Integrated whole-group method for pharmacogenomic screening
阅读说明：本技术 用于药物基因组学筛选的整合全组学方法 (Integrated whole-group method for pharmacogenomic screening ) 是由 卡米尔·R·施瓦兹 约翰·利特勒 查尔斯·约瑟夫·瓦斯克 约翰·扎卡里·桑伯恩 于 2018-06-07 设计创作，主要内容包括：可能在等位基因之间差异性分布的复杂基因型、尤其是多个单核苷酸变异可以使用来自这些等位基因的RNA转录物的下一代测序和RNA转录物的等位基因分数信息有效地绘制在基因的每个等位基因中。等位基因之间的这样重构的单核苷酸变异可以与癌症疗法的预期效力相关,以更新或产生患者的记录或者调整该癌症疗法的剂量和时间表以减少该癌症疗法的不期望的效力。(Such reconstructed single nucleotide variations between alleles can be correlated with expected efficacy of a cancer therapy to update or generate patient records or adjust the dose and schedule of the cancer therapy to reduce undesired efficacy of the cancer therapy.)
A method of alleviating adverse effects of cancer therapy in a patient having a tumor, the method comprising:
obtaining transcriptomic data for the patient, the transcriptomic data comprising allele fraction information for the th and second loci of RNA molecules transcribed from the gene, wherein the th and second loci have th and second single nucleotide variations, respectively;
using allele fraction information to reconstruct the haplotype at the th and second loci, and
generating or updating a record for the patient with the reconstructed haplotype correlated with the expected efficacy of the cancer therapy.
2. The method of claim 1, wherein the allele fraction information of the th and second loci is derived from a tumor of the patient.
3. The method of , wherein the gene is at least of CYP3A5, CYP2D6, TPMT, F5, DPYD, G6PD and NUDT 15.
4. The method of any of the preceding claims, wherein the and second locus are at least 300bp apart.
5. The method of any of the preceding claims, wherein the haplotype is reconstituted to have the and second nucleotide variations in the allele of the gene when the allele fractions of the and second loci that have the and second nucleotide variations differ by greater than 10%.
6. The method of any of the preceding claims, wherein the gene is CYP2D6 and the expected efficacy comprises an increase in toxicity of the cancer therapy due to slow metabolism of the cancer therapy.
7. The method of any of the preceding claims, wherein the transcriptomics data comprises the copy number of the and second locus, and the method further comprises:
determining amplification of at least of the th and second loci;
generating or updating a record for the patient with the amplified information of the genes associated with the expected efficacy of the cancer therapy.
8. The method of claim 7, wherein the gene is CYP2D6, and the expected effectiveness of cancer therapy comprises a decrease in effectiveness of the cancer therapy due to tachymetabolism of the cancer therapy.
9. The method of any of the preceding claims, further comprising adjusting a recommended dose and schedule of the cancer therapy based on the expected efficacy.
10. The method of of any one of claims 2-9 wherein the transcriptomics data further comprises allele fraction information of the and second loci derived from healthy tissue of the patient.
11. The method of claim 10, further comprising:
using allele fraction information derived from the healthy tissue to reconstruct healthy tissue haplotypes;
comparing the allele fraction information derived from the tumor tissue with the allele fraction information derived from the healthy tissue to obtain tumor-specific allele fraction information; and
generating or updating a record for the patient using the allele fraction information and the tumor-specific allele fraction information.
12. The method of claim 10, further comprising adjusting a recommended dose and schedule of the cancer therapy based on the comparison of the reconstructed healthy tissue haplotype and the tumor-specific haplotype.
13. The method of any of the preceding claims, wherein the cancer therapy is identified by a pathway analysis using at least two of the patient's genomic, transcriptomic, and proteomic data.
14. The method of claim 1, wherein the gene is at least of CYP3a5, CYP2D6, TPMT, F5, DPYD, G6PD, and NUDT 15.
15. The method of claim 1, wherein the th and second RNA loci are at least 300bp apart.
16. The method of claim 1, reconstructing the haplotype as having the and second nucleotide variations in the allele of the gene when the allele fractions of the and second loci having the and second nucleotide variations differ by less than 10%.
17. The method of claim 1, wherein the gene is CYP2D6, and the expected efficacy comprises an increase in toxicity of the cancer therapy due to slow metabolism of the cancer therapy.
18. The method of claim 1, wherein the transcriptomics data comprises the copy number of the th and second RNA locus, and the method further comprises:
determining amplification of at least of the th and second RNA loci;
generating or updating a record for the patient with the amplified information of the genes associated with the expected efficacy of the cancer therapy.
19. The method of claim 18, wherein the gene is CYP2D6, and the expected efficacy of cancer therapy comprises a decrease in efficacy of the cancer therapy due to tachymetabolism of the cancer therapy.
20. The method of claim 1, further comprising adjusting a recommended dose and schedule of the cancer therapy based on the expected efficacy.
21. The method of claim 2 wherein the transcriptomics data further comprises allele fraction information of the and second RNA locus derived from healthy tissue of the patient.
22. The method of claim 21, further comprising:
using allele fraction information derived from the healthy tissue to reconstruct healthy tissue haplotypes;
comparing the allele fraction information derived from the tumor tissue with the allele fraction information derived from the healthy tissue to obtain tumor-specific allele fraction information; and
generating or updating a record for the patient using the allele fraction information and the tumor-specific allele fraction information.
23. The method of claim 22, further comprising adjusting a recommended dose and schedule of the cancer therapy based on the comparison of the reconstructed healthy tissue haplotype and the tumor-specific haplotype.
24. The method of claim 1, wherein the cancer therapy is identified by a pathway analysis using at least two of genomics, transcriptomics, and proteomics data for the patient.
A method of treating patients having a tumor, the method comprising:
obtaining transcriptomic data for the patient, the transcriptomic data comprising allele fraction information for the th and second RNA loci of an RNA molecule transcribed from the gene, wherein the th and second loci have th and second nucleotide variations, respectively;
using the allele fraction information to reconstruct the haplotypes of the th and second RNA loci;
inferring an expected efficacy of a cancer therapy for the haplotype; and
adjusting a recommended dose and schedule for the cancer therapy based on the expected efficacy.
26. The method of claim 25, wherein the allele fraction information of the and second locus is derived from a tumor of the patient.
27. The method of any of claims 25-26, wherein the gene is at least of CYP3a5, CYP2D6, TPMT, F5, DPYD, G6PD and NUDT 15.
28. The method of any of claims 25-27, wherein the and second RNA locus are at least 300bp apart.
29. The method of any of claims 25-28, wherein the haplotype is reconstituted to have the and second nucleotide variation in the allele of the gene when the allele fractions of the and second loci that have the and second nucleotide variations differ by less than 10%.
30. The method of any of claims 25-29, wherein the gene is CYP2D6 and the expected efficacy comprises an increase in toxicity of the cancer therapy due to slow metabolism of the cancer therapy.
31. The method of any of claims 25-30, wherein the transcriptomics data comprises the copy number of the and second RNA locus, and the method further comprises:
determining amplification of at least of the th and second RNA loci;
adjusting a recommended dose and schedule for a cancer therapy using information on amplification of genes associated with expected efficacy of the cancer therapy.
32. The method of claim 31, wherein the gene is CYP2D6, and the expected efficacy of cancer therapy comprises a decrease in efficacy of the cancer therapy due to tachymetabolism of the cancer therapy.
33. The method of any of claims 25-32, further comprising generating or updating a record of the patient with the reconstituted haplotype correlated with the expected efficacy of the cancer therapy.
34. The method of any of claims 25-33, wherein the transcriptomics data further comprises allele fraction information of the and second RNA loci derived from healthy tissue of the patient.
35. The method of claim 34, further comprising:
using allele fraction information derived from the healthy tissue to reconstruct healthy tissue haplotypes;
comparing the allele fraction information derived from the tumor tissue with the allele fraction information derived from the healthy tissue to obtain tumor-specific allele fraction information; and
adjusting a recommended dose and schedule for the cancer therapy using the allele fraction information and the tumor-specific allele fraction information.
36. The method of claim 35, further comprising adjusting a recommended dose and schedule of the cancer therapy based on the comparison of the reconstructed healthy tissue haplotype and the tumor-specific haplotype.
37. The method of any of claims 25-36, wherein the cancer therapy is identified by a pathway analysis using at least two of the patient's genomic, transcriptomic, and proteomic data.
38. The method of claim 25, wherein the gene is at least of CYP3a5, CYP2D6, TPMT, F5, DPYD, G6PD, and NUDT 15.
39. The method of claim 25, wherein the th and second RNA loci are at least 300bp apart.
40. The method of claim 25, reconstructing the haplotype as having the and second nucleotide variations in the allele of the gene when the allele fractions of the and second loci having the and second nucleotide variations differ by less than 10%.
41. The method of claim 25, wherein the gene is CYP2D6, and the expected efficacy comprises an increase in toxicity of the cancer therapy due to slow metabolism of the cancer therapy.
42. The method of claim 25, wherein the transcriptomics data comprises the copy number of the th and second RNA locus, and the method further comprises:
determining amplification of at least of the th and second RNA loci;
generating or updating a record for the patient with the amplified information of the genes associated with the expected efficacy of the cancer therapy.
43. The method of claim 42, wherein the gene is CYP2D6, and the expected effectiveness of cancer therapy comprises a decrease in effectiveness of the cancer therapy due to tachymetabolism of the cancer therapy.
44. The method of claim 25, further comprising generating or updating a record for the patient with the reconstituted haplotype correlated with the expected efficacy of the cancer therapy.
45. The method of claim 25 wherein the transcriptomics data further comprises allele fraction information of the and second RNA locus derived from healthy tissue of the patient.
46. The method of claim 45, further comprising:
using allele fraction information derived from the healthy tissue to reconstruct healthy tissue haplotypes;
comparing the allele fraction information derived from the tumor tissue with the allele fraction information derived from the healthy tissue to obtain tumor-specific allele fraction information; and
adjusting the recommended dose and schedule of the cancer therapy based on a comparison of the reconstructed healthy tissue haplotype and the tumor-specific haplotype.
47. The method of claim 40, further comprising generating or updating a record for the patient with the allele fraction information and the tumor-specific allele fraction information.
48. The method of claim 25, wherein the cancer therapy is identified by a pathway analysis using at least two of genomics, transcriptomics, and proteomics data for the patient.
Technical Field
Background
The background description includes information that may be useful in understanding the present invention. There is no admission that any information provided herein is prior art or relevant to the presently claimed invention, nor that any publication specifically or implicitly referenced is prior art.
All publications and patent applications herein are incorporated by reference to the same extent as if each individual publication or patent application was specifically and individually indicated to be incorporated by reference at the definitions or uses of the terms in the incorporated references apply to the extent that the definitions or usages of the terms in the incorporated references are not or contrary to the definitions of the terms provided herein, but rather the definitions of the terms in the references apply.
Several genes that affect cancer drug-associated phenotypes (e.g., drug efficacy, drug toxicity, etc.) have been identified, including the thiopurine methyltransferase gene (TPMT), the gene encoding a member of the cytochrome P450 mixed-function oxidase system (CYP2D6), and the gene encoding the organic anion transport polypeptide 1B1 (SLCO1B1), and their single nucleotide variations have been identified , which suggests using genetic information to tailor cancer therapy to obtain its maximum and optimal results.
To circumvent such difficulties, efforts have been made to identify allele-specific single nucleotide point mutations using RNA allele frequency and DNA copy number variations.for example, Edsgard et al (Bioinformatics, 32(19),2016,3038, 3040) disclose haplotype inference using single-cell RNA-seq data showing a specific pattern of read number distribution.a specific pattern of read number distribution is correlated with sequencing data to infer whether two sequence variants are located in the same allele.
Thus, even though -like methods of using the allele frequencies of RNA transcripts to phase single nucleotide variations are known, it has largely remained elusive to investigate how to identify and modify cancer therapies by mapping allele-specific single nucleotide variations in specific genes associated with drug metabolism.
Disclosure of Invention
The present subject matter relates to methods of using omics data to comprehensively characterize single nucleotide variations in alleles of a gene of interest among various types of cancer patients by analyzing patterns of allele fraction distributions among RNA transcripts that include single nucleotide variations.accordingly, aspects of the present subject matter include methods of reducing adverse effects of cancer therapy in patients with tumors.
Most typically, transcriptomics data are obtained from RNAseq and the genes are at least of CYP3A5, CYP2D6, TPMT, F5, DPYD, G6PD, and NUDT15 although the expected efficacy of cancer therapy may vary depending on the type of gene and mutation, such expected efficacy may include drug efficacy, drug toxicity, metabolic rate of the drug, and expected life of the patient, therefore, in embodiments, the gene is CYP2D6 and the expected efficacy includes an increase in toxicity of the cancer therapy due to the slow metabolism of the cancer therapy.
Preferably, the th and second RNA loci are at least 300bp apart, at least 500bp apart, or at least 1kbp apart in the RNA transcript such that the RNA-seq sequence data of the th and second loci do not overlap when the allele fractions of the th and second RNA loci having the th and second nucleotide variations differ by less than 10%, less than 15%, or less than 20%, the haplotype is reconstituted to have the th and second nucleotide variations in the allele of the gene.
Additionally, the transcriptomics data may comprise copy numbers of the and second RNA loci, and the method may further comprise the step of determining an amplification of at least of the and second loci of the RNA transcripts and generating or updating a patient record with the amplified information of the gene associated with the expected efficacy of the cancer therapy.
In such embodiments, the method can further comprise the steps of using the allele fraction information of healthy tissue to reconstruct a healthy tissue haplotype and comparing the allele fraction information derived from tumor tissue to the allele fraction information derived from healthy tissue to obtain tumor-specific allele fraction information.
In yet another aspect of the inventive subject matter, the inventors contemplate a method of treating a patient having a tumor this method includes the step of obtaining transcriptomic data for the patient, the data comprising allele fraction information for the th and second RNA loci, respectively, of an RNA molecule transcribed from a gene having the th and second nucleotide variation.
Most typically, transcriptomics data are obtained from RNAseq and the genes are at least of CYP3A5, CYP2D6, TPMT, F5, DPYD, G6PD, and NUDT15 although the expected efficacy of cancer therapy may vary depending on the type of gene and mutation, such expected efficacy may include drug efficacy, drug toxicity, metabolic rate of the drug, and expected life of the patient, therefore, in embodiments, the gene is CYP2D6 and the expected efficacy includes an increase in toxicity of the cancer therapy due to the slow metabolism of the cancer therapy.
Preferably, the th and second RNA loci are at least 300bp apart, at least 500bp apart, or at least 1kbp apart such that the RNA-seq sequence data of the th and second loci do not overlap when the allele fractions of the th and second RNA loci having the th and second nucleotide variations differ by less than 10%, less than 15%, or less than 20%, the haplotype is reconstituted to have the th and second nucleotide variations in the allele of the gene.
Additionally, the transcriptomics data may comprise copy numbers of the and second loci of the RNA transcript, and the method may further comprise the step of determining an amplification of at least of the and second RNA loci and generating or updating a patient record with the amplified information of the gene associated with the expected efficacy of the cancer therapy.
In such embodiments, the method can further comprise the steps of using healthy tissue allele fraction information to reconstruct healthy tissue haplotypes and comparing tumor tissue-derived allele fraction information to healthy tissue-derived allele fraction information to obtain tumor-specific allele fraction information.
Various objects, features, aspects and advantages of the present subject matter will become more apparent from the following detailed description of preferred embodiments and the accompanying drawings.
Drawings
FIG. 1A depicts an exemplary graph of the fraction of DNA alleles in normal and tumor tissues of a patient.
FIG. 1B depicts an exemplary graph of tumor RNA allele fraction relative to tumor DNA allele fraction of a patient.
FIG. 2A shows an exemplary graph of tumor RNA allele fraction relative to the normal DNA allele fraction of a patient in which two single nucleotide variations (α and β) are in the same haplotype.
FIG. 2B shows an exemplary graph of tumor RNA allele fraction relative to tumor DNA allele fraction of a patient in which two single nucleotide variations (α and β) are in the same haplotype.
Figure 3A shows a graph of read coverage of each exon of CYP2D6 and CYP2D7 genes without any deletion or amplification of alleles.
Figure 3B shows a graph of read coverage for each exon of the CYP2D6 and CYP2D7 genes in the presence of allelic deletion.
Figure 3C shows a graph of read coverage for each exon of the CYP2D6 and CYP2D7 genes in the presence of allele amplification.
Detailed Description
In addition, while it is often necessary to conduct a genomic screen covering multiple genomic variations in multiple genes of a patient to optimize the type and treatment regimen of cancer treatments, a comprehensive large-scale genomic variation screen for different types of cancer patients has not been able to account for.
From a different perspective, the inventors discovered that allele fraction information for RNA molecules whose sequences overlap in regions where genomic variation exists can be readily determined using the allele fraction information and further steps to reconstruct haplotypes with the allele information, the inventors also discovered that allele fraction information for RNA molecules of multiple genes that are associated with drug efficacy and/or toxicity can be obtained from patients such that drug treatment plans can be customized and personalized.accordingly, in particularly preferred aspects of the inventive subject matter, methods of reconstructing haplotypes with multiple allele-specific single nucleotide variations in or multiple genes to mitigate adverse effects of cancer therapy in patients with tumors.
As used herein, the term "tumor" refers to and is used interchangeably with or more types of cancer cells, tissues, malignant cells or tissues, which may be located or found in or more anatomical locations in the human body it is noted that the term "patient" as used herein includes both individuals diagnosed with a condition (e.g., cancer) and individuals undergoing examination and/or testing for the purpose of detecting or identifying a condition.
As used herein, the term "locus (loci)" (or "loci)") refers to the portion of a gene, a transcript of a gene, or a nucleic acid molecule derived from a gene or a transcript of a gene or a location in a gene, a transcript of a gene, or a nucleic acid molecule derived from a gene or a transcript of a gene.
Obtaining omics data
For example, omics data can be obtained by obtaining tissue from an individual and processing the tissue to obtain DNA, RNA, protein, or any other biological substance from the tissue to further analyses of relevant information in another instances, the omics data can be obtained directly from a database storing omics information for the individual.
In the case of obtaining omics data from the tissue of an individual, any suitable method of obtaining a tumor sample (tumor cells or tumor tissue) or healthy tissue from a patient is contemplated.
In embodiments, tumor samples (or suspected tumor samples) can be obtained from a patient at multiple time points in order to determine any change in the tumor sample over a relevant time period, for example, the tumor samples (or suspected tumor samples) can be obtained before and after the sample is determined or diagnosed as cancerous in another instances the tumor samples (or suspected tumor samples) can be obtained before, during, and/or after (e.g., after completion of, etc.) times or series of anti-tumor treatments (e.g., radiation therapy, chemotherapy, immunotherapy, etc.) in yet another instances, after identifying new metastatic tissue or cells, the tumor samples (or suspected tumor samples) can be obtained during tumor progression.
Alternatively and/or additionally, the step of obtaining omics data can include receiving omics data from a database storing omics information for one or more patients and/or healthy individuals, for example, the omics data for a patient tumor can be obtained from DNA, RNA, and/or protein isolated from a patient tumor tissue, and the obtained omics data can be stored in the database (e.g., cloud database, server, etc.) along with other omics data sets for other patients having the same type of tumor or different types of tumors.
As used herein, omics data includes, but is not limited to, information related to genomics, proteomics, and transcriptomics, as well as specific gene expression or transcript analysis and other characteristics and biological functions of the cell. With respect to genomic data, suitable genomic data includes DNA sequence analysis information, which can be obtained by whole genome sequencing and/or exome sequencing (typically at least 10-fold, more typically at least 20-fold, depth of coverage) of tumor and matched normal samples. Alternatively, the DNA data may also be provided from an established sequence record (e.g., SAM, BAM, FASTA, FASTQ, or VCF file) from a previous sequence determination. Thus, a data set may comprise an unprocessed or processed data set, and exemplary data sets include those having a BAM format, a SAM format, a FASTQ format, or a FASTA format. However, it is particularly preferred that the data sets are provided in BAM format or as bambambam diff objects (e.g., US 2012/0059670a1 and US 2012/0066001a 1). Omics data can be derived from whole genome sequencing, exome sequencing, transcriptome sequencing (e.g., RNA-seq), or from gene-specific analysis (e.g., PCR, qPCR, hybridization, LCR, etc.). Also, computational analysis of the sequence data can be performed in a variety of ways. However, in the most preferred method, analysis is performed in a computer using BAM files and BAM servers through location-guided simultaneous alignment of tumor and normal samples as disclosed for example in US 2012/0059670a1 and US 2012/0066001a 1. Such an analysis advantageously reduces false positive neo-epitopes and significantly reduces the need for memory and computing resources.
Where tumor-specific omics data are to be obtained, many approaches are considered suitable for use herein, so long as such methods will be capable of generating differential sequence objects or other recognition of location-specific differences between the tumor and the matching normal sequence. Exemplary methods include sequence comparison to an external reference sequence (e.g., hg18 or hg19), sequence comparison to an internal reference sequence (e.g., a matching normal sequence), and sequence processing to a known common mutation pattern (e.g., SNV). Thus, contemplated methods and procedures for detecting mutations between tumors and matched normal, tumor and fluid biopsies, as well as matched normal and fluid biopsies, include iCallSV (URL: githu. com/rhshah/iCallSV), VarScan (URL: VarScan. sourceform. net), MuTect (URL: githu. com/branched/MuTect), Strenka (URL: githu. com/Illumina/strenka), Somatic Snaper (URL: gm. genome. dustl. edu/homogeneous-Sniper /), and BAMBAM (US 2012/0059670).
However, in a particularly preferred aspect of the inventive subject matter, sequence analysis is performed by incremental simultaneous alignment of sequence data (tumor sample) with second sequence data (matched normal sample), for example using algorithms as described, for example, in cancer Res [ cancer research ]2013, 10.1, 73(19):6036-45, US 2012/0059670 and US 2012/0066001 to so generate patient and tumor specific mutation data.
In addition, it should be noted that sets of data preferably reflect tumors and matched normal samples from patients in order to thus obtain patient and tumor specific information.
In such embodiments, the genomic dataset comprises all read information for at least portions of the gene, preferably at least 10x, at least 20x, or at least 30x the allele-specific copy number (more specifically, majority and minority copy numbers) is calculated using a dynamic windowing method that expands and shrinks the genomic width of the window according to coverage in the germline data, as described in detail in US9824181, which is incorporated herein by reference.
In addition, omics data for cancer and/or normal cells comprises a transcriptome dataset comprising sequence information and expression levels (including expression analysis, copy number or splice variant analysis) of or more RNAs (preferably cellular mrnas) obtained from a patient (from a cancer tissue (diseased tissue) and/or a matched healthy tissue) or a healthy individual+RNA acquisition, the reverse-transcribed polyA+RNA was in turn obtained from a tumor sample from a patient with and a matched normal (healthy) sample, again, it should be noted that although polyA is typically preferred+RNA as a transcriptPreferred methods include quantitative RNA (hnRNA or mRNA) analysis and/or quantitative proteomic analysis, including rnaseq in particular, RNA quantification and sequencing are otherwise performed using RNA-seq, qPCR and/or rtPCR based methods, although various alternative methods (e.g., solid phase hybridization based methods) are also deemed suitable from another perspective transcriptomic analysis (alone or in combination with genomic analysis) may be suitable for identifying and quantifying genes with cancer-specific and patient-specific mutations.
In such embodiments, the transcriptomic dataset comprises all of the read information for at least portions of the genes, preferably at least 10x, at least 20x, or at least 30 x.
Alternatively, where it is desired to find or scan for new mutations or changes in expression of a particular gene, RNAseq is preferred so as to so cover at least the portion of the patient's transcriptome.
In addition, omics data for cancer and/or normal cells comprise a proteomics data set comprising levels of protein expression (quantification of protein molecules), post-translational modifications, protein-protein interactions, protein-nucleotide interactions, protein-lipid interactions, etc. it is also understood that proteomics analysis as presented herein may also include activity assays for selected proteins.
Omics data analysis and selection of cancer drugs as treatments
The inventors contemplate that omic data, preferably two or more types of omic data, can be used to determine the molecular profile or molecular signature of tumor tissue. Although any type or subtype of omics data can be used to determine the molecular profile or molecular signature of tumor tissue, it is contemplated that the type of omics data that is preferred can vary based on the type of tumor, based on the desired information (e.g., information about intrinsic drug sensitivity, tumor cell stem cell characteristics, etc.), and/or the prognosis of the tumor (e.g., metastatic, immune resistant, etc.). Exemplary subtypes of genomic data that may be associated with tumor development may include, but are not limited to, genomic amplification (e.g., genomic copy number aberrations as represented), somatic mutations (e.g., point mutations (e.g., nonsense mutations, missense mutations, etc.), deletions, insertions, etc.), genomic rearrangements (e.g., intrachromosomal rearrangements, extrachromosomal rearrangements, translocations, etc.), the appearance and copy number of extrachromosomal genomes (e.g., double minichromosomes, etc.). In addition, the genomic data may also include tumor mutation burden as measured by the number of mutations carried by or present in the tumor cells over a predetermined or related period of time.
In addition to genomic data, or more subtypes of transcriptomic data can also be used to determine the molecular profile or molecular signature of tumor tissue exemplary transcriptomic data include, but are not limited to, the expression level of multiple mrnas as measured by the amount of mRNA, the level of maturation of mrnas (e.g., presence of poly a tail, etc.), and/or splice variants of the transcripts the number of genes (at least two, at least five, at least ten, at least fifteen, etc.), the type of transcript or RNA (mRNA, miRNA, etc.), or the selection of genes for determining the molecular profile or molecular signature of tumor tissue can vary based on the type of tumor, based on the information desired (e.g., information about intrinsic drug sensitivity, tumor cell stem cell characteristics, etc.), and/or prognosis of the tumor (e.g., metastatic, immune resistance, etc.), for example, the gene selection and/or number of genes for determining the molecular signature associated with tumor stem cell characteristics can vary, or the genes selected and/or the genes for determining the molecular signature associated with the cellular sensitivity to a particular chemotherapeutic drug, such as the transcriptional factor, including, the RNA, RNA.
Exemplary proteomic data includes, but is not limited to, amounts of or more proteins or peptides, post-translational modifications of or more proteins or peptides (e.g., phosphorylation, glycosylation, formation of dimers, ubiquitination, etc.), and/or subcellular localization of proteins or peptides.
Most typically, the pathway model comprises a plurality of pathway elements (e.g., proteins) linked by or more regulatory nodes, e.g., pathway model [ A ] is a factor graph-based pathway model (e.g., PARADIGM pathway model) comprising any factor other than A or B that may affect the activity of B and C by modulating node I (between elements A and B) and additionally regulatory nodes II (between elements B and C), regulatory nodes I and II represent pathway elements that may affect the activity of B and C (A-I-B-II-C) that may be coupled to each other via a single pathway model [ A ] and/or a multiple pathway model [ A ] and/or RNA expression profile coupled to each other via a single pathway model [ A ] and/or multiple pathway model [ B ] and multiple pathway model [ A ] and/or multiple pathway model [ C ] coupled to each other pathway model [ A ] may include a single pathway model [ A ] and/or multiple pathway model [ B ] coupled pathway model [ A ] and/or RNA expression profile [ A ] coupled to each other pathway model [ A ] via a single pathway model [ A ] and/or multiple pathway model [ C ] coupled pathway pathways [ A ] coupled to each other pathway model [ A ] which may affect the activity of B and/or C via a pathway [ A ] and/or B [ 12 [ B ] coupled pathway model [ 3 ] and/or multiple pathway pathways [ 3, thus, e.g., a ] may include multiple pathway model of pathway pathways [ A ] coupled pathway (e.g., a) coupled pathway model of a pathway.
For example, where a gene encoding protein A carries multiple genomic mutations in the exome and the level of RNA expression of the gene increases following drug treatment, it can be inferred from such genomic and transcriptional profiles that the amount of the protein may increase due to missense mutations in key post-translationally modified residues, while the activity of such protein may provide a dominant negative effect in the signaling pathway in which protein A is an element of that signaling pathway, based on the individual pathway element activity so inferred, the activity of downstream signaling pathway elements can be inferred in another signaling pathways connected to the signaling pathway or through regulatory nodes.
Thus, various types of omics data can be integrated into a single pathway model so as to allow calculation of inferred attributes (e.g., DNA copy number and/or mutation, RNA transcription level, protein mass and/or activity without data obtained from the sample) based on measured attributes (e.g., DNA copy number and/or mutation, RNA transcription level, protein mass and/or activity), and also to calculate inferred pathway activities. Advantageously, such calculations can utilize all available omics data, or just omics data that have significant deviation from the corresponding normal values (e.g., due to copy number changes, over-or under-expression, loss of protein activity, etc.). Using such a system, it will be appreciated that changes in cell signaling activity and such signaling pathways can be detected rather than analyzing only a single or multiple labels, which would otherwise go unnoticed when only a single or multiple labels are considered without regard to their function.
Preferably, the pathway model may be pre-trained with omics data of healthy individuals as input and validation data via machine learning algorithms (e.g., linear kernel SVM, class polynomial kernel SVM, class ii polynomial kernel SVM, ridge regression, Lasso, elastic networks, sequential minimal optimization, random forest methods, J48 trees, naive bayes, JRip rules, superpipes, and nmfpredictors.) in such embodiments, factors for each pathway element and regulatory nodes will be provided with weights and orientations to determine the activity of downstream pathway elements by the machine learning algorithms.
Thus, the pathway model thus trained can be used as a template to predict how pathways or pathway elements will change in tumor tissue. For example, the omics data obtained from the patient (and preferably compared to matched normal tissue or healthy tissue from a healthy individual) can be integrated into a factor graph-based model using PARADIGM (or any suitable pathway model that can be machine-trained and produces reliable output data) to infer or predict which pathway elements will change and how they will change due to changes in tumor-specific omics data compared to matched normal tissue or healthy tissue from a healthy individual. Thus, suitable pathway models include models based on gene set enrichment analysis (GSEA, BroadInstitute), signaling pathway impact analysis (SPIA, Bioconductor), and pathologist pathway models (NCBI), as well as factor graph-based models, in particular PARADIGM as described in WO 2011/139345a2, WO 2013/062505a1, and WO 2014/059036, all incorporated herein by reference.
Thus, genomic mutation profiling, RNA expression profiling, and optionally proteomic analysis (measured from a sample or inferred by pathway analysis) can be used collectively at steps to identify or predict the signaling pathway elements of the relevant signaling pathways that vary most significantly in tumor tissue, such that the most desirable target for or more tumor treatments can be selected.
Phasing RNA molecules of different loci and determining allele haplotypes
Even if a cancer drug with a high probability of successfully treating a tumor is identified from pathway analysis using omics data of a patient, the cancer drug may not be effectively used to treat a tumor of the patient if it cannot be metabolized in an effective manner and/or is toxic to normal tissues or cells of the patient due to patient-specific genetic variations.several genes and single nucleotide variations on those genes have been identified that may affect the efficacy of some currently available cancer drugs.in of those genes, the effect of each single nucleotide variation and/or a combination of ones of the single nucleotide variations and/or a combination of different types of alleles with different combinations of single nucleotide variations may vary with respect to the expected efficacy and/or toxicity of the cancer drug.for example, multiple allele types of CYP2D6 with different combinations of single nucleotide variations and their functional levels (normal function, reduced function, non-function, etc.) have been identified that, in the case where both types of alleles contain the common single nucleotide variations 1662G- > C and 4181 > G- > C > and the functional level of the single nucleotide variations are interesting (in the case of this single nucleotide variations with T-C39882), and in the case where this single nucleotide variations are not present in the same gene (T-C- > 4651 and T- > C) of the same allele variations).
For example, if a patient has 10 alleles in his/her CYP2D6 gene, it is likely that tamoxifen may not be as effective for that patient as the internal concentration of tamoxifen (endoxifen) in that patient is low and the chance of tumor recurrence after tamoxifen treatment is relatively high.
TABLE 1
Thus, in a aspect of the inventive subject matter, a patient's allelic haplotype can be determined to provide the expected efficacy of cancer therapy prior to administration of cancer therapy to the patient, although suitable methods of accurately mapping multiple single nucleotide variations in an allele-specific manner are contemplated, preferred methods use phasing of multiple RNA molecules in different loci transcribed from a single gene by analyzing the allele fraction of the loci, most typically these loci are non-overlapping portions of the gene in which at least allele-specific single nucleotide variations are located.
In preferred embodiments, the sequencing depth for each locus is at least 10x, preferably at least 15x, more preferably at least 20x, most preferably at least 30x, in other words, each single nucleotide variation in each locus of the germline allele (maternal or paternal allele) will be covered by at least 10 reads, at least 15 reads, at least 20 reads, or at least 30 reads the inventors contemplate that where only alleles have the requisite read support (all reads corresponding to the same nucleic acid sequence), the allele is homozygous, and where two alleles have the requisite read support, the allele is heterozygous, thus, where the allele is heterozygous, the reads for each locus (10 reads, 20 reads, 30 reads, etc.) can be divided into two groups (e.g., five reads corresponding to sequence a, and five reads corresponding to sequence B), thus, for each locus, there can be no single nucleotide variation calculated for each allele variation (e.g., a single nucleotide variation calculated for each allele has a score of 390, and no single nucleotide variation is calculated for each allele variation at a single nucleotide variation score of 3614, e.g., 3 reads corresponding to no single nucleotide variation of 367.
Without wishing to be bound by any particular theory, the inventors envision that the number of reads measured by the RNA-seq of a heterozygous allele is generally unbalanced, and this imbalance persists between multiple loci of RNA molecules transcribed from a single gene, from a different perspective, the RNA transcript from each allele is expressed in a particular pattern (e.g., a paternal to maternal ratio of 7:3, etc.) in a single gene, thus, it is possible that if the fraction ratio is the same or substantially similar to all or other sequence reads of the same locus, then the portion of reads from locus C of the RNA transcript are from the same allele as the portion of reads from locus D, and thus, the haplotype of the locus can be reconstructed based on the allele fraction pattern.607. for example, the allele fraction with reads of T201 (with sequence base pair T at position 201) is 0.3, the allele fraction with base pair C of reads of C201 (with base pair C at position 201) is 0.7, the allele fraction with base pair C of 0.607 is determined based on the allele fraction of reads at position 607, and the allele fraction of 0.607 is determined in the case where the allele fraction of reads with C201 (base pair) is 0.607 and the same allele fraction of 0.607 is determined in the same .
Preferably, the allele fractions for reconstructing a gene haplotype are sufficiently far from 0.5 that two sequences from different alleles are not incorrectly reconstructed as a single allele, or any sequence errors in the reads do not result in the haplotype of two loci being reconstructed from two different alleles as a single allele. Thus, the allele fraction is preferably less than 0.45, preferably less than 0.4, more preferably less than 0.35, or greater than 0.55, preferably greater than 0.6, or more preferably greater than 0.65. In other embodiments, the allele fractions between two alleles differ by more than 5%, preferably more than 10%, more preferably more than 20%, or more than 30%.
For example, in the case of studies of drug toxicity and/or drug efficacy associated with genomic variations, the gene of interest may include genes encoding enzymes that metabolize cancer drugs in patients, which may include, but are not limited to, CYP3A5, CYP2C19, CYP2D6, TPMT, F5, DPYD, G6PD, and DTNU15. Table 2 presents the frequency of specific allele types among patients as measured using DNA sequencing data analysis as described above.
Gene
Alleles
Frequency of
Group frequency
CYP3A5
*3
85.74％
85％-95％
CYP3A5
*6
0.43％
1.19％
CYP2D6
*10
4.23％
[2.5-42.4]％
TPMT
*3A
5.69％
4.50％
TPMT
*3B
5.53％
2.75％
TPMT
*3C
7.08％
3.67％
TPMT
*2
0.16％
0.14％
F5
rs6025
2.13％
2.15％
DPYD
*2A
0.48％
0.58％
DPYD
rs67376798
0.48％
0.29％
G6PD
Sea of Mediterranean origin
1.22％
0.24％
G6PD
A-
1.12％
1.13％
NUDT15
*3
1.44％
2.62％
NUDT15
*4
0.08％
0.24％
TABLE 2
The inventors have further studies the incidence of genomic variations between patients with various types of cancer that may affect the efficacy or toxicity of cancer drugs as shown in table 3, almost all (over 96%) of patients with various types of cancer in the test group (panel) have at least genomic variations in at least genes, furthermore, almost 8% of patients have genomic variants that may lead to fatal or severe drug toxicity.
In examples, haplotype determinations using RNA phasing can be performed with omics data of matched normal or healthy tissue of the patient and omics data of tumor tissue of the obtained patient to determine potential differential effects and/or toxicity of cancer therapy.
Fig. 2A and 2B show exemplary allele fraction plots according to which haplotypes with two different single nucleotide variations are in the same allele, in this example, the allele fractions of two loci of a tumor RNA transcript having of the single nucleotide variation of the TPMT gene are plotted against the normal DNA allele fraction (fig. 2A) or the tumor DNA allele fraction (fig. 2B) the TMPT 3A allele comprises two single nucleotide variations (rs1142345 and rs1800460), each of which is individually also identified as.
In another instances, where the tumor tissue has a genomic variation due to allele-specific deletion and/or amplification at steps, the tumor tissue may have different sensitivity or tolerance to the toxicity of the cancer therapy due to a reduction or enhancement of the phenotype from the deleted or amplified haplotype relative to the intact haplotype as shown in FIG. 1A, the DNA allele fraction in healthy tissue is mostly between 0.4 and 0.6, indicating that the copy number of both alleles of a given gene is essentially the same and there is little allele-specific amplification or deletion event in the healthy tissue genome.
As shown, the RNA allele fraction of many genes differs from their corresponding DNA allele fraction, indicating that an imbalance in at least two factors, allele-specific DNA copy number (e.g., by allele-specific amplification or deletion) and allele-specific transcript levels of gene transcripts, may affect tumor-specific drug sensitivity and/or toxicity compared to healthy tissue from patients.
Thus, in embodiments, with respect to deletion or amplification of or more alleles, the inventors contemplate genomic data analysis of genes associated with toxicity of cancer therapy (e.g., CYP3a5, CYP2C19, CYP2D6, TPMT, F5, DPYD, G6PD, and NUDT 15.) deletion or amplification of alleles of a gene can be determined by counting allele-specific copy numbers of a particular genomic region most typically, allele-specific copy numbers are calculated using a dynamic windowing method that expands and contracts the genomic width of a window based on coverage in tumor or normal germline data with or expected genes with heterozygous alleles.
This final point is particularly important to help distinguish between alleles potentially causing disease, as those alleles are amplified or not deleted in tumor sequence data, furthermore, the region that undergoes hemizygous loss (e.g., parental chromosome arms) can be used to directly estimate the amount of normal contaminants in sequenced tumor samples.
Figures 3A-3C show exemplary figures for copy numbers (shown as read coverage or number of reads) of individual exons of CYP2D6 (exons 1-9) and CYP2D7 (exons 1-9) as shown, in sample NA17234 with normal allelic genotype (x 1/x 41) but no deletion or amplification in exons of CYP2D6 and CYP2D7, the average number of copies is about 30, and the standard deviation is ± 10 (figure 3A), compared to an increase in the average number of copies above 40 in sample NA17244, indicating the presence of an amplification in exons (figure 3B) in CYP2D6 and CYP2D7, specifically, for example, the presence of an amplification in exons 6, exon 8 of CYP2D6 and exon 4 and exon 9 of CYP2D7, compared to the copy number of sample NA17244, the increase in exon 6, exon 8 of CYP2D6 and copy numbers of exons 4 and exon 9 of CYP2D7, and shows that there may be a decrease in the copy numbers of exons 1722D 6342, in samples NA17244, such as indicated by a decrease in the average number of deletions, in CYP2D6, in the sample NA 1722D 464, in comparison to about 8218, which there may be a decrease in the absence of exons, in the copy number of exons 2D 464, which indicates that there is a decrease in the absence of the sample NA 1722D 464, in the sample NA1724, in the sample NA 1726, in the sample NA 1722D 6D 6858, in.
In addition, by analyzing the genome-wide copy number or exome copy number of each exon, alleles associated with amplification and/or deletion in or more of partial exons can be identified and determined.
The inventors further envision that the allele haplotypes so identified may be associated with the potency and/or toxicity of a particular drug in a particular cancer.e., CYP2D6 catalyzes the metabolism of many clinically important drugs (including cancer drugs and opioid drugs). regarding the activity of the CYP2D6 enzyme (e.g., normal function, reduced function, non-function, etc.), multiple alleles with different combinations of single nucleotide variations and/or deletions have been identified.
For example, tumor tissue may have genes with or more different haplotypes compared to healthy tissue (e.g., different combinations of single nucleotide variations and/or amplifications or deletions of exons, etc.), which may result in a different response to the drug or a different toxicity due to exposure to the drug.
The haplotype analysis using RNA phasing and gene copy number analysis may then determine the haplotype for each allele of the selected gene and the susceptibility to, efficacy of, and/or toxicity for, the selected cancer treatment and/or drug, and the susceptibility to, the selected cancer treatment and/or drug, may be calculated for each allele of the haplotype, and may be calculated for each allele of the haplotype and/or for the selected cancer treatment and/or drug, and the calculated for each allele of the haplotype and/or drug may be calculated for each allele of the haplotype as a lower or lower susceptibility to, for example, a lower allele of the haplotype, and/or a lower allele of the susceptibility to, and/or toxicity for the selected cancer treatment and/or drug, may be calculated for each allele of the haplotype than the normal susceptibility to, and/or a lower allele of the haplotype, and/or allele of the susceptibility to, and/or drug, and/or a lower susceptibility to, and/or a lower allele of the same susceptibility to, and/or drug, and/or a lower susceptibility to the same allele of the same allele.
For example, where the allele of the gene in healthy tissue is associated with a high risk of toxicity, while the allele of the gene in tumor tissue is associated with low efficacy of the cancer drug, then the gene's optimal score for the cancer drug will become lower as a combination of a low score (or even a negative score) for high toxicity to healthy tissue and a low score (e.g., the sum of the two scores) for low efficacy of the tumor tissue.
The inventors further contemplate that a patient record may be generated or updated based on allele haplotype information, particularly the score for each allele of a gene, the score for genes with heterogeneous alleles, or the best score for genes associated with cancer drug efficacy and/or toxicity, that a new treatment plan may be recommended, or that a previously used treatment plan may be updated.
In embodiments, the patient's treatment regimen can be adjusted or modified based on allele haplotype information, particularly the score for each allele of the gene, the score for genes with heterogeneous alleles, or the best score for genes associated with cancer drug efficacy and/or toxicity, for example, where the best score for genes associated with cancer drug efficacy and/or toxicity is moderate (indicating that treatment of tumor cells with the cancer drug is likely to be successful, but may have high toxicity to healthy tissue), the dose and/or schedule of administration of the cancer drug can be varied (e.g., smaller doses to reduce toxicity to healthy tissue and/or at a reduced frequency of administration of the drug (e.g., times per day rather than two times per day, etc.), more frequent administration schedules with the same dose of drug to overcome rapid metabolism of the drug, etc.).
Alternatively and/or additionally, methods of treating cancer drugs can be altered to based on allele haplotype information, particularly the score for each allele of the gene, the score for genes with heterogeneous alleles, or the best score for genes associated with cancer drug efficacy and/or toxicity, for example, to minimize exposure of healthy tissue to the cancer drug before the cancer drug reaches the tumor, it may be suggested that the method of administering the cancer drug to the patient can be changed from systemic (e.g., intravenous injection, etc.) to local (e.g., intratumoral injection) in the event that the best score for genes associated with cancer drug efficacy and/or toxicity is moderate, indicating that treatment of tumor cells with the cancer drug is likely to be successful, but may have high toxicity to healthy tissue.
In addition, the present subject matter uses a comprehensive pathway analysis of or more allelic haplotypes to heterogeneous alleles carrying allele-specific single nucleotide variations and/or amplifications/deletions in order to predict the efficacy and/or toxicity of cancer treatment in a patient-specific manner.
It should be apparent to those skilled in the art that many more modifications besides those already described are possible without departing from the inventive concepts herein.
The claims (modification according to treaty clause 19)
A method of alleviating adverse effects of cancer therapy in a patient having a tumor, the method comprising:
obtaining transcriptomic data for the patient, the transcriptomic data comprising allele fraction information for the th and second loci of RNA molecules transcribed from the gene, wherein the th and second loci have th and second single nucleotide variations, respectively;
using allele fraction information to reconstruct the haplotype at the th and second loci, and
generating or updating a record for the patient with the reconstructed haplotype correlated with the expected efficacy of the cancer therapy.
2. The method of
3. The method of , wherein the gene is at least of CYP3A5, CYP2D6, TPMT, F5, DPYD, G6PD and NUDT 15.
4. The method of any of the preceding claims, wherein the and second locus are at least 300bp apart.
5. The method of any of the preceding claims, wherein the haplotype is reconstituted to have the and second nucleotide variations in the allele of the gene when the allele fractions of the and second loci that have the and second nucleotide variations differ by greater than 10%.
6. The method of any of the preceding claims, wherein the gene is CYP2D6 and the expected efficacy comprises an increase in toxicity of the cancer therapy due to slow metabolism of the cancer therapy.
7. The method of any of the preceding claims, wherein the transcriptomics data comprises the copy number of the and second locus, and the method further comprises:
determining amplification of at least of the th and second loci, and
generating or updating a record for the patient with the amplified information of the genes associated with the expected efficacy of the cancer therapy.
8. The method of claim 7, wherein the gene is CYP2D6, and the expected effectiveness of cancer therapy comprises a decrease in effectiveness of the cancer therapy due to tachymetabolism of the cancer therapy.
9. The method of any of the preceding claims, further comprising adjusting a recommended dose and schedule of the cancer therapy based on the expected efficacy.
10. The method of of any one of claims 2-9 wherein the transcriptomics data further comprises allele fraction information of the and second loci derived from healthy tissue of the patient.
11. The method of claim 10, further comprising:
using allele fraction information derived from the healthy tissue to reconstruct healthy tissue haplotypes;
comparing the allele fraction information derived from the tumor tissue with the allele fraction information derived from the healthy tissue to obtain tumor-specific allele fraction information; and
generating or updating a record for the patient using the allele fraction information and the tumor-specific allele fraction information.
12. The method of claim 10, further comprising adjusting a recommended dose and schedule of the cancer therapy based on the comparison of the reconstructed healthy tissue haplotype and the tumor-specific haplotype.
13. The method of any of the preceding claims, wherein the cancer therapy is identified by a pathway analysis using at least two of the patient's genomic, transcriptomic, and proteomic data.
14. The method of
15. The method of
16. The method of
17. The method of
18. The method of
determining amplification of at least of the th and second RNA loci, and
generating or updating a record for the patient with the amplified information of the genes associated with the expected efficacy of the cancer therapy.
19. The method of claim 18, wherein the gene is CYP2D6, and the expected efficacy of cancer therapy comprises a decrease in efficacy of the cancer therapy due to tachymetabolism of the cancer therapy.
20. The method of
21. The method of
22. The method of claim 21, further comprising:
using allele fraction information derived from the healthy tissue to reconstruct healthy tissue haplotypes;
comparing the allele fraction information derived from the tumor tissue with the allele fraction information derived from the healthy tissue to obtain tumor-specific allele fraction information; and
generating or updating a record for the patient using the allele fraction information and the tumor-specific allele fraction information.
23. The method of claim 22, further comprising adjusting a recommended dose and schedule of the cancer therapy based on the comparison of the reconstructed healthy tissue haplotype and the tumor-specific haplotype.
24. The method of
A method of treating patients having a tumor, the method comprising:
obtaining transcriptomic data for the patient, the transcriptomic data comprising allele fraction information for the th and second RNA loci of an RNA molecule transcribed from the gene, wherein the th and second loci have th and second nucleotide variations, respectively;
using the allele fraction information to reconstruct the haplotypes of the th and second RNA loci;
inferring an expected efficacy of a cancer therapy for the haplotype; and
adjusting a recommended dose and schedule for the cancer therapy based on the expected efficacy.
26. The method of claim 25, wherein the allele fraction information of the and second locus is derived from a tumor of the patient.
27. The method of any of claims 25-26, wherein the gene is at least of CYP3a5, CYP2D6, TPMT, F5, DPYD, G6PD and NUDT 15.
28. The method of any of claims 25-27, wherein the and second RNA locus are at least 300bp apart.
29. The method of any of claims 25-28, wherein the haplotype is reconstituted to have the and second nucleotide variation in the allele of the gene when the allele fractions of the and second loci that have the and second nucleotide variations differ by less than 10%.
30. The method of any of claims 25-29, wherein the gene is CYP2D6 and the expected efficacy comprises an increase in toxicity of the cancer therapy due to slow metabolism of the cancer therapy.
31. The method of any of claims 25-30, wherein the transcriptomics data comprises the copy number of the and second RNA locus, and the method further comprises:
determining amplification of at least of the th and second RNA loci, and
adjusting a recommended dose and schedule for a cancer therapy using information on amplification of genes associated with expected efficacy of the cancer therapy.
32. The method of claim 31, wherein the gene is CYP2D6, and the expected efficacy of cancer therapy comprises a decrease in efficacy of the cancer therapy due to tachymetabolism of the cancer therapy.
33. The method of any of claims 25-32, further comprising generating or updating a record of the patient with the reconstituted haplotype correlated with the expected efficacy of the cancer therapy.
34. The method of any of claims 25-33, wherein the transcriptomics data further comprises allele fraction information of the and second RNA loci derived from healthy tissue of the patient.
35. The method of claim 34, further comprising:
using allele fraction information derived from the healthy tissue to reconstruct healthy tissue haplotypes;
comparing the allele fraction information derived from the tumor tissue with the allele fraction information derived from the healthy tissue to obtain tumor-specific allele fraction information; and
adjusting a recommended dose and schedule for the cancer therapy using the allele fraction information and the tumor-specific allele fraction information.
36. The method of claim 35, further comprising adjusting a recommended dose and schedule of the cancer therapy based on the comparison of the reconstructed healthy tissue haplotype and the tumor-specific haplotype.
37. The method of any of claims 25-36, wherein the cancer therapy is identified by a pathway analysis using at least two of the patient's genomic, transcriptomic, and proteomic data.
38. The method of claim 25, wherein the gene is at least of CYP3a5, CYP2D6, TPMT, F5, DPYD, G6PD, and NUDT 15.
39. The method of claim 25, wherein the th and second RNA loci are at least 300bp apart.
40. The method of claim 25, reconstructing the haplotype as having the and second nucleotide variations in the allele of the gene when the allele fractions of the and second loci having the and second nucleotide variations differ by less than 10%.
41. The method of claim 25, wherein the gene is CYP2D6, and the expected efficacy comprises an increase in toxicity of the cancer therapy due to slow metabolism of the cancer therapy.
42. The method of claim 25, wherein the transcriptomics data comprises the copy number of the th and second RNA locus, and the method further comprises:
determining amplification of at least of the th and second RNA loci, and
generating or updating a record for the patient with the amplified information of the genes associated with the expected efficacy of the cancer therapy.
43. The method of
44. The method of claim 25, further comprising generating or updating a record for the patient with the reconstituted haplotype correlated with the expected efficacy of the cancer therapy.
45. The method of claim 25 wherein the transcriptomics data further comprises allele fraction information of the and second RNA locus derived from healthy tissue of the patient.
46. The method of claim 45, further comprising:
using allele fraction information derived from the healthy tissue to reconstruct healthy tissue haplotypes;
comparing the allele fraction information derived from the tumor tissue with the allele fraction information derived from the healthy tissue to obtain tumor-specific allele fraction information; and
adjusting the recommended dose and schedule of the cancer therapy based on a comparison of the reconstructed healthy tissue haplotype and the tumor-specific haplotype.
47. The method of claim 40, further comprising generating or updating a record for the patient with the allele fraction information and the tumor-specific allele fraction information.
48. The method of claim 25, wherein the cancer therapy is identified by a pathway analysis using at least two of genomics, transcriptomics, and proteomics data for the patient.
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