CN111100909A - Method for calculating genetic heterogeneity in tumor - Google Patents
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Abstract
The invention discloses a method for calculating genetic heterogeneity in tumors, which belongs to the field of cancers and comprises the following steps: collecting tumor tissue samples to generate mutation group data of each tissue sample; combinatorial indices of genetic heterogeneity within the tumor were calculated from the mutant panel data. The invention calculates the combination index based on the mutation of the sample data of each tissue sample and the variation allele frequency corresponding to the mutation, and provides a quantitative tool for the hierarchical analysis of tumor patients as an index of genetic heterogeneity in tumors.
Description
Technical Field
The invention relates to the technical field of cancers, in particular to a method for calculating genetic heterogeneity in tumors.
Background
Cancer is an unsolved public health big problem in the medical field today. Intratumoral Genetic Heterogeneity (IGH) is a major cause of the problematic cancer problem. Immunotherapy (including personalized immunotherapy), such as immune checkpoint inhibitors and personalized tumor vaccines, is becoming a promising solution to meet this medical need. To facilitate effective immunotherapy, knowledge and metering of the IGH system is required.
In the microenvironment of a tumor, the immune system can often recognize and eradicate tumor cells. However, tumor cells can take a number of different strategies to suppress or "hide" the immune system of the human body and thus survive various stages of anti-tumor immune surveillance and response. This function is called immune escape of the tumor cells. Daniel Chen and Ira Mellman proposed the concept of cancer immune cycle, which includes the following seven steps: 1. release of cancer cell antigens (cancer cell death); 2. cancer antigen presentation; 3. priming and activation (APC and T cells); 4. trafficking T cells to tumors (CTLs); infiltration of T cells (CTL and endothelial cells); recognition of cancer cells by T cells (CTL and cancer cells); 7. killing cancer cells (immune cells and cancer cells). The tumor can inhibit the effective recognition and killing of the immune system to tumor cells through the abnormality of different links, thereby generating immune tolerance and even promoting the occurrence and development of the tumor.
Tumor immunotherapy is a therapeutic approach to control and eliminate tumors by restarting and maintaining the cancer immune cycle and restoring normal anti-tumor immune responses in humans, and includes monoclonal antibody-type immune checkpoint inhibitors, therapeutic antibodies, cancer vaccines, cell therapies, and small molecule therapy inhibitors. Many drugs have been designed for specific immunotherapeutic targets, such as checkpoint inhibitors (PD-1/PD-L1, CTLA-4), but not all patients benefit from these universal therapies and inhibit the development of cancer due to complex mutational events and multiple immune escapes. Therefore, there has been increasing interest in selecting and designing immunotherapies for individual differences in specific patients. Such as patients who find an effect on immune checkpoint inhibitors, to allow a more targeted choice of whether to use this class of drugs. Another strategy is to design a personalized vaccine that targets neoantigen-derived mutations specific to a particular patient's cancer cells. This completely personalized vaccine strategy can activate the immune process, killing tumor cells with high specificity, and the non-specific binding induced side effects are also small. Finding suitable targets in many patient-specific somatic mutations has become a major challenge in the design of new antigen-based vaccines. One of the important reasons behind this target discovery challenge is Intratumoral Genetic Heterogeneity (IGH).
Microsatellite instability (MSI) is an important indicator reflecting genomic instability, associated with many genetic diseases and the level of tumor mutational burden/IGH. This unique molecular change and highly variable phenotype is caused by a defective DNA mismatch repair (MMR) system. MSI can be defined as the absence of certain repetitive DNA sequences in the corresponding germline DNA. Samples are generally classified as high microsatellite instability (MSI-H), low microsatellite instability (MSI-L) and microsatellite stability (MSS). Determination of MSI status has prognostic and therapeutic guidance.
One method of measuring the IGH of a particular mutation is Variant Allele Frequency (VAF). To assess the overall statistical distribution of IGH in Tumor samples, Mutant Allele Tumor Heterogeneity (MATH) scores were developed by analyzing VAF for somatic mutations sampled in tumors. There is a meaningful correlation between a higher MATH score and the risk of tumor metastasis. However, the MATH score only reflects the dispersion of the VAF distribution of the tumor sample (the tighter the distribution, the lower the MATH score). The MATH score measures the VAF distribution normalized by the median value of VAF, and therefore does not reflect the absolute level of somatic mutant VAF that is critical for vaccine and immunotherapeutic target selection. The MATH score also did not include a normal control in its mutation calling method, only including mutations with high levels of mRNA expression.
Disclosure of Invention
The invention provides a method for calculating genetic heterogeneity in tumors, and solves the problem that the absolute level of somatic mutation VAF which is crucial to vaccine and immunotherapy target selection is not reflected in the prior art. The technical scheme of the invention is realized as follows:
a method for calculating genetic heterogeneity in tumor comprises the following steps:
s1, collecting a tumor tissue sample;
s2, generating mutation group data of the sample of each tissue sample;
s3, calculating a combined index of genetic heterogeneity within the tumor from the mutant set data.
As a preferred embodiment of the present invention, the combination index specifically refers to:
combination index 1:
VAFiis the variant allele frequency of each sample, and n is the sample mutation number of the tissue sample.
As a preferred embodiment of the present invention, the combination index specifically refers to:
combination index 2:
VAFiis the variant allele frequency of each sample, and n is the sample mutation number of the tissue sample.
As a preferred embodiment of the present invention, the combination index is used for stratification analysis of cancer patients.
As a preferred embodiment of the present invention, the use of a combination index for the stratification of cancer patients refers in particular to the identification of subpopulations that benefit from cancer treatment drugs.
As a preferred embodiment of the present invention, the cancer treatment drug specifically refers to a cancer immunotherapy drug.
As a preferred embodiment of the present invention, the cancer immunotherapy drug specifically refers to a neoantigen-based cancer therapy drug.
As a preferred embodiment of the present invention, the cancer immunotherapy drug specifically refers to an immune checkpoint inhibitor.
As a preferred embodiment of the invention, the immunodetection point inhibitor specifically refers to a PD-1/PD-L1 inhibitor.
As a preferred embodiment of the present invention, the sub-population benefiting from cancer treatment drugs refers in particular to the MSS patient sub-population benefiting from PD-1/PD-L1 inhibitors.
The invention has the beneficial effects that: and calculating a combination index based on the mutation of the sample data of each tissue sample and a corresponding Variant Allele (VAF), wherein the combination index is used as a quantitative index of genetic heterogeneity in the tumor, and a quantitative tool is provided for the hierarchical analysis of tumor patients.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of one embodiment of a method of calculating intratumoral genetic heterogeneity of the present invention;
FIG. 2 is one of the VAF profiles of tumor samples;
FIG. 3 is a second VAF profile of the tumor sample.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1-3, the present invention provides a method for calculating genetic heterogeneity in tumor, which specifically comprises the following steps:
s1, collecting a tumor tissue sample;
the ethical committee of the first hospital in quanzhou city approved the study plan and procedure for patient tumor sample collection (QYL 2019-108). Three tumor tissue samples and one normal/tumor adjacent tissue sample were obtained from each of three patients receiving stage II or stage III colon cancer surgery.
S2, generating mutation group data of the sample of each tissue sample;
total RNA was extracted from tissues using Trizol (Invitrogen, Carlsbad, CA, USA). Approximately 60mg of tissue was ground to powder with liquid nitrogen in a 2mL tube, then homogenized for 2 minutes and placed horizontally for 5 minutes. The mixture was centrifuged at 12,000 Xg for 5 minutes at 4 ℃ and the supernatant was transferred to a fresh EP tube containing 0.3mL of chloroform/isoamyl alcohol (24: 1). The mixture was stirred vigorously for 15s and then centrifuged at 12,000 Xg for 10 min at 4 ℃. After centrifugation, the upper aqueous phase, which retained the RNA, was transferred to a new tube containing an equal volume of isopropanol supernatant and then centrifuged at 13, 600rpm for 20 minutes at 4 ℃. After discarding the supernatant, the RNA precipitate was washed twice with 1mL of 75% ethanol, and then the mixture was centrifuged at 13 ℃ rpm at 4 ℃ for 3 minutes to collect the residual ethanol, and then the precipitate was air-dried for 5 to 10 minutes. A biological safety cabinet. Finally, 25. mu.L to 100. mu.L of LDEPC-treated water was added to dissolve the RNA. Subsequently, total RNA was qualitatively and quantitatively determined using a Nano Drop and Agilent 2100 bioanalyzer (ThermoFisher Scientific, MA, USA).
mRNA was purified using oligodeoxynucleotide (OligodT) attached magnetic beads. At the appropriate temperature, the purified mRNA fragments are fragmented into small fragments using a fragment buffer. Random hexamer initiated reverse transcription was then used to generate first strand cDNA, followed by synthesis of second strand cDNA. Thereafter, end repair was performed by incubation with the addition of A-Tailing Mix and RNA Index Adapters. The cDNA fragment obtained from the previous step was amplified by PCR and the product was purified by Ampure XP Beads, which was then dissolved in EB solution. The product has been quality control verified on an Agilent Technologies 2100 bioanalyzer. The double-stranded PCR product from the previous step was heat denatured and circularized by splint oligonucleotide sequences to obtain the final library. Single-stranded loop dna (sscir dna) was formatted into the final library. The final library was amplified with phi29 to make DNA Nanospheres (DNBs) with 300 copies of one molecule, the DNBs were loaded into patterned nanoarrays, and reads of the terminal 100 bases were generated on the BGIseq500 platform (BGI-shenzhen, china). Samples from patients with stage II colon cancer generated 20GB of data per sample and were sampled as 10GB subsamples. The other samples per patient yielded only 10GB of data.
RNA-seq sequencing data were analyzed using the GATK procedure (v4.0.12.0). Somatic mutations were calculated from GATK4Mutect2, strelska (v2.9.2), SomaticSniper (v1.0.5.0) and Varscan2(v2.4.3), respectively, and all mutations calculated by the software were integrated using Somaticseq (v3.1.1) with reference to normal tissue samples. Somatseq is a machine learning-based somatic mutation integration software that integrates multiple VCF files of a tumor sample into one VCF file. SNVs and indels of quality "PASS" (PASS) were matched to the cosmc database. Variant Allele Frequency (VAF) was calculated as the ratio of alternative allele observations to read depth for each position. Statistical analysis was performed using Python. The MATH score is implemented by python and the MSI score is determined by MSISensor (v 0.6). Samples with scores > 3.5% are considered MSIs, otherwise MSSs. The MSI score was determined using the MSIsensor program (table 1). Samples with scores > 3.5% are considered MSIs, otherwise MSSs. In these three samples, patient 801 displayed the MSI, while patients 716 and 725 were considered MSS.
S3, calculating a combined index of genetic heterogeneity within the tumor from the mutant set data.
The calculation of the combined index of genetic heterogeneity within the tumor refers in particular to:
combination index 1:
or
Combination index 2:
VAF is variant allele frequency (Varianaleleffequency), and n is a mutation number. The part of the tumor not covered by the mutation is indicated as "1-VAF". Log values are used to reflect the geometric relationships between individual mutations (rather than additive relationships)
Cancer patient stratification example COMIC is the world's largest, most comprehensive resource for somatic mutations in human cancers. Using COSMIC as a reference database, mutations that overlap with the COSMIC database can reasonably be expected to be rich in high quality mutations, and functional and driver mutations are relatively rich. Similar to literature reports, only about 1-2% of the identified mutations in this study were observed to be present in the COSMIC database. However, this overlapping set should have relatively abundant mutations that can drive the development and proliferation of cancer. To provide an overall summary of the state of IGH, the average vaf (mean vaf) of all mutations in the sample and the average mutation vaf (mean COSMIC vaf) in the COSMIC database were measured.
In the sample from patient 716 (stage II), variants were enriched (ABC) over a VAF interval of 30% to 58% and the mean VAF (total or cosinc) was about 40%. The VAF distribution pattern and the mean VAF remained unchanged between the three different samples from patient 716. The variation distribution pattern was also very similar between samples with 20G and 10G sequencing depth (ABC vs DEF and table 2), demonstrating that the robustness of the method is independent of the sequencing depth. In the sample from patient 725 (stage III), variants were enriched (GHI) over the VAF interval of 2% to 26% and the mean VAF (total or cosinc) was about 20% (table 2). The VAF distribution pattern is also well conserved among the three different samples from patient 716 (G, H and I). In the sample from patient 801 (stage III), the variation was relatively smoothly distributed between 2% and 58%, and the mean VAF (total or cosinc) was about 30% for two out of three samples. One sample (801_ B) from patient 801 showed a very different distribution pattern (L, table 2), which may be due to heterogeneity between the three samples or contamination of normal tissue samples. Overall, significant differences were observed in the three VAF distribution patterns and mean values of the three patients (40%, 20%, 30%). The difference in VAF distribution between the two MSS patients (716, 725) was also evident (mean VAF 20% and 40%, respectively).
For all these patient samples, the MATH score, which is an indicator of tumor heterogeneity, was very high (>16 was considered to be highly heterogeneous) (table 1), indicating that there are limitations in using the MATH score to measure IGH gradients in tumor mutation landscape analysis, failing to reveal inter-tumor patient sample variability.
In addition to the VAF distribution pattern, the absolute number of mutations in the tumor sample is also important for immunotherapy design. The number of mutations (total or COSMIC) varied very among patients (Table 2), with all mutations ranging from hundreds to thousands (571-. The numerical value of the combinatorial index is used to reflect the geometric relationship (rather than the additive relationship) between individual mutations. The higher the value of the combination index, the better the coverage of the mutation. In this analysis, the combination index 1 ranged from 0.41 to 24.57 and the combination index 2 ranged from 0.42 to 27.86.
The complexity of personalizing immunotherapy for CRC patients with different IGH backgrounds and the personalized nature of the mutation profile are revealed. Considering the key impact of target coverage and target number on the final immunotherapy outcome, combinatorial indices are designed as tools to guide patient stratification and neoantigen analysis in personalized immunotherapy. For patients with a higher combination index, this may translate into better immunotherapy efficacy, and vice versa.
In various types of cancer, including CRC, the status of MSI-H is associated with a high response rate to treatment with PD-1/PD-L1 checkpoint inhibitors. One of the basic conditions for effective treatment of PD-1/PD-L1 inhibitors is the availability of neoantigens and their tumor coverage, which are systematically reflected in the combinatorial index. Patients similar to 716 were MSS, but had a large number of mutations compared to other MSS patients (e.g., patient 725), and the Vibrant Index values differed by two orders of magnitude. The combined index can be used to identify a subpopulation of MSS patients likely to benefit from treatment with a PD-1/PD-L1 inhibitor.
TABLE 1 patient sample information
QC30 is the RNA-seq sequencing quality score (above 0.9 is considered high quality). The MSI (microsatellite instability) score was determined by MSISensor and more than 3.5% was considered MSS. The MATH score measures heterogeneity of mutant allelic tumors, with greater than 16 being considered highly heterogeneous.
Table 2.vafs. distribution
The mean VAF is the mean VAF of all mutations, whereas STDEVVAF is the standard deviation of all mutated VAFs. The mean VAF (COSMIC) is the mean VAF for mutations in the COSMIC database, and stdevvaf (COSMIC) is the standard deviation of the VAFs for mutations in the COSMIC database. Mutations are the total number of mutations identified for a sample. Mutations (COSMICs) are the total number of mutations in the COSMIC in a sample.
For the bar graphs in fig. 2 and 3, the x-axis represents VAF intervals and the y-axis represents the proportion (%) of mutations in each VAF interval. For the line graph, the y-axis represents the proportion (%) of mutations above the VAF threshold.
The present invention calculates a combination index as an index for genetic heterogeneity in a tumor based on a mutation in sample data of each tissue sample and a VAF distribution pattern corresponding to the mutation. This combined index can be used to identify subpopulations that may benefit from cancer treatment drugs, particularly in cancer immunotherapy drugs, such as neoantigen-based immunotherapy or immune checkpoint inhibitors. The present combination parameters can be used to find a subset of MSS patients who may benefit from treatment with a PD-1/PD-L1 inhibitor.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the present invention, and any modifications, equivalents, improvements, etc. made by selecting mutation reference database other than COSMIC may be included in the scope of the present invention, as mutation group data may be obtained by various means, and mutation discovery tools may be widely selected within the spirit and principle of the present invention.
Claims (10)
1. A method for calculating genetic heterogeneity in a tumor, comprising the steps of:
s1, collecting a tumor tissue sample;
s2, generating mutation group data of the sample of each tissue sample;
s3, calculating a combined index of genetic heterogeneity within the tumor from the mutant set data.
2. The method of claim 1, wherein the combination index is a combination index of 1:
VAFiis the variant allele frequency of each sample, and n is the sample mutation number of the tissue sample.
3. The method of claim 1, wherein the combination index specifically refers to:
combination index 2:
VAFiis the variant allele frequency of each sample, and n is the sample mutation number of the tissue sample.
4. The method of claim 2 or 3, wherein the combination index is used for stratification of cancer patients.
5. The method of claim 4, wherein the combination index is used for the stratification of cancer patients, particularly for identifying subpopulations that benefit from cancer treatment drugs.
6. The method of claim 5, wherein the cancer treatment drug is cancer immunotherapy drug.
7. The method of claim 6, wherein the cancer immunotherapy drug is a neoantigen-based cancer therapy drug.
8. The method of claim 6, wherein the cancer immunotherapy drug is an immune checkpoint inhibitor.
9. The method of claim 8, wherein the immunodetection point inhibitor is PD-1/PD-L1 inhibitor.
10. The method of claim 9, wherein the subpopulation that benefits from cancer treatment drugs is specifically the MSS patient subpopulation that benefits from treatment with PD-1/PD-L1 inhibitor.
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