WO2008138578A2 - Procédés, coffrets et dispositifs pour identifier des biomarqueurs de réponse à un traitement et utilisation de ceux-ci pour prédire l'efficacité du traitement - Google Patents

Procédés, coffrets et dispositifs pour identifier des biomarqueurs de réponse à un traitement et utilisation de ceux-ci pour prédire l'efficacité du traitement Download PDF

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WO2008138578A2
WO2008138578A2 PCT/EP2008/003789 EP2008003789W WO2008138578A2 WO 2008138578 A2 WO2008138578 A2 WO 2008138578A2 EP 2008003789 W EP2008003789 W EP 2008003789W WO 2008138578 A2 WO2008138578 A2 WO 2008138578A2
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mir
hsa
prec
microrna
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WO2008138578A3 (fr
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Steen Knudsen
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Medical Prognosis Institute
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/118—Prognosis of disease development
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/178—Oligonucleotides characterized by their use miRNA, siRNA or ncRNA

Definitions

  • the invention features methods, kits, and devices for identifying biomarkers of patient sensitivity to medical treatments, e.g., sensitivity to chemotherapeutic agents, and predicting treatment efficacy using the biomarkers.
  • DNA microarrays have been used to measure gene expression in tumor samples from patients and to facilitate diagnosis. Gene expression can reveal the presence of cancer in a patient, its type, stage, and origin, and whether genetic mutations are involved. Gene expression may even have a role in predicting the efficacy of chemotherapy.
  • NCI National Cancer Institute
  • the NCI has tested compounds, including chemotherapy agents, for their effect in limiting the growth of 60 human cancer cell lines.
  • the NCI has also measured gene expression in these 60 cancer cell lines using DNA microarrays.
  • Various studies have explored the relationship between gene expression and compound effect using the NCI datasets. Critical time is often lost due to a trial and error approach to finding an effective chemotherapy for patients with cancer. In addition, cancer cells often develop resistance to a previously effective therapy. In such situations, patient outcome could be greatly improved by early detection of such resistance.
  • the invention features methods, kits, and devices for determining the sensitivity or resistance of a patient, e.g., a cancer patient, to a treatment, e.g., treatment with a compound, such as a chemotherapeutic agent, or radiation.
  • a treatment e.g., treatment with a compound, such as a chemotherapeutic agent, or radiation.
  • the methods, kits, and devices can be used to determine the sensitivity or resistance of a cancer patient to any medical treatment, including, e.g., treatment with a compound, drug, or radiation.
  • the methods, kits, and devices of the invention have been used to accurately determine treatment efficacy in cancer patients (e.g., patients with lung, lymphoma, and brain cancer) and can be used to determine treatment efficacy in patients diagnosed with any cancer.
  • biomarkers e.g., genes and microRNAs
  • Vincristine Cisplatin, Azaguanine, Etoposide, Adriamycin, Aclarubicin, Mitoxantrone, Mitomycin, Paclitaxel, Gemcitabine, Taxotere, Dexamethasone, Ara-C, Methylprednisolone, Methotrexate, Bleomycin, Methyl-GAG, Carboplatin, 5-FU (5-Fluorouracil), Rituximab, , PXDlOl, (a histone deacetylase (HDAC) inhibitor), 5-Aza-2'-deoxycytidine (Decitabine), Melphalan, IL4-PE38 fusion protein, IL13-PE38QQR fusion protein (cintredekin besudotox), Valproic acid (VPA), All-
  • the methods, kits, and devices can be used to predict the sensitivity or resistance of a subject (e.g., a cancer patient) diagnosed with a disease condition, e.g., cancer (e.g., cancers of the breast, prostate, lung and bronchus, colon and rectum, urinary bladder, skin, kidney, pancreas, oral cavity and pharynx, ovary, thyroid, parathyroid, stomach, brain, esophagus, liver and intrahepatic bile duct, cervix larynx, heart, testis, small and large intestine, anus, anal canal and anorectum, vulva, gallbladder, pleura, bones and joints, hypopharynx, eye and orbit, nose, nasal cavity and middle ear, nasopharynx, ureter, peritoneum, omentum and mesentery, or gastrointestines, as well as any form of cancer including, e.g., chronic myeloid leukemia, acute
  • the invention features a method of determining sensitivity of a cancer in a patient to a treatment for cancer by measuring the level of expression of at least one gene in a cell (e.g., a cancer cell) of the patient, in which the gene is selected from the group consisting of ACTB, ACTN4, ADA, ADAM9, ADAMTS 1 , ADD 1 , AFlQ, AIFl, AKAPl , AKAP13, AKRlCl, AKTl , ALDH2, ALDOC, ALG5, ALMSl, ALOXl 5B, AMIG02, AMPD2, AMPD3, ANAPC5, ANP32A, ANP32B, ANXAl, AP1G2, APOBEC3B, APRT, ARHE, ARHGAPl 5, ARHGAP25, ARHGDIB, ARHGEF6, ARL7, ASAHl, ASPH, ATF3, ATIC, ATP2A2, ATP2A3, ATP5D, ATP
  • the method further includes determining a patient's resistance or sensitivity to radiation therapy or the chemotherapy agents Vincristine, Cisplatin, Adriamycin, Etoposide, Azaguanine, Aclarubicin, Mitoxantrone, Paclitaxel, Mitomycin, Gemcitabine, Taxotere, Dexamethasone, Methylprednisolone, Ara-C, Methotrexate, Bleomycin, Methyl-GAG, Rituximab, PXDlOl (a histone deacetylase (HDAC) inhibitor), 5-Aza-2'-deoxycytidine (Decitabine), Melphalan, IL4-PE38 fusion protein, IL13-PE38QQR fusion protein (cintredekin besudotox), Valproic acid (VPA), All-trans retinoic acid (ATRA), Cytoxan, Topotecan (Hycamtin), Suberoylanilide hydroxamic acid (Vol
  • a patient's resistance or sensitivity to radiation therapy or any of the chemotherapy agents listed above can be determined by measuring the level of expression of at least one microRNA in a cell (e.g., a cancer cell) known to change (e.g., the level of expression is increased or decreased) in a patient sensitive to a treatment with these agents, in which the microRNA is selected from the group consisting of ath-MIR180aNo2, HcdlO2 left, Hcdl 1 1 left, Hcdl 15 left, Hcdl20 left, Hcdl 42 right, Hcdl 45 left, Hcdl48_HPR225 left, Hcdl 81 left, Hcdl 81 right, Hcd210_HPR205 right, Hcd213_HPR182 left, Hcd230 left, Hcd243 right, Hcd246 right, Hcd248 right, Hcd249 right, Hcd250 left, Hcd255 left, Hcd257 left, Hcd2
  • the method includes determining the expression of two of the listed genes or microRNAs, more preferably three, four, five, six, seven, eight, nine, or ten of the listed genes, and most preferably twenty, thirty, forty, fifty, sixty, seventy, eighty, ninety, or one hundred or more of the listed genes.
  • the change in the level of gene or microRNA expression e.g., an increase or decrease
  • the change in the level of gene or microRNA expression is determined relative to the level of gene or microRNA expression in a cell or tissue known to be resistant to the treatment, such that a similar level of gene or microRNA expression exhibited by a cell or tissue of the patient indicates the patient is resistant to the treatment.
  • the invention features a method of determining sensitivity of a cancer in a patient to a treatment for cancer by measuring the level of expression of at least one microRNA in a cell (e.g., a cancer cell) of the patient, in which the microRNA is selected from the group set forth in the first aspect of the invention.
  • a cell e.g., a cancer cell
  • the method further includes determining a patient's resistance or sensitivity to radiation therapy or any of the chemotherapy agents set forth in the first aspect of the invention by measuring the level of expression of one or more of the microRNAs known to change (e.g., to increase or decrease) in a patient sensitive to treatment with these agents (e.g., a patient is determined to be sensitive, or likely to be sensitive, to the indicated treatment if the level of expression of one or more of the microRNA(s) increases or decreases relative to the level of expression of the microRNA(s) in a control sample (e.g., a cell or tissue) in which increased or decreased expression of the microRNA(s) indicates sensitivity to the treatment, and vice versa).
  • a control sample e.g., a cell or tissue
  • the method includes determining the expression of two of the listed genes or microRNAs, more preferably three, four, five, six, seven, eight, nine, or ten of the listed genes, and most preferably twenty, thirty, forty, fifty, sixty, seventy, eighty, ninety, or one hundred or more of the listed genes.
  • the change in the level of microRNA expression e.g., an increase or decrease
  • the change in the level of microRNA expression is determined relative to the level of microRNA expression in a cell or tissue known to be resistant to the treatment, such that a similar level of microRNA expression exhibited by a cell or tissue of the patient indicates the patient is resistant to the treatment.
  • the invention features a method for determining the development of resistance by a patient (e.g., resistance of a cell, such as a cancer cell, in the patient) to a treatment to which the patient was previously sensitive.
  • the method includes measuring the level of expression of one or more of the microRNAs set forth in the first aspect of the invention, such that the level of expression of a micro RN A which is decreased in a cell or tissue known to be sensitive to the treatment indicates that the patient is resistant to or has a propensity to become resistant to the treatment.
  • a decrease in the expression level of a microRNA which is increased in a cell or tissue known to be sensitive to the treatment indicates that the patient is resistant to or has a propensity to become resistant to the treatment.
  • the invention features a kit that includes a single-stranded nucleic acid molecule (e.g., one or a plurality thereof; e.g., a deoxyribonucleic acid molecule or a ribonucleic acid molecule) that is substantially complementary to (e.g., that has at least 80%, 90%, 95% 97%, 99%, or 100% identical to the complement of) or that is substantially identical to (e.g., that has at least 80%, 90%, 95% 97%, 99%, or 100% identity to) at least 5 consecutive nucleotides (more preferably at least 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, or more consecutive nucleotides; the nucleic acid can also be 5-20, 25, 5-50, 50-100, or over 100 consecutive nucleotides long) of at least one of the genes (e.g., at least 2, 3, 4,
  • the kit includes one or more single-stranded nucleic acid molecules that are substantially complementary to or substantially identical to at least 5 consecutive nucleotides of at least one of the microRNAs set forth in the first aspect of the invention, such that the single-stranded nucleic acid molecule is sufficient for measuring the level of expression of the microRNA(s) by allowing specific hybridization between the single-stranded nucleic acid molecule and the microRNA, or a complement thereof.
  • the kit further includes instructions for applying nucleic acid molecules collected from a sample from a cancer patient (e.g., from a cell of the patient), determining the level of expression of the gene(s) or microRNA(s) hybridized to the single-stranded nucleic acid, and determining the patient's sensitivity to a treatment for cancer when use of the kit indicates that the level of expression of the gene(s) or microRNA(s) changes (e.g., increases or decreases relative to a control sample (e.g., tissue or cell) known to be sensitive or resistant to the treatment, as is discussed above in connection with the first aspect of the invention).
  • a control sample e.g., tissue or cell
  • the instructions further indicate that a change in the level of expression of the gene(s) or microRNA(s) relative to the expression of the gene(s) or microRNA(s) in a control sample (e.g., a cell or tissue known to be sensitive or resistant to the treatment) indicates a change in sensitivity of the patient to the treatment (e.g., a decrease in the level of expression of a gene or microRNA known to be expressed in cells sensitive to the treatment indicates that the patient is becoming resistant to the treatment or is likely to become resistant to the treatment, and vice versa).
  • a control sample e.g., a cell or tissue known to be sensitive or resistant to the treatment
  • the kit can be utilized to determine a patient's resistance or sensitivity to radiation therapy or the chemotherapy agents Vincristine, Cisplatin, Adriamycin, Etoposide, Azaguanine, Aclarubicin, Mitoxantrone, Paclitaxel, Mitomycin, Gemcitabine, Taxotere, Dexamethasone, Methylprednisolone, Ara-C, Methotrexate, Bleomycin, Methyl-GAG, Rituximab, PXDlOl (a histone deacetylase (HDAC) inhibitor), 5-Aza-2'-deoxycytidine (Decitabine), Melphalan, IL4-PE38 fusion protein, IL13-PE38QQR fusion protein (cintredekin besudotox), Valproic acid (VPA), All-trans retinoic acid (ATRA), Cytoxan, Topotecan (Hycamtin), Suberoylanilide hydroxamic acid
  • the invention features a kit that includes a single-stranded nucleic acid molecule (e.g., one or a plurality thereof; e.g., a deoxyribonucleic acid molecule or a ribonucleic acid molecule) that is substantially complementary to (e.g., that has at least 80%, 90%, 95% 97%, 99%, or 100% identical to the complement of) or that is substantially identical to (e.g., that has at least 80%, 90%, 95% 97%, 99%, or 100% identity to) at least 5 consecutive nucleotides (more preferably at least 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, or more consecutive nucleotides; the nucleic acid can also be 5-20, 25, 5-50, 50-100, or over 100 consecutive nucleotides long) of at least one of the microRNAs (e.g., at least 2,
  • the kit further includes instructions for applying nucleic acid molecules collected from a sample from a cancer patient (e.g., from a cell of the patient), determining the level of expression of the micro RNA(s) hybridized to the single-stranded nucleic acid, and determining the patient's sensitivity to a treatment for cancer when use of the kit indicates that the level of expression of microRNA(s) changes (e.g., increases or decreases relative to a control sample (e.g., tissue or cell) known to be sensitive or resistant to the treatment, as is discussed above in connection with the first aspect of the invention).
  • a control sample e.g., tissue or cell
  • the instructions further indicate that a change in the level of expression of microRNA(s) relative to the expression of microRNA(s) in a control sample (e.g., a cell or tissue known to be sensitive or resistant to the treatment) indicates a change in sensitivity of the patient to the treatment (e.g., a decrease in the level of expression of a microRNA known to be expressed in cells sensitive to the treatment indicates that the patient is becoming resistant to the treatment or is likely to become resistant to the treatment, and vice versa).
  • a control sample e.g., a cell or tissue known to be sensitive or resistant to the treatment
  • the kit can be utilized to determine a patient's resistance or sensitivity to radiation therapy or the chemotherapy agents Vincristine, Cisplatin, Adriamycin, Etoposide, Azaguanine, Aclarubicin, Mitoxantrone, Paclitaxel, Mitomycin, Gemcitabine, Taxotere, Dexamethasone, Methylprednisolone, Ara-C, Methotrexate, Bleomycin, Methyl-GAG, Rituximab, PXDlOl (a histone deacetylase (HDAC) inhibitor), 5-Aza-2'-deoxycytidine (Decitabine), Melphalan, IL4-PE38 fusion protein, IL13-PE38QQR fusion protein (cintredekin besudotox), Valproic acid (VPA), All-trans retinoic acid (ATRA), Cytoxan, Topotecan (Hycamtin), Suberoylanilide hydroxamic acid
  • the nucleic acid molecules are characterized by their ability to specifically identify nucleic acid molecules complementary to the microRNAs in a sample collected from a cancer patient.
  • the invention features a method of identifying biomarkers (e.g., genes and microRNAs) indicative of sensitivity of a cancer patient to a treatment for cancer by obtaining pluralities of measurements of the expression level of a gene or microRNA (e.g., by detection of the expression of a gene or microRNA using a single probe or by using multiple probes directed to a single gene or microRNA) in different cell types and measurements of the growth of those cell types in the presence of a treatment for cancer relative to the growth of the cell types in the absence of the treatment for cancer; correlating each plurality of measurements of the expression level of the gene or microRNA in cells with the growth of the cells to obtain a correlation coefficient; selecting the median correlation coefficient calculated for the gene or microRNA; and identifying the gene or microRNA as a biomarker for use in determining the sensitivity of a cancer patient to said treatment for cancer if said median correlation coefficient exceeds 0.3 (preferably the gene or microRNA is identified as a biomarker for a patient's sensitivity to a treatment
  • the invention features a method of determining sensitivity of a patient (e.g., a cancer patient) to a treatment for cancer by obtaining a measurement of the level of expression of a gene or microRNA in a sample (e.g., a cell or tissue) from the patient; applying a model predictive of sensitivity to a treatment for cancer to the measurement, in which the model is developed using an algorithm selected from the group consisting of linear sums, nearest neighbor, nearest centroid, linear discriminant analysis, support vector machines, and neural networks; and determining whether or not the patient will be responsive to the treatment for cancer.
  • a patient e.g., a cancer patient
  • a treatment for cancer by obtaining a measurement of the level of expression of a gene or microRNA in a sample (e.g., a cell or tissue) from the patient; applying a model predictive of sensitivity to a treatment for cancer to the measurement, in which the model is developed using an algorithm selected from the group consisting of linear sums, nearest neighbor, nearest centroid, linear discriminant analysis
  • the measurement is obtained by measuring the level of expression of any of the genes or microRNAs set forth in the first aspect of the invention in a cell known to be sensitive or resistant to the treatment.
  • the method is performed in the presence of a second treatment.
  • the model combines the outcomes of linear sums, linear discriminant analysis, support vector machines, neural networks, k-nearest neighbors, and nearest centroids, or the model is cross-validated using a random sample of multiple measurements.
  • treatment e.g., a compound, has previously failed to show efficacy in a patient.
  • the linear sum is compared to a sum of a reference population with known sensitivity; the sum of a reference population is the median of the sums derived from the population members' biomarker gene expression.
  • the model is derived from the components of a data set obtained by independent component analysis or is derived from the components of a data set obtained by principal component analysis.
  • the invention features a kit, apparatus, and software used to implement the method of the sixth aspect of the invention.
  • the level of expression of the gene(s) is determined by measuring the level of mRNA transcribed from the gene(s), by detecting the level of a protein product of the gene(s), or by detecting the level of the biological activity of a protein product of the gene(s).
  • an increase or decrease in the expression level of the gene(s) or microRNA(s), relative to the expression level of the gene(s) or microRNA(s) in a cell or tissue sensitive to the treatment indicates increased sensitivity of the cancer patient to the treatment.
  • an increase or decrease in the expression level of the gene(s) or microRNA(s), relative to the expression level of the gene(s) or microRNA(s) in a cell or tissue resistant to the treatment indicates increased resistance of the cancer patient to the treatment.
  • the cell is a cancer cell.
  • the expression level of the gene(s) is measured using a quantitative reverse transcription-polymerase chain reaction (qRT-PCR).
  • the level of expression of two of the listed genes or microRNAs is measured, more preferably the level of expression of three, four, five, six, seven, eight, nine, or ten of the listed genes or microRNAs is measured, and most preferably twenty, thirty, forty, fifty, sixty, seventy, eighty, ninety, or one hundred or more of the listed genes or microRNAs is measured.
  • the expression level of the gene(s) or microRNA(s) is determined using the kit of the third or fourth aspects of the invention.
  • the treatment is radiation therapy or a compound, such as a chemotherapy agent selected from the group consisting of Vincristine, Cisplatin, Adriamycin, Etoposide, Azaguanine, Aclarubicin, Mitoxantrone, Paclitaxel, Mitomycin, Gemcitabine, Taxotere, Dexamethasone, Methylprednisolone, Ara-C, Methotrexate, Bleomycin, Methyl-GAG, Rituximab, PXDl Ol (a histone deacetylase (HDAC) inhibitor), 5-Aza-2'-deoxycytidine (Decitabine), Melphalan, IL4-PE38 fusion protein, IL13-PE38QQR fusion protein (cintredekin besudotox), Valproic acid (VPA), All-trans retinoic acid (ATRA), Cytoxan, Topotecan (Hycamtin), Suberoylanilide, a chemotherapy agent selected from the group
  • the treatment has previously failed to show effect in a subject (e.g., a subject selected from a subpopulation determined to be sensitive to the treatment, a subject selected from a subpopulation predicted to die without treatment, a subject selected from a subpopulation predicted to have disease symptoms without treatment, a subject selected from a subpopulation predicted to be cured without treatment.
  • a subject e.g., a subject selected from a subpopulation determined to be sensitive to the treatment, a subject selected from a subpopulation predicted to die without treatment, a subject selected from a subpopulation predicted to have disease symptoms without treatment, a subject selected from a subpopulation predicted to be cured without treatment.
  • the treatment is, e.g., administration of a compound, a protein, an antibody, an oligonucleotide, a chemotherapeutic agent, or radiation to a patient.
  • the treatment is, e.g., a chemotherapeutic agent, such as, e.g., Vincristine, Cisplatin, Azaguanine, Etoposide, Adriamycin, Aclarubicin, Mitoxantrone, Mitomycin, Paclitaxel, Gemcitabine, Taxotere, Dexamethasone, Ara-C, Methylprednisolone, Methotrexate, Bleomycin, Methyl-GAG, Carboplatin, 5-FU (5-Fluorouracil), a histone deacetylase (HDAC) inhibitor such as PXDlOl, 5-Aza-2'-deoxycytidine (De), a histone deacetylase (HDAC) inhibitor such as PXDlOl
  • the gene is selected from the group consisting of ABLl, ACTB, ACTNl, ACTN4, ACTR2, ADA, ADAM9, ADAMTSl , ADDl, AD0RA2A, AFlQ, AIFl, AKAPl, AKAP13, AKRlBl , AKRlCl, AKTl , ALDH2, ALDH3 Al, ALDOC, ALG5, ALMSl, ALOX15B, AMIG02, AMPD2, AMPD3, ANAPC5, ANP32A, ANP32B, ANPEP, ANXAl, ANXA2, AP1G2, APOBEC3B, APRT, ARHE, ARHGAP15, ARHGAP25, ARHGDIB, ARHGEF6, ARL7, ASAHl, ASPH, ATF3, ATIC, ATOXl , ATPl B3, ATP2A2, ATP2A3, ATP5D, ATP5G2,
  • the nucleic acid sequence of each listed genes is publicly available through the GenBank or RefSeq database (http://www.ncbi.nlm.nih.gov/sites/gquery). The gene sequences are also included as part of the HG-Ul 33A GeneChip from Affymetrix, Inc.
  • Resistant or “resistance” as used herein means that a cell, a tumor, a patient (e.g., a human), or a living organism is able to withstand treatment, e.g., with a compound, such as a chemotherapeutic agent, or radiation treatment, in that the treatment inhibits the growth of a cell, e.g., a cancer cell, in vitro or in a tumor, patient, or living organism by less than 10%, 20%, 30%, 40%, 50%, 60%, or 70% relative to the growth of a similar cell not exposed to the treatment.
  • a compound such as a chemotherapeutic agent, or radiation treatment
  • Resistance to treatment can be determined by a cell-based assay that measures the growth of treated cells as a function of the cells' absorbance of an incident light beam as used to perform the NCI60 assays described herein. In this example, greater absorbance indicates greater cell growth, and thus, resistance to the treatment. A reduction in growth indicates more resistance to a treatment.
  • chemoresistant or “chemoresistance” is meant resistance to a compound.
  • Sensitive or “sensitivity” as used herein means that a cell, a tumor, a patient (e.g., a human), or a living organism is responsive to treatment, e.g., with a compound, such as a chemotherapeutic agent, or radiation treatment, in that the treatment inhibits the growth of a cell, e.g., a cancer cell, in vitro or in a tumor, patient, or living organism by 70%, 80%, 90%, 95%, 99%, or 100%.
  • Sensitivity to treatment may be determined by a cell-based assay that measures the growth of treated cells as a function of the cells' absorbance of an incident light beam as used to perform the NCI60 assays described herein. In this example, lesser absorbance indicates reduced cell growth, and thus, sensitivity to the treatment. A greater reduction in growth indicates more sensitivity to the treatment.
  • chemosensitive or “chemosensitivity” is meant sensitivity to a compound.
  • “Complement” of a nucleic acid sequence or a “complementary” nucleic acid sequence as used herein refers to an oligonucleotide which is in "antiparallel association" when it is aligned with the nucleic acid sequence such that the 5' end of one sequence is paired with the 3' end of the other.
  • Nucleotides and other bases can have complements and may be present in complementary nucleic acids. Bases not commonly found in natural nucleic acids that can be included in the nucleic acids of the present invention include, for example, inosine and 7-deazaguanine.
  • “Complementarity” may not be perfect; stable duplexes of complementary nucleic acids can contain mismatched base pairs or unmatched bases.
  • Skilled artisans can determine duplex stability empirically considering a number of variables including, for example, the length of the oligonucleotide, percent concentration of cytosine and guanine bases in the oligonucleotide, ionic strength, and incidence of mismatched base pairs. Typically, complementarity is determined by comparing contiguous nucleic acid sequences.
  • nucleic acids When complementary nucleic acid sequences form a stable duplex, they are said to be “hybridized” or to “hybridize” to each other or it is said that “hybridization” has occurred.
  • Nucleic acids are referred to as being “complementary” if they contain nucleotides or nucleotide homologues that can form hydrogen bonds according to Watson-Crick base-pairing rules (e.g., G with C, A with T, or A with U) or other hydrogen bonding motifs such as, for example, diaminopurine with T, 5-methyl C with G, 2-thiothymidine with A, inosine with C, and pseudoisocytosine with G.
  • Anti-sense RNA can be complementary to other oligonucleotides, e.g., mRNA.
  • Biomarker indicates a transcription product (e.g., RNA, such as an RNA primary transcript, mRNA, tRNA, rRNA, microRNA (miRNA), or complementary RNA or DNA (e.g., cDNA) strands thereof) or a translation product (e.g., a polypeptide or metabolite thereof) of a biomarker gene, as defined herein, whose level of expression indicates the sensitivity or resistance of a cell (e.g., a cancer cell), tissue, organism, or patient (e.g., a human) to a treatment (e.g., chemotherapy, radiation therapy, or surgery).
  • a transcription product e.g., RNA, such as an RNA primary transcript, mRNA, tRNA, rRNA, microRNA (miRNA), or complementary RNA or DNA (e.g., cDNA) strands thereof
  • a translation product e.g., a polypeptide or metabolite thereof
  • Compound as used herein means a chemical or biological substance, e.g., a drug, a protein, an antibody, or an oligonucleotide, which can be used to treat a disease or which has biological activity in vivo or in vitro.
  • Compounds may or may not be approved by the U.S. Food and Drug Administration (FDA).
  • FDA U.S. Food and Drug Administration
  • Preferred compounds include, e.g., chemotherapy agents that can inhibit cancer growth.
  • Preferred chemotherapy agents include, e.g., Vincristine, Cisplatin, Azaguanine, Etoposide, Adriamycin, Aclarubicin, Mitoxantrone, Mitomycin, Paclitaxel, Gemcitabine, Taxotere, Dexamethasone, Ara-C, Methylprednisolone, Methotrexate, Bleomycin, Methyl-GAG, Carboplatin, 5-FU (5-Fluorouracil), Rituximab (e.g., MABTHERATM), , histone deacetylase (HDAC) inhibitors, and 5-Aza-2'-deoxycytidine (Decitabine).
  • Vincristine Cisplatin
  • Azaguanine Etoposide
  • Adriamycin Aclarubicin
  • Mitoxantrone Mitomycin
  • Paclitaxel Paclitaxel
  • Gemcitabine Taxotere
  • Dexamethasone Ara-C, Meth
  • radioactive chemotherapeutic agents include compounds containing alpha emitters such as astatine-211 , bismuth-212, bismuth-213, lead-212, radium-223, actinium-225, and thorium-227, beta emitters such as tritium, strontium-90, cesium-137, carbon-11 , nitrogen-13, oxygen-15, fluorine-18, iron-52, cobalt-55, cobalt-60, copper-61, copper- 62, copper-64, zinc-62, zinc-63, arsenic-70, arsenic-71 , arsenic-74, bromine-76, bromine-79, rubidium-82, yttrium-86, zirconium-89, indium- 1 10, iodine- 120, iodine- 124, iodine-129, iodine-131, iodine-125, xenon-122, technetium-94m, technetium-94, technetium-99
  • chemotherapeutic agents also include antibodies such as Alemtuzumab, Daclizumab, Rituximab (e.g., MABTHERATM), Trastuzumab (e.g., HERCEPTINTM), Gemtuzumab, Ibritumomab, Edrecolomab, Tositumomab, CeaVac, Epratuzumab, Mitumomab, Bevacizumab, Cetuximab, Edrecolomab, Lintuzumab, MDX-210, IGN-101, MDX-010, MAb, AME, ABX-EGF, EMD 72 000, Apolizumab, Labetuzumab, ior-tl, MDX-220, MRA, H-11 scFv, Oregovomab, huJ591 MAb, BZL, Visilizumab, TriGem, TriAb, R3, MT-201 , G-250,
  • chemotherapeutic agents also include Acivicin; Aclarubicin; Acodazole Hydrochloride; Acronine; Adozelesin; Adriamycin; Aldesleukin; Altretamine; Ambomycin; A. metantrone Acetate; Aminoglutethimide; Amsacrine; Anastrozole; Anthramycin; Asparaginase; Asperlin; Azacitidine; Azetepa; Azotomycin; Batimastat; Benzodepa; Bicalutamide; Bisantrene Hydrochloride; Bisnafide Dimesylate; Bizelesin; Bleomycin Sulfate; Brequinar Sodium; Bropirimine; Busulfan; Cactinomycin; Calusterone; Camptothecin; Caracemide; Carbetimer; Carboplatin; Carmustine; Carubicin Hydrochloride; Carzelesin; Cedefingol; Chlorambucil; Cirole
  • chemotherapeutic agents include, but are not limited to, 20-pi-l ,25 dihydroxyvitamin D3; 5-ethynyluracil; abiraterone; aclarubicin; acylfulvene; adecypenol; adozelesin; aldesleukin; ALL-TK antagonists; altretamine; ambamustine; amidox; amifostine; aminolevulinic acid; amrubicin; amsacrine; anagrelide; anastrozole; andrographolide; angiogenesis inhibitors; antagonist D; antagonist G; antarelix; anti-dorsalizing morphogenetic protein-1 ; antiandrogen,; antiestrogen; antineoplaston; antisense oligonucleotides; aphidicolin glycinate; apoptosis gene modulators; apoptosis regulators; apurinic acid; ara-CDP-DL-PTBA;
  • inhibit growth means causing a reduction in cell growth in vivo or in vitro by, e.g., 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, or 99% or more, as evident by a reduction in the size or number of cells exposed to a treatment (e.g., exposure to a compound), relative to the size or number of cells in the absence of the treatment.
  • Growth inhibition can be the result of a treatment that induces apoptosis in a cell, induces necrosis in a cell, slows cell cycle progression, disrupts cellular metabolism, induces cell lysis, or induces some other mechanism that reduces the size or number of cells.
  • Biomarker gene as used herein means a gene in a cell (e.g., a cancer cell) the expression of which, as measured by, e.g., detecting the level of one or more biomarkers produced from the gene, correlates to sensitivity or resistance of the cell, tissue, organism, or patient (e.g., a human) to a treatment (e.g., chemotherapy, radiation therapy, or surgery).
  • a treatment e.g., chemotherapy, radiation therapy, or surgery.
  • oligonucleotide as used herein means a device employed by any method that quantifies one or more subject oligonucleotides, e.g., DNA or RNA, or analogues thereof, at a time.
  • One exemplary class of microarrays consists of DNA probes attached to a glass or quartz surface. Many microarrays, e.g., those made by Affymetrix, use several probes for determining the expression of a single gene.
  • the DNA microarray can contain oligonucleotide probes that may be, e.g., full-length cDNAs complementary to an RNA or cDNA fragments that hybridize to part of an RNA. Exemplary RNAs include mRNA, miRNA, and miRNA precursors.
  • Exemplary microarrays also include a "nucleic acid microarray" having a substrate-bound plurality of nucleic acids, hybridization to each of the plurality of bound nucleic acids being separately detectable.
  • the substrate can be solid or porous, planar or non-planar, unitary or distributed.
  • Exemplary nucleic acid microarrays include all of the devices so called in Schena (ed.), DNA Microarrays: A Practical Approach (Practical Approach Series), Oxford University Press (1999); Nature Genet. 21(l)(suppl.):l-60 (1999); and Schena (ed.), Microarray Biochip: Tools and Technology, Eaton Publishing Company/BioTechniques Books Division (2000).
  • exemplary nucleic acid microarrays can include a substrate-bound plurality of nucleic acids in which the plurality of nucleic acids is disposed on a plurality of beads, rather than on a unitary planar substrate, as is described, inter alia, in Brenner et al., Proc. Natl. Acad. Sci. USA 97(4): 1665- 1670 (2000). Examples of nucleic acid microarrays may be found in U.S. Patent Nos.
  • Exemplary microarrays can also include "peptide microarrays" or "protein microarrays” having a substrate-bound plurality of polypeptides, the binding of a oligonucleotide, a peptide, or a protein to the plurality of bound polypeptides being separately detectable.
  • the peptide microarray can have a plurality of binders, including, but not limited to, monoclonal antibodies, polyclonal antibodies, phage display binders, yeast 2 hybrid binders, aptamers, that can specifically detect the binding of specific oligonucleotides, peptides, or proteins. Examples of peptide arrays may be found in International Patent Publication Nos.
  • Gene expression means the level of expression of a biomarker gene (e.g,. the level of a transcription product, such as an mRNA, tRNA, or microRNA, or its complement (e.g., a cDNA complement of the transcription product), or a translation product, such as a polypeptide or metabolite thereof) in a cell, tissue, organism, or patient (e.g., a human).
  • Gene expression can be measured by detecting the presence, quantity, or activity of a DNA, RNA, or polypeptide, or modifications thereof (e.g., splicing, phosphorylation, and acetylation) associated with a given gene.
  • NCI60 as used herein means a panel of 60 cancer cell lines from lung, colon, breast, ovarian, leukemia, renal, melanoma, prostate, and brain cancers including the following cancer cell lines: NSCLC_NCIH23, NSCLC_NCIH522, NSCLC_A549ATCC, NSCLC_EKVX, NSCLC_NCIH226, NSCLC_NCIH332M, NSCLC_H460, NSCLC_HOP62, NSCLC_HOP92, COLON HT29, COLON_HCC- 2998, COLON_HCT1 16, COLON_SW620, COLON_COLO205, COLON_HCT15, COLON KM 12, BREAST MCF7, BREAST_MCF7ADRr, BREAST MDAMB231 , BREAST HS578T, BREAST MDAMB435, BREAST_MDN, BREAST BT549, BREAST_T47D
  • Treatment means administering to a patient (e.g., a human) or living organism or exposing to a cell or tumor a compound (e.g., a drug, a protein, an antibody, an oligonucleotide, a chemotherapeutic agent, and a radioactive agent) or some other form of medical intervention used to treat or prevent cancer or the symptoms of cancer (e.g., cryotherapy and radiation therapy).
  • Radiation therapy includes the administration to a patient of radiation generated from sources such as particle accelerators and related medical devices that emit X-radiation, gamma radiation, or electron (Beta radiation) beams.
  • a treatment may further include surgery, e.g., to remove a tumor from a patient or living organism.
  • Figure 1 depicts an illustration of the method of identifying biomarkers and predicting patient sensitivity to a medical treatment.
  • the method has an in vitro component where the growth inhibition of a compound or medical treatment is measured on cell lines (6 of the 60 cell lines tested are shown). The gene expression is measured on the same cell lines without compound treatment.
  • Those genes that have a correlation above a certain cutoff e.g., a preffered cutoff of 0.3, in which a correlation coefficient equal to or greater than the cutoff of 0.3 is deemed statistcally significant by, e.g., cross-validation
  • marker genes e.g., may predict the sensitivity or resistance of a patient's cancer to a compound or other medical treatment.
  • the in vivo component is applied to a patient to determine whether or not the treatment will be effective in treating disease in the patient.
  • the gene expression in cells of a sample of the suspected disease tissue (e.g., a tumor) in the patient is measured before or after treatment.
  • the activity of the marker genes in the sample is compared to a reference population of patients known to be sensitive or resistant to the treatment.
  • the expression of marker genes in the cells of the patient known to be expressed in the cells of reference patients sensitive to the treatment indicates that the patient to be treated is sensitive to the treatment and vice versa. Based on this comparison the patient is predicted to be sensitive or resistant to treatment with the compound.
  • Figure 2 depicts the treatment sensitivity predictions for a 5-year-old American boy with a brain tumor.
  • the subject had surgery to remove the tumor and, based on the analysis of gene expression in cells from a sample of the tumor, the subject was predicted to be chemosensitive to ten chemotherapy drugs.
  • the subject received Vincristine and Cisplatin and survived.
  • Figure 3 depicts the treatment sensitivity predictions for a 7-month-old American girl with a brain tumor.
  • the subject had surgery to remove the tumor, and based on the analysis of gene expression in cells from a sample of the tumor, the subject was predicted to be chemoresistant to twelve chemotheraphy drugs.
  • the subject received Vincristine and Cisplatin, but passed away 9 months later.
  • Figure 4 depicts the survival rate of 60 brain cancer patients divided into a group predicted to be chemosensitive to Cisplatin and a group predicted to be chemoresistant to Cisplatin. All patients received Cisplatin after surgery.
  • Figure 5 depicts the survival rate of 56 lymphoma patients divided into a group predicted to be chemosensitive to Vincristine and Adriamycin and a group predicted to be chemoresistant. All patients received Vincristine and Adriamycin.
  • Figure 6 depicts the survial rate of 19 lung cancer patients divided into a group predicted to be chemosensitive to Cisplatin and a group predicted to be chemoresistant. All patients received Cisplatin.
  • Figure 7 depicts the survival rate of 14 diffuse large-B-cell lymphoma (DLBCL) patients divided into a group predicted to be chemosensitive to the drug combination R- CHOP and a group predicted to be chemoresistant. All patients were treated with R- CHOP.
  • DLBCL diffuse large-B-cell lymphoma
  • Figure 8 depicts the predictions of sensitivity or resistance to treatment of a patient diagnosed with DLBCL.
  • Various drug combinations and radiation therapy are considered.
  • the drug combinations are those commonly used to treat DLBCL.
  • Figure 9 depicts the survival rate of 60 brain cancer patients divided into a group predicted to be sensitive to radiation treatment and a group predicted to be resistant. All patients were treated with radiation.
  • Figure 10 depicts the survival rate of 60 brain cancer patients divided into a group predicted to be sensitive to radiation treatment and a group predicted to be resistant. All patients were treated with radiation. Gene biomarkers used in predicting radiation sensitivity or resistance were obtained using the correlation of the median gene expression measurement to cancer cell growth as opposed to the median of the correlations as employed in Figure 9.
  • Figure 1 1 depicts the predicted sensitivity of cancer patients to sunitinib.
  • the cancer patients are grouped according to cancer type or origin and cancer types with predicted high sensitivity are labeled.
  • kits of the invention include microarrays having oligonucleotide probes that are biomarkers of sensitivity or resistance to treatment (e.g., treatment with a chemotherapeutic agent) that hybridize to nucleic acids derived from or obtained from a subject and instructions for using the device to predict the sensitivity or resistance of the subject to the treatment.
  • the invention also features methods of using the microarrays to determine whether a subject, e.g., a cancer patient, will be sensitive or resistant to treatment with, e.g., a chemotherapy agent. Also featured are methods of identifying biomarkers of sensitivity or resistance to a medical treatment based on the correlation of gene or microRNA expression to treatment efficacy, e.g., the growth inhibition of cancer cells. Gene or microRNA biomarkers that identify subjects as sensitive or resistant to a treatment can also be identified within patient populations already thought to be sensitive or resistant to that treatment. Thus, the methods, devices, and kits of the invention can be used to identify patient subpopulations that are responsive to a treatment thought to be ineffective for treating disease (e.g., cancer) in the general population.
  • disease e.g., cancer
  • cancer patient sensitivity to a compound or other medical treatment can be predicted using biomarker expression regardless of prior knowledge about patient responsiveness to treatment.
  • the method according to the present invention can be implemented using software that is run on an apparatus (e.g., a computer) for measuring biomarker expression in connection with a microarray.
  • the microarray e.g., a DNA microarray
  • included in a kit for processing a tumor sample from a patient, and the apparatus for reading the microarray and turning the result into a chemosensitivity profile for the patient may be used to implement the methods of the invention.
  • the microarrays of the invention include one or more oligonucleotide probes that have nucleotide sequences that are substantially identical to or substantially complementary to, e.g., at least 5, 8, 12, 20, 30, 40, 60, 80, 100, 150, or 200 consecutive nucleotides (or nucleotide analogues) of the biomarker genes or biomarker gene products (e.g., transcription or translation gene products, such as microRNAs) listed below.
  • the oligonucleotide probes may be, e.g., 5-20, 25, 5-50, 50-100, or over 100 nucleotides long.
  • the oligonucleotide probes may be deoxyribonucleic acids (DNA) or ribonucleic acids (RNA). Consecutive nucleotides within the oligonucleotide probes (e.g., 5-20, 25, 5-50, 50-100, or over 100 consecutive nucleotides), which are used as biomarkers of chemosensitivity, may also appear as consecutive nucleotides in one or more of the genes described herein beginning at or near, e.g., the first, tenth, twentieth, thirtieth, fortieth, fiftieth, sixtieth, seventieth, eightieth, ninetieth, hundredth, hundred- fiftieth, two-hundredth, five-hundredth, or one-thousandth nucleotide of the genes or microRNAs listed in Tables 1-136 below.
  • Tables 80-136 indicate microRNA biomarkers that can be used to determine a patient's (e.g., a human's) sensitivity to a treatment. The following combinations of biomarkers have been used to detect a patient's sensitivity to the indicated treatment:
  • Gene sequences B2M, MYC, CD99, RPS24, PPIF, PBEF 1 , and ANP32B preferably gene sequences CD99, INSIGl , LAPTM5, PRGl, MUFl, HCLSl , CD53, SLA, SSBP2, GNB5, MFNG, GMFG, PSMB9, EVI2A, PTPN7, PTGER4, CXorf9, PTPRCAP, ZNFNlAl, CENTBl , PTPRC, NAPlLl, HLA-DRA, IFI 16, COROlA, ARHGEF6, PSCDBP, SELPLG, LAT, SEC31L2, CD3Z, SH2D1A, GZMB, SCN3A, ITK, RAFTLIN, D0CK2, CD3D, RAC2, ZAP70, GPR65, PRFl, ARHGAPl 5, NOTCHl, and UBASH3A, and most preferably gene sequences CD99, INSIGl
  • One or more of the gene sequences GAPD, GAPD, GAPD, TOP2A, SUIl , TOP2A, FTL, HNRPC, TNFRSFlA, SHCl, CCT7, P4HB, CTSL, DDX5, G6PD, and SNRPF preferably gene sequences STCl , GPR65, DOCKlO, COL5A2, FAM46A, and LOC54103, and most preferably gene sequences STCl, GPR65, DOCKlO, COL5A2, FAM46A, and LOC54103, whose expression indicates chemosensitivity to Mitomycin.
  • One or more of the gene sequences RPS23, SFRS3, KIAAOl 14, SFRS3, RPS6, DDX39, and RPS7 preferably gene sequences ANP32B, GTF3A, RRM2, TRIM 14, SKP2, TRIPl 3, RFC3, CASP7, TXN, MCM5, PTGES2, OBFCl , EPB41L4B, and CALML4, and most preferably gene sequences ANP32B, GTF3A, RRM2, TRIM 14, SKP2, TRIPl 3, RFC3, CASP7, TXN, MCM5, PTGES2, OBFCl, EPB41L4B, and CALML4, whose expression indicates chemosensitivity to Taxotere.
  • One or more of the gene sequences TM4SF2, ARHGDIB, ADA, H2 AFZ, NAPlLl , CCND3, FABP5, LAMRl, REA, MCM5, SNRPF, and USP7 preferably gene sequences ITM2A, RHOH, PRIMl , CENTBl, GNA15, NAPlLl, ATP5G2, GATA3, PRKCQ, SH2D1A, SEPT6, PTPRC, NME4, RPL13, CD3D, CDlE, ADA, and FHODl, and most preferably gene sequences ITM2A, RHOH, PRIMl, CENTBl , NAPlLl , ATP5G2, GATA3, PRKCQ, SH2D1A, SEPT6, NME4, CD3D, CDlE, ADA, and FHODl , whose expression indicates chemosensitivity to Ara-C.
  • ACTB One or more of the gene sequences ACTB, COL5A1, MTlE, CSDA, COL4A2, MMP2, COLlAl, TNFRSFlA, CFHLl , TGFBI, FSCNl, NNMT, PLAUR, CSPG2, NFIL3, C5orfl3, NCOR2, TUBB4, MYLK, TUB A3, PLAU, COL4A2, COL6A2, COL6A3, IFITM2, PSMB9, CSDA, and COLlAl, preferably gene sequences MSN, PFNl, HKl, ACTR2, MCLl, ZYX, RAPlB, GNB2, EPASl, PGAMl, CKAP4, DUSPl, MYL9, K-ALPHA-I , LGALSl, CSDA, AKRlBl, IFITM2, ITGA5, VIM, DPYSL3, JUNB, ITGA3, NFKBIA, LAMBl,
  • One or more of the gene sequences NOS2A, MUCl , TFF3, GPlBB, IGLLl, BATF, MYB, PTPRS, NEFL, AIP, CEL, DGKA, RUNXl, ACTRlA, and CLCNKA preferably gene sequences PTMA, SSRPl, NUDC, CTSC, AP1G2, PSME2, LBR, EFNB2, SERPINAl, SSSCAl, EZH2, MYB, PRIMl, H2AFX, HMGAl, HMMR, TK2, WHSCl, DIAPHl, LAMB3, DPAGTl , UCK2, SERPINBl, MDNl , BRRNl, G0S2, RAC2, MGC21654, GTSEl, TACC3, PLEK2, PLAC8, HNRPD, and PNAS-4, and most preferably gene sequences SSRPl, NUDC, CTSC, AP1G2, PSME2, LBR, EFNB2,
  • Probes that may be employed on microarrays of the invention include oligonucleotide probes having sequences complementary to any of the biomarker gene or microRNA sequences described above. Additionally, probes employed on microarrays of the invention may also include proteins, peptides, or antibodies that selectively bind any of the oligonucleotide probe sequences or their complementary sequences. Exemplary probes are listed in Tables 22-44, wherein for each treatment listed, the biomarkers indicative of treatment sensitivity, the correlation of biomarker expression to growth inhibition, and the sequence of an exemplary probe (Tables 22-44) to detect biomarker (Tables 1-21) expression are shown.
  • the gene expression measurements of the NCI60 cancer cell lines were obtained from the National Cancer Institute and the Massachusetts Institute of Technology (MIT). Each dataset was normalized so that sample expression measured by different chips could be compared.
  • GI50 Growth inhibition data
  • the correlation between the logit- transformed expression level of each gene in each cell line and the logarithm of GI50 can be calculated, e.g., using the Pearson correlation coefficient or the Spearman Rank- Order correlation coefficient.
  • any other measure of patient sensitivity to a given compound may be correlated to the patient's gene expression. Since a plurality of measurements may be available for a single gene, the most accurate determination of correlation coefficient was found to be the median of the correlation coefficients calculated for all probes measuring expression of the same gene.
  • the median correlation coefficient of gene expression measured on a probe to growth inhibition or patient sensitivity is calculated for all genes, and genes that have a median correlation above 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, or 0.99 are retained as biomarker genes.
  • the correlation coefficient of biomarker genes will exceed 0.3. This is repeated for all the compounds to be tested. The result is a list of marker genes that correlates to sensitivity for each compound tested.
  • the biomarker whose expression has been shown to correlate to chemosensitivity can be used to classify a patient, e.g., a cancer patient, as sensitive to a medical treatment, e.g., administration of a chemotherapeutic agent or radiation.
  • a medical treatment e.g., administration of a chemotherapeutic agent or radiation.
  • a tumor sample or a blood sample e.g., in case of leukemia or lymphoma
  • expression of the biomarker in the cells of the patient in the presence of the treatment agent is determined (using, for example, an RNA extraction kit, a DNA microarray and a DNA microarray scanner).
  • the biomarker expression measurements are then logit transformed as described above.
  • the sum of the expression measurements of the biomarkers is then compared to the median of the sums derived from a training set population of patients having the same tumor. If the sum of biomarker expression in the patient is closest to the median of the sums of expression in the surviving members of the training set, the patient is predicted to be sensitive to the compound or other medical treatment. If the sum of expression in the patient is closest to the median of the sums of expression in the non-surviving members of the training set, the patient is predicted to be resistant to the compound.
  • Machine learning techniques such as Neural Networks, Support Vector Machines, K Nearest Neighbor, and Nearest Centroids may also be employed to develop models that discriminate patients sensitive to treatment from those resistant to treatment using biomarker expression as model variables which assign each patient a classification as resistant or sensitive.
  • Machine learning techniques used to classify patients using various measurements are described in U.S. Patent No. 5,822,715; U.S. Patent Application Publication Nos. 2003/0073083, 2005/0227266, 2005/0208512, 2005/0123945, 2003/0129629, and 2002/0006613; and in Vapnik V N.
  • Other variables can be used to determine relative biomarker expression between a patient (e.g., a cancer patient) and a normal subject (e.g., a control subject), including but not limited to, measurement of biomarker DNA copy number and the identification of biomarker genetic mutations.
  • a more compact microarray can be designed using only the oligonucleotide probes having measurements yielding the median correlation coefficients with cancer cell growth inhibition. Thus, in this embodiment, only one probe needs to be used to measure expression of each biomarker.
  • Biomarkers include polypeptides and metabolites thereof. A skilled artisan can use employ assays that measure changes in polypeptide biomarker expression (e.g., Western blot, immunofluorescent staining, and flow cytometry) to determine a patient's sensitivity to a treatment (e.g., chemotherapy, radiation therapy, or surgery).
  • the invention can also be used to identify a subpopulation of patients, e.g., cancer patients, that are sensitive to a compound or other medical treatment previously thought to be ineffective for the treatment of cancer.
  • genes or microRNAs whose expression correlates to sensitivity to a compound or other treatment can be identified so that patients sensitive to a compound or other treatment may be identified.
  • gene or microRNA expression within cell lines can be correlated to the growth of those cell lines in the presence of the same compound or other treatment.
  • genes or microRNAs whose expression correlates to cell growth with a correlation coefficient exceeding 0.3 may be considered possible biomarkers.
  • genes or microRNAs can be identified as biomarkers according to their ability to discriminate patients known to be sensitive to a treatment from those known to be resistant. The significance of the differences in gene or microRNA expression between the sensitive and resistant patients may be measured using, e.g., t- tests.
  • naive Bayesian classifiers may be used to identify gene biomarkers that discriminate sensitive and resistant patient subpopulations given the gene expressions of the sensitive and resistant subpopulations within a treated patient population.
  • the patient subpopulations considered can be further divided into patients predicted to survive without treatment, patients predicted to die without treatment, and patients predicted to have symptoms without treatment.
  • the above methodology may be similarly applied to any of these further defined patient subpopulations to identify biomarkers able to predict a subject's sensitivity to compounds or other treatments for the treatment of cancer. Patients with elevated expression of biomarkers correlated to sensitivity to a compound or other medical treatment would be predicted to be sensitive to that compound or other medical treatment.
  • the invention is particularly useful for recovering compounds or other treatments that failed in clinical trials by identifying sensitive patient subpopulations using the gene or microRNA expression methodology disclosed herein to identify biomarkers that can be used to predict clinical outcome.
  • This invention can also be used to predict patients who are resistant or sensitive to a particular treatment by using a kit that includes a kit for RNA extraction from tumors (e.g., Trizol from Invitrogen Inc.), a kit for RNA amplification (e.g., MessageAmp from Ambion Inc.), a microarray for measuring biomarker expression (e.g., HG-U 133 A GeneChip from Affymetrix Inc.), a microarray hybridization station and scanner (e.g., GeneChip System 3000Dx from Affymetrix Inc.), and software for analyzing the expression of marker genes as described in herein (e.g., implemented in R from R-Project or S-Plus from Insightful Corp.).
  • a kit for RNA extraction from tumors e.g., Trizol from Invitrogen Inc.
  • a kit for RNA amplification e.g., MessageAmp from Ambion Inc.
  • a microarray for measuring biomarker expression e.
  • the human tumor cell lines of the cancer screening panel are grown in RPMI 1640 medium containing 5% fetal bovine serum and 2 mM L-glutamine. Cells are inoculated into 96 well microtiter plates in 100 ⁇ L at plating densities ranging from 5,000 to 40,000 cells/well depending on the doubling time of individual cell lines. After cell inoculation, the microtiter plates are incubated at 37°C, 5% CO 2 , 95% air, and 100% relative humidity for 24 hrs prior to addition of experimental compounds.
  • SRB Sulforhodamine B
  • 1% acetic acid 1% acetic acid
  • Bound stain is subsequently solubilized with 10 mM trizma base, and the absorbance is read on an automated plate reader at a wavelength of 515 nm.
  • the methodology is the same except that the assay is terminated by fixing settled cells at the bottom of the wells by gently adding 50 ⁇ L of 80% TCA (final concentration, 16 % TCA).
  • TCA final concentration, 16 % TCA
  • GI50 Growth inhibition of 50%
  • C-Tz C-Tz
  • TGI total growth inhibition
  • the LC50 concentration of compound resulting in a 50% reduction in the measured protein at the end of the compound treatment as compared to that at the beginning
  • RNA is extracted using e.g., Trizol Reagent (Invitrogen) following manufacturers instructions.
  • RNA is amplified using e.g., MessageAmp kit (Ambion) following manufacturers instructions.
  • Amplified RNA is quantified using e.g., HG-U 133 A GeneChip (Affymetrix) and compatible apparatus e.g., GCS3000Dx (Affymetrix), using manufacturers instructions.
  • the resulting gene expression measurements are further processed as described in this document.
  • the procedures described can be implemented using R software available from R-Project and supplemented with packages available from Bioconductor.
  • qRT-PCR quantitative reverse transcriptase polymerase chain reaction
  • Example 1 Identification of gene biomarkers for chemosensitivity to common chemotherapy drugs.
  • DNA chip measurements of the 60 cancer cell lines of the NCI60 data set were downloaded from the Broad Institute (Cambridge, Massachusetts) and logit normalized. Growth inhibition data of thousands of compounds against the same cell lines were downloaded from the National Cancer Institute. Compounds where the difference concentration to achieve 50% in growth inhibition (GI50) was less than 1 log were deemed uninformative and rejected. Each gene's expression in each cell line was correlated to its growth (-log(GI50)) in those cell lines in the presence of a given compound. The median Pearson correlation coefficient was used when multiple expression measurements were available for a given gene, and genes having a median correlation coefficient greater than 0.3 were identified as biomarkers for a given compound.
  • Example 2 Prediction of treatment sensitivity for brain cancer patients.
  • DNA chip measurements of gene expression in tumors from 60 brain cancer patients were downloaded from the Broad Institute. All data files were logit normalized. For each of the common chemotherapy drugs Cisplatin, Vincristine, Adriamycine, Etoposide, Aclarubicine, Mitoxantrone and Azaguanine, the gene expression for the marker genes was summed. The sum was normalized by dividing by the standard deviation of all patients and compared to the median of the sums of patients who survived and the median of the sums of patients who died:
  • Sensitivity(compound) [NormalizedSum(compound)- median(NormalizedSumdeadpatients(compound))] 2 [NormalizedSum(compound) - median(TslormalizedSumsurvivingpatients(compound))] 2
  • Figures 2 and 3 show the resulting treatment sensitivity predictions for two of the 60 patients. All patients received Cisplatin and the prediction of survival amongst the 60 patients based on their Cisplatin chemosensitivity yielded the Kaplan-Meier survival curve shown in Figure 4.
  • fastICA Independent Component Analysis
  • Chemosensitivity or sensitivity to radiation treatment was predicted by combining the classifications of the five methods wherein each classification method was assigned a single vote: unanimous chemosensitive/treatment sensitive prediction resulted in a prediction of chemosensitive/treatment sensitive. All other predictions resulted in a prediction of chemoresistant/treatment resistant.
  • the performance of the combined classifier was validated using leave-one-out cross validation and the survival of the two predicted groups shown in Figure 4. The survival rate of the patients predicted to be chemosensitive was higher than the patients predicted to be chemoresistant.
  • Example 3 Prediction of chemosensitivity for lymphoma (DLBCL) patients.
  • Chemosensitivity was predicted by combining the classifications of the five methods wherein each classification method was assigned a single vote: unanimous chemosensitive prediction resulted in a prediction of chemosensitive. All other predictions resulted in a prediction of chemoresistant.
  • the performance of the combined classifier was validated using leave-one-out cross validation and the survival of the two predicted groups is shown in Figure 5. The survival rate of the patients predicted to be chemosensitive was higher than the patients predicted to be chemoresistant.
  • Example 4 Prediction of chemosensitivity for lung cancer patients.
  • the patient was predicted to be resistant to Cisplatin.
  • the survival rates of the two predicted groups are shown in Figure 6.
  • the survival rate of the patients predicted to be chemosensitive was higher than the patients predicted to be chemoresistant.
  • Example 5 Prediction of Rituximab sensitivity for lymphoma (DLBCL) patients.
  • the method is not limited to cytotoxic chemicals. It is also applicable to predicting the efficacy of protein therapeutics, such as monoclonal antibodies, approved for treating cancer.
  • protein therapeutics such as monoclonal antibodies
  • the monoclonal antibody Rituximab e.g., MABTHERATM and RITUXANTM
  • Data for cytotoxicity of Rituximab in cell lines in vitro were obtained from published reports (Ghetie et al., Blood 97(5): 1392-1398, 2001). This cytotoxicity in each cell line was correlated to the expression of genes in these cell lines (downloaded from the NCBI Gene Expression Omnibus database using accession numbers GSE2350, GSE 1880, GDS 181).
  • the identified marker genes were used to predict the sensitivity of DLBCL to Rituximab in a small set of 14 patients treated with Rituximab and CHOP (R-CHOP) (downloaded from NCBI Gene Expression Omnibus under accession number GSE4475). Conversion between different chip types was performed using matching tables available through Affymetrix.
  • the survival of patients predicted to be sensitive to be R-CHOP is compared to the survival of patients predicted to be resistant to R-CHOP in Figure 7.
  • the survival rate of the patients predicted to be chemosensitive was higher than the patients predicted to be chemoresistant.
  • R-CHOP contains Rituximab (e.g., MABTHERATM), Vincristine, Doxorubicin (Adriamycin), Cyclophosphamide, and Prednisolone
  • R-ICE contains Rituximab, Ifosfamide, Carboplatin, and Etoposide
  • R-MIME contains Rituximab, Mitoguazone, Ifosfamide, Methotrexate, and Etoposide
  • CHOEP contains Cyclophosphamide, Doxorubicin, Etoposide, Vincristine and Prednisone
  • R-CHOP contains Rituximab (e.g., MABTHERATM), Vincristine, Doxorubicin (Adriamycin), Cyclophosphamide, and Prednisolone
  • R-ICE contains Rituximab, Ifosfamide, Carboplatin, and Etoposide
  • R-MIME contains Rituxim
  • the method of identifying biomarkers can also be applied to other forms of treatment such as radiation therapy.
  • sensitivity to radiation therapy was predicted for brain tumor patients.
  • Radiation therapy in the form of craniospinal irradiation yielding 2,400-3,600 centiGray (cGy) with a tumor dose of 5,300-7,200 cGy was administered to the brain tumor patients using a medical device that emits beams of radiation.
  • Sensitivity of the 60 cancer cell lines used in the NCI60 dataset to radiation treatment was obtained from published reports. This sensitivity was correlated to the expression of genes in the cell lines as described above to identify marker genes.
  • DNA microarray measurements of gene expression in brain tumors obtained from patients subsequently treated with radiation therapy were obtained from the Broad Institute.
  • the identified gene biomarkers were used to classify the patients as sensitive or resistant to radiation therapy.
  • the survival of the patients in the two predicted categories is shown in Figure 9.
  • the survival rate of the patients predicted to be sensitive to radiation therapy was higher than the patients predicted to be resistant to radiation
  • Every member of a population may not be equally responsive to a particular treatment. For example, new compounds often fail in late clinical trials because of lack of efficacy in the population tested. While such compounds may not be effective in the overall population, there may be subpopulations sensitive to those failed compounds due to various reasons, including inherent differences in gene expression.
  • the method as described herein can be used to rescue failed compounds by identifying a patient subpopulation sensitive to a compound using their gene expression as an indicator. Subsequent clinical trials restricted to a sensitive patient subpopulation may demonstrate efficacy of a previously failed compound within that particular patient subpopulation, advancing the compound towards approval for use in that subpopulation.
  • in vitro measurements of the inhibitory effects of a compound on various cancer cell lines are compared to the gene expression of cells.
  • the growth of the cancer cell samples can be correlated to gene expression measurements as described above. This will identify marker genes that can be used to predict patient sensitivity to the failed compound.
  • biomarkers Once biomarkers are identified, the expression of biomarker genes in cells obtained from patients can be measured according to the procedure detailed above. The patients are predicted to be responsive or non-responsive to compound treatment according to their gene biomarker expression profile. Clinical effect must then be demonstrated in the group of patients that are predicted to be sensitive to the failed compound.
  • the method may be further refined if patients responsive to the compound treatment are further subdivided into those predicted to survive without the compound and those predicted to die or suffer a relapse without the compound.
  • Clinical efficacy in the subpopulation that is predicted to die or suffer relapse can be further demonstrated. Briefly, the gene expression at the time of diagnosis of patients who later die from their disease is compared to gene expression at the time of diagnosis of patients who are still alive after a period of time (e.g., 5 years). Genes differentially expressed between the two groups are identified as prospective biomarkers and a model is built using those gene biomarkers to predict treatment efficacy.
  • Examples of compounds that have failed in clinical trials include Gefinitib (e.g., Iressa, AstraZeneca) in refractory, advanced non-small-cell lung cancer (NSCLC), Bevacizumab (e.g., Avastin, Genentech) in first-line treatment for advanced pancreatic cancer, Bevacizumab (e.g., Avastin, Genentech) in relapsed metastatic breast cancer patients, and Erlotinib (e.g., Tarceva, Genentech) in metastatic non-small cell lung cancer (NSCLC).
  • the method of the invention may be applied to these compounds, among others, so that sensitive patient subpopulations responsive to those compounds may be identified.
  • Example 8 Median of the correlations versus correlation of the median.
  • the median of the correlation to measured radiosensitivity of cell lines in vitro is 0.32.
  • the correlation of the median is 0.39. Adjusting the cutoff from 0.3 to 0.4 to compensate for the difference does not improve on Figure 10, however.
  • Example 9 Other methods of identifying biomarkers.
  • Sunitinib inhibits at least eight receptor protein-tyrosine kinases including vascular endothelial growth factor receptors 1-3 (VEGFRl -VEGFR3), platelet-derived growth factor receptors (PDGFRA and PDGFRB), stem cell factor receptor (Kit), Flt-3, and colony-stimulating factor- 1 receptor (CSF-IR).
  • VEGFRl -VEGFR3 vascular endothelial growth factor receptors 1-3
  • PDGFRA and PDGFRB platelet-derived growth factor receptors
  • Kit stem cell factor receptor
  • Flt-3 colony-stimulating factor- 1 receptor
  • CSF-IR colony-stimulating factor- 1 receptor
  • the predicted sunitinib sensitivity of cell lines HT29, Ul 18, 786, and H226 is 0.24, 2.3, 0.14 and 0.60, respectively, based on the sum of the four targets PDGFRB, KDR, KIT and FLT3.
  • This correlates well with the measured response in mouse xenografts of these cells (correlation coefficient 0.86) as well as with the measured anti-angiogenetic effect measured in mouse xenografts (Potapova et al. Contribution of individual targets to the antitumor efficacy of the multitargeted receptor tyrosine kinase inhibitor SUl 1248 (MoI. Cancer Ther. 5(5): 1280-9, 2006). This is better than a model based only on two targets PDGFRA and KIT (correlaton coefficient 0.56).
  • This four-gene predictor of sunitinib response can be applied to a large number of tumor samples from patients with different tumors from which gene expression analysis has been performed in order to get an idea of the range of sensitivities within each cancer type as well as which cancer types are most susceptible to treatment with sunitinib.
  • Figure 11 shows just a small fraction of the cancer samples available from www.intgen.org/expo.html. The comparison is based on normalizing the samples in such a way (e.g., logit normalization) that different cancer types become comparable.
  • Sunitinib is currently approved by the FDA for renal cancer and gastrointestinal cancer. Both kidney and colon show a good response in this plot.
  • RNA antagonists such as SPC2996 targeted against Bcl-2.
  • a response predictor can be built based on measuring the gene expression of Bcl-2 in samples from cancer patients. The same approach can be used for the targets of all mRN A antagonists or inhibitors.
  • Example 10 Identifying candidate drugs for a known target.
  • the methods of the invention described herein can also be used for identifying candidate drugs to a known target.
  • the method of identifying biomarkers is run backwards in order to identify candidate drugs. If one starts with a known target, the expression of its corresponding gene is determined in the NCI 60 cell lines and correlated to the measured growth inhibition of all the thousands of drugs tested in the NCI 60 cell lines. This provides a list, ranked by correlation coefficient, of candidate drugs for the target. It is even possible to test new drugs and compare their correlation coefficient to the target gene expression to the correlation coefficients of the already tested drugs.
  • Example 11 Using microRNAs as biomarkers of drug response.
  • microRNAs play an important role in regulating the translation of mRNAs.
  • microRNAs may contain important information relevant for the prediction of drug sensitivity. This information may be complementary to the information contained in mRNA expression. Shown below is the correlation between predicted and measured chemosensitivity of the NCI 60 cell lines. The prediction is based either on mRNA measurements with DNA microarrays as described herein or predictions based on measurements of microRNA concentration (ArrayExpress accession number E-MEXP- 1029) using a microRNA specific microarray (ArrayExpress accession number A-MEXP-620). Whenever more than one probe is used to determine the concentration of a given microRNA, the median correlation procedure is used for calculating correlation between microRNA concentration and -log(GI50).
  • Tables 22A-76A list the microRNA probes that are useful for detection of sensitivity to individual drugs, as determined by their median correlation to -log(GI50) for the indicated drug.
  • HU6800 biomarkers obtained with old HU6800 chip measurements
  • IFITM2 IFITM2 0.38 [2,] UBE2L6 UBE2L6 0.32 [3,] LAPTM5 LAPTM5 LAPTM5 0.36 [4,] USP4 USP4 0.33 [5,] ITM2A ITM2A 0.38 [6,] ITGB2 ITGB2 0.42 [7,] ANPEP ANPEP 0.31 [8,] CD53 CD53 0.34 [9,] IL2RG IL2RG IL2RG 0.36 [10,] CD37 CD37 0.34

Abstract

La présente invention concerne des procédés, des coffrets et des dispositifs pour prédire la sensibilité d'un patient à un composé ou à un traitement médical. L'invention concerne également des procédés pour identifier des biomarqueurs de gènes dont l'expression est corrélée à la sensibilité au traitement ou à la résistance au traitement dans une population ou un groupe de patients.
PCT/EP2008/003789 2007-05-11 2008-05-09 Procédés, coffrets et dispositifs pour identifier des biomarqueurs de réponse à un traitement et utilisation de ceux-ci pour prédire l'efficacité du traitement WO2008138578A2 (fr)

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Cited By (31)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2009080437A1 (fr) * 2007-12-21 2009-07-02 Exiqon A/S Procédé d'analyse de la résistance aux médicaments par les micro-arn
WO2010073721A1 (fr) * 2008-12-26 2010-07-01 国立大学法人徳島大学 Prédiction basée sur un micro-arn de l'efficacité d'une chimiothérapie préopératoire sur le cancer rectal
WO2010133562A1 (fr) * 2009-05-18 2010-11-25 INSERM (Institut National de la Santé et de la Recherche Médicale) Méthode permettant de prédire la sensibilité d'un patient à un traitement avec un anticorps anti-cd20 et méthode permettant de diagnostiquer une polyarthrite rhumatoïde
US7888035B2 (en) 2008-10-30 2011-02-15 Caris Mpi, Inc. Methods for assessing RNA patterns
US7897356B2 (en) 2008-11-12 2011-03-01 Caris Life Sciences Methods and systems of using exosomes for determining phenotypes
EP2333103A1 (fr) * 2009-12-11 2011-06-15 Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. Diagnostic différentiel et thérapie pour les inhibiteurs de la kinase
US8034910B2 (en) 2008-05-06 2011-10-11 Academia Sinica, Taiwan SUMO fusion protein expression system for producing native proteins
JP2012016294A (ja) * 2010-07-06 2012-01-26 Toray Ind Inc 胃がんの補助化学療法感受性判定用組成物又はキット
US8168180B2 (en) 2002-11-27 2012-05-01 Technion Research & Development Foundation Ltd. Methods and compositions for modulating angiogenesis
EP2532747A1 (fr) * 2011-06-09 2012-12-12 Deutsches Krebsforschungszentrum Modulateurs de glucokinase dépendant de l'ADP (ADPGK) et déshydrogénase glycérol-3-phosphate (GPD2) pour thérapie
WO2013026685A1 (fr) * 2011-08-19 2013-02-28 Febit Holding Gmbh Ensembles de complexes de micro-arn utilisés comme biomarqueurs non invasifs du cancer du côlon
US8461303B2 (en) 2007-08-02 2013-06-11 Gilead Biologics, Inc. LOX and LOXL2 inhibitors and uses thereof
WO2014046623A1 (fr) * 2012-09-21 2014-03-27 Singapore Health Services Pte Ltd Méthode de diagnostic du cancer du foie chez un sujet et kit permettant de diagnostiquer le cancer du foie
US8700335B2 (en) 2006-05-18 2014-04-15 Caris Mpi, Inc. System and method for determining individualized medical intervention for a disease state
CN103784465A (zh) * 2012-11-02 2014-05-14 上海交通大学医学院附属瑞金医院 乙醛脱氢酶2作为蒽环类化疗药物处理肿瘤细胞时药物靶标的应用
US8768629B2 (en) 2009-02-11 2014-07-01 Caris Mpi, Inc. Molecular profiling of tumors
WO2014108855A1 (fr) * 2013-01-10 2014-07-17 Amrita Vishwa Vidyapeetham Réactivité différentielle du liquide céphalorachidien vis-à-vis de pfdn5-alpha pour la détection d'une leucémie lymphoblastique aiguë à cellules b du système nerveux central
US8895509B2 (en) 2010-11-23 2014-11-25 Georgia Tech Research Corporation MIR-200 family induces mesenchymal-to-epithelial transition (MET) in ovarian cancer cells
EP2748336A4 (fr) * 2011-09-08 2015-05-13 Targeted Molecular Diagnostics Llc Méthodes de détermination de la réactivité à un inhibiteur de la dihydrofolate réductase
US9128101B2 (en) 2010-03-01 2015-09-08 Caris Life Sciences Switzerland Holdings Gmbh Biomarkers for theranostics
WO2015192081A1 (fr) * 2014-06-13 2015-12-17 Byrd, John C. Biomarqueur permettant de prédire la réponse d'une llc à un traitement avec un inhibiteur de la btk
US9289447B2 (en) 2009-01-06 2016-03-22 Gilead Biologics, Inc. Chemotherapeutic methods and compositions
KR20160046500A (ko) * 2014-10-21 2016-04-29 한국원자력의학원 항암제 내성 예측용 바이오마커로서의 ｉｃａｍ－３ 및 이의 용도
US9469876B2 (en) 2010-04-06 2016-10-18 Caris Life Sciences Switzerland Holdings Gmbh Circulating biomarkers for metastatic prostate cancer
US9598734B2 (en) 2010-04-29 2017-03-21 Medical Prognosis Institute A/S Methods and devices for predicting treatment efficacy
US10004746B2 (en) 2010-06-03 2018-06-26 Pharmacyclics Llc Use of inhibitors of Bruton's tyrosine kinase (Btk)
US10570457B2 (en) 2014-09-26 2020-02-25 Medical Prognosis Institute A/S Methods for predicting drug responsiveness
US10907214B2 (en) 2016-12-30 2021-02-02 Oncology Venture ApS Methods for predicting drug responsiveness in cancer patients
US10954567B2 (en) 2012-07-24 2021-03-23 Pharmacyclics Llc Mutations associated with resistance to inhibitors of Bruton's Tyrosine Kinase (BTK)
US11207269B2 (en) 2009-09-17 2021-12-28 Bio-Bedst Aps Medical use of sPLA2 hydrolysable liposomes
US11421284B2 (en) 2016-10-07 2022-08-23 Allarity Therapeutics Europe ApS Methods for predicting drug responsiveness in cancer patients

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8512990B2 (en) 2009-08-21 2013-08-20 Gilead Biologics, Inc. Catalytic domains from lysyl oxidase and LOXL2

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
DAHLEN ANNA ET AL: "Activation of the GLl oncogene through fusion with the beta-actin gene (ACTB) in a group of distinctive pericytic neoplasms - Pericytoma with t(7;12)" AMERICAN JOURNAL OF PATHOLOGY, vol. 164, no. 5, May 2004 (2004-05), pages 1645-1653, XP002494073 ISSN: 0002-9440 *
FOURNIER MARCIA V ET AL: "Gene expression signature in organized and growth-arrested mammary acini predicts good outcome in breast cancer" CANCER RESEARCH, vol. 66, no. 14, July 2006 (2006-07), pages 7095-7102, XP002494058 ISSN: 0008-5472 *
KORNMANN MARKO ET AL: "Thymidylate synthase and dihydropyrimidine dehydrogenase mRNA expression levels: predictors for survival in colorectal cancer patients receiving adjuvant 5-fluorouracil" CLINICAL CANCER RESEARCH, THE AMERICAN ASSOCIATION FOR CANCER RESEARCH, US, vol. 9, no. 11, 15 September 2003 (2003-09-15), pages 4116-4124, XP002391741 ISSN: 1078-0432 *
VEER VAN 'T L J ET AL: "Gene expression profiling predicts clinical outcome of breast cancer" NATURE, NATURE PUBLISHING GROUP, LONDON, UK, vol. 415, no. 6871, 31 January 2002 (2002-01-31), pages 530-536, XP002259781 ISSN: 0028-0836 *

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