WO2013035114A1

WO2013035114A1 - Tp53 genetic variants predictive of cancer

Info

Publication number: WO2013035114A1
Application number: PCT/IS2012/050013
Authority: WO
Inventors: Patrick Sulem; Simon Stacey
Original assignee: Decode Genetics Ehf; Illumina Inc
Priority date: 2011-09-08
Filing date: 2012-09-10
Publication date: 2013-03-14

Abstract

It has been discovered that particular variants in the human TP53 gene are associated with risk of cancer. The present invention provides methods of determining susceptibility of cancer using such variants. Also provided are computer-implemented methods for determining cancer susceptibility.

Description

GENETIC VARIANTS PREDICTIVE OF CANCER RISK

INTRODUCTION

Cancer, the uncontrolled growth of malignant cells, is a major health problem of the modern medical era and is one of the leading causes of death in developed countries. In the United States, one in four deaths is caused by cancer (Jemal, A. et al., CA Cancer J. Clin. 52: 23-47 (2002)). Cancer initiation results from the complex interplay of genetic and environmental factors. The estimated contribution of genetic factors varies widely between cancer sites, with prostate cancer generally considered to have the largest genetic component (Lichtenstein et al., N Engl J Med, 343, 78-85 (2000)). However, genetic factors also play a role in cancer types with strong environmental factors such as lung cancer (Jonsson, S., et al. JAMA

292: 2977-83 (2004); Hemminki, K., et a/. Genet Epidemiol 20: 107-116 (2001)).

All cancers are subject to the accumulation of genetic changes that lead to aberrant cell growth and survival. Thus, it could be expected that genetic polymorphisms that affect certain basic cellular processes, such as DNA repair, cell cycle regulation and apoptosis could increase an individual's life-long risk of developing cancer - the actual site of cancer could be determined by other factors, environmental or genetic (Hanahan, D. and Weinber, R.A., Cell 100: 57-70 (2000). Indeed, studies on cancer risk in relatives of cancer patients lends strong evidence for shared genetic factors that increase the risk of more than one cancer type (Cannon-Albright, L.A., et al. Cancer Res 54: 2378-85 1994); Amundadottir, L.T., et al. PLoS Med l :e65 (2004)). Furthermore, mutations in strongly cancer-predisposing genes are associated with an increased risk of more than one type of cancer, as exemplified by the spectrum of cancer types in Li-Fraumeni syndrome that are caused by mutations in TP53 (Malkin, D., et al. Science 250: 1233-38 (1990).

Glioma is a brain tumor that has its origins in glial cells in the brain or the spine. Gliomas are graded by their cell type, grade and location. The main types of glioma are ependymomas, which originate in ependymal cells, astrocytomas, that have their origin in astrocytes and the most common of which is glioblastoma multiforme, oligodendrogliomas, which originate in oligodendrocytes, and mixed gliomas, which contain cells from different types of glial cells.

The causes of gliomas are not known, although hereditary disorders such as

neurofibromatosis and tuberuous sclerosix complex are known to lead to increased predisposition of glioma. Gliomas are rarely curable, the prognosis for patients with high- grade gliomas being very poor.

SUMMARY OF THE INVENTION

The present inventors have discovered that variants on chromosome 17pl3 in the human TP53 gene are associated with risk of cancer in humans, including basal cell carcinoma, prostate cancer, glioma and colorectal adenoma. The present invention relates to the utilization of such variants in the risk management of cancer. For simplicity, many details of the invention, including details related to TP53 or techniques or materials for practicing the invention are described in the context of predicting susceptibility to cancer. It should be understood that such details are also are applicable to predicting susceptibility for the specific cancers identified herein.

In one aspect, the invention provides a method of determining a susceptibility to a cancer, the method comprising steps of (i) analyzing data representative of at least one allele of a human TP53 gene (SEQ ID NO : 3) in a human subject, wherein different alleles of the TP53 gene are associated with different susceptibilities to the cancer in humans, and (ii) determining a susceptibility to the cancer for the human subject from the data.

In another aspect of the invention, a method of determining whether a human individual is at increased risk of developing a cancer is provided, the method comprising steps of (i) obtaining a biological sample containing nucleic acid from the individua l; (ii) determining, in the biological sample, nucleic acid sequence about the TP53 gene; and (iii) comparing the sequence information to the wild-type sequence of TP53 (SEQ ID NO : 3), wherein an identification of a mutation in TP53 in the individual is indicative that the individual is at increased risk of developing the cancer.

The invention in a further aspect provides a method for determining a susceptibility to a cancer in a human individual, comprising (i) determining the presence or absence of at least one allele of at least one polymorphic marker in a nucleic acid sample obtained from the individual, wherein the at least one allele causes an impaired function or reduced expression of TP53, and (ii) determining a susceptibility to the cancer from the presence or absence of the at least one allele, wherein the presence of the at least one allele is indicative of a susceptibility to the cancer. Yet a further aspect provides a method of determining a susceptibility to a cancer, the method comprising steps of (i) screening a biological sample from a human subject for evidence of an allele of TP53 (SEQ ID NO : 3) that results in impaired TP53 mRNA processing, wherein the presence of an allele of TP53 with impaired mRNA processing is associated with elevated susceptibility to the cancer in humans, and (ii) determining a susceptibility to the cancer for the human subject from the presence or absence of the allele of TP53 that results in the impaired TP53 mRNA processing .

In certain embodiments of the methods of the invention the allele indicative of susceptibility of the cancer is a mutant allele in TP53 that results in impaired polyadenylation of a TP53 transcript. In certain embodiments, the allele is an allele that affects the AATAAA

polyadenylation signal in TP53. In certain preferred embodiments, the allele is the C allele of rs78378222. The invention also provides a therapeutic regimen for a human subject with a cancer, the method comprising steps of (1) analyzing data representative of at least one allele of a TP53 gene (SEQ ID NO: 3) in a human subject with the cancer to identify the presence or absence of a TP53 mutant allele that leads to impaired 3' mRNA processing of TP53, and (2) selecting a therapeutic regimen of a therapeutic agent for treating the cancer for a subject identified from the data as having the mutant allele.

Also provides is a method of selecting a human subject with a cancer for treatment with a cancer therapeutic agent, the method comprising steps of (1) determining the presence or absence of at least one allele of at least one polymorphic marker in a nucleic acid sample obtained from the individual, wherein the at least one allele causes impaired 3' processing of TP53 mRNA, and (2) selecting for treatment with the therapeutic agent a subject identified as having the at least one allele in the nucleic acid sample.

Another method of treatment of a cancer of a human individual diagnosed with the cancer is provided, the method comprising steps of (1) determining the presence or absence of a mutation that causes a impaired 3' mRNA processing of TP53 in a nucleic acid sample from the human individual, (2) selecting for treatment an individual determined to have the mutation, (3) administering to the selected individual a pharmaceutically acceptable amount of a therapeutic agent for the cancer.

The invention also provides systems for carrying out methods of determining susceptibility to cancer. In one such aspect a system for identifying susceptibility to a cancer in a human subject is provided, the system comprising : (1) at least one processor; (2) at least one computer-readable medium; (3) a susceptibility database operatively coupled to a computer- readable medium of the system and containing population information correlating the presence or absence of one or more alleles of the human TP53 gene and susceptibility to a cancer in a population of humans; (4) a measurement tool that receives an input about the human subject and generates information from the input about the presence or absence of at least one mutant TP53 allele indicative of a TP53 defect in the human subject; and (5) an analysis tool that (i) is operatively coupled to the susceptibility database and the the measurement tool, (ii) is stored on a computer-readable medium of the system, and (iii) is adapted to be executed on a processor of the system, to compare the information about the human subject with the population information in the susceptibility database and generate a conclusion with respect to susceptibility to the cancer for the human subject. In certain embodiment, the system further includes a communication tool operatively coupled to the analysis tool, stored on a computer-readable medium of the system and adapted to be executed on a processor of the system to communicate to the subject, or to a medical practitioner for the subject, the conclusion with respect to susceptibility to the cancer for the subject. Another computer system provided by the invention is a system for assessing or selecting a treatment protocol for a subject diagnosed with a cancer, the system comprising (1) at least one processor; (2) at least one computer-readable medium; (3) a medical treatment database operatively connected to a computer-readable medium of the system and containing information correlating the presence or absence of at least one mutant TP53 allele and efficacy of treatment regimens for the cancer; (4) a measurement tool to receive an input about the human subject and generate information from the input about the presence or absence of the at least one mutant TP53 allele indicative of a TP53 defect in a human subject diagnosed with the cancer; and (5) a medical protocol tool operatively coupled to the medical treatment database and the measurement tool, stored on a computer-readable medium of the system, and adapted to be executed on a processor of the system, to compare the information with respect to presence or absence of the at least one mutant TP53 allele for the subject and the medical treatment database, and generate a conclusion with respect to at least one of (a) the probability that one or more medical treatments will be efficacious for treatment of the cancer for the patient; and (b) which of two or more medical treatments for the cancer will be more efficacious for the patient.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular description of preferred embodiments of the invention.

FIG 1 provides a diagram illustrating a system comprising computer implemented methods utilizing risk variants as described herein.

FIG 2 shows an exemplary system for determining risk of cancer as described further herein.

FIG 3 shows a system for selecting a treatment protocol for a subject diagnosed with a cancer.

FIG 4 shows an overview of single-point SNP association data obtained from genomic sequencing in the 17pl3 region (pos 7,186,095-7,680,389, HG18 Build 36). The upper panel shows BCC association p-values for SNPs in the region identified by whole genome sequencing of 457 individuals. The positions of the TP53 SNP rs78378222 and the novel SNP giving the second-highest signal in the region (chrl7:7640788; located at position 201 in SEQ ID NO:l) are indicated. The locations of UCSC genes in the region are shown in the middle panel. The lower panel shows recombination rates calculated from HapMap data.

FIG 5a displays a box plot of quantitative RT-PCR of TP53 RNA in blood samples. Normalized expression levels (y-axis) are plotted separately for each genotype (x-axis). The central horizontal line indicates the median of each distribution, upper and lower boundaries of the boxes indicate the 25^th and 75^th percentiles and the whiskers indicate the 5^th and 95^th percentiles. N = 7 [C/A] heterozygotes and 51 [A/A] homozygotes. FIG 5b is a schematic diagram showing the locations of primers used for investigation of termination and polyadenylation of TP 53 rs78378222 mutant and wild-type alleles. FIG 5c shows RNA RACE- pattern of samples from blood and adipose tissue from rs78378222 heterozygotes producing 1300bp bands. Sequencing of the RACE products showed a predominance of mRNAs bearing the wild type [A] allele and a reduced abundance of the mutant [C] allele for rs78378222 (arrowed) . The mutated site affected by rs78378222 is indicated with an arrow. FIG 5d shows sequence analysis on transcription from an rs78378222 heterozygote conducted on blood and adipose-derived RNA. The rs78378222 site is indicated with an arrow.

DETAILED DESCRIPTION

Definitions

Unless otherwise indicated, nucleic acid sequences are written left to right in a 5' to 3' orientation. Numeric ranges recited within the specification are inclusive of the numbers defining the range and include each integer or any non-integer fraction within the defined range. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by the ordinary person skilled in the art to which the invention pertains.

The following terms shall, in the present context, have the meaning as indicated :

A "polymorphic marker", sometime referred to as a "marker", as described herein, refers to a genomic polymorphic site. Each polymorphic marker has at least two sequence variations characteristic of particular alleles at the polymorphic site. Thus, genetic association to a polymorphic marker implies that there is association to at least one specific allele of that particular polymorphic marker. The marker can comprise any allele of any variant type found in the genome, including SNPs, mini- or microsatellites, insertion-deletions, translocations and copy number variations (insertions, deletions, duplications) . Polymorphic markers can be of any measurable frequency in the population. For mapping of disease genes, polymorphic markers with population frequency higher than 5- 10% are in general most useful. However, polymorphic markers may also have lower population frequencies, such as 1-5% frequency, or even lower frequency. The term shall, in the present context, be taken to include polymorphic markers with any population frequency.

An "allele" refers to the nucleotide sequence of a given locus (position) on a chromosome. A polymorphic marker allele thus refers to the composition (i.e., sequence) of the marker on a chromosome. Genomic DNA from an individual contains two alleles (e.g. , allele-specific sequences) for any given polymorphic marker, representative of each copy of the marker on each chromosome. Sequence codes for nucleotides used herein are: A = 1, C = 2, G = 3, T = 4.

Sequence conucleotide ambiguity as described herein is according to WIPO ST.25 :

or or or , un nown or o er

A nucleotide position at which more than one sequence is possible in a population (either a natural population or a synthetic population, e.g. , a library of synthetic molecules) is referred to herein as a "polymorphic site".

A "Single Nucleotide Polymorphism" or "SIMP" is a DNA sequence variation occurring when a single nucleotide at a specific location in the genome differs between members of a species or between paired chromosomes in an individual. Most SNP polymorphisms have two alleles. Each individual is in this instance either homozygous for one allele of the polymorphism (i.e. both chromosomal copies of the individual have the same nucleotide at the SNP location), or the individual is heterozygous (i.e. the two sister chromosomes of the individual contain different nucleotides). The SNP nomenclature as reported herein refers to the official Reference SNP (rs) ID identification tag as assigned to each unique SNP by the National Center for Biotechnological Information (NCBI).

A "variant", as described herein, refers to a segment of DNA that comprises a polymorphic site. A "marker" or a "polymorphic marker", as defined herein, is a variant.

A "microsatellite" is a polymorphic marker that has multiple small repeats of bases that are 2- 8 nucleotides in length (such as CA repeats) at a particular site, in which the number of repeat lengths varies in the general population.

An "indel", or an "insertion-deletion" is a common form of polymorphism comprising a small insertion or deletion that is typically only one or a few nucleotides long.

A "haplotype," as described herein, refers to a segment of genomic DNA that is characterized by a specific combination of alleles arranged along the segment. For diploid organisms such as humans, a haplotype comprises one member of the pair of alleles for two or more polymorphic markers or loci along the segment. In a certain embodiment, the haplotype can comprise two or more alleles, three or more alleles, four or more alleles, or five or more alleles. Allelic identities are described herein in the context of the marker name and the particular allele of the marker, e.g., "2 rs78378222" refers to the 2 allele of marker rs78378222, and is equivalent to "rs78378222 allele 2". Furthermore, allelic codes are as for individual markers, i.e. 1 = A, 2 = C, 3 = G and 4 = T.

The term "TP53", as described herein, refers to the Tumor Protein p53 gene on chromosome 17pl3.1. The name of this gene is sometimes also abbreviated as "p53". The nucleotide sequence of the gene is shown in SEQ ID NO: 3 herein.

The term "susceptibility", as described herein, refers to the proneness of an individual towards the development of a certain state (e.g., a certain trait, phenotype or disease), or towards being less able to resist a particular state than the average individual. The term encompasses both increased susceptibility and decreased susceptibility. Thus, particular alleles at polymorphic markers may be characteristic of increased susceptibility (i.e., increased risk) of cancer, e.g. glioma, basal cell carcinoma, prostate cancer or colorectal adenoma, as characterized by a relative risk (RR) or odds ratio (OR) of greater than one for the particular allele. Alternatively, the markers and/or haplotypes of the invention are characteristic of decreased susceptibility (i.e., decreased risk) of cancer, as characterized by a relative risk of less than one.

The term "and/or" shall in the present context be understood to indicate that either or both of the items connected by it are involved. In other words, the term herein shall be taken to mean "one or the other or both".

The term "look-up table", as described herein, is a table that correlates one form of data to another form, or one or more forms of data to a predicted outcome to which the data is relevant, such as phenotype or trait. For example, a look-up table can comprise a correlation between allelic data for at least one polymorphic marker and a particular trait or phenotype, such as a particular disease diagnosis, that an individual who comprises the particular allelic data is likely to display, or is more likely to display than individuals who do not comprise the particular allelic data. Look-up tables can be multidimensional, i.e. they can contain information about multiple alleles for single markers simultaneously, or they can contain information about multiple markers, and they may also comprise other factors, such as particulars about diseases diagnoses, racial information, biomarkers, biochemical

measurements, therapeutic methods or drugs, etc.

The term "database" refers to a collection of data organized for one or more purposes. In the context of the invention, databases may be organized in a digital format for access, analysis, or processing by a computer. The data are typically organized to model features relevant to the invention. For instance, one component of data in a database may be information about variations in a population, such as genetic variation with respect to TP53, but also variation with respect to other medically informative parameters, including other genetic loci, race, ethnicity, sex, age, behaviors and lifestyle (tobacco consumption (smoking), alcohol consumption (drinking), exercise, body mass indices), glucose tolerance/diabetes, and any other factors that medical personnel may measure in the context of standard medical care or specific diagnoses. Other components of the database may include one or more sets of data relating to susceptibility to a disease in a population, and/or suitability or success of a disease treatment, and/or suitability or success of a protocol for screening for or presenting a disease. Preferably the data is organized to permit analysis of how the biological variation in the population correlates with the susceptibility to disease and/or the suitability or success of the treatment, protocol, etc. A look-up datable (or the information in a look-up table) may be stored in a database to facilitate aspects of the invention.

A "computer-readable medium", is an information storage medium that can be accessed by a computer using a commercially available or custom-made interface. Exemplary computer- readable media include memory (e.g., RAM, ROM, flash memory, etc.), optical storage media (e.g., CD-ROM), magnetic storage media (e.g., computer hard drives, floppy disks, etc.), punch cards, or other commercially available media. Information may be transferred between a system of interest and a medium, between computers, or between computers and the computer-readable medium for storage or access of stored information. Such transmission can be electrical, or by other available methods, such as IR links, wireless connections, etc.

The term "biological sample" refers to a sample obtained from an individual that contains nucleic acid and/or protein and/or fluid containing organic and/or inorganic metabolites and substances. In many variations of the invention, the biological sample comprises nucleic acid suitable for genetic analysis.

A "nucleic acid sample" as described herein, refers to a sample obtained from an individual that contains nucleic acid (DNA or RNA). In certain embodiments, i.e. the detection of specific polymorphic markers and/or haplotypes, the nucleic acid sample comprises genomic DNA. Such a nucleic acid sample can be obtained from any source that contains genomic DNA, including a blood sample, sample of amniotic fluid, sample of cerebrospinal fluid, or tissue sample from skin, muscle, buccal or conjunctival mucosa, placenta, gastrointestinal tract or other organs.

The term "antisense agent" or "antisense oligonucleotide" refers, as described herein, to molecules, or compositions comprising molecules, which include a sequence of purine an pyrimidine heterocyclic bases, supported by a backbone, which are effective to hydrogen bond to corresponding contiguous bases in a target nucleic acid sequence. The backbone is composed of subunit backbone moieties supporting the purine and pyrimidine hetercyclic bases at positions which allow such hydrogen bonding. These backbone moieties are cyclic moieties of 5 to 7 atoms in size, linked together by phosphorous-containing linkage units of one to three atoms in length. In certain preferred embodiments, the antisense agent comprises an oligonucleotide molecule. Variants on chromosome 17q23.2 associate with cancer It has been discovered that variants on chromosome 17q23.2 are associated with risk of cancer. Analysis of variants in the human TP53 gene has resulted in the identification of variants that predispose to cancer risk in humans. Strongest risk was observed for marker rs78378222 (identified at position 201 in SEQ ID NO : 2 herein), which is located in the 3' UTR of TP53. The minor allele C of this SNP, which is located at position 34 in the TP53 gene sequence as shown in SEQ ID NO : 3 herein (reverse complement sequence of that shown in SEQ ID NO : 2), has a frequency of 1.9% in the Icelandic population and confers an odds ratio of basal cell carcinoma of 2.4 in the Icelandic discovery cohort.

Follow-up ana lysis in Icelandic and non-Icelandic populations confirmed the association with basal cell carcinoma, and further identified significant association to prostate cancer (OR 1.44, P-value 2.4xl0^"6), glioma (OR 2.35, P-value l .OxlO^"5) and colorectal adenoma (OR 1.39, P- value 1.6xl0^"4), as further described in the Examples herein.

These findings show that variants in the human TP53 gene confer risk of cancer, including prostate cancer, basal cell carcinoma, glioma and colorectal adenoma .

Variants in TP53 are predictive of cancer risk

The TP53 gene encodes the p53 tumor suppressor, which plays a crucial role in multicellular organisms, where it regulates the cell cycle and is involved in the prevention of cancer by preventing genome mutation. Human p53 contains 393 amino acids and contains seven functional domains: an acidic N-terminus transcription-activation domain, an activation domain important for apoptotic activity, a proline rich domain important for apoptotic activity, a central DNA-binding core domain, a nuclear localization signaling doma in, a homo- oligomerization domain and a C-terminal domain involved in downregulation of DNA binding of the central domain. p53 has many known functions, including apotposis, genome stability and inhibition of angiogenesis. The protein has also been shown to interact with a large number of other proteins and is involved in the regulation of expression of a myriad of genes.

Human cancers are frequently associated with mutations in TP53. Residues 175, 248 and 273 are most frequently mutated in cancers, and are all located at or near the protein-DNA interface. Furthermore, a number of missense mutations are located in one of the 3 DNA loops.

The process of polyadenylation begins as transcription of a gene finishes. The 3' end of the newly synthesized mRNA is first cleaved off, and then a poly(A) tail is added to the 3' end of the RNA molecule. The AATAAA sequence (AAUAAA at the mRNA level) is important for binding of the enzyme CPSF (cleavage/polyadenylation specificity factor), which cleaves off the 3' end of the newly synthesized mRNA, and is followed by polyadenylation which is catalyzed by polyadenylate polymerase.

Inspection of the sequence surrounding rs78378222 indicated that it occurs in the sole polyadenylation signal of TP53, the risk-associated mutation changing the sequence AATAAA to AATACA, thus disrupting the signal sequence. Using RT-PCR with primers internal to the TP 53 mRNA, it was observed that rs78378222 [A/C] heterozygotes expressed somewhat less TP53 transcript than wild-type homozygotes. 3' RACE experiments on RNA from [A/C] heterozygotes indicated that correctly terminated polyA(+) mRNAs were produced

predominantly from the wild type allele, with 73% of mRNAs containing the wild type [A] allele and 27% containing the mutant [C] allele {P = 1.6 x 10^~6). Sequencing of RT-PCR products from heterozygotes showed that RNA species containing the normal 3' end of the TP53 gene were comprised almost entirely of mutant [C] a llele transcripts. These data suggest that the rs78378222[C] variant impairs proper termination and polyadenylation of the TP53 transcript. It is therefore also speculated that other variants that disrupt normal 3' end processing of TP53 will confer increased risk of cancer.

Methods of determining susceptibility to cancer

Accordingly, in one aspect, the invention provides a method of method of determining a susceptibility to a cancer, the method comprising analyzing data representative of at least one allele of a human TP53 gene (SEQ ID NO : 3) in a human subject, wherein different alleles of the TP53 gene are associated with different susceptibilities to at least one cancer in humans, and determining a susceptibility to the cancer for the human subject from the data . In certain embodiments, the cancer is selected from the group consisting of basal cell carcinoma, prostate cancer, glioma and colorectal adenoma. In some embodiments, the cancer is selected from the group consisting of prostate cancer, glioma and colorectal adenoma. In one preferred embodiment, the cancer is glioma . In certain embodiments, the methods comprise analyzing the data for the presence or absence of at least one mutant allele in TP53 that results in impaired polyadenylation of a TP53 transcript, wherein determination of the presence of the at least one mutant allele is indicative of an increased susceptibility to the cancer.

The data can be any type of data that is representative of polymorphic alleles in the TP53 gene. In certain embodiments, the data is nucleic acid sequence data . The sequence data is data that is sufficient to provide information about particular alleles. In certain embodiments, the nucleic acid sequence data is obtained from a biological sample comprising or containing nucleic acid from the human individual. The nucleic acid sequence may suitably be obtained using a method that comprises at least one procedure selected from (i) amplification of nucleic acid from the biological sample; (ii) hybridization assay using a nucleic acid probe and nucleic acid from the biological sample; (iii) hybridization assay using a nucleic acid probe and nucleic acid obtained by amplification of the biological sample, and (iv) sequencing, in particular high-throughput sequencing. The nucleic acid sequence data may also be obtained from a preexisting record . For example, the preexisting record may comprise a genotype dataset for at least one polymorphic marker. In certain embodiments, the determining comprises comparing the sequence data to a database containing correlation data between the at least one polymorphic marker and susceptibility to thecancer. In certain embodiments, the sequence data is provided as genotype data, identifying the presence or absence of particular alleles at polymorphic locations.

In some embodiments, the analyzing comprises analyzing the data for the presence or absence of at least one mutant allele indicative of a TP 53 defect. The TP 53 defect may for example be a defect in 3' end processing of TP 53 mRNA, such as a defect in polyadenylation. In certain embodiments, the defect is a defect in the AATAAA polyadenylation signal. In one preferred embodiment, the defect is a change of the AATAAA polyadenylation signal to AATACA. In one preferred embodiment, the defect is a change of the AATAAA

polyadenylation signal, between position 197 and 202 in SEQ ID NO : 2, to AATACA. The defect correspond to a T to G nucleotide change at position 34 in the sequence set forth in SEQ ID NO : 3 herein. Thus, in one preferred embodiment, the defect is a change of the TTTATT polyadenylation signal, between position 33 and 38 in SEQ ID NO : 3, to TTTAGT. The TP53 defect may also be an altered level of expression of a TP53 protein compared with wild- type TP53 protein levels.

Determination of TP53 3' end processing or determination of TP53 expression levels can be performed using standard assays well known to the skilled person, some of which are described herein. Such assays can be used to confirm that a particular TP53 mutation impairs or eliminates TP53 processing activity and therefor would be expected to carry an increased susceptibility for cancers as described herein.

The data to be analyzed by the method of the invention is suitably obtained by analysis of a biological sample from a human subject to obtain information about particular alleles in the genome of the individual. In certain embodiments, the information is nucleic acid information which comprises sufficient sequence to identify the presence or absence of at least one allele in the subject (e.g. a mutant allele) . The information can also be nucleic acid information that identifies at least one allele of a polymorphic marker that is in linkage disequilibrium with a mutant allele. Linkage disequilibrium may suitably be determined by the correlation coefficient between polymorphic sites. In one embodiment, the sites are correlated by values of the correlation coefficient r² of greater than 0.5. Other suitable values of r² that are also appropriate to characterize polymorphic sites in LD are however also contemplated, as discussed further herein. The information may also be information about measurement of quantity of length of TP53 mRNA, wherein the measurement is indicative of the presence or absence of the mutant allele, e.g. a mutant allele in a polyadenylation site in TP53. The information may further be measurement of quantity of TP53 protein, wherein the

measurement of protein is indicative of the presence or absence of a mutant allele.

In a further embodiment of the invention, a biologica l sample is obtained from the human subject prior to the analyzing steps. The analyzing may also suitably be performed by analyzing data from a preexisting record about the human subject. The preexisting record may for example include sequence information or genotype information about the individual, which can identify the presence or absence of mutant alleles. In certain embodiments, information about risk for the human subject can be determined using methods known in the art. Some of these methods are described herein. For example, information about odds ratio (OR), relative risk (RR) or lifetime risk (LR) can be determined from information about the presence or absence of particular mutant alleles of TP53.

In certain embodiments, the mutant allele of TP53 is a mutation in a polyadenylation site. In one preferred embodiment, the mutant allele is mutation in the AATAAA polyadenylation site. In another preferred embodiment, the mutant allele is a AATAAA to AATACA mutation. In another embodiment, the mutant allele is a mutation in TP53 that results in reduced expression of a TP53 protein compared to wild-type expression levels of TP53 protein.

It should be apparent from the foregoing that another aspect of the invention may relate to a method of determining whether an individual is at increased risk of developing cancer, the method comprising steps of (a) obtaining a biological sample containing nucleic acid from the individual; (b) determining, in the biological sample, nucleic acid sequence about the TP53 gene, and (c) comparing the sequence information to the wild-type sequence of TP53, as set forth in SEQ ID NO : 3 herein, wherein the identification of a mutation in TP53 in the individual is indicative that the individual is at increased risk of developing cancer.

Alternatively, the invention provides a method of determining whether an individual is at increased risk of developing cancer, the method comprising steps of determining, in a biological sample from the individual, nucleic acid sequence about the TP53 gene, and comparing the sequence information to the wild-type sequence of TP53, as set forth in SEQ ID NO : 3 herein, wherein the identification of a mutation in TP53 in the individual is indicative that the individual is at increased risk of developing cancer.

The mutation may be a missense mutation, a promoter mutation, a 3' end mutation, a nonsense mutation or a frameshift mutation in TP53. The mutation may be a 3' end mutation that results in a TP53 defect as described in the above.

In any of the methods described herein, the human subject or human individual whose susceptibility of cancer is being assessed may be a male or a female.

In another aspect, the invention provides a method of determining a susceptibility to cancer, the method comprising analyzing sequence data from a human subject for at least one variant in the human TP53 gene, or in an encoded human TP53 protein, wherein different alleles of the at least one variant are associated with different susceptibilities to cancer in humans, and determining a susceptibility to cancer for the human subject from the sequence data . In a preferred embodiment, the variant is a variant that results in impaired 3' end processing of TP53. In another preferred embodiment, the variant is a variant in the AATAAA

polyadenylation signal sequence.

In certain embodiments, the data that is obtained is nucleic acid sequence data. In certain embodiments, the nucleic acid sequence data is obtained from a biological sample comprising or containing nucleic acid from the human individual. The nucleic acids sequence may suitably be obtained using a method that comprises at least one procedure selected from (i) amplification of nucleic acid from the biological sample; (ii) hybridization assay using a nucleic acid probe and nucleic acid from the biological sample; (iii) hybridization assay using a nucleic acid probe and nucleic acid obtained by amplification of the biological sample, and (iv) sequencing, in particular high-throughput sequencing. The nucleic acid sequence data may also be obtained from a preexisting record. For example, the preexisting record may comprise a genotype dataset for at least one polymorphic marker. In certain embodiments, the determining comprises comparing the sequence data to a database containing correlation data between the at least one polymorphic marker and susceptibility to the cancer.

Certain risk alleles have been found to be predictive of increased risk of cancer. Thus, in certain embodiments, determination of the presence of at least one allele selected from the group consisting of the C allele of rs78378222 and the T allele of chrl7: 7640788, is indicative of an increased susceptibility of cancer for the human subject

The C allele of rs78378222 results in change of the AATAAA polyadenylation site to AATACA and is indicative of increased risk of cancer. Thus, in certain embodiment, determination of the presence of the AA insertion is indicative of increased risk of cancer for the individual. Determination of the absence of the AA insertion, or another variant allele conferring increased risk of cancer is indicative that the individual does not have the increased risk conferred by the allele.

Alternatively, the allele that is detected can be the allele of the complementary strand of DNA, such that the nucleic acid sequence data identifies at least one allele which is complementary to any of the alleles of the polymorphic markers referenced above. For example, the allele that is detected may be the complementary TT allele of the at-risk AA allele of the -/AA insertion/deletion polymorphism.

It is contemplated that in certain embodiments of the invention, it may be convenient to prepare a report of results of risk assessment. Thus, certain embodiments of the methods of the invention comprise a further step of preparing a report containing results from the determination of risk, wherein said report is written in a computer readable medium, printed on paper, or displayed on a visual display. In certain embodiments, it may be convenient to report results of susceptibility to at least one entity selected from the group consisting of the individual, a guardian of the individual, a genetic service provider, a physician, a medical organization, and a medical insurer.

Obtaining nucleic acid sequence data

Sequence data can be nucleic acid sequence data, which may be obtained by means known in the art. Sequence data is suitably obtained from a biological sample of genomic DNA, RNA, or cDNA (a "test sample") from an individual ("test subject). For example, nucleic acid sequence data may be obtained through direct analysis of the sequence of the polymorphic position (allele) of a polymorphic marker. Suitable methods, some of which are described herein, include, for instance, whole genome sequencing methods, whole genome analysis using SNP chips (e.g., Infinium HD BeadChip), cloning for polymorphisms, non-radioactive PCR-single strand conformation polymorphism analysis, denaturing high pressure liquid chromatography (DHPLC), DNA hybridization, computational analysis, single-stranded conformational polymorphism (SSCP), restriction fragment length polymorphism (RFLP), automated fluorescent sequencing; clamped denaturing gel electrophoresis (CDGE); denaturing gradient gel electrophoresis (DGGE), mobility shift analysis, restriction enzyme analysis; heteroduplex analysis, chemical mismatch cleavage (CMC), RNase protection assays, use of polypeptides that recognize nucleotide mismatches, such as E. coli mutS protein, allele-specific PCR, and direct manual and automated sequencing. These and other methods are desribed in the art (see, for instance, Li et al., Nucleic Acids Research, 28(2) : el (i-v) (2000); Liu et al., Biochem Cell Bio 80: 17-22 (2000); and Burczak et al., Polymorphism Detection and Analysis, Eaton Publishing, 2000; Sheffield et al., Proc. Natl. Acad. Sci. USA, 86: 232-236 (1989); Orita et al., Proc. Natl. Acad. Sci. USA, 86: 2766-2770 (1989); Flavell et al., Cell, 15: 25-41 (1978);

Geever et al., Proc. Natl. Acad. Sci. USA, 78: 5081-5085 (1981); Cotton et al., Proc. Natl.

Acad. Sci. USA, 85:4397-4401 (1985); Myers et al., Science 230: 1242- 1246 (1985); Church and Gilbert, Proc. Natl. Acad. Sci. USA, 81 : 1991-1995 (1988); Sanger et al., Proc. Natl. Acad. Sci. USA, 74: 5463-5467 (1977); and Beavis et al., U.S. Patent No. 5,288,644).

Recent technological advances have resulted in technologies that allow massive parallel sequencing to be performed in relatively condensed format. These technologies share sequencing-by-synthesis principle for generating sequence information, with different technological solutions implemented for extending, tagging and detecting sequences.

Exemplary technologies include 454 pyrosequencing technology (Nyren, P. et al. Anal

Biochem 208: 171-75 (1993); http://www.454.com), Illumina Solexa sequencing technology (Bentley, D.R. Curr Opin Genet Dev 16: 545-52 (2006); http://www.illumina.com), and the SOLiD technology developed by Applied Biosystems (ABI)

(http://www.appliedbiosystems.com; see also Strausberg, R. L., et al. Drug Disc Today 13: 569-77 (2008)). Other sequencing technologies include those developed by Pacific Biosciences (http://www.pacificbiosciences.com), Complete Genomics

(http://www.completegenomics.com), Intelligen Bio-Systems

(http://www. intelligentbiosystems.com), Genome Corp (http://www.genomecorp.com), ION Torrent Systems (http://www.iontorrent.com) and Helicos Biosciences

(http://www. helicosbio.som). It is contemplated that sequence data useful for performing the present invention may be obtained by any such sequencing method, or other sequencing methods that are developed or made available. Thus, any sequence method that provides the allelic identity at particular polymorphic sites {e.g., the absence or presence of particular alleles at particular polymorphic sites) is useful in the methods described and claimed herein.

Alternatively, hybridization methods may be used (see Current Protocols in Molecular Biology, Ausubel et al., eds., John Wiley & Sons, including all supplements). For example, a biological sample of genomic DNA, RNA, or cDNA (a "test sample") may be obtained from a test subject. The subject can be an adult, child, or fetus. The DNA, RNA, or cDNA sample is then examined. The presence of a specific marker allele can be indicated by sequence-specific hybridization of a nucleic acid probe specific for the particular allele. The presence of more than one specific marker allele or a specific haplotype can be indicated by using several sequence-specific nucleic acid probes, each being specific for a particular allele. A sequence- specific probe can be directed to hybridize to genomic DNA, RNA, or cDNA. A "nucleic acid probe", as used herein, can be a DNA probe or an RNA probe that hybridizes to a

complementary sequence. One of skill in the art would know how to design such a probe so that sequence specific hybridization will occur only if a particular allele is present in a genomic sequence from a test sample.

In certain embodiments, determination of a susceptibility to cancer comprises forming a hybridization sample by contacting a test sample, such as a genomic DNA sample, with at least one nucleic acid probe. A non-limiting example of a probe for detecting mRNA or genomic DNA is a labeled nucleic acid probe that is capable of hybridizing to mRNA or genomic DNA sequences described herein. The nucleic acid probe can be, for example, a full- length nucleic acid molecule, or a portion thereof, such as an oligonucleotide of at least 10, 15, 30, 50, 100, 250 or 500 nucleotides in length that is sufficient to specifically hybridize under stringent conditions to appropriate mRNA or genomic DNA. For example, the nucleic acid probe can comprise all or a portion of the nucleotide sequence of the TP 53 gene, or the probe can be the complementary sequence of such a sequence. Hybridization can be performed by methods well known to the person skilled in the art (see, e.g., Current Protocols in Molecular Biology, Ausubel et al., eds., John Wiley & Sons, including all supplements). In one embodiment, hybridization refers to specific hybridization, i.e., hybridization with no mismatches (exact hybridization). In one embodiment, the hybridization conditions for specific hybridization are high stringency.

Specific hybridization, if present, is detected using standard methods. If specific hybridization occurs between the nucleic acid probe and the nucleic acid in the test sample, then the sample contains the allele that is complementary to the nucleotide that is present in the nucleic acid probe.

Additionally, or alternatively, a peptide nucleic acid (PNA) probe can be used in addition to, or instead of, a nucleic acid probe in the hybridization methods described herein. A PNA is a DNA mimic having a peptide-like, inorganic backbone, such as N-(2-aminoethyl)glycine units, with an organic base (A, G, C, T or U) attached to the glycine nitrogen via a methylene carbonyl linker (see, for example, Nielsen et al., Bioconjug. Chem. 5: 3-7 ( 1994)). The PNA probe can be designed to specifically hybridize to a molecule in a sample suspected of containing one or more of the marker alleles shown herein to be associated with risk of cancer.

In one embodiment of the invention, a test sample containing genomic DNA obtained from the subject is collected and the polymerase chain reaction (PCR) is used to amplify a fragment comprising one or more polymorphic marker. As described herein, identification of particular marker alleles can be accomplished using a variety of methods. In another embodiment, determination of susceptibility is accomplished by expression analysis, for example using quantitative PCR (kinetic thermal cycling). This technique can, for example, utilize commercially available technologies, such as TaqMan® (Applied Biosystems, Foster City, CA). The technique can for example assess the presence of an alteration in the expression or composition of a polypeptide or splicing variant(s) that is encoded by an associated nucleic acid described herein. Alternatively, this technique may assess expression levels of genes or particular splice variants of genes, that are affected by one or more of the variants described herein. Further, the expression of the variant(s) can be quantified as physically or functionally different.

Allele-specific oligonucleotides can also be used to detect the presence of a particular allele in a nucleic acid. An "allele-specific oligonucleotide" (also referred to herein as an "allele- specific oligonucleotide probe") is an oligonucleotide of any suitable size, for example an oligonucleotide of approximately 10-50 base pairs or approximately 15-30 base pairs, that specifically hybridizes to a nucleic acid which contains a specific allele at a polymorphic site (e.g., a polymorphic marker). An allele-specific oligonucleotide probe that is specific for one or more particular alleles at polymorphic markers can be prepared using standard methods (see, e.g., Current Protocols in Molecular Biology, supra). PCR can be used to amplify the desired region. Specific hybridization of an allele-specific oligonucleotide probe to DNA from a subject is indicative of the presence of a specific allele at a polymorphic site (see, e.g., Gibbs et al., Nucleic Acids Res. 17: 2437-2448 (1989) and WO 93/22456).

In certain embodiments, arrays of oligonucleotide probes that are complementary to target nucleic acid sequence segments from a subject can be used to identify polymorphisms in a nucleic acid. For example, an oligonucleotide array can be used. Oligonucleotide arrays typically comprise a plurality of different oligonucleotide probes that are coupled to a surface of a substrate in different known locations. These arrays can generally be produced using mechanical synthesis methods or light directed synthesis methods that incorporate a combination of photolithographic methods and solid phase oligonucleotide synthesis methods, or by other methods known to the person skilled in the art (see, e.g., Bier et al., Adv Biochem Eng Biotechnol 109:433-53 (2008); Hoheisel, Nat Rev Genet 7: 200-10 (2006); Fan et al., Methods Enzymol 410: 57-73 (2006); Raqoussis & Elvidge, Expert Rev Mol Diagn 6: 145-52 (2006); Mockler et al., Genomics 85: 1- 15 (2005), and references cited therein, the entire teachings of each of which are incorporated by reference herein). Many additional descriptions of the preparation and use of oligonucleotide arrays for detection of

polymorphisms can be found, for example, in US 6,858,394, US 6,429,027, US 5,445,934, US 5,700,637, US 5,744,305, US 5,945,334, US 6,054,270, US 6,300,063, US 6,733,977, US 7,364,858, EP 619 321, and EP 373 203, the entire teachings of which are incorporated by reference herein. Also, standard techniques for genotyping can be used to detect particular marker alleles, such as fluorescence-based techniques (e.g. , Chen et al., Genome Res. 9(5) : 492-98 (1999); Kutyavin et al., Nucleic Acid Res. 34:el28 (2006)), utilizing PCR, LCR, Nested PCR and other techniques for nucleic acid amplification. Specific commercial methodologies available for SNP genotyping include, but are not limited to, TaqMan genotyping assays and SNPlex platforms (Applied Biosystems), gel electrophoresis (Applied Biosystems), mass spectrometry (e.g., MassARRAY system from Sequenom), minisequencing methods, real-time PCR, Bio-Plex system (BioRad), CEQ and SNPstream systems (Beckman), array hybridization

technology(e.g., Affymetrix GeneChip; Perlegen ), BeadArray Technologies (e.g., Illumina GoldenGate and Infinium assays), array tag technology (e.g., Parallele), and endonuclease- based fluorescence hybridization technology (Invader; Third Wave).

Suitable biological sample in the methods described herein can be any sample containing nucleic acid (e.g., genomic DNA) and/or protein from the human individual. For example, the biological sample can be a blood sample, a serum sample, a leukapheresis sample, an amniotic fluid sample, a cerbrospinal fluid sample, a hair sample, a tissue sample from skin, muscle, buccal, or conjuctival mucosa, placenta, gastrointestinal tract, or other organs, a semen sample, a urine sample, a saliva sample, a nail sample, a tooth sample, and the like. Preferably, the sample is a blood sample, a saliva sample or a buccal swab.

Protein analysis

Missense, nonsense and frameshift nucleic acid variations may lead to an altered amino acid sequence, as compared to the non-variant (e.g., wild-type) protein, due to amino acid substitutions, deletions, or insertions, or truncations. Variations at splice sites may also lead to splice variation. In such instances, detection of an amino acid substitution or a truncated amino acid sequence of the variant protein may be useful. Thus, nucleic acid sequence data may be obtained through indirect analysis of the nucleic acid sequence of the allele of the polymorphic marker, i.e. by detecting a protein variation.

Methods of detecting variant proteins are known in the art. For example, direct amino acid sequencing of the variant protein followed by comparison to a reference amino acid sequence can be used. Alternatively, SDS-PAGE followed by gel staining can be used to detect variant proteins of different molecular weights. Also, Immunoassays, e.g., antibody assays, e.g., immunofluorescent immunoassays, immunoprecipitations, radioimmunoasays, ELISA, and Western blotting, in which an antibody specific for an epitope comprising the variant sequence among the variant protein and non-variant or wild-type protein can be used. In certain embodiments, the amino acid sequence data about TP53 protein is obtained or deduced from a preexisting record.

In certain embodiments of the present invention, an amino acid substitution in the human TP53 protein is detected. In another embodiment, a truncated polypeptide encoded by an altered TP53 gene sequence is detected. The detection of altered proteins may be suitably performed, for example using any of the methods described in the above, or any other suitable method known to the skilled artisan.

In certain embodiments, the risk variant in TP53 is a risk variant that leads to decreased expression of TP53 protein. Methods of detecting expression levels are known in the art. For example, ELISA, radioimmunoassays, immunofluorescence, and Western blotting can be used to compare the expression of protein levels. Alternatively, Northern blotting can be used to compare the levels of mRNA. These processes are described in Sambrook et al., Molecular Cloning: A Laboratory Manual, 3^rd ed. Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York (2001) .

Any of these methods may be performed using a nucleic acid (e.g., DNA, mRNA) or protein of a biological sample obtained from the human individual for whom a susceptibility is being determined. The biological sample can be any nucleic acid or protein containing sample obtained from the human individual. For example, the biological sample can be any of the biological samples described herein.

Number of Polymorphic Markers/Genes Analyzed

With regard to the methods of determining a susceptibility described herein, the methods can comprise obtaining sequence data about any number of polymorphic markers and/or about any number of genes. For example, the method can comprise obtaining sequence data for about at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 100, 500, 1000, 10,000 or more polymorphic markers. In certain embodiments, the sequence data is obtained from a microarray comprising probes for detecting a plurality of markers. The polymorphic markers can be the ones of the group specified herein or they can be different polymorphic markers that are not specified herein. In a specific embodiment, the method comprises obtaining sequence data about at least two polymorphic markers. In certain embodiments, each of the markers may be associated with a different gene. For example, in some instances, if the method comprises obtaining nucleic acid data about a human individual identifying at least one allele of a polymorphic marker, then the method comprises identifying at least one allele of at least one polymorphic marker. Also, for example, the method can comprise obtaining sequence data about a human individual identifying alleles of multiple, independent markers, which are not in linkage disequilibrium.

Linkage Disequilibrium

Linkage Disequilibrium (LD) refers to a non-random assortment of two genetic elements. For example, if a particular genetic element (e.g., an allele of a polymorphic marker, or a haplotype) occurs in a population at a frequency of 0.50 (50%) and another element occurs at a frequency of 0.50 (50%), then the predicted occurrance of a person's having both elements is 0.25 (25%), assuming a random distribution of the elements. However, if it is discovered that the two elements occur together at a frequency higher than 0.25, then the elements are said to be in linkage disequilibrium, since they tend to be inherited together at a higher rate than what their independent frequencies of occurrence (e.g., allele or haplotype frequencies) would predict. Roughly speaking, LD is generally correlated with the frequency of recombination events between the two elements. Allele or haplotype frequencies can be determined in a population by genotyping individuals in a population and determining the frequency of the occurence of each allele or haplotype in the population. For populations of diploids, e.g. , human populations, individuals will typically have two alleles for each genetic element (e.g., a marker, haplotype or gene).

Many different measures have been proposed for assessing the strength of linkage disequilibrium (LD; reviewed in Devlin, B. & Risch, N., Genomics 29: 311-22 (1995)). Most capture the strength of association between pairs of biallelic sites. Two important pairwise measures of LD are r² (sometimes denoted Δ²) and | D'| (Lewontin, R., Genetics 49:49-67 (1964); Hill, W.G. & Robertson, A. Theor. Appl. Genet. 22: 226-231 (1968)). Both measures range from 0 (no disequilibrium) to 1 ('complete' disequilibrium), but their interpretation is slightly different. | D'| is defined in such a way that it is equal to 1 if just two or three of the possible haplotypes are present, and it is < 1 if all four possible haplotypes are present.

Therefore, a value of | D'| that is < 1 indicates that historical recombination may have occurred between two sites (recurrent mutation can also cause | D'| to be < 1, but for single nucleotide polymorphisms (SNPs) this is usually regarded as being less likely than recombination). The measure r² represents the statistical correlation between two sites, and takes the value of 1 if only two haplotypes are present. Markers which are correlated by an r² value of 1 are said to be perfectly correlated. In such an instance, the genotype of one marker perfectly predicts the genotype of the other.

The r² measure is arguably the most relevant measure for association mapping, because there is a simple inverse relationship between r² and the sample size required to detect association between susceptibility loci and SNPs. These measures are defined for pairs of sites, but for some applications a determination of how strong LD is across an entire region that contains many polymorphic sites might be desirable (e.g., testing whether the strength of LD differs significantly among loci or across populations, or whether there is more or less LD in a region than predicted under a particular model). Measuring LD across a region is not

straightforward, but one approach is to use the measure r, which was developed in population genetics. Roughly speaking, r measures how much recombination would be required under a particular population model to generate the LD that is seen in the data. This type of method can potentially also provide a statistically rigorous approach to the problem of determining whether LD data provide evidence for the presence of recombination hotspots.

A significant r² indicative of markers being in linkage disequilibrium may be at least 0.1, such as at least 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85,

0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99 or 1.0. A significant r² indicates that the markers are highly correlated, and therefore in linkage disequilibrium. Highly correlated markers must, be definition, show highly comparable results in association mapping, since the genotypes for one marker predicts the genotype for another, correlated, marker. In one specific embodiment of invention, the significant r² value can be at least 0.2. In another specific embodiment of invention, the significant r² value can be at least 0.5. In one specific embodiment of invention, the significant r² value can be at least 0.8. Alternatively, linkage disequilibrium as described herein, refers to linkage disequilibrium characterized by values of r² of at least 0.2, such as 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.85, 0.9, 0.95, 0.96, 0.97, 0.98, 0.99. Thus, linkage disequilibrium represents a correlation between alleles of distinct markers. It is measured by correlation coefficient or | D'| (r² up to 1.0 and | D'| up to 1.0). Linkage disequilibrium can be determined in a single human population, as defined herein, or it can be determined in a collection of samples comprising individuals from more than one human population. In one embodiment of the invention, LD is determined in a sample from one or more of the HapMap populations. These include samples from the Yoruba people of Ibadan, Nigeria (YRI), samples from individuals from the Tokyo area in Japan (JPT), samples from individuals Beijing, China (CHB), and samples from U.S. residents with northern and western European ancestry (CEU), as described (The International HapMap Consortium, Nature 426: 789-796 (2003)). In one such embodiment, LD is determined in the Caucasian CEU population of the HapMap samples. In another embodiment, LD is determined in the African YRI population. In yet another embodiment, LD is determined in samples from the Icelandic population.

If all polymorphisms in the genome were independent at the population level {i.e., no LD between polymorphisms), then every single one of them would need to be investigated in association studies, to assess all different polymorphic states. However, due to linkage disequilibrium between polymorphisms, tightly linked polymorphisms are strongly correlated, which reduces the number of polymorphisms that need to be investigated in an association study to observe a significant association. Another consequence of LD is that many polymorphisms may give an association signal due to the fact that these polymorphisms are strongly correlated.

Genomic LD maps have been generated across the genome, and such LD maps have been proposed to serve as framework for mapping disease-genes (Risch, N. & Merkiangas, K, Science 273: 1516-1517 ( 1996); Maniatis, N., et a/., Proc Natl Acad Sci USA 99: 2228-2233 (2002); Reich, DE et a/, Nature 411 : 199-204 (2001)).

It is now established that many portions of the human genome can be broken into series of discrete haplotype blocks containing a few common haplotypes; for these blocks, linkage disequilibrium data provides little evidence indicating recombination (see, e.g., Wall., J.D. and Pritchard, J.K., Nature Reviews Genetics 4: 587-597 (2003); Daly, M. et a/., Nature Genet. 29: 229-232 (2001); Gabriel, S.B. et a/., Science 296: 2225-2229 (2002); Patil, N. et a/.,

Science 294: 1719-1723 (2001); Dawson, E. et a/., Nature 418: 544-548 (2002); Phillips, M.S. et a/., Nature Genet. 33: 382-387 (2003)).

Haplotype blocks (LD blocks) can be used to map associations between phenotype and haplotype status, using single markers or haplotypes comprising a plurality of markers. The main haplotypes can be identified in each haplotype block, and then a set of "tagging" SNPs or markers (the smallest set of SNPs or markers needed to distinguish among the haplotypes) can then be identified. These tagging SNPs or markers can then be used in assessment of samples from groups of individuals, in order to identify association between phenotype and haplotype. If desired, neighboring haplotype blocks can be assessed concurrently, as there may also exist linkage disequilibrium among the haplotype blocks.

It has thus become apparent that for any given observed association to a polymorphic marker in the genome, it is likely that additional markers in the genome also show association. This is a natural consequence of the uneven distribution of LD across the genome, as observed by the large variation in recombination rates. The markers used to detect association thus in a sense represent "tags" for a genomic region (i.e., a haplotype block or LD block) that is associating with a given disease or trait, and as such are useful for use in the methods and kits of the invention.

By way of example, rs78378222 may be detected directly to determine risk of cancer.

Alternatively, any marker in linkage disequilibrium with rs78378222 may be detected to determine risk.

Suitable surrogate markers may be selected using public information, such as from the International HapMap Consortium (http://www.hapmap.org) and the International

lOOOgenomes Consortium (http://www.1000genomes.org). The markers may also be suitably selected from results of whole-genome sequencing. The stronger the linkage disequilibrium (i.e., the higher the correlation) to the anchor marker, the better the surrogate, and thus the mores similar the association detected by the surrogate is expected to be to the association detected by the anchor marker. Markers with values of r² equal to 1 are perfect surrogates for the at-risk variants, i.e. genotypes for one marker perfectly predicts genotypes for the other. In other words, the surrogate will, by necessity, give exactly the same association data to any particular disease as the anchor marker. Markers with smaller values of r² than 1 can also be surrogates for the at-risk anchor variant.

The present invention encompasses the assessment of such surrogate markers for the markers as disclosed herein. Such markers are annotated, mapped and listed in public databases, as well known to the skilled person, or can alternatively be readily identified by sequencing the region or a part of the region identified by the markers of the present invention in a group of individuals, and identify polymorphisms in the resulting group of sequences. As a consequence, the person skilled in the art can readily and without undue experimentation identify and select appropriate surrogate markers.

Association analysis

For single marker association to a disease, the Fisher exact test can be used to calculate two- sided p-values for each individual allele. Correcting for relatedness among patients can be done by extending a variance adjustment procedure previously described (Risch, N. & Teng, J. Genome Res., 8 : 1273- 1288 ( 1998)) for sibships so that it can be applied to general familial relationships. The method of genomic controls (Devlin, B. & Roeder, K. Biometrics 55 : 997 ( 1999)) can also be used to adj ust for the relatedness of the individuals and possible stratification.

For both single-marker and haplotype analyses, relative risk (RR) and the population attributable risk (PAR) can be calculated assuming a multiplicative model (haplotype relative risk model) (Terwilliger, J . D. & Ott, J ., Hum. Hered. 42: 337-46 ( 1992) and Falk, C.T. & Rubinstein, P, Ann. Hum. Genet. 51 (Pt 3) : 227 -33 ( 1987)), i.e., that the risks of the two alleles/haplotypes a person carries multiply. For example, if RR is the risk of A relative to a, then the risk of a person homozygote AA will be RR times that of a heterozygote Aa and RR² times that of a homozygote aa . The multiplicative model has a nice property that simplifies analysis and computations— haplotypes are independent, i.e., in Hardy-Weinberg

equilibrium, within the affected population as well as within the control population. As a consequence, haplotype counts of the affecteds and controls each have multinomial distributions, but with different haplotype frequencies under the alternative hypothesis.

Specifically, for two haplotypes, h, and h_jr

= {f p /if_j/P_j), where f and p denote, respectively, frequencies in the affected population and in the control population. While there is some power loss if the true model is not multiplicative, the loss tends to be mild except for extreme cases. Most importantly, p-values are always valid since they are computed with respect to null hypothesis.

Risk assessment and Diagnostics

Within any given population, there is an absolute risk of developing a disease or trait, defined as the chance of a person developing the specific disease or trait over a specified time-period . For example, a woman's lifetime absolute risk of breast cancer is one in nine. That is to say, one woman in every nine will develop breast cancer at some point in their lives. Risk is typically measured by looking at very large numbers of people, rather than at a particular individual. Risk is often presented in terms of Absolute Risk (AR) and Relative Risk (RR) . Relative Risk is used to compare risks associating with two variants or the risks of two different groups of people. For example, it can be used to compare a group of people with a certain genotype with another group having a different genotype. For a disease, a relative risk of 2 means that one group has twice the chance of developing a disease as the other group. The risk presented is usually the relative risk for a person, or a specific genotype of a person, compared to the population with matched gender and ethnicity. Risks of two individuals of the same gender and ethnicity could be compared in a simple manner. For example, if, compared to the population, the first individual has relative risk 1.5 and the second has relative risk 0.5, then the risk of the first individual compared to the second individual is 1.5/0.5 = 3. Risk Calculations

The creation of a model to calculate the overall genetic risk involves two steps: i) conversion of odds-ratios for a single genetic variant into relative risk and ii) combination of risk from multiple variants in different genetic loci into a single relative risk value.

Deriving risk from odds-ratios

Most gene discovery studies for complex diseases that have been published to date in authoritative journals have employed a case-control design because of their retrospective setup. These studies sample and genotype a selected set of cases (people who have the specified disease condition) and control individuals. The interest is in genetic variants (alleles) which frequency in cases and controls differ significantly.

The results are typically reported in odds ratios, that is the ratio between the fraction

(probability) with the risk variant (carriers) versus the non-risk variant (non-carriers) in the groups of affected versus the controls, i.e. expressed in terms of probabilities conditional on the affection status:

OR = (Pr(c|A)/Pr(nc|A)) / (Pr(c|C)/Pr(nc|C))

Sometimes it is however the absolute risk for the disease that we are interested in, i.e. the fraction of those individuals carrying the risk variant who get the disease or in other words the probability of getting the disease. This number is typically not directly measured in case- control studies, in part, because the ratio of cases versus controls is typically not the same as that in the general population. However, under certain assumption, we can estimate the risk from the odds ratio.

It is well known that under the rare disease assumption, the relative risk of a disease can be approximated by the odds ratio. This assumption may however not hold for many common diseases. Still, it turns out that the risk of one genotype variant relative to another can be estimated from the odds ratio expressed above. The calculation is particularly simple under the assumption of random population controls where the controls are random samples from the same population as the cases, including affected people rather than being strictly unaffected individuals. To increase sample size and power, many of the large genome-wide association and replication studies use controls that were neither age-matched with the cases, nor were they carefully scrutinized to ensure that they did not have the disease at the time of the study. Hence, they often approximate a random sample from the general population. It is noted that this assumption is rarely expected to be satisfied exactly, but the risk estimates are usually robust to moderate deviations from this assumption.

Calculations show that for the dominant and the recessive models, where we have a risk variant carrier, "c", and a non-carrier, "nc", the odds ratio of individuals is the same as the risk ratio between these variants:

OR = Pr(A |c)/Pr(A| nc) = r And likewise for the multiplicative model, where the risk is the product of the risk associated with the two allele copies, the allelic odds ratio equals the risk factor:

OR = Pr(A |aa)/Pr(A|ab) = Pr(A|ab)/Pr(A| bb) = r

Here "a" denotes the risk allele and "b" the non-risk allele. The factor "r" is therefore the relative risk between the allele types.

For many of the studies published in the last few years, reporting common variants associated with complex diseases, the multiplicative model has been found to summarize the effect adequately and most often provide a fit to the data superior to alternative models such as the dominant and recessive models.

Determining risk

In the present context, an individual who is at an increased susceptibility (i.e., increased risk) for cancer is an individual who is carrying at least one at-risk variant as described herein. In certain embodiments, the variant is within the human TP53 gene, or a variant encoded by a variation in the human TP53 gene. In one embodiment, significance associated with a marker is measured by a relative risk (RR). In another embodiment, significance associated with a marker or haplotye is measured by an odds ratio (OR). In a further embodiment, the significance is measured by a percentage. In one embodiment, a significant increased risk is measured as a risk (relative risk and/or odds ratio) of at least 2.0, including but not limited to at least 3.0, at least 3.5, at least 4.0, at least 5.0, at least 6.0, at least 7.0, at least 8.0, at least 9.0, at least 10.0, at least 11.0, at least 12.0, at least 13.0, at least 14.0, at least 15.0, at least 16.0, at least 18.0, at least 20.0, at least 22.0, or at least 24.0. In a particular embodiment, a risk (relative risk and/or odds ratio) of at least 5.0 is significant. In another particular embodiment, a risk of at least 7.0 is significant.

An at-risk variant as described herein is one where at least one allele of at least one marker is more frequently present in an individual at risk for cancer (affected), or diagnosed with cancer, compared to the frequency in a comparison group (control), such that the presence of the marker allele is indicative of susceptibility to cancer. The control group may in one embodiment be a population sample, i.e. a random sample from the general population. In another embodiment, the control group is represented by a group of individuals who are disease-free, i.e. individuals who have not been diagnosed with cancer.

The person skilled in the art will appreciate that for markers with two alleles present in the population being studied (such as SNPs), and wherein one allele is found in increased frequency in a group of individuals with a trait or disease in the population, compared with controls, the other allele of the marker will be found in decreased frequency in the group of individuals with the trait or disease, compared with controls. In such a case, one allele of the marker (the one found in increased frequency in individuals with the trait or disease) will be the at-risk allele, while the other allele will be a protective allele. Database

Determining susceptibility can alternatively or additionally comprise comparing nucleic acid sequence data and/or protein sequence data {e.g., genotype data) to a database containing correlation data between polymorphic markers and susceptibility to cancer. The database can be part of a computer-readable medium described herein.

In a specific aspect of the invention, the database comprises at least one measure of

susceptibility to cancer for the polymorphic markers. For example, the database may

comprise risk values associated with particular genotypes at such markers. The database may also comprise risk values associated with particular genotype combinations for multiple such markers.

In another specific aspect of the invention, the database comprises a look-up table containing at least one measure of susceptibility to cancer for the polymorphic markers.

Further steps

The methods disclosed herein can comprise additional steps which may occur before, after, or simultaneously with one of the aforementioned steps of the method of the invention. In a specific embodiment of the invention, the method of determining a susceptibility to cancer further comprises reporting the susceptibility to at least one entity selected from the group consisting of the individual, a guardian of the individual, a genetic service provider, a physician, a medical organization, and a medical insurer. The reporting may be accomplished by any of several means. For example, the reporting can comprise sending a written report on physical media or electronically or providing an oral report to at least one entity of the group, which written or oral report comprises the susceptibility. Alternatively, the reporting can comprise providing the at least one entity of the group with a login and password, which provides access to a report comprising the susceptibility posted on a password-protected computer system.

Study population

In a general sense, the methods and kits described herein can be utilized from samples containing nucleic acid material (DNA or RNA) or protein material from any source and from any individual, or from genotype or sequence data derived from such samples. In preferred embodiments, the individual is a human individual. The individual can be an adult, child, or fetus. In some embodiments, the individual is a female individual. The nucleic acid or protein source may be any sample comprising nucleic acid or protein material, including biological samples, or a sample comprising nucleic acid or protein material derived therefrom. The present invention also provides for assessing markers in individuals who are members of a target population. Such a target population is in one embodiment a population or group of individuals at risk of developing cancer, based on other genetic factors, biomarkers,

biophysical parameters, or lifestyle factors.

The Icelandic population is a Caucasian population of Northern European ancestry. A large number of studies reporting results of genetic linkage and association in the Icelandic population have been published in the last few years. Many of those studies show replication of variants, originally identified in the Icelandic population as being associating with a particular disease, in other populations (Sulem, P., et al. Nat Genet May 17 2009 (Epub ahead of print); Rafnar, T., et a/. Nat Genet 41 : 221-7 (2009); Greta rsdottir, S., et al. Ann Neurol 64:402-9 (2008); Stacey, S.N., et a/. Nat Genet 40: 1313-18 (2008); Gudbjartsson, D.F., et al. Nat Genet 40: 886-91 (2008); Styrkarsdottir, U., et a/. N Engl J Med 358: 2355-65 (2008); Thorgeirsson, T., et a/. Nature 452: 638-42 (2008); Gudmundsson, J., et a/. Nat Genet.

40: 281-3 (2008); Stacey, S.N., et a/., Nat Genet. 39: 865-69 (2007); Helgadottir, A., et a/., Science 316: 1491-93 (2007); Steinthorsdottir, V., et a/., Nat Genet. 39: 770-75 (2007);

Gudmundsson, J., et a/., Nat Genet. 39: 631-37 (2007); Frayling, TM, Nature Reviews Genet 8:657-662 (2007); Amundadottir, L.T., et a/., Nat Genet. 38: 652-58 (2006) ; Grant, S.F., et al., Nat Genet. 38: 320-23 (2006)). Thus, genetic findings in the Icelandic population have in general been replicated in other populations, including populations from Africa and Asia.

It is thus believed that the markers described herein to be associated with risk of cancer will show similar association in other human populations. It is further contemplated that additional variants in the human TP53 gene may be conferring risk of cancer in other populations. Particular embodiments comprising individual human populations are thus also contemplated and within the scope of the invention. Such embodiments relate to human subjects that are from one or more human population including, but not limited to, Caucasian populations, European populations, American populations, Eurasian populations, Asian populations, Central/South Asian populations, East Asian populations, and African populations. In certain embodiments, the invention pertains to individuals from Caucasian populations. In certain embodiments, the invention pertains to Icelandic individuals.

In certain embodiments, the invention relates to markers identified in specific populations, as described in the above. The person skilled in the art will appreciate that measures of linkage disequilibrium (LD) may give different results when applied to different populations. This is due to different population history of different human populations as well as differential selective pressures that may have led to differences in LD in specific genomic regions. It is also well known to the person skilled in the art that certain markers, e.g. SNP markers, have different population frequency in different populations, or are polymorphic in one population but not in another. The person skilled in the art will however apply the methods available and as taught herein to practice the present invention in any given human population. This may include assessment of polymorphic markers in the LD region of the present invention, so as to identify those markers that give strongest association within the specific population. Thus, the at-risk variants of the present invention may reside on different haplotype background and in different frequencies in various human populations. However, utilizing methods known in the art and the markers of the present invention, the invention can be practiced in any given human population. Diagnostic Methods

Polymorphic markers associated with increased susceptibility of cancer, e.g. BCC, prostate cancer, glioma and colorectal adenoma, are useful in diagnostic methods. While methods of diagnosing cancer are known in the art, the detection risk markers for cancer advantageously may be useful for detection of cancer at its early stages and may also reduce the occurrence of misdiagnosis. In this regard, the invention further provides methods of diagnosing cancer comprising obtaining sequence data identifying at least one risk allele as described herein, in conjunction with carrying out one or more clinical diagnostic steps for the identification of cancer. Such diagnostic steps may include imaging methods, clinical evaluation methods, determination of biophysical parameters and determination of biomarker levels.

The present invention pertains in some embodiments to methods of clinical applications of diagnosis, e.g., diagnosis performed by a medical professional. In other embodiments, the invention pertains to methods of diagnosis or methods of determination of a susceptibility performed by a layman. The layman can be the customer of a sequencing or genotyping service. The layman may also be a genotype or sequencing service provider, who performs analysis on a DNA sample from an individual, in order to provide service related to genetic risk factors for particular traits or diseases, based on the genotype status of the individual {i.e., the customer). Sequencing methods include for example those discussed in the above, but in general any suitable sequencing method may be used in the methods described and claimed herein. Recent technological advances in genotyping technologies, including high- throughput genotyping of SNP markers, such as Molecular Inversion Probe array technology (e.g., Affymetrix GeneChip), and BeadArray Technologies (e.g., Illumina GoldenGate and Infinium assays) have made it possible for individuals to have their own genome assessed for up to one million SNPs simultaneously, at relatively little cost. The resulting genotype information, which can be made available to the individual, can be compared to information about disease or trait risk associated with various SNPs, including information from public literature and scientific publications.

The application of disease-associated alleles as described herein, can thus for example be performed by the individual, through analysis of his/her genotype data, by a health professional based on results of a clinical test, or by a third party, including the genotype or sequencing service provider. The third party may also be service provider who interprets genotype or sequence information from the customer to provide service related to specific genetic risk factors, including the genetic markers described herein. In other words, the diagnosis or determination of a susceptibility of genetic risk can be made by health professionals, genetic counselors, third parties providing genotyping and/or sequencing service, third parties providing risk assessment service or by the layman (e.g. , the individual), based on information about the genotype status of an individual and knowledge about the risk conferred by particular genetic risk factors (e.g., particular SNPs). In the present context, the term "diagnosing", "diagnose a susceptibility" and "determine a susceptibility" is meant to refer to any available method for determining a susceptibility or risk of disease, including those mentioned above.

In certain embodiments, a sample containing genomic DNA or protein from an individual is collected. Such sample can for example be a buccal swab, a saliva sample, a blood sample, or other suitable samples containing genomic DNA or protein, as described further herein. In certain embodiments, the sample is obtained by non-invasive means (e.g., for obtaining a buccal sample, saliva sample, hair sample or skin sample). In certain embodiments, the sample is obtained by non-surgical means, i.e. in the absence of a surgical intervention on the individual that puts the individual at substantial health risk. Such embodiments may, in addition to non-invasive means also include obtaining sample by extracting a blood sample (e.g., a venous blood sample). The genomic DNA or protein obtained from the individual is then analyzed using any common technique available to the skilled person, such as high- throughput technologies for genotyping and/or sequencing. Results from such methods are stored in a convenient data storage unit, such as a data carrier, including computer databases, data storage disks, or by other convenient data storage means. In certain embodiments, the computer database is an object database, a relational database or a post- relational database. The genotype data is subsequently analyzed for the presence of certain variants known to be susceptibility variants for a particular human condition, such as the genetic variants described herein associated with risk of cancer. Genotype and/or sequencing data can be retrieved from the data storage unit using any convenient data query method. Calculating risk conferred by a particular genotype for the individual can be based on comparing the genotype of the individual to previously determined risk (expressed as a relative risk (RR) or and odds ratio (OR), for example) for the genotype, for example for an heterozygous carrier of an at-risk variant. The calculated risk for the individual can be the relative risk for a person, or for a specific genotype of a person, compared to the average population with matched gender and ethnicity. The average population risk can be expressed as a weighted average of the risks of different genotypes, using results from a reference population, and the appropriate calculations to calculate the risk of a genotype group relative to the population can then be performed. Alternatively, the risk for an individual is based on a comparison of particular genotypes, for example heterozygous carriers of an at-risk allele of a marker compared with non-carriers of the at-risk allele. The calculated risk estimated can be made available to the customer via a website, preferably a secure website.

Prognostic methods

In addition to the utilities described above, the polymorphic markers of the invention are useful in determining a prognosis of a human individual with cancer. The variants described herein are indicative of risk of cancer, including BCC, glioma, prostate cancer and colorectal adenoma. Individuals carrying mutant alleles that predispose to cancer are at increased risk of the cancer. Such mutant alleles are predicted to be indicative of prognosis of the cancer. The prognosis predicted can be any type of prognosis relating to the progression of the cancer, including glioma, and/or relating to the chance of recovering from the cancer. The prognosis can, for instance, relate to the severity of the cancer, or how the cancer will respond to therapeutic treatment.

Accordingly, the invention provides a method of predicting prognosis of an individual experiencing symptoms associated with, or an individual diagnosed with, cancer. The method comprises analyzing data representative of at least one allele of a TP 53 gene in a human subject, wherein different alleles of the human TP53 gene are associated with different susceptibilities to at least one cancer in humans, and determining a prognosis of the human subject from the data. In certain embodiments, the cancer is glioma. The analyzing may comprise analysis for a mutation in TP53 that leads to loss of function or loss of expression of TP53. In certain embodiments, the analyzing comprises analyzing for the presence or absence of at least one mutant allele indicative of a TP53 defect selected from impaired 3' end processing of TP53 and impaired polyadenylation of TP53.

The determination of the presence of a mutation in TP53 that leads to loss of function or loss of expression of TP53 is in certain embodiments indicative of a worsened prognosis of cancer, including glioma. In other words, the presence of such mutations is in certain embodiments indicative that the individual has a worse prognosis of the cancer than do individuals with the cancer that do not carry such mutations.

In some variations, the prognostic method further includes one or more additional steps, such as a step relating to generating the data by analyzing a biological sample; and/or a step involving selecting or administering a medial protocol to the subject, as described elsewhere herein.

Methods of treatment

It may be useful to select individuals for treatment based on the presence of altered forms of TP53, including mutations in TP53 that cause impaired polyadenylation of TP53 transcripts, or otherwise result in reduced or altered protein expression. As discussed in the above, it is contemplated that mutations that affect 3' end processing of TP53, including mutations in polyladenylation sites result in reduced expression of TP53. Therefore, it is contemplated that it may be beneficial to select individuals for therapy based on whether the individuals are carriers of such mutations.

Accordingly, the invention provides in one aspect a method of treatment of a cancer, the method comprising steps of (a) determining the presence or absence of at least one allele of at least one polymorphic marker in a nucleic acid sample obtained from the individual, wherein the at least one allele causes impaired 3' processing of TP53 mRNA; (b) selecting for treatment with the therapeutic agent a subject identified as having the at least one allele in the nucleic acid sample; and (c) administering to the selected individual a pharmaceutically acceptable amount of a therapeutic agent for the cancer. In certain embodiments, the cancer is glioma.

Another aspect provides a method of selecting a therapeutic regimen for a human subject with a cancer, the method comprising analyzing data representative of at least one allele of a TP53 gene (SEQ ID NO: 3) in a human subject with the cancer to identify the presence or absence of a TP 53 mutant allele that leads to impaired 3' mRNA processing of TP53, and selecting a therapeutic regimen of a therapeutic agent for treating the cancer for a subject identified from the data as having the mutant allele.

In certain embodiments, the cancer is glioma. In preferred embodiments, the TP53 allele is an allele that disrupts a 3' end processing signal in TP53. In certain preferred embodiments, the allele is the C allele of rs78378222, which alters the AATAAA polyadenylation site in TP53 to AATACA.

Therapeutic agents for treating glioma include temozolomide ((4-methyl-5-oxo- 2,3,4,6,8- pentazabicyclo [4.3.0] nona-2,7,9-triene- 9-carboxamide) and cannabinoids. Cannabinoids can for example be selected from the group consisting of tetrahydrocannabinol ((-)-(6a ?, 10a ?)- 6,6,9-trimethyl-3-pentyl-6a,7,8, 10a-tetrahydro-6 - -benzo[c]chromen- l-ol), cannabidiol (2- [(l ?,6 ?)-6-isopropenyl-3-methylcyclohex-2-en-l-yl]-5-pentylbenzene-l,3-diol), cannabinol (6,6,9-trimethyl-3-pentyl-benzo[c]chromen-l-ol), cannabigerol (2-[(2E)-3,7-dimethylocta- 2,6-dienyl]-5-pentyl-benzene-l,3-diol), cannabichromene (2-Methyl-2-(4-methylpent-3- enyl)-7-pentyl-5-chromenol), cannabicyclol ((laR-(la alpha, 3a alpha, 8b alpha, 8c alphas- la, 2, 3, 3a, 8b, 8c-hexahydro- 1,1, 3a-trimethyl-6-pentyl-lH-4-oxabenzo(f)cyclobut^^ ol), cannabivarin (6,6,9-trimethyl-3-propyl-l-benzo[c]chromenol), tetrahydrocannabivarin (6,6,9-trimethyl-3-propyl-6a,7,8,10a-tetrahydro-6 - -benzo[c]chromen-l-ol), cannabidivarin (2-((lS,6S)-3-methyl-6-(prop-l-en-2-yl)cyclohex-2-enyl)-5-propylbenzene-l,3-diol), cannabichromevarin, cannabigerovarin and cannabigerol monethyl ether. Accordingly, preferred embodiments of the methods of treating glioma comprise selecting a therapeutic agent selected from the above recited group of therapeutic agents.

Kits

Kits useful in the methods of the invention comprise components useful in any of the methods described herein, including for example, primers for nucleic acid amplification, hybridization probes (e.g. probes for detecting particular mutant alleles), restriction enzymes (e.g., for RFLP analysis), allele-specific oligonucleotides, antibodies, e.g., antibodies that bind to an altered TP53 polypeptide (e.g. a missense variant in TP53 or a truncated TP53 polypeptide) or to a non-altered (native) TP53 polypeptide, means for amplification of nucleic acids, means for analyzing the nucleic acid sequence of nucleic acids, means for analyzing the amino acid sequence of polynucleotides, etc. The kits can for example include necessary buffers, nucleic acid primers for amplifying nucleic acids (e.g., a nucleic acid segment comprising one or more of the polymorphic markers as described herein), and reagents for allele-specific detection of the fragments amplified using such primers and necessary enzymes (e.g. , DNA polymerase) . Additionally, kits can provide reagents for assays to be used in combination with the methods of the present invention, e.g. , reagents for use with other diagnostic assays for cancer or related conditions.

In one embodiment, the invention pertains to a kit for assaying a sample from a subject to detect a susceptibility to cancer (e.g ., cancer) in the subject, wherein the kit comprises reagents necessary for selectively detecting at least one at-risk variant for cancer in the individual, wherein the at least one at-risk variant is a polymorphic marker in the human TP53 gene or an amino acid substitution in an encoded TP53 protein.

In a particular embodiment, the reagents comprise at least one contiguous oligonucleotide that hybridizes to a fragment of the genome of the individual comprising at least one polymorphism of the present invention. In another embodiment, the reagents comprise at least one pair of oligonucleotides that hybridize to opposite strands of a genomic segment obta ined from a subject, wherein each oligonucleotide primer pair is designed to selectively amplify a fragment of the genome of the individual that includes at least one polymorphism associated with risk of cancer. In one embodiment the fragment is at least 20 base pairs in size. Such oligonucleotides or nucleic acids (e.g. , oligonucleotide primers) can be designed using portions of the nucleic acid sequence flanking the polymorphism. In another embodiment, the kit comprises one or more labeled nucleic acids capable of allele-specific detection of one or more specific polymorphic markers or haplotypes, and reagents for detection of the label. Suitable labels include, e.g. , a radioisotope, a fluorescent label, an enzyme label, an enzyme co-factor label, a magnetic label, a spin label, an epitope label.

In certain embodiments, determination of the presence of a particular marker allele is indicative of a increased susceptibility of cancer. In another embodiment, determination of the presence of a particular marker allele is indicative of prognosis of cancer, or selection of appropriate therapy for cancer. In another embodiment, the presence of the marker allele or haplotype is indicative of response to therapy for the cancer. In yet another embodiment, the presence of the marker allele is indicative of progress of treatment of the cancer.

In certain embodiments, the kit comprises reagents for detecting no more than 100 alleles in the genome of the individual. In certain other embodiments, the kit comprises reagents for detecting no more than 20 alleles in the genome of the individua l.

In a further aspect of the present invention, a pharmaceutical pack (kit) is provided, the pack comprising a therapeutic agent and a set of instructions for administration of the therapeutic agent to humans diagnostically tested for an at-risk variant for cancer. The therapeutic agent can be a small molecule drug, an antibody, a peptide, an antisense or RNAi molecule, or other therapeutic molecules. In one embodiment, an individual identified as a carrier of at least one variant of the present invention is instructed to take a prescribed dose of the therapeutic agent. In one such embodiment, an individual identified as a homozygous carrier of at least one variant of the present invention (e.g., an at-risk variant) is instructed to take a prescribed dose of the therapeutic agent. In another embodiment, an individual identified as a non- carrier of at least one variant of the present invention {e.g., an at-risk variant) is instructed to take a prescribed dose of the therapeutic agent.

In certain embodiments, the kit further comprises a set of instructions for using the reagents comprising the kit. In certain embodiments, the kit further comprises a collection of data comprising correlation data between the at least one at-risk variant and susceptibility to cancer.

Antisense agents

The nucleic acids and/or variants described herein, or nucleic acids comprising their complementary sequence, may be used as antisense constructs to control gene expression in cells, tissues or organs. The methodology associated with antisense techniques is well known to the skilled artisan, and is for example described and reviewed in AntisenseDrug

Technology: Principles, Strategies, and Applications, Crooke, ed., Marcel Dekker Inc., New York (2001). In general, antisense agents (antisense oligonucleotides) are comprised of single stranded oligonucleotides (RNA or DNA) that are capable of binding to a complimentary nucleotide segment. By binding the appropriate target sequence, an RNA-RNA, DNA-DNA or RNA-DNA duplex is formed. The antisense oligonucleotides are complementary to the sense or coding strand of a gene. It is also possible to form a triple helix, where the antisense oligonucleotide binds to duplex DNA.

Several classes of antisense oligonucleotide are known to those skilled in the art, including cleavers and blockers. The former bind to target RNA sites, activate intracellular nucleases {e.g., RnaseH or Rnase L), that cleave the target RNA. Blockers bind to target RNA, inhibit protein translation by steric hindrance of the ribosomes. Examples of blockers include nucleic acids, morpholino compounds, locked nucleic acids and methylphosphonates (Thompson,

Drug Discovery Today, 7:912-917 (2002)). Antisense oligonucleotides are useful directly as therapeutic agents, and are also useful for determining and validating gene function, for example by gene knock-out or gene knock-down experiments. Antisense technology is further described in Lavery et a/. , Curr. Opin. Drug Discov. Devel. 6: 561-569 (2003), Stephens et al., Curr. Opin. Mol. Ther. 5: 118-122 (2003), Kurreck, Eur. J. Biochem.

270: 1628-44 (2003), Dias et al., Mol. Cancer Ten 1 : 347-55 (2002), Chen, Methods Mol. Med. 75: 621-636 (2003), Wang et al., Curr. Cancer Drug Targets 1 : 177-96 (2001), and Bennett, Antisense Nucleic Acid Drug. Dev. 12: 215-24 (2002).

In certain embodiments, the antisense agent is an oligonucleotide that is capable of binding to a particular nucleotide segment. In certain embodiments, the nucleotide segment comprises the human TP53 gene. In certain other embodiments, the antisense nucleotide is capable of binding to a nucleotide segment of a human TP53 transcript, as set forth in SEQ ID NO: 3. In one embodiment, the antisense nucleotide is capable of binding the a nucleotide segment of a human TP53 transcript with sequence as set forth in SEQ ID NO: 3 that has a T to G substitution at position 34 (A to C substitution on the reverse complement as shown in SEQ ID NO: 2 for rs78378222). Antisense nucleotides can be from 5-400 nucleotides in length, including 5-200 nucleotides, 5-100 nucleotides, 10-50 nucleotides, and 10-30 nucleotides. In certain preferred embodiments, the antisense nucleotides is from 14-50 nucleotides in length, including 14-40 nucleotides and 14-30 nucleotides.

The variants described herein can also be used for the selection and design of antisense reagents that are specific for particular variants. Using information about the variants described herein, antisense oligonucleotides or other antisense molecules that specifically target mRNA molecules that contain one or more variants of the invention can be designed. In this manner, expression of mRNA molecules that contain one or more variant of the present invention can be inhibited or blocked. In one embodiment, the antisense molecules are designed to specifically bind a particular allelic form of the target nucleic acid, thereby inhibiting translation of a product originating from this specific allele, but which do not bind other or alternate variants at the specific polymorphic sites of the target nucleic acid molecule. In one embodiment, the antisense molecule is designed to specifically bind to nucleic acids comprising the rs78378222 allele C in TP53. As antisense molecules can be used to inactivate mRNA so as to inhibit gene expression, and thus protein expression, the molecules can be used for disease treatment. The methodology can involve cleavage by means of ribozymes containing nucleotide sequences complementary to one or more regions in the mRNA that attenuate the ability of the mRNA to be translated. Such mRNA regions include, for example, protein-coding regions, in particular protein-coding regions

corresponding to catalytic activity, substrate and/or ligand binding sites, or other functional domains of a protein.

The phenomenon of RNA interference (RNAi) has been actively studied for the last decade, since its original discovery in C. elegans (Fire et al., Nature 391 : 806-11 (1998)), and in recent years its potential use in treatment of human disease has been actively pursued (reviewed in Kim & Rossi, Nature Rev. Genet. 8: 173-204 (2007)). RNA interference (RNAi), also called gene silencing, is based on using double-stranded RNA molecules (dsRNA) to turn off specific genes. In the cell, cytoplasmic double-stranded RNA molecules (dsRNA) are processed by cellular complexes into small interfering RNA (siRNA). The siRNA guide the targeting of a protein-RNA complex to specific sites on a target mRNA, leading to cleavage of the mRNA (Thompson, Drug Discovery Today, 7:912-917 (2002)). The siRNA molecules are typically about 20, 21, 22 or 23 nucleotides in length. Thus, one aspect of the invention relates to isolated nucleic acid molecules, and the use of those molecules for RNA interference, i.e. as small interfering RNA molecules (siRNA). In one embodiment, the isolated nucleic acid molecules are 18-26 nucleotides in length, preferably 19-25 nucleotides in length, more preferably 20-24 nucleotides in length, and more preferably 21, 22 or 23 nucleotides in length. Another pathway for RNAi-mediated gene silencing originates in endogenously encoded primary microRNA (pri-miRNA) transcripts, which are processed in the cell to generate precursor miRNA (pre-miRNA). These miRNA molecules are exported from the nucleus to the cytoplasm, where they undergo processing to generate mature miRNA molecules (miRNA), which direct translational inhibition by recognizing target sites in the 3' untranslated regions of mRNAs, and subsequent mRNA degradation by processing P-bodies (reviewed in Kim & Rossi, Nature Rev. Genet. 8: 173-204 (2007)).

Clinical applications of RNAi include the incorporation of synthetic siRNA duplexes, which preferably are approximately 20-23 nucleotides in size, and preferably have 3' overlaps of 2 nucleotides. Knockdown of gene expression is established by sequence-specific design for the target mRNA. Several commercial sites for optimal design and synthesis of such molecules are known to those skilled in the art.

Other applications provide longer siRNA molecules (typically 25-30 nucleotides in length, preferably about 27 nucleotides), as well as small hairpin RNAs (shRNAs; typically about 29 nucleotides in length). The latter are naturally expressed, as described in Amarzguioui et al. {FEBS Lett. 579: 5974-81 (2005)). Chemically synthetic siRNAs and shRNAs are substrates for in vivo processing, and in some cases provide more potent gene-silencing than shorter designs (Kim et a/., Nature Biotechnol. 23: 222-226 (2005); Siolas et a/., Nature Biotechnol. 23: 227-231 (2005)). In general siRNAs provide for transient silencing of gene expression, because their intracellular concentration is diluted by subsequent cell divisions. By contrast, expressed shRNAs mediate long-term, stable knockdown of target transcripts, for as long as transcription of the shRNA takes place (Marques et a/., Nature Biotechnol. 23: 559-565 (2006); Brummelkamp et a/., Science 296: 550-553 (2002)).

Since RNAi molecules, including siRNA, miRNA and shRNA, act in a sequence-dependent manner, the variants presented herein can be used to design RNAi reagents that recognize specific nucleic acid molecules comprising specific alleles and/or haplotypes (e.g., the alleles and/or haplotypes of the present invention), while not recognizing nucleic acid molecules comprising other alleles or haplotypes. These RNAi reagents can thus recognize and destroy the target nucleic acid molecules. As with antisense reagents, RNAi reagents can be useful as therapeutic agents (i.e., for turning off disease-associated genes or disease-associated gene variants), but may also be useful for characterizing and validating gene function (e.g., by gene knock-out or gene knock-down experiments).

Delivery of RNAi may be performed by a range of methodologies known to those skilled in the art. Methods utilizing non-viral delivery include cholesterol, stable nucleic acid-lipid particle (SNALP), heavy-chain antibody fragment (Fab), aptamers and nanoparticles. Viral delivery methods include use of lentivirus, adenovirus and adeno-associated virus. The siRNA molecules are in some embodiments chemically modified to increase their stability. This can include modifications at the 2' position of the ribose, including 2'-0-methylpurines and 2'- fluoropyrimidines, which provide resistance to Rnase activity. Other chemical modifications are possible and known to those skilled in the art.

The following references provide a further summary of RNAi, and possibilities for targeting specific genes using RNAi : Kim & Rossi, Nat. Rev. Genet. 8: 173-184 (2007), Chen &

Rajewsky, Nat. Rev. Genet. 8: 93-103 (2007), Reynolds, et a\._r Nat. Biotechnol. 22: 326-330 (2004), Chi et al., Proc. Natl. Acad. Sci. USA 100: 6343-6346 (2003), Vickers et a\., J. Biol. Chem. 278: 7108-7118 (2003), Agami, Curr. Opin. Chem. Biol. 6: 829-834 (2002), Lavery, et al., Curr. Opin. Drug Discov. Devel. 6: 561-569 (2003), Shi, Trends Genet. 19:9-12 (2003), Shuey et al., Drug Discov. Today 7: 1040-46 (2002), McManus et al., Nat. Rev. Genet. 3: 737- 747 (2002), Xia et al., Nat. Biotechnol. 20: 1006- 10 (2002), Plasterk et al., curr. Opin. Genet. Dev. 10: 562-7 (2000), Bosher et al., Nat. Cell Biol. 2: E31-6 (2000), and Hunter, Curr. Biol. 9: R440-442 (1999).

Nucleic acids and polypeptides

The nucleic acids and polypeptides described herein can be used in methods and kits of the present invention. An "isolated" nucleic acid molecule, as used herein, is one that is separated from nucleic acids that normally flank the gene or nucleotide sequence (as in genomic sequences) and/or has been completely or partially purified from other transcribed sequences (e.g., as in an RNA library). For example, an isolated nucleic acid of the invention can be substantially isolated with respect to the complex cellular milieu in which it naturally occurs, or culture medium when produced by recombinant techniques, or chemical precursors or other chemicals when chemically synthesized. In some instances, the isolated material will form part of a composition (for example, a crude extract containing other substances), buffer system or reagent mix. In other circumstances, the material can be purified to essential homogeneity, for example as determined by polyacrylamide gel electrophoresis (PAGE) or column chromatography (e.g., HPLC). An isolated nucleic acid molecule of the invention can comprise at least about 50%, at least about 80% or at least about 90% (on a molar basis) of all macromolecular species present. With regard to genomic DNA, the term "isolated" also can refer to nucleic acid molecules that are separated from the chromosome with which the genomic DNA is naturally associated. For example, the isolated nucleic acid molecule can contain less than about 250 kb, 200 kb, 150 kb, 100 kb, 75 kb, 50 kb, 25 kb, 10 kb, 5 kb, 4 kb, 3 kb, 2 kb, 1 kb, 0.5 kb or 0.1 kb of the nucleotides that flank the nucleic acid molecule in the genomic DNA of the cell from which the nucleic acid molecule is derived.

The invention also pertains to nucleic acid molecules that hybridize under high stringency hybridization conditions, such as for selective hybridization, to a nucleotide sequence described herein (e.g., nucleic acid molecules that specifically hybridize to a nucleotide sequence containing a polymorphic site associated with a marker or haplotype described herein). Such nucleic acid molecules can be detected and/or isolated by allele- or sequence- specific hybridization (e.g., under high stringency conditions). Stringency conditions and methods for nucleic acid hybridizations are well known to the skilled person (see, e.g., Current Protocols in Molecular Biology, Ausubel, F. et a/, John Wiley & Sons, ( 1998), and Kraus, M . and Aaronson, S., Methods Enzymol. , 200 : 546-556 ( 1991), the entire teachings of which are incorporated by reference herein.

The percent identity of two nucleotide or amino acid sequences can be determined by aligning the sequences for optimal comparison purposes (e.g. , gaps can be introduced in the sequence of a first sequence) . The nucleotides or amino acids at corresponding positions are then compared, and the percent identity between the two sequences is a function of the number of identical positions shared by the sequences (i.e., % identity = # of identical positions/total # of positions x 100) . In certain embodiments, the length of a sequence aligned for comparison purposes is at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, or at least 95%, of the length of the reference sequence. The actual comparison of the two sequences can be accomplished by methods well known to the skilled person, for example, using the NBLAST and XBLAST programs, as described in Altschul, S. et al. , Nucleic Acids Res., 25: 3389-3402 ( 1997) . Another example of an algorithm is BLAT (Kent, W.J . Genome Res. 12 : 656-64 (2002)) .

The present invention also provides isolated nucleic acid molecules that contain a fragment or portion that hybridizes under highly stringent conditions to a nucleic acid that comprises, or consists of, the nucleotide sequence of the human TP53 gene as set forth in SEQ ID NO : 3, or a nucleotide sequence comprising, or consisting of, the complement of the nucleotide sequence of SEQ ID NO : 3. In certain embodiments, the nucleotide sequence comprises at least one polymorphic allele as described herein (e.g., rs78378222 [C]) . The nucleic acid fragments of the invention may suitably be at least about 15, at least about 18, 20, 23 or 25 nucleotides, and can be up to 30, 40, 50, 100, 200, 300 or 400 nucleotides in length.

The nucleic acid fragments of the invention are used as probes or primers in assays such as those described herein. "Probes" or "primers" are oligonucleotides that hybridize in a base- specific manner to a complementary strand of a nucleic acid molecule. In addition to DNA and RNA, such probes and primers include polypeptide nucleic acids (PIMA), as described in Nielsen, P. et a/. , Science 254: 1497- 1500 ( 1991) . A probe or primer comprises a region of nucleotide sequence that hybridizes to at least about 15, typically about 20-25, and in certain embodiments about 40, 50 or 75, consecutive nucleotides of a nucleic acid molecule. In one embodiment, the probe or primer comprises at least one allele of at least one polymorphic marker or at least one haplotype described herein, or the complement thereof. In particular embodiments, a probe or primer can comprise 100 or fewer nucleotides; for example, in certain embodiments from 6 to 50 nucleotides, or, for example, from 12 to 30 nucleotides. In other embodiments, the probe or primer is at least 70% identical, at least 80% identical, at least 85% identical, at least 90% identical, or at least 95% identical, to the contiguous nucleotide sequence or to the complement of the contiguous nucleotide sequence. In another embodiment, the probe or primer is capable of selectively hybridizing to the contiguous nucleotide sequence or to the complement of the contiguous nucleotide sequence. Often, the probe or primer further comprises a label, e.g., a radioisotope, a fluorescent label, an enzyme label, an enzyme co-factor label, a magnetic label, a spin label, an epitope label.

Antibodies

The invention also provides antibodies which bind to an epitope comprising either a TP53 variant amino acid sequence (e.g., a polypeptide comprising an amino acid substitution or a truncated polypeptide) encoded by a variant allele or the reference amino acid sequence encoded by the corresponding non-variant or wild-type allele of TP53. The term "antibody" as used herein refers to immunoglobulin molecules and immunologically active portions of immunoglobulin molecules, i.e., molecules that contain antigen-binding sites that specifically bind an antigen. A molecule that specifically binds to a polypeptide of the invention is a molecule that binds to that polypeptide or a fragment thereof, or a fragment thereof), but does not substantially bind other molecules in a sample, e.g. , a biological sample, which naturally contains the polypeptide. Examples of immunologically active portions of immunoglobulin molecules include F(ab) and F(ab')₂ fragments which can be generated by treating the antibody with an enzyme such as pepsin. The invention provides polyclonal and monoclonal antibodies that bind to a polypeptide of the invention. The term "monoclonal antibody" or "monoclonal antibody composition", as used herein, refers to a population of antibody molecules that contain only one species of an antigen binding site capable of immunoreacting with a particular epitope of a polypeptide of the invention. A monoclonal antibody composition thus typically displays a single binding affinity for a particular polypeptide of the invention with which it immunoreacts.

Polyclonal antibodies can be prepared as described above by immunizing a suitable subject with a desired immunogen, e.g. , polypeptide of the invention or a fragment thereof. The antibody titer in the immunized subject can be monitored over time by standard techniques, such as with an enzyme linked immunosorbent assay (ELISA) using immobilized polypeptide. If desired, the antibody molecules directed against the polypeptide can be isolated from the mammal (e.g., from the blood) and further purified by well-known techniques, such as protein A chromatography to obtain the IgG fraction. At an appropriate time after immunization, e.g. , when the antibody titers are highest, antibody-producing cells can be obtained from the subject and used to prepare monoclonal antibodies by standard techniques, such as the hybridoma technique originally described by Kohler and Milstein, Nature 256:495-497 (1975), the human B cell hybridoma technique (Kozbor et al., Immunol. Today 4: 72 (1983)), the EBV-hybridoma technique (Cole et al., Monoclonal Antibodies and Cancer Therapy, Alan R. Liss,1985, Inc., pp. 77-96) or trioma techniques. The technology for producing hybridomas is well known (see generally Current Protocols in Immunology (1994) Coligan et al., (eds.) John Wiley & Sons, Inc., New York, NY). Briefly, an immortal cell line (typically a myeloma) is fused to lymphocytes (typically splenocytes) from a mammal immunized with an immunogen as described above, and the culture supernatants of the resulting hybridoma cells are screened to identify a hybridoma producing a monoclonal antibody that binds a polypeptide of the invention. Any of the many well known protocols used for fusing lymphocytes and immortalized cell lines can be applied for the purpose of generating a monoclonal antibody to a polypeptide of the invention (see, e.g. , Current Protocols in Immunology, supra; Galfre et al., Nature 266: 55052 (1977); R. H. Kenneth, in Monoclonal Antibodies: A New Dimension In Biological Analyses, Plenum Publishing Corp., New York, New York (1980); and Lerner, Yale J. Biol. Med. 54: 387- 402 (1981)). Moreover, the ordinarily skilled worker will appreciate that there are many variations of such methods that also would be useful.

Alternative to preparing monoclonal antibody-secreting hybridomas, a monoclonal antibody to a polypeptide of the invention can be identified and isolated by screening a recombinant combinatorial immunoglobulin library (e.g., an antibody phage display library) with the polypeptide to thereby isolate immunoglobulin library members that bind the polypeptide. Kits for generating and screening phage display libraries are commercially available (e.g., the Pharmacia Recombinant Phage Antibody System, Catalog No. 27-9400-01; and the

Stratagene SurfZAP™ Phage Display Kit, Catalog No. 240612). Additionally, examples of methods and reagents particularly amenable for use in generating and screening antibody display library can be found in, for example, U.S. Patent No. 5,223,409; PCT Publication No. WO 92/18619; PCT Publication No. WO 91/17271; PCT Publication No. WO 92/20791; PCT Publication No. WO 92/15679; PCT Publication No. WO 93/01288; PCT Publication No. WO 92/01047; PCT Publication No. WO 92/09690; PCT Publication No. WO 90/02809; Fuchs et al., Bio/Technology 9: 1370-1372 (1991); Hay et a/. , Hum. Antibod. Hybridomas 3: 81-85 (1992); Huse et al., Science 246: 1275- 1281 (1989); and Griffiths et al., EMBO J. 12: 725- 734 (1993).

Additionally, recombinant antibodies, such as chimeric and humanized monoclonal antibodies, comprising both human and non-human portions, which can be made using standard recombinant DNA techniques, are within the scope of the invention. Such chimeric and humanized monoclonal antibodies can be produced by recombinant DNA techniques known in the art.

In general, antibodies of the invention (e.g. , a monoclonal antibody) can be used to isolate a polypeptide as described herein by standard techniques, such as affinity chromatography or immunoprecipitation. A polypeptide-specific antibody can facilitate the purification of natural polypeptide from cells and of recombinantly produced polypeptide expressed in host cells. Moreover, an antibody specific for a polypeptide of the invention can be used to detect the polypeptide (e.g., in a cellular lysate, cell supernatant, or tissue sample) in order to evaluate the abundance and pattern of expression of the polypeptide. Antibodies can be used diagnostically to monitor protein levels in tissue as part of a clinical testing procedure, e.g., to, for example, determine the efficacy of a given treatment regimen. The antibody can be coupled to a detectable substance to facilitate its detection. Examples of detectable substances include various enzymes, prosthetic groups, fluorescent materials, luminescent materials, bioluminescent materials, and radioactive materials. Examples of suitable enzymes include horseradish peroxidase, alkaline phosphatase, beta-galactosidase, or acetylcholinesterase; examples of suitable prosthetic group complexes include streptavidin/biotin and avidin/biotin; examples of suitable fluorescent materials include umbelliferone, fluorescein, fluorescein isothiocyanate, rhodamine, dichlorotriazinylamine fluorescein, dansyl chloride or phycoerythrin; an example of a luminescent material includes luminol; examples of bioluminescent materials include luciferase, luciferin, and aequorin, and examples of suitable radioactive material include ¹²⁵I, ¹³¹I, ³⁵S or ³H.

Antibodies can furthermore be useful for assessing expression of proteins, e.g. TP53 expression. Antibodies specific TP53, or variants or truncated forms of TP53, may be used to determine the expression levels of TP53 in a sample from an indvidual.

Antibodies can be used in other methods. Thus, antibodies are useful as diagnostic tools for evaluating proteins, such as variant proteins of the invention, in conj unction with analysis by electrophoretic mobility, isoelectric point, tryptic or other protease digest, or for use in other physica l assays known to those skilled in the art. Antibodies may also be used in tissue typing. In one such embodiment, a specific variant protein has been correlated with expression in a specific tissue type, and antibodies specific for the variant protein can then be used to identify the specific tissue type. For example, TP53 antibodies may be used to determine the expression levels of TP53 in cancerous tissue.

Subcellular localization of proteins, including variant proteins, can also be determined using antibodies, and can be applied to assess aberrant subcellular localization of the protein in cells in various tissues. Such use can be applied in genetic testing, but also in monitoring a particular treatment modality. For example, it may be useful to determine the expresion levels of TP53 in tumor samples from an individual. In certain embodiments, expression levels of TP53 in glioma tumor samples are determined .

Antibodies are further useful for inhibiting variant protein function, for example by blocking the binding of a variant protein to a binding molecule or partner. Such uses can also be applied in a therapeutic context in which treatment involves inhibiting a variant protein's function. An antibody can be for example be used to block or competitively inhibit binding, thereby modulating (i.e., agonizing or antagonizing) the activity of the protein. Antibodies can be prepared against specific protein fragments containing sites required for specific function or against an intact protein that is associated with a cell or cell membrane. For administration in vivo, an antibody may be linked with an additional therapeutic payload, such as radionuclide, an enzyme, an immunogenic epitope, or a cytotoxic agent, including bacterial toxins (diphtheria or plant toxins, such as ricin) . The in vivo half-life of an antibody or a fragment thereof may be increased by pegylation through conjugation to polyethylene glycol. The present invention further relates to kits for using antibodies in the methods described herein. This includes, but is not limited to, kits for detecting the presence or absence of a protein {e.g., TP53, or variants or truncated forms thereof) in a test sample. One preferred embodiment comprises antibodies such as a labeled or labelable antibody and a compound or agent for detecting proteins in a biological sample, means for determining the amount or the presence and/or absence of protein {e.g., TP53, or variants or truncated forms thereof) in the sample, and means for comparing the amount of variant protein in the sample with a standard, as well as instructions for use of the kit.

Computer-implemented aspects

As understood by those of ordinary skill in the art, the methods and information described herein may be implemented, in all or in part, as computer executable instructions on known computer readable media. For example, the methods described herein may be implemented in hardware. Alternatively, the method may be implemented in software stored in, for example, one or more memories or other computer readable medium and implemented on one or more processors. As is known, the processors may be associated with one or more controllers, calculation units and/or other units of a computer system, or implanted in firmware as desired. If implemented in software, the routines may be stored in any computer readable memory such as in RAM, ROM, flash memory, a magnetic disk, a laser disk, or other storage medium, as is also known. Likewise, this software may be delivered to a computing device via any known delivery method including, for example, over a communication channel such as a telephone line, the Internet, a wireless connection, etc., or via a transportable medium, such as a computer readable disk, flash drive, etc.

More generally, and as understood by those of ordinary skill in the art, the various steps described above may be implemented as various blocks, operations, tools, modules and techniques which, in turn, may be implemented in hardware, firmware, software, or any combination of hardware, firmware, and/or software. When implemented in hardware, some or all of the blocks, operations, techniques, etc. may be implemented in, for example, a custom integrated circuit (IC), an application specific integrated circuit (ASIC), a field programmable logic array (FPGA), a programmable logic array (PLA), etc.

When implemented in software, the software may be stored in any known computer readable medium such as on a magnetic disk, an optical disk, or other storage medium, in a RAM or

ROM or flash memory of a computer, processor, hard disk drive, optical disk drive, tape drive, etc. Likewise, the software may be delivered to a user or a computing system via any known delivery method including, for example, on a computer readable disk or other transportable computer storage mechanism.

Thus, another aspect of the invention is a system that is capable of carrying out a part or all of a method of the invention, or carrying out a variation of a method of the invention as described herein in greater detail. Exemplary systems include, as one or more components, computing systems, environments, and/or configurations that may be suitable for use with the methods and include, but are not limited to, personal computers, server computers, hand- held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. In some variations, a system of the invention includes one or more machines used for analysis of biological material (e.g., genetic material), as described herein. In some variations, this analysis of the biological material involves a chemical analysis and/or a nucleic acid amplification.

With reference to Fig. 1, an exemplary system of the invention, which may be used to implement one or more steps of methods of the invention, includes a computing device in the form of a computer 110. Components shown in dashed outline are not technically part of the computer 110, but are used to illustrate the exemplary embodiment of Fig. 1. Components of computer 110 may include, but are not limited to, a processor 120, a system memory 130, a memory/graphics interface 121, also known as a Northbridge chip, and an I/O interface 122, also known as a Southbridge chip. The system memory 130 and a graphics processor 190 may be coupled to the memory/graphics interface 121. A monitor 191 or other graphic output device may be coupled to the graphics processor 190.

A series of system busses may couple various system components including a high speed system bus 123 between the processor 120, the memory/graphics interface 121 and the I/O interface 122, a front-side bus 124 between the memory/graphics interface 121 and the system memory 130, and an advanced graphics processing (AGP) bus 125 between the memory/graphics interface 121 and the graphics processor 190. The system bus 123 may be any of several types of bus structures including, by way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus and Enhanced ISA (EISA) bus. As system architectures evolve, other bus architectures and chip sets may be used but often generally follow this pattern. For example, companies such as Intel and AMD support the Intel Hub Architecture (IHA) and the

Hypertransport™ architecture, respectively.

The computer 110 typically includes a variety of computer-readable media. Computer- readable media can be any available media that can be accessed by computer 110 and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer readable media may comprise computer storage media. Computer storage media includes both volatile and nonvolatile, removable and nonremovable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data.

Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other physical medium which can be used to store the desired information and which can accessed by computer 110.

The system memory 130 includes computer storage media in the form of volatile and/or nonvolatile memory such as read only memory (ROM) 131 and random access memory (RAM) 132. The system ROM 131 may contain permanent system data 143, such as identifying and manufacturing information. In some embodiments, a basic input/output system (BIOS) may also be stored in system ROM 131. RAM 132 typically contains data and/or program modules that are immediately accessible to and/or presently being operated on by processor 120. By way of example, and not limitation, Fig. 1 illustrates operating system 134, application programs 135, other program modules 136, and program data 137.

The I/O interface 122 may couple the system bus 123 with a number of other busses 126, 127 and 128 that couple a variety of internal and external devices to the computer 110. A serial peripheral interface (SPI) bus 126 may connect to a basic input/output system (BIOS) memory 133 containing the basic routines that help to transfer information between elements within computer 110, such as during start-up.

A super input/output chip 160 may be used to connect to a number of 'legacy' peripherals, such as floppy disk 152, keyboard/mouse 162, and printer 196, as examples. The super I/O chip 160 may be connected to the I/O interface 122 with a bus 127, such as a low pin count (LPC) bus, in some embodiments. Various embodiments of the super I/O chip 160 are widely available in the commercial marketplace.

In one embodiment, bus 128 may be a Peripheral Component Interconnect (PCI) bus, or a variation thereof, may be used to connect higher speed peripherals to the I/O interface 122. A PCI bus may also be known as a Mezzanine bus. Variations of the PCI bus include the Peripheral Component Interconnect-Express (PCI-E) and the Peripheral Component

Interconnect - Extended (PCI-X) busses, the former having a serial interface and the latter being a backward compatible parallel interface. In other embodiments, bus 128 may be an advanced technology attachment (ATA) bus, in the form of a serial ATA bus (SATA) or parallel ATA (PATA).

The computer 110 may also include other removable/non-removable, volatile/nonvolatile computer storage media. By way of example only, Fig. 1 illustrates a hard disk drive 140 that reads from or writes to non-removable, nonvolatile magnetic media. The hard disk drive 140 may be a conventional hard disk drive..

Removable media, such as a universal serial bus (USB) memory 153, firewire (IEEE 1394), or CD/DVD drive 156 may be connected to the PCI bus 128 directly or through an interface 150. A storage media 154 may coupled through interface 150. Other removable/non-removable, volatile/nonvolatile computer storage media that can be used in the exemplary operating environment include, but are not limited to, magnetic tape cassettes, flash memory cards, digital versatile disks, digital video tape, solid state RAM, solid state ROM, and the like.

The drives and their associated computer storage media discussed above and illustrated in Fig. 1, provide storage of computer readable instructions, data structures, program modules and other data for the computer 110. In Fig. 1, for example, hard disk drive 140 is illustrated as storing operating system 144, application programs 145, other program modules 146, and program data 147. Note that these components can either be the same as or different from operating system 134, application programs 135, other program modules 136, and program data 137. Operating system 144, application programs 145, other program modules 146, and program data 147 are given different numbers here to illustrate that, at a minimum, they are different copies. A user may enter commands and information into the computer 20 through input devices such as a mouse/keyboard 162 or other input device combination. Other input devices (not shown) may include a microphone, joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processor 120 through one of the I/O interface busses, such as the SPI 126, the LPC 127, or the PCI 128, but other busses may be used. In some embodiments, other devices may be coupled to parallel ports, infrared interfaces, game ports, and the like (not depicted), via the super I/O chip 160.

The computer 110 may operate in a networked environment using logical connections to one or more remote computers, such as a remote computer 180 via a network interface controller (NIC) 170, . The remote computer 180 may be a personal computer, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the computer 110. The logical connection between the NIC 170 and the remote computer 180 depicted in Fig. 1 may include a local area network (LAN), a wide area network (WAN), or both, but may also include other networks. Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets, and the Internet. The remote computer 180 may also represent a web server supporting interactive sessions with the computer 110, or in the specific case of location-based applications may be a location server or an application server. In some embodiments, the network interface may use a modem (not depicted) when a broadband connection is not available or is not used. It will be appreciated that the network connection shown is exemplary and other means of establishing a communications link between the computers may be used.

In some variations, the invention is a system for identifying susceptibility to a cancer in a human subject. For example, in one variation, the system includes tools for performing at least one step, preferably two or more steps, and in some aspects all steps of a method of the invention, where the tools are operably linked to each other. Operable linkage describes a linkage through which components can function with each other to perform their purpose.

In some variations, a system of the invention is a system for identifying susceptibility to a cancer in a human subject, and comprises:

(a) at least one processor;

(b) at least one computer-readable medium;

(c) a susceptibility database operatively coupled to a computer-readable medium of the system and containing population information correlating the presence or absence of one or more alleles of the human TP53 gene and susceptibility to a cancer in a population of humans; (d) a measurement tool that receives an input about the human subject and generates information from the input about the presence or absence of at least one mutant TP53 allele indicative of a TP53 defect in the human subject; and

(e) an analysis tool or routine that:

(i) is operatively coupled to the susceptibility database and the information generated by the measurement tool,

(ii) is stored on a computer-readable medium of the system,

(iii) is adapted to be executed on a processor of the system, to compare the information about the human subject with the population information in the susceptibility database and generate a conclusion with respect to susceptibility to the cancer for the human subject.

Exemplary processors (processing units) include all variety of microprocessors and other processing units used in computing devices. Exemplary computer-readable media are described above. When two or more components of the system involve a processor or a computer-readable medium, the system generally can be created where a single processor and/or computer readable medium is dedicated to a single component of the system; or where two or more functions share a single processor and/or share a single computer readable medium, such that the system contains as few as one processor and/or one computer readable medium. In some variations, it is advantageous to use multiple processors or media, for example, where it is convenient to have components of the system at different locations. For instance, some components of a system may be located at a testing laboratory dedicated to laboratory or data analysis, whereas other components, including components (optional) for supplying input information or obtaining an output communication, may be located at a medical treatment or counseling facility (e.g. , doctor's office, health clinic, HMO, pharmacist, geneticist, hospital) and/or at the home or business of the human subject (patient) for whom the testing service is performed.

Referring to Figure 2, an exemplary system includes a susceptibility database 208 that is operatively coupled to a computer-readable medium of the system and that contains population information correlating the presence or absence of one or more alleles of the human TP53 gene and susceptibility to a cancer in a population of humans. For example, the one or more alleles of the TP53 gene include mutant alleles that cause, or are indicative of, a TP53 defect such as reduced or lost function, as described elsewhere herein.

In a simple variation, the susceptibility database contains 208 data relating to the frequency that a particular allele of TP53 has been observed in a population of humans with the cancer and a population of humans free of the cancer. Such data provides an indication as to the relative risk or odds ratio of developing the cancer for a human subject that is identified as having the allele in question. In another variation, the susceptibility database includes similar data with respect to two or more alleles of TP53, thereby providing a useful reference if the human subject has any of the two or more alleles. In still another variation, the susceptibility database includes additional quantitative personal, medical, or genetic information about the individuals in the database diagnosed with the cancer or free of the cancer. Such information includes, but is not limited to, information about parameters such as age, sex, ethnicity, race, medical history, weight, diabetes status, blood pressure, family history of the cancer, smoking history, and alcohol use in humans and impact of the at least one parameter on susceptibility to the cancer. The information also can include information about other genetic risk factors for the cancer besides TP53 variants. These more robust susceptibility databases can be used by an analysis routine 210 to calculate a combined score with respect to susceptibility or risk for developing the cancer.

In addition to the susceptibility database 208, the system further includes a measurement tool 206 programmed to receive an input 204 from or about the human subject and generate an output that contains information about the presence or absence of the at least one TP53 allele of interest. (The input 204 is not part of the system per se but is illustrated in the schematic Figure 2.) Thus, the input 204 will contain a specimen or contain data from which the presence or absence of the at least one TP53 allele can be directly read, or analytically determined. In a simple variation, the input contains annotated information about genotypes or allele counts for TP53 in the genome of the human subject, in which case no further processing by the measurement tool 206 is required, except possibly transformation of the relevant information about the presence/absence of the TP53 allele into a format compatible for use by the analysis routine 210 of the system.

In another variation, the input 204 from the human subject contains data that is unannotated or insufficiently annotated with respect to TP53, requiring analysis by the measurement tool 206. For example, the input can be genetic sequence of a chromosomal region or chromosome on which TP53 resides, or whole genome sequence information, or unannotated information from a gene chip analysis of a variable loci in the human subject's genome. In such variations of the invention, the measurement tool 206 comprises a tool, preferably stored on a computer-readable medium of the system and adapted to be executed on a processor of the system, to receive a data input about a subject and determine information about the presence or absence of the at least one mutant TP53 allele in a human subject from the data. For example, the measurement tool 206 contains instructions, preferably executable on a processor of the system, for analyzing the unannotated input data and determining the presence or absence of the TP53 allele of interest in the human subject. Where the input data is genomic sequence information, and the measurement tool optionally comprises a sequence analysis tool stored on a computer readable medium of the system and executable by a processor of the system with instructions for determining the presence or absence of the at least one mutant TP53 allele from the genomic sequence information.

In yet another variation, the input 204 from the human subject comprises a biological sample, such as a fluid (e.g., blood) or tissue sample, that contains genetic material that can be analyzed to determine the presence or absence of the TP53 allele of interest. In this variation, an exemplary measurement tool 206 includes laboratory equipment for processing and analyzing the sample to determine the presence or absence (or identity) of the TP53 allele(s) in the human subject. For instance, in one variation, the measurement tool includes: an oligonucleotide microarray (e.g., "gene chip") containing a plurality of oligonucleotide probes attached to a solid support; a detector for measuring interaction between nucleic acid obtained from or amplified from the biological sample and one or more oligonucleotides on the oligonucleotide microarray to generate detection data; and an analysis tool stored on a computer-readable medium of the system and adapted to be executed on a processor of the system, to determine the presence or absence of the at least one TP53 allele of interest based on the detection data.

To provide another example, in some variations the measurement tool 206 includes: a nucleotide sequencer (e.g., an automated DNA sequencer) that is capable of determining nucleotide sequence information from nucleic acid obtained from or amplified from the biological sample; and an analysis tool stored on a computer-readable medium of the system and adapted to be executed on a processor of the system, to determine the presence or absence of the at least one mutant TP53 allele based on the nucleotide sequence information.

In some variations, the measurement tool 206 further includes additional equipment and/or chemical reagents for processing the biological sample to purify and/or amplify nucleic acid of the human subject for further analysis using a sequencer, gene chip, or other analytical equipment.

The exemplary system further includes an analysis tool or routine 210 that: is operatively coupled to the susceptibility database 208 and operatively coupled to the measurement tool 206, is stored on a computer-readable medium of the system, is adapted to be executed on a processor of the system to compare the information about the human subject with the population information in the susceptibility database 208 and generate a conclusion with respect to susceptibility to the cancer for the human subject. In simple terms, the analysis tool 210 looks at the TP53 alleles identified by the measurement tool 206 for the human subject, and compares this information to the susceptibility database 208, to determine a susceptibility to the cancer for the subject. The susceptibility can be based on the single parameter (the identity of one or more TP53 alleles), or can involve a calculation based on other genetic and non-genetic data, as described above, that is collected and included as part of the input 204 from the human subject, and that also is stored in the susceptibility database 208 with respect to a population of other humans. Generally speaking, each parameter of interest is weighted to provide a conclusion with respect to susceptibility to the cancer. Such a conclusion is expressed in the conclusion in any statistically useful form, for example, as an odds ratio, a relative risk, or a lifetime risk for subject developing the cancer.

In some variations of the invention, the system as just described further includes a communication tool 212. For example, the communication tool is operatively connected to the analysis routine 210 and comprises a routine stored on a computer-readable medium of the system and adapted to be executed on a processor of the system, to: generate a communication containing the conclusion; and to transmit the communication to the human subject 200 or the medical practitioner 202, and/or enable the subject or medical practitioner to access the communication. (The subject and medical practitioner are depicted in the schematic Fig. 2, but are not part of the system per se, though they may be considered users of the system. The communication tool 212 provides an interface for communicating to the subject, or to a medical practitioner for the subject (e.g., doctor, nurse, genetic counselor), the conclusion generated by the analysis tool 210 with respect to susceptibility to the cancer for the subject. Usually, if the communication is obtained by or delivered to the medical practitioner 202, the medical practitioner will share the communication with the human subject 200 and/or counsel the human subject about the medical significance of the communication. In some variations, the communication is provided in a tangible form, such as a printed report or report stored on a computer readable medium such as a flash drive or optical disk. In some variations, the communication is provided electronically with an output that is visible on a video display or audio output (e.g., speaker). In some variations, the communication is transmitted to the subject or the medical practitioner, e.g., electronically or through the mail. In some variations, the system is designed to permit the subject or medical practitioner to access the communication, e.g., by telephone or computer. For instance, the system may include software residing on a memory and executed by a processor of a computer used by the human subject or the medical practitioner, with which the subject or practitioner can access the communication, preferably securely, over the internet or other network connection. In some variations of the system, this computer will be located remotely from other components of the system, e.g., at a location of the human subject's or medical practitioner's choosing.

In some variations of the invention, the system as described (including embodiments with or without the communication tool) further includes components that add a treatment or prophylaxis utility to the system. For instance, value is added to a determination of susceptibility to a cancer when a medical practitioner can prescribe or administer a standard of care that can reduce susceptibility to the cancer; and/or delay onset of the cancer; and/or increase the likelihood of detecting the cancer at an early stage, to facilitate early treatment when the cancer has not spread and is most curable. Exemplary lifestyle change protocols include loss of weight, increase in exercise, cessation of unhealthy behaviors such as smoking, and change of diet. Exemplary medicinal and surgical intervention protocols include administration of pharmaceutical agents for prophylaxis; and surgery, including in extreme cases surgery to remove a tissue or organ before it has become cancerous. Exemplary diagnostic protocols include non-invasive and invasive imaging; monitoring metabolic biomarkers; and biopsy screening.

For example, in some variations, the system further includes a medical protocol database 214 operatively connected to a computer-readable medium of the system and containing information correlating the presence or absence of the at least one TP53 allele of interest and medical protocols for human subjects at risk for the cancer. Such medical protocols include any variety of medicines, lifestyle changes, diagnostic tests, increased frequencies of diagnostic tests, and the like that are designed to achieve one of the aforementioned goals. The information correlating a TP53 allele with protocols could include, for example, information about the success with which the cancer is avoided or delayed, or success with which the cancer is detected early and treated, if a subject has a TP53 susceptibility allele and follows a protocol.

The system of this embodiment further includes a medical protocol tool or routine 216, operatively connected to the medical protocol database 214 and to the analysis tool or routine 210. The medical protocol tool or routine 216 preferably is stored on a computer- readable medium of the system, and adapted to be executed on a processor of the system, to : (i) compare (or correlate) the conclusion that is obta ined from the analysis routine 210 (with respect to susceptibility to cancer for the subject) and the medical protocol database 214, and (ii) generate a protocol report with respect to the probability that one or more medical protocols in the medical protocol database will achieve one or more of the goals of reducing susceptibility to the cancer; delaying onset of the cancer; and increasing the likelihood of detecting the cancer at an early stage to facilitate early treatment. The probability can be based on empirical evidence collected from a population of humans and expressed either in absolute terms (e.g. , compared to making no intervention), or expressed in relative terms, to highlight the comparative or additive benefits of two or more protocols.

Some variations of the system just described include the communication tool 212. In some examples, the communication tool generates a communication that includes the protocol report in addition to, or instead of, the conclusion with respect to susceptibility.

Information about TP53 allele status not only can provide useful information about identifying or quantifying susceptibility to cancers; it can also provide useful information about possible causative factors for a human subject identified with a cancer, and useful information about therapies for the cancer patient. In some variations, systems of the invention are useful for these purposes.

For instance, in some variations the invention is a system for assessing or selecting a treatment protocol for a subject diagnosed with a cancer. An exemplary system,

schematically depicted in Figure 3, comprises :

(a) at least one processor;

(b) at least one computer-readable medium;

(c) a medical treatment database 308 operatively connected to a computer-readable medium of the system and containing information correlating the presence or absence of at least one TP53 allele and efficacy of treatment regimens for the cancer; (d) a measurement tool 306 to receive an input (304, depicted in Fig. 3 but not part of the system per se) about the human subject and generate information from the input 304 about the presence or absence of the at least one TP53 allele indicative of a TP53 defect in a human subject diagnosed with the cancer; and

(e) a medical protocol routine or tool 310 operatively coupled to the medical treatment database 308 and the measurement tool 306, stored on a computer-readable medium of the system, and adapted to be executed on a processor of the system, to compare the information with respect to presence or absence of the at least one TP53 allele for the subject and the medical treatment database, and generate a conclusion with respect to at least one of:

(i) the probability that one or more medical treatments will be efficacious for treatment of the cancer for the patient; and

(ii) which of two or more medical treatments for the cancer will be more efficacious for the patient.

Preferably, such a system further includes a communication tool 312 operatively connected to the medical protocol tool or routine 310 for communicating the conclusion to the subject 300, or to a medical practitioner for the subject 302 (both depicted in the schematic of Fig. 3, but not part of the system per se). An exemplary communication tool comprises a routine stored on a computer-readable medium of the system and adapted to be executed on a processor of the system, to generate a communication containing the conclusion; and transmit the communication to the subject or the medical practitioner, or enable the subject or medical practitioner to access the communication.

In certain embodiments, the TP53 allele indicative of increased risk of cancer is an allele that leads to a defective polyadenylation site in TP53. In certain embodiments, the TP53 is an allele that leads to a defective AATAAA polyadenylation signal site. In certain embodiments, the TP53 allele is is the C allele of marker rs78378222.

The present invention will now be exemplified by the following non-limiting examples.

EXAMPLE 1

Genome-wide Association Study

To search for novel sequence variants that confer susceptibility to cutaneous basal cell carcinoma (BCC), a genome-wide association study was conducted of 16 million SNPs that were identified through whole-genome sequencing of 457 Icelanders.

To start with, Illumina SNP chip data for 1,366 cases and 40,309 controls was generated for BCC. Haplotype association analysis based on long range phasing revealed that several 0.3cM haplotypes at 17pl3 were strongly associated with BCC. The most significant signals were produced by haplotype \6 (OR = 2.04, P = 2.0xl0^"10) covering the region chrl7 : 7, 186,095- 7,425,536 and by a highly correlated haplotype /\8 (OR = 2.00, P = 3.0xl0^"10) covering an adjacent region, chrl7 : 7, 431, 901-7, 680, 389. The region covered by these haplotypes is illustrated in Figure 4.

To search for variants that might not be covered well by the chips, high capacity DNA sequencing techniques were used to sequence the entire genomes of 457 Icelanders to an average depth of over 10X. This identified some 16 million SNPs. To ensure that all rare risk alleles that might be carried on the A6 or A8 backgrounds would be sequenced, 10 individuals were included who carried these haplotypes among the 457 people selected for sequencing. Using imputation assisted by long-range haplotype phasing, sequence data was used to determine the genotypes of the 16 million SNPs in the 41,675 Icelanders who had been genotyped on the SNP chips. Moreover, knowledge of the Icelandic genealogy allowed for propagation of genotypic information into individuals for whom that had neither SNP chip nor sequence data, a process referred to as "genealogy-based in silico genotyping". Reference is made to the combined method of imputing sequence-derived data into phased chromosomes from chip-typed individuals and using genealogy-based in silico genotyping to infer the sequence of ungenotyped individuals as "two-way imputation".

A two-way imputation-based genome-wide BCC association analysis of the 16 million SNPs was conducted, and designated as the "Discovery Phase". This revealed a number of SNPs with strong associations in the region covered by the two haplotypes. The strongest signal (OR = 2.36, P = 5.2xl0^"17) came from rs78378222, located in the 3 ^' UTR of TP 53 Fig. 4, Table 1 ). This signal was not only the strongest in the region covered by the two candidate haplotypes, it was also the strongest signal of the 16 million SNPs observed genome-wide. The minor (C, at-risk) allele is present in the Icelandic population at a frequency of 0.0192. There was no deviation from Hardy-Weinberg equilibrium and C/C homozygotes were seen (although they are rare), so the variant is not recessive lethal. The SNP was not on the Illumina chips and is not in HapMap2 or 3. The best on-chip tagger was rs4796305, which produced only a modest signal (OR = 1. 18, P = 0.0024, MAF = 0. 119, r² = 0.15) . Aside from the 17pl3 locus, all other SNPs that surpassed the 3.0 x 10^~9 Bonferroni adjusted P-value threshold for genome-wide significance were in previously published loci^5,6.

To gain further evidence of the rs78378222 association a "Follow up Phase" was conducted . A Centaurus¹⁰ single-track assay for rs78378222 was devised and used to genotype all available Icelandic BCC samples (n = 2,322) and 7,200 controls. One thousand and forty four out of the 2,322 single-track genotyped cases had not previously been genotyped by Illumina chip. An association analysis with these 1,044 cases and 7,200 controls, yielded an OR of 2.19 and P = 3.2 x 10^~7 {Table 1 , Iceland Follow-up Single-Track Genotyped) . Note that this is not a fully independent replication of the Discovery Phase result, since some of the individuals who had been assigned genealogy-based in silico genotypes in the Discovery Phase were single-track genotyped in the Follow-up Phase . The two-way imputation-based analysis using a non- overlapping sample which excluded the 1,044 single-tracked cases was then repeated {Table 1, Iceland Follow-up Two-Way Imputation). Combining the results from the two Follow-up groups provided strong evidence for the association with BCC, OR = 2.25, P = 5.4 x 10^~19 {Table 1, Iceland Follow-up Phase Combined). Association based on all 2,322 cases who had been single-track genotyped (irrespective of whether or not they had been chip-typed) produced comparable results.

Table 1 : Association between BCC and rs78378222[C]

95% Number Number Frequency

Confidence of Frequency of in

Sample Set P-value OR Interval Cases in Cases Controls Controls

Iceland Discovery Phase Two-Way Imputation 5.2xl0 ¹⁷ 2.36 (1.93, 2.89) 2121^b 0.0442 >39,614^b 0.0192^c

Iceland Follow-up Phase Two-Way Imputation^d 3.1xl0 ¹³ 2.28 (1.83, 2.85) 1813^b 0.0428 >36,709^b 0.0192^c

Iceland Follow-up Phase Single-Track Genotyped^d 3.2xl0^"7 2.19 (1.62, 2.96) 1044 0.0398 7200 0.0185

Iceland Follow-up Phase Combined 5.4xl0^"19 2.25 ( 1.88, 2.69) 2857^b NA >43,909^b NA

Denmark 0.0057 2.06 (1.23, 3.44) 308 0.0341 3441 0.0168

Eastern Europe^e 0.12 2.04 (0.84, 4.93) 526 0.0133 532 0.0066

Spain 0.77 0.86 (0.34, 2.21) 628 0.0040^f 3928 0.0046^f

Combined Non-Icelandic Replication 0.0060 1.75 ( 1.17, 2.62) 1462 NA 7901 NA

Combined Iceland Follow-up and Replication 2.2x10 ²⁰ 2.16 ( 1.83, 2.54) 4319^b NA >51,810^b NA ^aP-value for heterogeneity based on combining groups using the Mantel-Haenszel model. Effective sample size estimate taking into account efficiency of Icelandic genealog based in silico genotyping. 'The control frequency given is derived from 40,309 directly chip-typed non-BCC control individuals. ^dThe Iceland Follow-up Phase Two-Way Imputation set consists of individuals who were chip-typed and individuals whose genotypes were determined by genealogy-based in silico genotyping. This set differs from Iceland Discovery Phase Two-Way Imputation set in that it lacks those individuals whose genotypes were determined by genealogy-based in silico genotyping in the Discov Phase but who were subsequently directly single-track genotyped. Such individuals form part of the Iceland Follow-up Phase Single-Track Genotyped set, which consists of individuals whose genotypes were determined solely by Centaurus single-track assay. The Follow-up Phase Two-Way Imputation and Single-Track Genotyped sets are non- overlapping. ^eSamples from Hungary, Slovakia and Romania. 'Arithmetic mean of the frequencies in the samples from the two centres. NA, not applicable

Table 2: Overview of the replication sample sets used in this study

Sample Set Disease/Phenotype Cases³ Controls³ Type Location Refer

Denmark Basal Cell Carcinoma 308 3,441 Nested Case Control Study from EPIC with Copenhagen, Denmark a, supplementary population-based controls from

Inter99 cohort

Eastern Europe Basal Cell Carcinoma 528 533 Multi-center Hospital-based Case : Population- Hungary, Romania, Slovakia

based control

Spain Basal Cell Carcinoma 628 3,928 Multi-center Hospital-based Case : Population- Zaragoza & Valencia, Spain d based control

Netherlands Prostate Cancer 1,085 1,796 Registry Ascertained Case : Population-based Eastern Netherlands e

Control

Romania Prostate Cancer 639 815 Hospital-Based Case : Population Based Control Bucharest, Romania e

Spain Prostate Cancer 785 1,787 Hospital-Based Case : Population Based Control Zaragoza, Spain e

U.K. Prostate Cancer 521 1,407 PSA based testing and treatment trial Nine locations in the U.K. e

U.S.A. Prostate Cancer 1,454 1,293 Hospital-Based Case :Control Chicago, U.S.A. e

U.S.A. UCSF Glioma 658 573 Multi-center Hospital-based an Population- San Francisco Bay Area, f, based Case : Population-based control U.S.A.

U.S.A. Mayo Clinic Glioma 530 283 Hospital-Based Case :Control Minnesota, U.S.A. f,

Netherlands Colorectal Cancer 464 1,796 Hospital-Based Case : Population Based Control Eastern Netherlands h

Spain Colorectal Cancer 184 1,940 Hospital-Based Case : Population Based Control Zaragoza, Spain i

Sweden Colorectal Cancer 1,781 1,737 Hospital-Based Case : Population Based Control Stockholm, Sweden j

US Colon Cancer 475 807 Hospital-Based Case : Population Based Control North Carolina, U.S.A.

US Rectal Cancer 942 922 Hospital-Based Case : Population Based Control North Carolina, U.S.A.

Netherlands Breast Cancer 725 1,796 Registry Ascertained Case :Control Eastern Netherlands 1

Spain Breast Cancer 1,007 1,940 Hospital-Based Case : Population Based Control Zaragoza, Spain 1

Netherlands Melanoma 683 1,796 Registry Ascertained Case :Control Eastern Netherlands d

Spain Valencia Melanoma 823 1,988 Hospital-Based Case : Population Based Control Valencia, Spain d

Spain Zaragoza Melanoma 290 1,787 Hospital-Based Case : Population Based Control Zaragoza, Spain d

Iceland Pigmentation Pigmentation Traits 9,805 NA Population-Based Self Reported Questionnaire Nationwide, Iceland

Netherlands Pigmentation Traits 1,326 NA Population-Based Self Reported Questionnaire Eastern Netherlands

aNumbers successfully genotyped for rs78378222 are given

References: (a) Vogel U. et al. Mutat Res 617, 138-46 (2007), (b) Glumer C. et al. Diabetes Care 26, 2335-40 (2003), (c) Scherer, D et al. Int. J. Cancer 122, 1787-1793 (2008), (d) Sta S.N . et al., Nat Genet 41, 909-14 (2009), (e) Gudmundsson, J. et al. Sci Transl Med 2, 62ra92 (2010), (f) Wrensch M. et al., Nat Genet 41, 905-8 (2009), (g) Jenkins R.B., et al., Cance Genetics 204, 13-18 (2011), (h) van der Logt, E.M.J , et al. Carcinogenesis 25, 2407- 15 (2004), (i) Rafnar, T et al. Nat Genet 41, 221-7 (2009), (j) Ghazi S. et al. Am J Pathol 177, 268 (2010), (k) Satia J.A. et al. Cancer Epidemiol Biomarkers Prev 14, 429-36 (2005), (I) Stacey, S.N. et al. Nat Genet 39, 865-9 (2007), (m) Sulem, P. et al. Nat Genet 39, 1443-52 (2007)

The SNP rs78378222 was then typed in replication samples from Denmark, eastern Europe and Spain (the replication sets are described in Table 2). The risk allele was found in all populations tested with frequencies that seemed to decline with distance from Iceland {Table 1). Combined, the evidence for replication of the BCC association in non-Icelandic samples was significant and showed no evidence of heterogeneity (OR = 1.75, P = 0.0060, P_het = 0.27, Table 1). Combined with the Icelandic Follow-up Phase data, the overall association was highly significant (OR = 2.16, P = 2.2 x 10^"20), concluding that rs78378222[C] predisposes to BCC.

The second strongest signal in the genome originated from a novel SNP (designated chrl 7: 7640788, OR = 2.41, P = l. lxlO^"13) that was also in the region covered by the two candidate haplotypes {Fig. 4). Based on single-track genotyping of 2,281 cases and 6,858 controls for both SNPs, chrl7:7640788 has an r² of 0.61 with rs78378222 and so, may capture the same signal. When adjusted for the effect of rs78378222, there was no residual signal from chrl7:7640788 (OR_adj = 1.07, P_adj = 0.72) whereas rs7838222 remained significant after adjustment for chrl7:7640788 (OR_adj = 2.00, P_adj = 3.0 x 10^"5). Therefore chrl7:7640788 was not investigated further. A common germline variant in TP53, Pro72Arg (rsl042522), has been studied extensively for cancer susceptibility, generally with equivocal results¹¹. No evidence showed that rsl042522 was associated with BCC (OR = 1.00, P = 0.98). This was confirmed by single-track genotyping. It appears that Pro72Arg does not confer risk of BCC and is unrelated to the rs78378222 signal.

EXAMPLE 2

Inspection of the sequence surrounding rs78378222 indicated that it occurs in the sole polyadenylation signal of TP53, the risk-associated mutation changing the sequence AATAAA to AATACA, thus disrupting the signal sequence. This class of mutations was first observed in the polyadenylation signal of alpha 2 globin {HBA2), leading to alpha thalassemia¹⁶. RNA was obtained from blood and adipose tissue from rs78378222[A/C] heterozygotes and [A/A] homozygotes. Using RT-PCR with primers internal to the TP53 mRNA, we observed that rs78378222 [A/C] heterozygotes expressed somewhat less TP53 transcript than wild-type homozygotes {P = 0.041, Fig. 5a). To investigate polyadenylation site usage of wild-type and variant TP53 genes, total RNA samples were selected from [A/C] heterozygotes and carried out 3 ^' RACE {Fig. 5b). Amplification using a TP53 gene-specific forward primer produced a band of 1.3kb, the expected length of correctly terminated mRNA {Fig. 5c). However sequencing of this band indicated that correctly terminated polyA(+) mRNAs were produced predominantly from the wild type allele, with 73% of mRNAs containing the wild type [A] allele and 27% containing the mutant [C] allele {P = 1.6 x 10^"6, Fig. 5c). RT-PCR was then carried out using a "Run-on" reverse primer, located in genomic sequence approximately 320bp beyond the normal 3 ^'end of the TP53 gene (see Fig. 5b). Sequencing of RT-PCR products from heterozygotes showed that this RNA species was comprised almost entirely of mutant [C] allele transcripts {Fig. 5d). Taken together, these data suggest that the rs78378222[C] variant impairs proper termination and polyadenylation of the TP 53 transcript.

EXAMPLE 3

The association between rs78378222 and 20 major types of tumour was investigated by cross referencing genotypes to the Icelandic Cancer Registry and National Pathology records. After correcting for multiple phenotype testing, significa nt associations were seen for prostate cancer, brain cancers and colorectal adenoma (but not colorectal cancer) . A Follow-up Phase was conducted in an attempt to get further evidence of these associations. Samples numbers are detailed in Table 4. rs78378222 was directly genotyped in all available Icelandic cases of prostate cancer, colorectal adenoma and brain cancer and determined Follow-up Phase two- way imputation and single-track genotype-based association values {Table 3) . Replication Prostate cancer samples from 5 countries were further genotyped. The association with prostate cancer outside Iceland was significant (OR = 1.63, P = 1.1 x 10^~4) as was the combined Iceland Follow-up and replication sample result (OR = 1.44, P = 2.4 x 10^~6, Table 3) . For the colorectal adenoma Follow-up Phase, single track genotyping gave a comparable result to two-way imputation and the combined analysis yielded a significant association result (OR = 1.39, P = 1.6 x 10^"4, Table 3). This raises the possibility that rs78378222[C] might predispose to colorectal adenomas with a low propensity for progression to invasive cancer.

The discovery two-way imputation category of All Brain Cancers contained meningiomas (ICD 10 code C70) and gliomas (C71-C72) . When glioma and meningioma were considered separately, the association appeared stronger for glioma (OR = 2.50, P = 0.0055) than for meningioma (OR = 1.48, P = 0.36) . Although this difference was not significant {P = 0.88) the focus was on glioma in the Follow-up Phase {Table 3) . In Iceland, the Follow-up Phase yielded a suggestive association with glioma (OR = 2.36, P = 0.0036) . This was confirmed in two case: control samples of adult glioma from the United States (OR = 2.34, P = 9.2 x 10^~4) . Combined with the Icelandic data, firm evidence was obtained that rs78378222[C] predisposes to glioma (OR = 2.35, P = 1.0 x 10^~5, Table 3).

Table 3: Association between rs78378222[C] and prostate cancer, glioma and colorectal adenoma

95% Number Number Frequency Confidence of Frequency of in

Sample Set Tumour Type P-value OR Interval Cases in Cases Controls Controls

Iceland Follow-up Phase Two-Way Imputation¹¹ Prostate Cancer 0.0058 1.35 (1.09, 1.67) 2671^c 0.0258 >36,331^c 0.0192^d

Iceland Follow-up Phase Single-Track Genotyped^b Prostate Cancer 0.27 1.28 (0.82, 1.99) 635 0.0236 7200 0.0185

Iceland Follow-up Phase Combined Prostate Cancer 0.0030 1.34 ( 1.10, 1.62) 3306^c NA >43,531^c NA

Netherlands Prostate Cancer 0.093 1.41 (0.94, 2.10) 1085 0.0207 1796 0.0147

UK Prostate Cancer 0.24 1.39 (0.80, 2.42) 521 0.0192 1407 0.0139

Romania Prostate Cancer 0.41 1.38 (0.64, 2.95) 639 0.0110 815 0.0080

USA Prostate Cancer 0.0026 2.24 (1.33, 3.79) 1454 0.0155 1293 0.0070

Spain Prostate Cancer 0.037 2.52 (1.06, 5.99) 785 0.0070 1787 0.0028

Combined Non-Icelandic Replication Prostate Cancer 1.1x10 ⁴ 1.63 ( 1.27, 2.09) 4484 NA 7098 NA

Combined Iceland Follow-up and Replication Prostate Cancer 2.4xl0^"6 1.44 ( 1.24, 1.68) 7790^c NA >50,629^c NA

Iceland Follow-up Phase Two-Way Imputation¹¹ Glioma 0.0074 2.61 (1.29, 5.27) 135^c 0.0486 >37,881^c 0.0192^d

Iceland Follow-up Phase Single-Track Genotyped^b Glioma 0.22 1.90 (0.69, 5.27) 72 0.0347 7200 0.0185

Iceland Follow-up Phase Combined Glioma 0.0036 2.36 ( 1.32, 4.20) 207^c NA >45,081^c NA

USA UCSF Glioma 0.0063 2.65 (1.32, 5.32) 658 0.0228 573 0.0087

USA Mayo Clinic Glioma 0.053 2.05 (0.99, 4.25) 530 0.0321 283 0.0159

Combined Non-Icelandic Replication Glioma 9.2x10 ⁴ 2.34 ( 1.42, 3.88) 1188 NA 856 NA

Combined Iceland Follow-up and Replication Glioma 1.0x10 ⁵ 2.35 ( 1.61, 3.44) 1395^c NA >45,937^c NA

Iceland Follow-up Phase Two-Way Imputation¹¹ Colorectal Adenoma 0.0064 1.32 (1.08, 1.61) 3057^c 0.0252 >36,022^c 0.0192^d

Iceland Follow-up Phase Single-Track Genotyped^b Colorectal Adenoma 0.0052 1.60 (1.15, 2.23) 1038 0.0294 7200 0.0185

Iceland Follow-up Phase Combined Colorectal Adenoma 1.6x10 ⁴ 1.39 ( 1.17, 1.65) 4095^c NA >43,022^c NA ^aP-value for heterogeneity based on combining groups using the Mantel-Haenszel model. 'The Iceland Follow-up Phase Two-Way Imputation set consists of individuals who chip-typed and individuals whose genotypes were determined by genealogy-based in silico genotyping. This set differs from the Iceland Discovery Phase Two-Way Imputati shown in Table 2 in that it lacks those individuals whose genotypes were determined by genealogy-based in silico genotyping in the Discovery Phase but who were subsequ directly single-track genotyped. Such individuals form part of the Iceland Follow-up Phase Single-Track Genotyped set, which consists of individuals whose genotypes were determined solely by Centaurus single-track assay. The Follow-up Phase Two-Way Imputation and Single-Track Genotyped sets are non-overlapping. ^cEffective sample size estimate taking into account efficiency of Icelandic genealogy-based in silico genotyping. ^dThe control frequency given is derived from 40,309 directly chip-typed non-BCC c individuals. NA, not applicable.

Table 4: Numbers of samples analysed in Icelandic Discovery and Follow-up Phases

Phase: General Discovery Phase

B C D E F G H

Cases Not Cases

Chipped Genealogy-

Phenotype Total Number but with a Based in silico

of Cases Cases Cases Not FSDR^C on Genotyped Cases Total

Ascertained³ Chipped" Chipped (B-C) Chip ESS^d ESS (C+F) Matched Control ESS

BCC 4265 1366 2899 1986 755 2121 >39,61

Prostate Cancer 5046 1860 3186 2231 848 2708 >39,06

Colorectal Adenoma 6703 2201 4502 3245 1233 3776 >37,41

All Brain Cancers 682 137 545 413 157 327 >20,82

Glioma 425 51 374 274 104 182 > 10,35

Colorectal Cancer 3888 1128 2760 1836 698 1950 >40,54

Melanoma 1030 602 428 292 111 724 >41,07

Breast Cancer 5456 2414 3042 1952 742 3253 >39,26

ER Negative Breast Cancer 435 368 67 45 17 385 >41,21

High Risk Breast Cancer^f 1739 875 864 563 220 1095 >40,25

Phase: Follow-up Phase

I J K L M N O

Cases

Cases Cases Cases neither Genealogy-

Total Number Single Single Chipped nor Based in 2-Way 2 r neriuiy pe

of Cases Single Tracked Tracked Cases neither Single Tracked silico Imputation Imput

Track but not and Chipped nor but with a Genotyped Cases Total Ma

Genotyped Chipped Chipped Single Tracked FSDR on Chip ESS ESS (C+N) Contr

BCC 2322 1044 1278 1855 1115 447 1813 >3

Prostate Cancer 2445 635 1810 2550 1749 811 2671 >3

Colorectal Adenoma 2396 1038 1358 2106 2237 856 3057 >3

Glioma 121 72 49 302 207 84 135 >3 ^aCase ascertainment was through the Icelandic Cancer Registry or National Pathology Department registers. ^b"Chipped" means that the samples were genotyped with Illumi Human Hap300, HapCNV370, Hap610, 1M or Omni-1 Quad bead chips. ^c FSDR" means first or second degree relative. ^d"ESS" means effective sample size estimate. The matched control set (see Online Methods) was drawn from a total number of 437,218 population based controls, of whom 40,309 were chip typed and did not have a recor BCC diagnosis. 'Probands with breast cancer diagnosed under 50 years of age or a record of multiple independent primary breast cancers.

EXAMPLE 4

Methods

Samples: Icelandic patients were ascertained through the Icelandic Cancer Registry or through National Pathology registers as previously detailed ⁵. Controls were drawn from non-cancer population based projects conducted by deCODE genetics. Details of the non-Icelandic replication sample sets used are given in Table 2.

Illumina SNP Chip Genotyping: The Icelandic chip-typed samples were assayed with the Illumina Human Hap300, Hap CNV370, Hap 610, 1M or Omni-1 Quad bead chips at deCODE genetics. Only the 317,503 SNPs from the Human Hap300 chip were used in the long range phasing and the subsequent SNP imputations. SNPs were excluded if they had (i) yield lower than 95%, (ii) minor allele frequency less than 1% in the population or (iii) significant deviation from Hardy- Weinberg equilibrium in the controls {P < 0.001), (iv) if they produced an excessive inheritance error rate (over 0.001), (v) if there was substantial difference in allele frequency between chip types (from just a single chip if the problem that resolved all differences, but from all chips otherwise). All samples with a call rate below 97% were excluded from the analysis. The final set of SNPs used for long range phasing was composed of 297,835 autosomal SNPs.

Whole Genome Sequencing and SNP Calling: SNPs were imputed based on whole genome sequence data from 457 Icelanders, selected for various neoplastic, cardiovascular and psychiatric conditions. All of the individuals were sequenced at a depth of at least 10X.

Approximately sixteen million SNPs were imputed based on this set of individuals.

a. Sample preparation. Paired-end libraries for sequencing were prepared according to the manufacturer's instructions (Illumina). In short, approximately 5 pg of genomic DNA, isolated from frozen blood samples, was fragmented to a mean target size of 300 bp using a Covaris E210 instrument. The resulting fragmented DNA was end repaired using T4 and Klenow polymerases and T4 polynucleotide kinase with 10 mM dNTP followed by addition of an 'A' base at the ends using Klenow exo fragment (3' to 5'-exo minus) and dATP (1 mM). Sequencing adaptors containing 'Τ' overhangs were ligated to the DNA products followed by agarose (2%) gel electrophoresis. Fragments of about 400 bp were isolated from the gels (QIAGEN Gel Extraction Kit), and the adaptor-modified DNA fragments were PCR enriched for ten cycles using Phusion DNA polymerase (Finnzymes Oy) and PCR primers PE 1.0 and PE 2.0 (Illumina).

Enriched libraries were further purified using agarose (2%) gel electrophoresis as described above. The quality and concentration of the libraries were assessed with the Agilent 2100 Bioanalyzer using the DNA 1000 LabChip (Agilent). Barcoded libraries were stored at -20 °C. All steps in the workflow were monitored using an in-house laboratory information management system with barcode tracking of all samples and reagents.

b. DNA sequencing. Template DNA fragments were hybridized to the surface of flow cells (Illumina PE flowcell, v4) and amplified to form clusters using the Illumina cBot. In brief, DNA (8-10 pM) was denatured, followed by hybridization to grafted adaptors on the flowcell. Isothermal bridge amplification using Phusion polymerase was then followed by linearization of the bridged DNA, denaturation, blocking of 3 ^' ends and hybridization of the sequencing primer. Sequencing-by-synthesis was performed on Illumina GAIIx instruments equipped with paired- end modules. Paired-end libraries were sequenced using 2 x 101 cycles of incorporation and imaging with Illumina sequencing kits, v4 or v5 (TruSeq). Each library or sample was initially run on a single lane for validation followed by further sequencing of >4 lanes with targeted raw cluster densities of 500-700 k/mm², depending on the version of the data imaging and analysis packages. Imaging and analysis of the data was performed using either the SCS2.6 /RTA1.6 or SCS2.8/RTA1.8 software packages from Illumina, respectively. Real-time analysis involved conversion of image data to base-calling in real-time.

c. Alignment. For each lane in the DNA sequencing output, the resulting qseq files were converted into fastq files using an in-house script. All output from sequencing was converted, and the Illumina quality filtering flag was retained in the output. The fastq files were then aligned against Build 36 of the human reference sequence using bwa version 0.5.7 (ref.²⁴). All genomic locations quoted refer to HG18 Build 36.

d. BAM file generation. SAM file output from the alignment was converted into BAM format using SAMtools version 0.1.8 (ref. ²⁵), and an in-house script was used to carry the Illumina quality filter flag over to the BAM file. The BAM files for each sample were then merged into a single BAM file using SAMtools. Finally, Picard version 1.17 (see http://picard.sourceforge. net/) was used to mark duplicates in the resulting sample BAM files.

e. 5NP identification and genotype calling: A two-step approach was applied. The first step was to detect SNPs by identifying sequence positions where at least one individual could be determined to be different from the reference sequence with confidence (quality threshold of 20) based on the SNP calling feature of the pileup tool in SAMtools. SNPs that always differed heterozygous or homozygous from the reference were removed. The second step was to use the pileup tool to genotype the SNPs at the positions that were flagged as polymorphic. Because sequencing depth varies and hence the certainty of genotype calls also varies, genotype likelihoods rather than deterministic calls were calculated (see Supplementary Note). Of the 2.5 million SNPs reported in the HapMap2 CEU samples, 96.3% were observed in the whole-genome sequencing data. Of the 6.9 million SNPs reported in the 1000 Genomes Project data, 89.4% were observed in the whole-genome sequencing data.

Methods for Genotype Imputation: Methods used for long range phasing, genotype imputation, genealogy-based in-silico genotyping and association testing are presented in Example 5 below.

Assessment of sun sensitivity: Sun sensitivity was self-assessed through questionnaires ^14,15 using the Fitzpatrick score²⁶, where the lowest score (I) represents very fair skin that is very sensitive to UVR and the highest score (IV) represents dark skin that tans rather than burns in reaction to UVR exposure. Individuals scoring I and II were classified as being sensitive to sun and individuals scoring III and IV were classified as being not sensitive to sun. Specification of novel SNP chrl7: 7640788: This SNP was identified by the sequencing with the sequence context shown in Table 5.

RNA Analysis: RNA was isolated from blood using Qiagen RNA maxi kit according to the

manufacturer's instructions. Concentration and quality of the RNA was determined with Agilent 2100 Bioanalyzers (Agilent Technologies). cDNA was synthesized using the High capacity cDNA reverse transcriptase kit (Applied Biosystems Inc.). Quantitative RT-PCR of TP53 cDNA was performed with Applied Biosystems assay Hs99999147_ml on an ABI 7900HT Real-time PCR system according to standard protocol. RACE reaction was performed using Smart-RACE cDNA amplification kit (Clontech) according to protocol. Primer sequences are given in Table 5. All sequencing was performed with Big-Dye R terminator chemistry on a 3730 system (Applied Biosystems Inc.).

Table 5: Specification of Nucleic Acid Sequences

Description Sequence

Sequence context of novel SNP cttcctgcccacgcccaccaagatgcattacctcttcaaccttcgagacatctccaaggtgactcgcggcctgacc chrl7 :7640788 ttgccccttctgcttggcccagcctccgcggaggctttctcttctcaaactaagccttaacactcactagcatg[C/

T]gcaccaaaagtcacccccatgctgaagtgccacactccctggccttacctttaaaacttctgggccaagtgcg gtggctcacacctgtaattccagcactttgggaggccaacgcaggcagatcacctgaggttaggagttcaaga ccag (SEQ ID NO :4)

TP53 3 ^' RACE Gene-specific primer GAATGAGGCCTTGGAACTCAAGGAT (SEQ ID NO: 5)

TP53 Sequencing primer TTCCCCTCCTTCTCCU 1 1 1 1 (SEQ ID NO :6)

Run-on primer TCCCGTAATCCTTGGTGAGA (SEQ ID NO:7)

TP53 Internal primer for Run-on

experiments TGCAAGCACATCTGCA 1 1 1 1 (SEQ ID NO :8)

EXAMPLE 5

Genotype Imputation Methods

Long range phasing: Long range phasing of all chip-genotyped individuals was performed with methods described previously^27,28. In brief, phasing is achieved using an iterative algorithm which phases a single proband at a time given the available phasing information about everyone else that shares a long haplotype identically by state with the proband. Given the large fraction of the Icelandic population that has been chip-typed, accurate long range phasing is available genome-wide for all chip-typed Icelanders. For long range phased haplotype association analysis, we then partitioned the genome into non-overlapping fixed 0.3cM bins. Within each bin, we observed the haplotype diversity described by the combination of all chip-typed markers in the bin. Haplotypes with frequencies over 0.001 were tested in a case: control analysis.

Genotype imputation: We imputed the SNPs identified and genotyped through sequencing into all Icelanders who had been phased with long range phasing using the same model as used by IMPUTE²⁷. The genotype data from sequencing can be ambiguous due to low sequencing

coverage. In order to phase the sequencing genotypes, an iterative algorithm was applied for each SNP with alleles 0 and 1. We let H be the long range phased haplotypes of the sequenced individuals and applied the following algorithm:

1. For each haplotype h in H, use the Hidden Markov Model of IMPUTE to calculate for every other k in H, the likelihood, denoted y_hrkl of h having the same ancestral source as k at the SNP.

2. For every h in H, initialize the parameter 9_h, which specifies how likely the one allele of the SNP is to occur on the background of h from the genotype likelihoods obtained from sequencing. The genotype likelihood L_g is the probability of the observed sequencing data at the SNP for a given individual assuming g is the true genotype at the SNP. If L₀, Li and L₂ are the likelihoods of the genotypes 0, 1 and 2 in the individual that carries h, then set e_h =

L₂+L_!+L₀

3. For every pair of haplotypes h and k in H that are carried by the same individual, use the other haplotypes in H to predict the genotype of the SNP on the backgrounds of h and k: t_h

Combining these predictions with the genotype likelihoods from sequencing gives un-normalized updated phased genotype probabilities: p₀₀ = (l - τ„)(ι - T_k)L₀, p₁₀ = τ„(ι - r_ft)f£i, P₀ = (i - and p = T_hz_kL₂. Now use these values to update Q_h and 9_k to e_h =— — and e_k =— — .

4. Repeat step 3 when the maximum difference between iterations is greater than a

convergence threshold ε. We used ε=10^~7.

Given the long range phased haplotypes and Θ, the allele of the SNP on a new haplotype h not in H, is imputed as∑_l€HY_h -

The above algorithm can easily be extended to handle simple family structures such as parent- offspring pairs and triads by letting the P distribution run over all founder haplotypes in the family structure. The algorithm also extends trivially to the X-chromosome. If source genotype data are only ambiguous in phase, such as chip genotype data, then the algorithm is still applied, but all but one of the Ls will be 0. In some instances, the reference set was intentionally enriched for carriers of the minor allele of a rare SNP in order to improve imputation accuracy. In this case, expected allele counts will be biased toward the minor allele of the SNP. Call the enrichment of the minor allele E and let θ' be the expected minor allele count calculated from the

Εθ naive imputation method, and let Θ be the unbiased expected allele count, then ø' = ^and hence θ

Ε+(1-Ε)θι

This adjustment was applied to all imputations based on enriched imputations sets. We note that if θ' is 0 or 1, then Θ will also be 0 or 1, respectively.

Using a sample of 9691 individuals who had been typed both on chip and by direct genotyping for rs78378222, we compared the imputed genotype expectation values with direct single track genotypes. The r² between the results of the two methods was 0.92. Genotype imputation information: The informativeness of genotype imputation was estimated by the ratio of the variance of imputed expected allele counts and the variance of the actual allele counts:

Var(E(e \chip data))

Var{6) '

where θ e {0, 1} is the allele count. Var(E{e \chip data)) was estimated by the observed variance of the imputed expected counts and Var(0) was estimated by p(i - p), where is the allele frequency. 78.2 % of SNPs were imputed with information values j> 0.8 and a further 16.6% were imputed with information values >. 0.6 and < 0.8. Thus 97.4% of SNPs were imputed with information values >. 0.6. The information value for rs78378222 was 0.97.

Genealogy-based in silico genotyping: In addition to imputing sequence variants from the whole genome sequencing effort into chip genotyped individuals, we also performed a second imputation step where genotypes were imputed into relatives of chip genotyped individuals, creating in silico genotypes. The inputs into the second imputation step are the fully phased (in particular every allele has been assigned its parent of origin²⁹) imputed and chip type genotypes of the available chip typed individual. The algorithm used to perform the second imputation step consists of:

1. For each ungenotyped individual (the proband), find all chip genotyped individuals within two meioses of the individual. The six possible types of two meiotic distance relatives of the proband are (ignoring more complicated relationships due to pedigree loops) :

Parents, full and half siblings, grandparents, children and grandchildren. If all pedigree paths from the proband to a genotyped relative go through other genotyped relatives, then that relative is excluded . E.g. if a parent of the proband is genotyped, then the proband's grandparents through that parent are excluded . If the number of meiosis in the pedigree around the proband exceeds a threshold (we used 12), then relatives are removed from the pedigree until the number of meiosis falls below 12, in order to reduce computational complexity.

2. At every point in the genome, calculate the probability of each genotyped relative sharing with the proband based on the autosomal SNPs used for phasing . A multipoint algorithm based on the hidden Markov model Lander-Green multipoint linkage algorithm using fast Fourier transforms is used to calculate these sharing probabilities^30,31. First single point sharing probabilities are calculated by dividing the genome into 0.5cM bins and using the haplotypes over these bins as alleles. Haplotypes that are the same, except at most at a single SNP, are treated as identical. When the haplotypes in the pedigree are

incompatible over a bin, then a uniform probability distribution was used for that bin. The most common causes for such incompatibilities are recombinations within the pedigree, phasing errors and genotyping errors. Note that since the input genotypes are fully phased, the single point information is substantially more informative than for unphased genotyped, in particular one haplotype of the parent of a genotyped child is always known . The single point distributions are then convolved using the multipoint algorithm to obtain multipoint sharing probabilities at the center of each bin. Genetic distances were obtained from the most recent version of the deCODE genetic map³².

3. Based on the sharing probabilities at the center of each bin, all the SNPs from the whole genome sequencing are imputed into the proband . To impute the genotype of the paternal allele of a SNP located at x, flanked by bins with centers at x_left and x_right.

Starting with the left bin, going through all possible sharing patterns v, let l_v be the set of haplotypes of genotyped individuals that share identically by descent within the pedigree with the proband's paternal haplotype given the sharing pattern v and P(y) be the probability of v at the left bin - this is the output from step 2 above - and let e_t be the expected allele count of the SNP for haplotype i . Then e_v = s the expected allele count of the paternal haplotype of the proband given v and an overall estimate of the allele count given the sharing distribution at the left bin is obtained from e_left =∑_v P(v)e_v. If l_v is empty then no relative shares with the proband's paternal haplotype given v and thus there is no information about the allele count. We therefore store the probability that some genotyped relative shared the proband's paternal haplotype, o_left = „_,;„=ø POO and an expected allele count, conditional on the proband's paternal haplotype being shared by at least one genotyped relative : c_left = ^ ^ν≠φ In the same way calculate o_right and c_right. Linear interpolation is then used to get an estimates at the SNP from the two flanking bins :

left

^■left + ^crlght ^cleft.

^■right left

If θ is an estimate of the population frequency of the SNP then Oc + (l - 0)θ is an estimate of the allele count for the proband's paternal haplotype. Similarly, an expected allele count can be obta ined for the proband's maternal haplotype.

Case : control association testing: Logistic regression was used to test for association between SNPs and disease, treating disease status as the response and expected genotype counts from imputation or allele counts from direct genotyping as covariates. Testing was performed using the likelihood ratio statistic. The conditional analysis of rs78378222 and chrl 7:7640788 was performed by adding rs78378222 as a covariate while testing chrl 7:7640788 for association with BCC. When testing for association using the in silico genotypes, controls were matched to cases based on the informativeness of the imputed genotypes, such that for each case c controls of matching informativeness where chosen. Failing to match cases and controls will lead to a highly inflated genomic control factor, and in some cases may lead to spurious false positive findings. The informativeness of each of the imputation of each one of an individual's haplotypes was estimated by taking the average of

over all SNPs imputed for the individual, where e is the expected allele count for the haplotype at the SNP and θ is the population frequency of the SNP. Note that (θ, θ) = o and (θ, θ) = α(ΐ, 0) = l. The mean informativeness values cluster into groups corresponding to the most common pedigree configurations used in the imputation, such as imputing from parent into child or from child into parent. Based on this clustering of imputation informativeness we divided the haplotypes of individuals into seven groups of varying informativeness, which created 27 groups of individuals of similar imputation informativeness; 7 groups of individuals with both haplotypes having similar informativeness, 21 groups of individuals with the two haplotypes having different informativeness, minus the one group of individuals with neither haplotype being imputed well. Within each group we calculate the ratio of the number of controls and the number of cases, and choose the largest integer c that was less than this ratio in all the groups. For example, if in one group there are 10.3 times as many controls as cases and if in all other groups this ratio was greater, then we would set c = 10 and within each group randomly select ten times as many controls as there are cases. For the different tumour types the value of cwas always higher than 15.

Inflation Factor Adjustment: In order to account for the relatedness and stratification within the case and control sample sets we applied the method of genomic control based on chip typed markers³³. The adjustment factors ranged from 1.06 (for PBC) to 1.27 (for Prostate Cancer). Quoted P values have been adjusted accordingly.

Effective sample size estimation: In order to estimate the effective sample size of the case control association analyses, we compared the variances of the logistic and generalized linear regression parameter estimates based on the in silico genotypes to their one step imputation counterparts. For the quantitative trait association analysis, assume that a single step imputation (SNPs are imputed, but in silico genotypes are not used) association analysis with ¾ subjects leads on average to an estimate of the regression parameter with variance

and that the corresponding in silico genotype association analysis leads to an estimate of the regression parameter with variance σ , then assuming that variance goes down linearly with sample size we estimate the effective sample size in the in silico genotype association analysis as n₂ ^{Fo r}

the case control association analysis, the number of controls is much greater than the number cases and we use the same formula to estimate the effective number of cases, with the n-s representing the number of cases and the a²-s representing the variances of the logistic regression coefficient.

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Claims

1. A method of determining a susceptibility to a cancer, the method comprising : analyzing data representative of at least one allele of a human TP53 gene (SEQ ID NO: 3) in a human subject, wherein different alleles of the TP53 gene are associated with different susceptibilities to the cancer in humans, and determining a susceptibility to the cancer for the human subject from the data.

2. The method according to claim 1, wherein the cancer is selected from the group consisting of glioma, basal cell carcinoma, prostate cancer and colorectal adenoma.

3. The method according to claim 1 or claim 2, wherein the cancer is glioma.

4. The method according to any one of claims 1-3, comprising analyzing the data for the presence or absence of at least one mutant allele in TP53 that results in impaired

polyadenylation of a TP53 transcript; wherein determination of the presence of the at least one mutant allele is indicative of an increased susceptibility to the cancer.

6. The method of claim 4, wherein the analyzing data comprises analyzing a biological sample from the human subject to obtain information selected from the group consisting of:

(a) nucleic acid sequence information, wherein the nucleic acid sequence information comprises sequence sufficient to identify the presence or absence of the mutant allele in the subject; (b) nucleic acid sequence information, wherein the nucleic acid sequence information identifies at least one allele of a polymorphic marker in linkage disequilibrium (LD) with the mutant allele, wherein the LD is characterized by a value for r² of at least 0.5;

(c) measurement of the quantity or length of TP53 mRNA, wherein the measurement is indicative of the presence or absence of the mutant allele; and (d) measurement of the quantity of TP53 protein, wherein the measurement is indicative of the presence or absence of the mutant allele.

7. The method of claim 6, comprising analyzing the biological sample to obtain the nucleic acid sequence information.

8. The method of any one of claims 6-7, further comprising obtaining a biological sample comprising nucleic acid from the human subject.

9. The method of claim 6, wherein the analyzing data comprises analyzing data from a preexisting record about the human subject.

10. The method of any one of claims 1-9, wherein the presence of the mutant allele is indicative of increased susceptibility to the cancer with a relative risk (RR) or odds ratio (OR) of at least 1.3, of at least 1.4, of at least 1.5, of at least 1.6, og at least 1.7, of at least 1.8, of at least 1.9, of at least 2.0, of at least 2.1, of at least 2.2, or of at least 2.3.

11. The method of any one of the previous claims, wherein the allele affects the AATAAA polyadenylation signal in TP53.

12. The method of any one of claims previous claims, wherein the allele is the C allele of rs78378222.

13. A method of determining whether a human individual is at increased risk of developing a cancer, the method comprising steps of obtaining a biological sample containing nucleic acid from the individual; determining, in the biological sample, nucleic acid sequence about the TP53 gene; and comparing the sequence information to the wild-type sequence of TP53 (SEQ ID NO: 3); wherein an identification of a mutation in TP53 in the individual is indicative that the individual is at increased risk of developing the cancer.

14. The method of claim 13, wherein the mutation is in the 3' UTR of TP53.

15. The method of claim 13 or claim 14, wherein the mutation results in impaired 3' processing of TP53 mRNA.

16. The method of any one of claims 13 to 15, wherein the mutation leads to an impaired polyadenylation signal.

17. The method of claim 16, wherein the mutation affects the TP53 AATAAA polyadenylation signal.

18. The method of claim 17, wherein the mutation changes the TP53 AATAAA polyadenylation signal to AATACA.

19. The method of any one of the claims 13 to 18, wherein the cancer is selected from the group consisting of basal cell carcinoma, prostate cancer, glioma and colorectal adenoma.

20. A method for determining a susceptibility to a cancer in a human individual, comprising determining the presence or absence of at least one allele of at least one polymorphic marker in a nucleic acid sample obtained from the individual, wherein the at least one allele causes a impaired function or reduced expression of TP53, and determining a susceptibility to the cancer from the presence or absence of the at least one allele, wherein the presence of the at least one allele is indicative of a susceptibility to the cancer.

21. The method of claim 20, wherein the at least one polymorphic marker is a marker that causes reduced expression of a TP53.

22. The method of claim 20, wherein the at least one polymorphic marker is a marker that causes impaired 3' processing of a TP53 with sequence as set forth in SEQ ID NO: 3.

23. The method according to any one of claims 20 to 22, wherein the cancer is selected from the group consisting of basal cell carcinoma, prostate cancer, glioma and colorectal adenoma.

24. A method of determining a susceptibility to a cancer, the method comprising : screening a biological sample from a human subject for evidence of an allele of TP53 (SEQ ID NO: 3) that results in impaired TP53 mRNA processing, wherein the presence of an allele of TP53 with impaired mRNA processing is associated with elevated susceptibility to the cancer in humans, and determining a susceptibility to the cancer for the human subject from the presence or absence of the allele of TP53 that results in the impaired TP53 mRNA processing.

25. The method according to claim 24, comprising screening for the presence of a TP53 allele which results in impaired 3' processing.

26. The method of claim 24 or claim 25, comprising screening for the presence of a TP53 allele that affects the AATAAA 3' polyadenylation signal in the 3' region of TP53.

27. The method of any of the claims 24 to 26, comprising screening for the presence of a TP53 allele that changes the AATAAA 3' polyadenylation signal in TP53 to AATACA.

28. A method of selecting a therapeutic regimen for a human subject with a cancer, the method comprising : analyzing data representative of at least one allele of a TP53 gene (SEQ ID NO: 3) in a human subject with the cancer to identify the presence or absence of a TP53 mutant allele that leads to impaired 3' mRNA processing of TP53, and selecting a therapeutic regimen of a therapeutic agent for treating the cancer for a subject identified from the data as having the mutant allele.

29. The method of claim 28, wherein the analyzing comprises screening for the presence or absence of at least one mutant allele that leads to impaired polyadenylation of TP53 mRNA.

30. The method of claim 28 or claim 29, comprising selecting the therapeutic regimen for a subject identified from the data as having a AATAAA polyadenylation signal changed to AATACA.

31. The method of any one of claims 28 - 30, wherein the selecting comprises prescribing the therapeutic regimen of the therapeutic agent for treating the cancer for the subject.

32. The method of any one of claims 28 - 31, wherein the selecting comprises administering the therapeutic regimen of the therapeutic agent for treating the cancer to the subject.

33. The method of any one of the claims 28 - 32, wherein the therapeutic regimen is selected for an individual diagnosed with glioma and wherein the therapeutic agent is a therapeutic agent for glioma selected from the group consisting of temozolomide, cannabinoids (e.g., tetrahydrocannabinol, cannabidiol, cannabinol, cannabigerol, cannabichromene, cannabicyclol, cannabivarin, tetrahydrocannabivarin, cannabidivarin, cannabichrromevarin, cannabigerovarin, cannabigerol monethyl ether).

34. A method of selecting a human subject with a cancer for treatment with a cancer therapeutic agent, the method comprising : determining the presence or absence of at least one allele of at least one polymorphic marker in a nucleic acid sample obtained from the individual, wherein the at least one allele causes impaired 3' processing of TP53 mRNA, and selecting for treatment with the therapeutic agent a subject identified as having the at least one allele in the nucleic acid sample.

35. The method of claim 34, further comprising prescribing the therapeutic regimen of the therapeutic agent for the subject.

36. The method of claim 34 or claim 35 , further comprising administering the therapeutic regimen of the therapeutic agent to the subject.

37. A method of treatment of a cancer of a human individual diagnosed with the cancer, the method comprising determining the presence or absence of a mutation that causes a impaired 3' mRNA processing of TP53 in a nucleic acid sample from the human individual; selecting for treatment an individual determined to have the mutation; and administering to the selected individual a pharmaceutically acceptable amount of a therapeutic agent for the cancer.

38. The method of claim 37, wherein the mutation that causes a impaired 3' mRNA processing of TP53 is a mutation in the AATAAA polyadenylation signal.

39. The method of any one of the claims 28 - 38 , wherein the cancer is selected from the group consisting of basal cell carcinoma, prostate cancer, glioma and colorectal adenoma.

40. A system for identifying susceptibility to a cancer in a human subject, the system comprising : at least one processor; at least one computer-readable medium; a susceptibility database operatively coupled to a computer-readable medium of the system and containing population information correlating the presence or absence of one or more alleles of the human TP53 gene and susceptibility to a cancer in a population of humans; a measurement tool that receives an input about the human subject and generates information from the input about the presence or absence of at least one mutant TP53 allele indicative of a TP53 defect in the human subject; and an analysis tool that: is operatively coupled to the susceptibility database and the the measurement tool, is stored on a computer-readable medium of the system, is adapted to be executed on a processor of the system, to compare the information about the human subject with the population information in the susceptibility database and generate a conclusion with respect to susceptibility to the cancer for the human subject.

41. The system according to claims 40, further including : a communication tool operatively coupled to the analysis tool, stored on a computer-readable medium of the system and adapted to be executed on a processor of the system to communicate to the subject, or to a medical practitioner for the subject, the conclusion with respect to susceptibility to the cancer for the subject.

42. The system according to claim 40 or 41, wherein the cancer is selected from the group consisting of basal cell carcinoma, prostate cancer, glioma and colorectal adenoma.

43. The system according to any one of claims 40-42, wherein the at least one mutant TP53 allele is an allele that leads to impaired 3' mRNA processing of human TP53.

44. The system according to any one of the claims 40-43, wherein the at least one mutant TP53 allele is an allele in the AATAAA polyadenylation signal.

45. The system according to any one of claims 40-44, wherein the measurement tool comprises a tool stored on a computer-readable medium of the system and adapted to be executed by a processor of the system to receive a data input about a subject and determine information about the presence or absence of the at least one mutant TP53 allele in a human subject from the data.

46. The system according to claim 45, wherein the data is genomic sequence information, and the measurement tool comprises a sequence analysis tool stored on a computer readable medium of the system and adapted to be executed by a processor of the system to determine the presence or absence of the at least one mutant TP53 allele from the genomic sequence information.

47. The system according to any one of claims 40-44, wherein the input about the human subject is a biological sample from the human subject, and wherein the measurement tool comprises a tool to identify the presence or absence of the at least one mutant TP53 allele in the biological sample, thereby generating information about the presence or absence of the at least one mutant TP53 allele in a human subject.

48. The system according to claim 47, wherein the measurement tool includes: an oligonucleotide microarray containing a plurality of oligonucleotide probes attached to a solid support; a detector for measuring interaction between nucleic acid obtained from or amplified from the biological sample and one or more oligonucleotides on the oligonucleotide microarray to generate detection data; and an analysis tool stored on a computer-readable medium of the system and adapted to be executed on a processor of the system, to determine the presence or absence of the at least one mutant TP53 allele based on the detection data.

49. The system according to claim 47, wherein the measurement tool includes: a nucleotide sequencer capable of determining nucleotide sequence information from nucleic acid obtained from or amplified from the biological sample; and an analysis tool stored on a computer-readable medium of the system and adapted to be executed on a processor of the system, to determine the presence or absence of the at least one mutant TP53 allele based on the nucleotide sequence information.

50. The system according to any one of claims 40 to 49, further comprising : a medical protocol database operatively connected to a computer-readable medium of the system and containing information correlating the presence or absence of the at least one mutant TP53 allele and medical protocols for human subjects at risk for the cancer; and a medical protocol routine, operatively connected to the medical protocol database and the analysis routine, stored on a computer-readable medium of the system, and adapted to be executed on a processor of the system, to compare the conclusion from the analysis routine with respect to susceptibility to cancer for the subject and the medical protocol database, and generate a protocol report with respect to the probability that one or more medical protocols in the database will : reduce susceptibility to the cancer; or delay onset of the cancer; or increase the likelihood of detecting the cancer at an early stage to facilitate early treatment.

51. The system according to any one of claims 41-50, wherein the communication tool is operatively connected to the analysis routine and comprises a routine stored on a computer- readable medium of the system and adapted to be executed on a processor of the system, to: generate a communication containing the conclusion; and transmit the communication to the subject or the medical practitioner, or enable the subject or medical practitioner to access the communication.

52. The system according to claim 51, wherein the communication expresses the susceptibility to the cancer in terms of odds ratio or relative risk or lifetime risk.

53. The system according to claim 51 or 52, wherein the communication further includes the protocol report.

54. The system according to any one of claims 40-54, wherein the susceptibility database further includes information about at least one parameter selected from the group consisting of age, sex, ethnicity, race, medical history, weight, diabetes status, blood pressure, family history of the cancer, and smoking history in humans and impact of the at least one parameter on susceptibility to the cancer.

55. A system for assessing or selecting a treatment protocol for a subject diagnosed with a cancer, comprising : at least one processor; at least one computer-readable medium; a medical treatment database operatively connected to a computer-readable medium of the system and containing information correlating the presence or absence of at least one mutant TP53 allele and efficacy of treatment regimens for the cancer; a measurement tool to receive an input about the human subject and generate information from the input about the presence or absence of the at least one mutant TP53 allele indicative of a TP53 defect in a human subject diagnosed with the cancer; and a medical protocol tool operatively coupled to the medical treatment database and the measurement tool, stored on a computer-readable medium of the system, and adapted to be executed on a processor of the system, to compare the information with respect to presence or absence of the at least one mutant TP53 allele for the subject and the medical treatment database, and generate a conclusion with respect to at least one of: the probability that one or more medical treatments will be efficacious for treatment of the cancer for the patient; and which of two or more medical treatments for the cancer will be more efficacious for the patient.

56. The system according to claim 55, wherein the measurement tool comprises a tool stored on a computer-readable medium of the system and adapted to be executed by a processor of the system to receive a data input about a subject and determine information about the presence or absence of the at least one mutant TP53 allele in a human subject from the data.

57. The system according to claim 56, wherein the data is genomic sequence information, and the measurement tool comprises a sequence analysis tool stored on a computer readable medium of the system and adapted to be executed by a processor of the system to determine the presence or absence of the at least one mutant TP53 allele from the genomic sequence information.

58. The system according to claim 55, wherein the input about the human subject is a biological sample from the human subject, and wherein the measurement tool comprises a tool to identify the presence or absence of the at least one mutant TP53 allele in the biological sample, thereby generating information about the presence or absence of the at least one mutant TP53 allele in a human subject.

59. The system according to any one of claims 55-58, further comprising a communication tool operatively connected to the medical protocol routine for communicating the conclusion to the subject, or to a medical practitioner for the subject.

60. The system according to claim 59, wherein the communication tool comprises a routine stored on a computer-readable medium of the system and adapted to be executed on a processor of the system, to: generate a communication containing the conclusion; and transmit the communication to the subject or the medical practitioner, or enable the subject or medical practitioner to access the communication.