Methods for assessing genomic instability

Abstract

A method for assessing genomic instability (GI) can include selectively amplifying a nucleic acid sequence at a targeting location in a tumor sample genome by a targeting panel with low sample input to produce a plurality of nucleic acid sequence reads; dividing the genome into segments having homogeneous copy numbers using the logarithmic probability of the hybrid SNP and the CNV logarithmic ratio determined for the nucleic acid sequence reads; applying a threshold count to the square of the allele logarithmic probability of each segment in the autosomes of the genome to identify unbalanced copy number (UCN) segments; adding the number of bases in the UCN segment to produce a sum of UCN bases; and dividing the sum of UCN bases by the total number of bases in all segments identified in the autosomal of the sample genome to produce a ratio indicative of genomic instability. The ratio may be expressed in percentage to give a GI fraction.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit under 35 USC §119(e) of U.S. Provisional Application No. 63 / 357,537, filed on June 30, 2022. The entire contents of the foregoing application are incorporated herein by reference. Technical Field

[0003] The present disclosure relates to methods, systems, and computer-readable media for assessing genomic instability, and more particularly to methods, systems, and computer-readable media for assessing genomic instability in the genome of a tumor sample using nucleic acid sequencing data from a targeted sequencing panel and next-generation sequencing (NGS) technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0004] Figure 1 is a block diagram of an example process for analyzing sample genomes to assess genomic instability.

[0005] Figure 2A An example graph of CNV log ratios for tumor sample genomes is shown.

[0006] Figure 2B An example plot of log odds for tumor sample genomes is shown.

[0007] Figure 2C Shows Figure 2A and Figure 2B Example graph of the copy number of each genomic segment in the identified genomic segments.

[0008] Figure 3 Shown are examples of box plots of genomic instability scores for BRCA1 / 2 mutants versus wild-type BRCA1 / 2, where no size constraint step was applied.

[0009] Figure 4 Shown are examples of box plots of genomic instability scores for BRCA1 / 2 mutants versus wild-type BRCA1 / 2, where a size constraint step was applied.

[0010] Figure 5 Shown are examples of box plots of genomic instability scores for BRCA1 / 2 mutants versus wild-type BRCA1 / 2, where a size constraint step was applied and segments with UCN changes shorter than 10 Mb were excluded.

[0011] Figure 6 Shown are examples of box plots of genomic instability scores for BRCA1 / 2 mutants versus wild-type BRCA1 / 2, excluding stretches where UCN changes have fewer than five heterozygous SNPs.

[0012] Figure 7 Shown is an example of a box plot of the genomic instability score for BRCA1 / 2 mutants versus wild-type BRCA1 / 2, where the weighted sum of UCN bases was calculated and divided by the total number of bases in all segments identified in the autosomes.

[0013] Figure 8 is a schematic diagram of an exemplary system for recombining nucleic acid sequences according to various embodiments.

[0014] Fig. 9 is an example of a block diagram of an analysis pipeline for signal data obtained from a nucleic acid sequencing instrument. DETAILED DESCRIPTION

[0015] According to the teachings and principles implemented in the present application, methods, systems and non-transitory machine-readable storage media are provided for assessing genomic instability by analyzing nucleic acid sequence reads from the genome of a tumor sample.

[0016] In various embodiments, DNA (deoxyribonucleic acid) can be referred to as a nucleotide chain consisting of 4 types of nucleotides. A (adenine), T (thymine), C (cytosine) and G (guanine) and the RNA (ribonucleic acid) consists of 4 nucleotides: A, U (uracil), G and C. Certain nucleotide pairs specifically bind to each other in a complementary manner (referred to as complementary base pairing). That is, adenine (A) pairs with thymine (T) (however, in the case of RNA, adenine (A) pairs with uracil (U)), and cytosine (C) pairs with guanine (G). When a first nucleic acid chain is bound to a second nucleic acid chain consisting of nucleotides complementary to the nucleotides in the first chain, the two chains combine to form a double chain. In various embodiments, "nucleic acid sequencing data", "nucleic acid sequencing information", "nucleic acid sequence", "genomic sequence", "gene sequence" or "fragment sequence", "nucleic acid sequence read" or "nucleic acid sequencing read" means any information or data indicating the order of nucleotide bases (e.g., adenine, guanine, cytosine and thymine / uracil) in a DNA or RNA molecule (e.g., a whole genome, a whole transcriptome, an exome, an oligonucleotide, a polynucleotide, a fragment, etc.). It should be understood that the present teachings encompass sequence information obtained using all available types of techniques, platforms or technologies including but not limited to the following: capillary electrophoresis, microarrays, ligation-based systems, polymerase-based systems, hybridization-based systems, direct or indirect nucleotide identification systems, pyrosequencing, ion- or pH-based detection systems, electronic signature-based systems, etc.

[0017] "Polynucleotide", "nucleic acid" or "oligonucleotide" refers to a linear polymer of nucleosides (including deoxyribonucleosides, ribonucleosides or their analogs) joined by internucleoside bonds. Typically, a polynucleotide includes at least three nucleosides. Typically, the size of an oligonucleotide ranges from a few monomer units, for example, 3-4 to several hundred monomer units. Whenever a polynucleotide (such as an oligonucleotide) is represented by a series of letters (such as "ATGCCTG"), it is understood that, unless otherwise indicated, the nucleotides are in a 5'->3' order from left to right and "A" represents deoxyadenosine, "C" represents deoxycytidine, "G" represents deoxyguanosine, and "T" represents thymidine. As is standard in the art, the letters A, C, G and T can be used to refer to a base itself, a nucleoside, or a nucleotide comprising a base.

[0018] The phrase "next generation sequencing" or NGS refers to sequencing technologies that have increased throughput compared to traditional Sanger and capillary electrophoresis-based methods, e.g., the ability to generate hundreds of thousands of relatively small sequence reads at a time. Some examples of next generation sequencing technologies include, but are not limited to, sequencing by synthesis, sequencing by ligation, and sequencing by hybridization.

[0019] The phrase "genomic variant" refers to a single or group of sequences (in DNA or RNA) that have been altered due to mutation, recombination / exchange, or genetic changes to a particular species or subpopulation within a particular species. Examples of genomic variant types include, but are not limited to, single nucleotide polymorphisms (SNPs), copy number variants (CNVs), insertions / deletions (Indels), inversions, etc.

[0020] In various embodiments, genomic variants can be detected using the analysis of nucleic acid sequencing systems and / or sequencing data. The sequencing workflow can start with shearing or digesting a test sample to hundreds, thousands or millions of smaller fragments, which are sequenced on a nucleic acid sequencer to provide hundreds, thousands or millions of sequence reads, such as nucleic acid sequence reads. Each read can then be mapped to a reference or targeted genome, and in the case of paired fragments, the read can be paired, thereby allowing the duplication region of the genome to be interrogated. The result of mapping and pairing can be used as the input of various independent or integrated genomic variants (e.g., SNP, CNV, Indel, inversion, etc.) analysis tools.

[0021] The phrase "sample genome" may refer to the entire or partial genome of an organism.

[0022] As used herein, the term "allele" refers to a genetic variation associated with a gene or DNA segment, ie, one of two or more alternative forms of a DNA sequence occupying the same locus.

[0023] As used herein, the term "locus" refers to a specific position on a chromosome or nucleic acid molecule. Alleles of a locus are located at the same site on homologous chromosomes.

[0024] As used herein, "targeting set" refers to a set of target-specific primers designed to selectively amplify target gene sequences in a sample. In some embodiments, following selective amplification of at least one target sequence, the workflow also includes nucleic acid sequencing of the amplified target sequence.

[0025] As used herein, "target sequence" or "target gene sequence" and derivatives thereof refer to any single-stranded or double-stranded nucleic acid sequence that can be amplified or synthesized according to the present disclosure, including any nucleic acid sequence suspected or expected to be present in a sample. In some embodiments, the target sequence exists in a double-stranded form and includes at least a portion of a specific nucleotide sequence to be amplified or synthesized, or its complement, prior to the addition of a target-specific primer or the attachment of an adapter. The target sequence may include a nucleic acid that is capable of hybridizing with a primer suitable for an amplification or synthesis reaction prior to polymerase extension. In some embodiments, the term refers to a nucleic acid sequence whose sequence identity, order or position of nucleotides is determined by one or more of the methods of the present disclosure.

[0026] As used herein, "target-specific primer" and its derivatives refer to a single-stranded or double-stranded polynucleotide, typically an oligonucleotide, which includes at least one sequence that is at least 50% complementary, typically at least 75% complementary or at least 85% complementary, more typically at least 90% complementary, more typically at least 95% complementary, more typically at least 98% or at least 99% complementary or identical to at least a portion of a nucleic acid molecule that includes a target sequence. In such cases, the target-specific primer and the target sequence are described as "corresponding" to each other. In some embodiments, a target-specific primer is capable of hybridizing to at least a portion of its corresponding target sequence (or to a complementary sequence of a target sequence); such hybridization may optionally be performed under standard hybridization conditions or under stringent hybridization conditions. In some embodiments, a target-specific primer is not capable of hybridizing to a target sequence or its complement, but is capable of hybridizing to a portion of a nucleic acid strand comprising a target sequence or its complement. In some embodiments, a forward target-specific primer and a reverse target-specific primer define a target-specific primer pair that can be used to amplify a target sequence via template-dependent primer extension. Typically, each primer of a target-specific primer pair includes at least one sequence that is substantially complementary to at least a portion of a nucleic acid molecule that includes a corresponding target sequence, but is less than 50% complementary to at least one other target sequence in the sample. In some embodiments, amplification can be performed using multiple target-specific primer pairs in a single amplification reaction, wherein each primer pair comprises a forward target-specific primer and a reverse target-specific primer, each comprising at least one sequence that is substantially complementary to or substantially identical to a corresponding target sequence in the sample, and each primer pair has a different corresponding target sequence.

[0027] Alterations in genes in the homologous recombination repair (HRR) pathway may interfere with the ability to repair DNA double-strand breaks (DSBs), leading to homologous repair deficiency (HRD). HRD is associated with sensitivity to poly(ADP-ribose) polymerase inhibitors and cisplatin, and its measurement is used as a biomarker for making treatment decisions. Genomic instability is an emerging biomarker for HRD.

[0028] HRD is the inability of cells to repair double-stranded DNA breaks. It is caused by mutations in genes in the HRR pathway, particularly the BRCA1 and BRCA2 genes. The consequence of HRD is the accumulation of errors in the genome during cell division and DNA replication, leading to genomic scarring. Genomic scarring can be characterized by a comprehensive analysis of structural changes in the tumor genome, such as copy number changes. Therefore, a comprehensive metric for assessing genomic instability is needed.

[0029] Targeted panels with low sample input requirements can be used to assess genomic instability in tumor samples. Targeted panels can provide a viable alternative to whole genome sequencing, which may have higher input sample requirements. In some embodiments, the targeted panel may include Oncomine Comprehensive AssayPlus or OCA Plus panels (Thermo FisherScientific). The OCA Plus panel interrogates 502 cancer-related genes. The OCA Plus panel has 1889 amplicons, which are specifically designed to include heterozygous SNPs, which have high minor allele frequencies and are evenly distributed on the genome. In addition, heterozygous SNPs present in the targeted medical content of the panel are also used. Heterozygous SNPs allow a comprehensive analysis of the assessment of tumor samples regarding structural changes, copy number (CN) changes, and genomic instability. The OCA Plus panel can use a recommended amount of 20ng, as little as 10ng of nucleotides separated from formaldehyde-fixed paraffin-embedded (FFPE) tumor samples (including fine needle biopsies). In some embodiments, the panel may include other targeted panels of custom panels or cancer driver genes or other genes associated with cancer.

[0030] Figure 1It is a block diagram of an example process for analyzing a sample genome to assess genomic instability. A plurality of nucleic acid sequence reads are generated by selectively amplifying nucleic acid sequences at a targeted position in a tumor sample genome by a targeted group with a low sample input from a tumor sample. The sequence reads are mapped to a reference genome to generate aligned sequence reads. In a variant calling step 102, a processor receives aligned sequence reads generated by the targeted sequencing of a tumor sample. For example, the aligned sequence reads can be retrieved from a file using a BAM file format. The aligned sequence reads can correspond to multiple targeted positions in a tumor sample genome. The variant calling step 102 can be configured by one or more variant caller parameters. The variant calling step 102 can provide observed variant populations detected in the aligned sequence reads, such as SNPs (single nucleotide polymorphisms). In addition, the variant calling step 102 can determine the logarithmic probability of the variant allele frequency of the observed SNP population. The logarithmic probability is calculated as the natural logarithm of the ratio of the number of sequence reads with variant alleles to the number of sequence reads with reference alleles. In some embodiments, the variant detection method used with this teaching may include one or more features described in U.S. Patent Application Publication No. 2013 / 0345066, published on December 26, 2013, U.S. Patent Application Publication No. 2014 / 0296080, published on October 2, 2014, and U.S. Patent Application Publication No. 2014 / 0052381, published on February 20, 2014, each of which is incorporated herein by reference in its entirety. Other variant detection methods can be used. In various embodiments, the variant caller can be configured to transmit the variant called for the sample genome in the form of *.vcf, *.gff or *.hdf data files. The variant information called can be communicated using any file format, as long as the variant information called can be parsed and / or extracted for analysis. Copy number variation (CNV) step 104 can provide copy number estimates and CNV logarithm ratios of the sequence reads compared. The CNV log ratio is calculated as the log2 ratio of the copy number estimate relative to the baseline copy number of each amplicon in the assay. In some embodiments, the CNV detection method used with the present teachings may include one or more features described in U.S. Patent Application Publication No. 2018 / 0268103, published on September 20, 2018, U.S. Patent Application Publication No. 2014 / 0256571, published on September 11, 2014, and U.S. Patent Application Publication No. 2016 / 0103957, published on April 14, 2016, each of which is incorporated herein by reference in its entirety. Other CNV detection methods may be used. Segmentation step 106 uses log odds and CNV log ratios to divide the genomic sequence into segments with homogeneous copy numbers.For example, the OCA Plus group provides 1889 amplicons with heterozygous SNPs that are specifically designed to cover the genome and segment the genome using a joint segmentation of CNV log ratios and allele log odds to achieve CN changes. The segmentation algorithm is a cyclic binary segmentation that aims to use Hotelling's T2 statistics to jointly segment the log2 ratio and log odds to detect change points. (See, e.g., R. Shen et al., FACETS: allele-specific copy number and clonal heterogeneity analysis tool for high-throughput DNA sequencing, Nucleic Acids Research, 2016, Vo1.44, No.16e 131 doi: 10.1093 / nar / gkw520). Optionally, the segmentation step 106 can exclude segments with less than a minimum number of heterozygous SNPs. For example, the minimum number of SNPs in a segment can be set to a number within the range of 5 to 15 SNPs.

[0031] Figure 2A An example plot of CNV log ratios for a tumor sample genome is shown. The genome segmentation overlay (horizontal bars) shows that segments with similar log2 ratios cluster together. Figure 2B An example graph of log odds for a tumor sample genome is shown. The genome segmentation overlap (horizontal bars) shows that segments with similar log odds are clustered together. Since variant alleles can be major or minor for any SNP, the corresponding log odds can be positive or negative and are therefore displayed as mirrored segments around zero. Figure 2C Shows Figure 2A and Figure 2B Example graph of the copy number of each genomic segment in the identified genomic segments. The black horizontal bar indicates the total copy number, while the gray bars indicate the minor copy number. Genomic segments with total CN ≥ 1 and minor CN = 0 are segments with loss of heterozygosity (LOH). There may be genomic segments for which a minor copy number estimate cannot be determined.

[0032] Return to Figure 1, based on the allele log odds corresponding to the individual segments defined by the segmentation step 106, segments with unbalanced copy numbers (UCNs) in the autosomes are identified. The thresholding step 108 applies a threshold count to the square of the allele log odds of each segment to identify the initial UCN segments in the autosomes. The threshold count can be determined empirically. Example threshold count values ​​are in the range of from 0.05 to 0.26. For example, the threshold count value can be 0.05, 0.06, 0.07, 0.08, 0.09, 0.10, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.20, 0.21, 0.22, 0.23, 0.24, 0.25 or 0.26. The size constraint step 110 can apply one or more size thresholds to the initial UCN segments. The size threshold may include a maximum length threshold. The size constraint step 110 may exclude those initial UCN segments whose lengths cross the maximum length threshold or a larger length threshold. For example, the size constraint step 110 may exclude initial UCN segments that meet one or more of the following conditions:

[0033] 1) Spanning the maximum length threshold of the entire chromosome or a larger length threshold;

[0034] 2) a maximum length threshold or greater across the p arm of the chromosome; and

[0035] 3) A maximum length threshold or greater than a length threshold spanning the q arm of a chromosome.

[0036] The value of this maximum length threshold value can be different for full chromosome, p arm and q arm.This maximum length threshold value can be the percentage of full chromosome length, the percentage of p arm length or the percentage of q arm length.For example, this maximum length threshold value can be set to 90%.For example, this maximum length threshold value can be at least 80%.The size threshold value can include a minimum length threshold value.Size constraint step 110 can exclude those initial UCN segments whose length crosses the minimum length threshold value or a smaller length threshold value.For example, this minimum length threshold value can be 10 megabases (Mb).This minimum length threshold value can be in the range of 5Mb to 15Mb.Size constraint step 110 filters out any initial UCN segments that do not meet the size constraint standard to produce a group of UCN segments.

[0037] The summing step 112 can add the number of bases in the set of UCN segments in the autosome to produce a sum of UCN bases. The dividing step 114 can divide the sum of UCN bases by the total number of bases in all segments identified in the autosomes of the sample genome to produce a ratio. The ratio can be expressed as a percentage to give a genomic instability (GI) score or GI metric.

[0038] Optionally, the summation step 112 can calculate the weighted sum of UCN bases. For example, the number of bases in each UCN segment in a given chromosome can be divided by the total number of bases in the chromosome containing the UCN segment to give a normalized number of bases per UCN segment. The normalized number of bases per UCN segment can be multiplied by a weight value to give a weighted number of bases per UCN segment. For example, for each UCN segment of a given chromosome, the same weight value can be applied to the normalized number of bases per UCN segment. For example, the weight value can be a function of the number of UCN segments in a chromosome. For example, the weight value can be the number of UCN segments in a chromosome. The sum of the weighted numbers of bases per UCN segment for all autosomes for all UCN segments can be calculated to form a weighted sum of UCN bases. The division step 114 can divide the weighted sum of UCN bases by the total number of bases in all segments identified in the autosomes of the sample genome to produce a ratio. This ratio can be expressed as a percentage to give a genomic instability (GI) score or GI metric.

[0039] To test the effectiveness of the methods described herein for assessing genomic instability, genomic instability scores were determined for ovarian tumor samples in an ovarian tumor FFPE cohort. Tumor samples in the ovarian tumor FFPE cohort with deleterious germline / somatic mutations in the BRCA1 and BRCA2 genes were compared to those in the cohort with wild-type (WT) BRCA1 and BRCA2 genes. The OCA Plus panel was used to generate targeted sequencing reads for tumor samples in the cohort. Figure 1 The described method was applied to aligned sequence reads corresponding to tumor samples in the same cohort. Number of tumor samples in the same cohort, N=41. The results showed that tumor samples with deleterious germline / somatic mutations BRCA1 and BRCA2 genes had significantly higher GI scores than samples with WT BRCA1 and BRCA2 genes. These results suggest that GI can be used to characterize genomic scars. The results also suggest that the targeted panel is large enough to detect genomic instability.

[0040] Figure 3 An example of a box plot of the genomic instability score of BRCA1 / 2 mutants versus wild-type BRCA1 / 2 is shown, where the size constraint step 110 is not applied. For this example, the size constraint step 110 is not applied, so for the summation step 112, the initial UCN segments covering the entire chromosome, p arm, and q arm are included. These results show the stratification of the GI scores of BRCA1 / 2 mutants versus wild-type (WT). The p-value using the Student's two-sided t-test is 0.043714.

[0041] Figure 4An example of a box plot of genomic instability scores for BRCA1 / 2 mutants versus wild-type BRCA1 / 2 is shown, where a size constraint step 110 is applied. For this example, a size constraint step 110 is applied, so initial UCN segments covering 90% or more of the whole chromosome, p arm, and q arm are excluded for the summation step 112. These results show stratification of GI scores for BRCA1 / 2 mutants versus wild-type (WT). The p-value using a student's two-sided t-test is 0.000086.

[0042] Figure 5 An example of a box plot of the genomic instability score of BRCA1 / 2 mutations versus wild-type BRCA1 / 2 is shown, wherein a size constraint step is applied and segments with UCN changes shorter than 10Mb are excluded. For this example, a size constraint step 110 is applied so that, before the summation step 112, initial UCN segments covering 90% or more of the whole chromosome, p arm, and q arm are excluded, and segments with UCN changes shorter than 10Mb are excluded. These results show the stratification of GI scores of BRCA1 / 2 mutants versus wild-type (WT). The p-value using the Student's two-sided t-test is 0.000112.

[0043] Figure 6 An example of a box plot of the genomic instability score of BRCA1 / 2 mutations versus wild-type BRCA1 / 2 is shown, wherein segments with less than five heterozygous SNPs in the UCN changes are excluded. For this example, the segmentation step 106 excludes segments with less than five heterozygous SNPs. A size constraint step 110 is applied so that, before the summation step 112, initial UCN segments covering 90% or more of the whole chromosome, p arm, and q arm are excluded, and segments with UCN changes shorter than 10Mb are excluded. These results show the stratification of the GI scores of BRCA1 / 2 mutants versus wild-type (WT). The p-value using the Student's two-sided t-test is 0.000271.

[0044] Figure 7 An example of a box plot of the genomic instability score of BRCA1 / 2 mutations versus wild-type BRCA1 / 2 is shown, where the weighted sum of the UCN bases is calculated and divided by the total number of bases in all segments identified in the autosomes. For this example, a size constraint step 110 is applied so that initial UCN segments covering 90% or more of the whole chromosome, p arm, and q arm are excluded before the summation step 112, and segments with UCN changes shorter than 10Mb are excluded. These results show the stratification of the GI scores of BRCA1 / 2 mutants versus wild-type (WT). The p-value using the Student's two-sided t-test is 0.000339.

[0045] The targeted groups and methods for assessing genomic instability described herein provide improvements to the technology of whole genome sequencing (WGS). The sequence assembly method must be able to effectively assemble and / or map a large number of reads, such as by minimizing the use of computing resources. For example, the sequencing of a human-sized genome may result in tens of millions or hundreds of millions of reads that need to be assembled, which can then be further analyzed. The computer processing of the nucleic acid sequence reads from targeted sequencing reduces the computing requirements and memory requirements compared with the processing of WGS data. For WGS, a tumor genome of 3Gb will be covered. The data generated by the nucleic acid sequence reads of WGS will need to be calculated and stored in a memory for nucleic acid sequence reads and variant data. In comparison, the targeted group covering a tumor genome of about 1Mb will require substantially less computing and substantially less memory for the nucleic acid sequence reads and variant data stored.

[0046] The targeted panels and methods described herein for assessing genomic instability of tumor-only samples provide improvements in the art of matched tumor-normal sample processing. In some cases, matched normal samples for tumor samples may not be available. When matched normal samples are available, detecting variants and CNVs in nucleic acid sequence reads from normal samples requires at least the same amount of processing as for tumor samples, thereby at least doubling the computational and memory requirements.

[0047] Embodiment 1 is a method for analyzing genomic instability of a tumor sample genome, the method comprising: selectively amplifying nucleic acid sequences at targeted positions in the tumor sample genome by a targeted group with low sample input from the tumor sample to generate multiple nucleic acid sequence reads; dividing the genome into segments with homogeneous copy numbers using the log odds of heterozygous SNPs and the CNV log ratios determined for the multiple nucleic acid sequence reads, wherein the heterozygous SNPs are distributed on the genome; applying a threshold count to the square of the allele log odds of each segment in the autosomes of the genome to identify unbalanced copy number (UCN) segments; adding the number of bases in the UCN segments to generate the sum of the UCN bases; and dividing the sum of the UCN bases by the total number of bases in all of the segments identified in the autosomes of the genome to generate a ratio indicative of genomic instability.

[0048] Embodiment 2 includes the subject matter of any one of Embodiment 1, and further specifies that the ratio is expressed as a percentage to give a genomic instability (GI) score.

[0049] Embodiment 3 includes the subject matter of any one of Embodiment 1, and further includes applying a maximum length threshold to each of the UCN segments prior to the adding step.

[0050] Embodiment 4 includes the subject matter of any one of Embodiment 3, and further includes excluding UCN segments that span the maximum length threshold or greater across the entire chromosome.

[0051] Embodiment 5 includes the subject matter of any one of Embodiment 3, and further includes excluding UCN segments of the maximum length threshold or greater that span the p-arm of a chromosome.

[0052] Embodiment 6 includes the subject matter of any one of Embodiment 3, and further includes excluding UCN segments of the maximum length threshold or greater that span the q arm of the chromosome.

[0053] Embodiment 7 includes the subject matter of any one of Embodiments 3, and further specifies that the maximum length threshold is a percentage of the entire chromosome length, a percentage of the p arm length, or a percentage of the q arm length.

[0054] Embodiment 8 includes the subject matter of any one of Embodiment 7, and further specifies that the maximum length threshold is 90%.

[0055] Embodiment 9 includes the subject matter of any one of Embodiment 1, and further includes applying a minimum length threshold to the UCN segments prior to the adding step and excluding UCN segments that cross the minimum length threshold or less.

[0056] Embodiment 10 includes the subject matter of any one of Embodiment 9, and further specifies that the minimum length threshold is 10 megabases (Mb).

[0057] Embodiment 11 includes the subject matter of any one of Embodiment 1, and further includes dividing the number of bases in the UCN segment by the total number of bases in the chromosome containing the UCN segment to produce a normalized number of UCN bases per UCN segment.

[0058] Embodiment 12 includes the subject matter according to any one of Embodiment 11, and also includes multiplying the normalized number of UCN bases per UCN segment by the weight value to give a weighted number of bases per UCN segment, wherein the adding step is applied to the weighted number of bases per UCN segment for all of the UCN segments to produce the sum of the UCN bases.

[0059] Embodiment 13 includes the subject matter of any one of Embodiment 12, and further specifies that the weight value is the total number of the UCN segments in the chromosome.

[0060] Embodiment 14 includes the subject matter of any one of Embodiments 1, and further includes excluding segments having less than a minimum number of heterozygous SNPs in the segments.

[0061] Embodiment 15 includes the subject matter of any one of Embodiment 1, and further specifies that the ratio indicative of genomic instability is determined based solely on analyzing the tumor sample.

[0062] Embodiment 16 is a system for analyzing genomic instability of a tumor sample genome, the system comprising a processor and a data repository, the data repository being communicatively connected to the processor, the processor being configured to execute instructions, the instructions causing the system to perform a method when executed by the processor, the method comprising: receiving a plurality of nucleic acid sequence reads, the plurality of nucleic acid sequence reads being generated by selectively amplifying nucleic acid sequences at targeted positions in the tumor sample genome by a targeted group having a low sample input from a tumor sample; dividing the genome into segments having homogeneous copy numbers using log odds of heterozygous SNPs and CNV log ratios determined for the plurality of nucleic acid sequence reads, wherein the heterozygous SNPs are distributed over the genome; applying a threshold count to the square of the log odds of the alleles in each segment in the autosomes of the genome to identify unbalanced copy number (UCN) segments; adding the number of bases in the UCN segments to produce the sum of the UCN bases; and dividing the sum of the UCN bases by the total number of bases in all of the segments identified in the autosomes of the genome to produce a ratio indicative of genomic instability.

[0063] Embodiment 17 includes the subject matter of any one of Embodiment 16, and further specifies that the ratio is expressed as a percentage to give a genomic instability (GI) score.

[0064] Embodiment 18 includes the subject matter of any one of Embodiment 16, and further includes applying a maximum length threshold to each of the UCN segments prior to the adding step.

[0065] Embodiment 19 includes the subject matter of any one of Embodiment 18, and further includes excluding UCN segments that span a maximum length threshold or greater of the entire chromosome.

[0066] Embodiment 20 includes the subject matter of any one of Embodiments 18, and further includes excluding UCN segments that span the p-arm of a chromosome at or above a maximum length threshold.

[0067] Embodiment 21 includes the subject matter of any one of Embodiments 18, and further includes excluding UCN segments of a maximum length threshold or greater that span the q arm of a chromosome.

[0068] Embodiment 22 includes the subject matter of any one of Embodiments 18, and further specifies that the maximum length threshold is a percentage of the entire chromosome length, a percentage of the p arm length, or a percentage of the q arm length.

[0069] Embodiment 23 includes the subject matter of any of Embodiment 22, and further specifies that the maximum length threshold is 90%.

[0070] Embodiment 24 includes the subject matter of any one of Embodiment 16, and further includes applying a minimum length threshold to the UCN segments prior to the adding step and excluding UCN segments that cross the minimum length threshold or less.

[0071] Embodiment 25 includes the subject matter of any one of Embodiment 24, and further specifies that the minimum length threshold is 10 megabases (Mb).

[0072] Embodiment 26 includes the subject matter of any one of Embodiment 16, and further includes dividing the number of bases in the UCN segment by the total number of bases in the chromosome containing the UCN segment to produce a normalized number of UCN bases per UCN segment.

[0073] Embodiment 27 includes the subject matter described in any one of Embodiment 26, and also includes multiplying the normalized number of UCN bases per UCN segment by the weight value to give a weighted number of bases per UCN segment, wherein the adding step is applied to the weighted number of bases per UCN segment for all of the UCN segments to produce the sum of the UCN bases.

[0074] Embodiment 28 includes the subject matter of any one of Embodiment 27, and further specifies that the weight value is the total number of the UCN segments in the chromosome.

[0075] Embodiment 29 includes the subject matter of any one of Embodiments 16, and further includes excluding segments having less than a minimum number of heterozygous SNPs in the segments.

[0076] Embodiment 30 includes the subject matter of any one of Embodiment 16, and further specifies that the ratio indicative of genomic instability is determined based solely on analyzing the tumor sample.

[0077] Embodiment 31 is a non-transitory computer-readable medium storing instructions, which, when executed by a computer, causes the computer to perform a method for analyzing genomic instability in a tumor sample genome, the method comprising: receiving a plurality of nucleic acid sequence reads, the plurality of nucleic acid sequence reads being generated by selectively amplifying nucleic acid sequences at targeted positions in the tumor sample genome by a targeted group having a low sample input from the tumor sample; dividing the genome into segments with homogeneous copy numbers using log odds of heterozygous SNPs and CNV log ratios determined for the plurality of nucleic acid sequence reads, wherein the heterozygous SNPs are distributed over the genome; applying a threshold count to the square of the log odds of the alleles in each segment in the autosomes of the genome to identify unbalanced copy number (UCN) segments; adding the number of bases in the UCN segments to produce a sum of the UCN bases; and dividing the sum of the UCN bases by the total number of bases in all of the segments identified in the autosomes of the genome to produce a ratio indicative of genomic instability.

[0078] Embodiment 32 includes the subject matter of any one of Embodiments 31, and further specifies that the ratio is expressed as a percentage to give a genomic instability (GI) score.

[0079] Embodiment 33 includes the subject matter of any one of Embodiment 31, and further includes applying a maximum length threshold to each of the UCN segments prior to the adding step.

[0080] Embodiment 34 includes the subject matter of any one of Embodiment 33, and further includes excluding UCN segments that span a maximum length threshold or greater of the entire chromosome.

[0081] Embodiment 35 includes the subject matter of any one of Embodiment 33, and further includes excluding UCN segments of a maximum length threshold or greater that span the p arm of a chromosome.

[0082] Embodiment 36 includes the subject matter of any one of Embodiments 33, and further includes excluding UCN segments of a maximum length threshold or greater that span the q arm of a chromosome.

[0083] Embodiment 37 includes a subject matter according to any one of Embodiments 33, and further specifies that the maximum length threshold is a percentage of the entire chromosome length, a percentage of the p arm length, or a percentage of the q arm length.

[0084] Embodiment 38 includes the subject matter described in any of Embodiments 37, and further specifies that the maximum length threshold is 90%.

[0085] Embodiment 39 includes the subject matter of any one of Embodiments 31, and further includes applying a minimum length threshold to the UCN segments prior to the adding step and excluding UCN segments that cross the minimum length threshold or less.

[0086] Embodiment 40 includes the subject matter of any one of Embodiment 39, and further specifies that the minimum length threshold is 10 megabases (Mb).

[0087] Embodiment 41 includes the subject matter of any one of Embodiments 31, and further includes dividing the number of bases in the UCN segment by the total number of bases in the chromosome containing the UCN segment to produce a normalized number of UCN bases per UCN segment.

[0088] Embodiment 42 includes the subject matter described in any one of Embodiment 41, and also includes multiplying the normalized number of UCN bases per UCN segment by the weight value to give a weighted number of bases per UCN segment, wherein the adding step is applied to the weighted number of bases per UCN segment for all of the UCN segments to produce the sum of the UCN bases.

[0089] Embodiment 43 includes the subject matter of any one of Embodiment 42, and further specifies that the weight value is the total number of the UCN segments in the chromosome.

[0090] Embodiment 44 includes the subject matter of any one of Embodiments 31, and further includes excluding segments having less than a minimum number of heterozygous SNPs in the segments.

[0091] Embodiment 45 includes the subject matter of any one of Embodiments 31, and further specifies that the ratio indicative of genomic instability is determined based solely on analyzing the tumor sample.

[0092] In various embodiments, nucleic acid sequence data can be generated using various techniques, platforms or technologies including but not limited to: capillary electrophoresis, microarrays, ligation-based systems, polymerase-based systems, hybridization-based systems, direct or indirect nucleotide identification systems, pyrophosphate sequencing, ion- or pH-based detection systems, electronic signature-based systems, fluorescence-based systems, single-molecule methods, etc.

[0093] Various embodiments of nucleic acid sequencing platforms (such as nucleic acid sequencers) may include, for example, Figure 8Components shown in the block diagram of . According to various embodiments, sequencing instrument 200 may include fluid delivery and control unit 202, sample processing unit 204, signal detection unit 206, and data acquisition, analysis and control unit 208. Various embodiments of instruments, reagents, libraries and methods for next generation sequencing are described in U.S. Patent Application Publication Nos. 2009 / 0127589 and 2009 / 0026082. Various embodiments of instrument 200 can provide automated sequencing that can be used to collect sequence information from multiple sequences in parallel, such as substantially simultaneously.

[0094] In various embodiments, the fluid delivery and control unit 202 may include a reagent delivery system. The reagent delivery system may include a reagent reservoir for storing various reagents. Reagents may include RNA-based primers, forward / reverse DNA primers, oligonucleotide mixtures for ligation sequencing, nucleotide mixtures for synthesis sequencing, optional ECC oligonucleotide mixtures, buffers, washing reagents, blocking reagents, stripping reagents, etc. In addition, the reagent delivery system may include a pipetting system or a continuous flow system that connects the sample processing unit to the reagent reservoir.

[0095] In various embodiments, the sample processing unit 204 may include a sample chamber, such as a flow trough, a substrate, a microarray, a porous plate, etc. The sample processing unit 204 may include a plurality of swimming lanes, a plurality of channels, a plurality of holes or other means for substantially processing a plurality of sample sets simultaneously. Additionally, the sample processing unit may include a plurality of sample chambers so that a plurality of rounds can be processed simultaneously. In a specific embodiment, the system may perform signal detection to a sample chamber, and substantially process another sample chamber simultaneously. Additionally, the sample processing unit may include an automated system for moving or manipulating a sample chamber.

[0096] In various embodiments, the signal detection unit 206 may include an imaging or detection sensor. For example, the imaging or detection sensor may include a CCD, a CMOS, an ion sensor (such as an ion sensitive layer covering a CMOS), a current detector, etc. The signal detection unit 206 may include an excitation system to allow the probe (such as a fluorescent dye) to emit a signal. It is desirable that the system may include an illumination source, such as an arc lamp, a laser, a light emitting diode (LED), etc. In a specific embodiment, the signal detection unit 206 may include an optical device for transmitting light from an illumination source to a sample or from a sample to an imaging or detection sensor. Alternatively, the signal detection unit 206 may not include an illumination source, such as, for example, when a signal is spontaneously generated due to a sequencing reaction. For example, a signal can be generated by the interaction of a release portion, such as a release ion interacting with an ion sensitive layer, or a pyrophosphate that generates a chemiluminescent signal with an enzyme or other catalyst reaction. For example, the change in current can be detected when the nucleic acid passes through a nanopore without the need for an illumination source.

[0097] In various embodiments, various system parameters can be monitored by the data acquisition analysis and control unit 208. The system parameters can include the temperature of various parts of the instrument 200 (such as a sample processing unit or a reagent reservoir), the volume of various reagents, the status of various system subcomponents (such as manipulators, stepper motors, pumps, etc.), or any combination thereof.

[0098] Those skilled in the art will appreciate that various embodiments of the instrument 200 may be used to practice a variety of sequencing methods, including ligation-based methods, sequencing by synthesis, single-molecule methods, nanopore sequencing, and other sequencing technologies.

[0099] In various embodiments, the sequencing instrument 200 can determine the sequence of nucleic acids, such as polynucleotides or oligonucleotides. The nucleic acids can include DNA or RNA, and can be single-stranded, such as ssDNA and RNA, or double-stranded, such as dsDNA or RNA / cDNA pairs. In various embodiments, the nucleic acids can include or be derived from a fragment library, a paired library, a ChIP fragment, etc. In a specific embodiment, the sequencing instrument 200 can obtain sequence information from a single nucleic acid molecule or from a group of substantially identical nucleic acid molecules.

[0100] In various embodiments, the sequencing instrument 200 can output nucleic acid sequencing read data in a variety of different output data file types / formats including, but not limited to, the following: *.fasta, *.csfasta, *seq.txt, *qseq.txt, *.fastq, *.sff, *prb.txt, *.sms, *srs, and / or *.qv.

[0101] Fig. 9 It is a block diagram of an analysis pipeline for signal data obtained from a nucleic acid sequencing instrument. The sequencing instrument generates a raw data file (DAT or .dat file) during the sequencing run of the assay. Signal processing can be applied to the raw data to generate the incorporation signal measurement data of a file such as a 1.wells file that is transferred to a server FTP location together with the log information of the run. The signal processing step can derive the background signal corresponding to the hole. The background signal can be subtracted from the measured signal of the corresponding hole. The remaining signal can be fitted by the incorporation signal model to estimate the incorporation at each nucleotide flow of each hole. The output from the above signal processing is the signal measurement result of each hole and each flow that can be stored in a file such as a 1.wells file.

[0102] In some embodiments, the base calling step can perform phase estimation, normalization, and run a solver algorithm to identify the best partial sequence fit and perform base calling. The base sequence of the sequence reads is stored in an unmapped BAM file. The base calling step can generate a total number of reads, a total number of bases, and an average read length as a quality control (QC) metric for indicating base calling quality. Base calling can be performed by analyzing any suitable signal characteristic (e.g., signal amplitude or intensity). Signal processing and base calling used together with the present teachings can include one or more features described in U.S. Patent Application Publication No. 2013 / 0090860, U.S. Patent Application Publication No. 2014 / 0051584, U.S. Patent Application Publication No. 2014 / 0051584, and U.S. Patent Application Publication No. 2012 / 0109598, published on April 11, 2013, February 20, 2014, and May 3, 2012, each of which is incorporated herein by reference in its entirety.

[0103] Once the base sequence of the sequence read is determined, the sequence read can be provided to the comparison step, for example, in the form of an unmapped BAM file. The comparison step compares the sequence read with the reference genome to determine the compared sequence read and related mapping quality parameters. The comparison step can generate a certain percentage of mappable reads as QC metrics to indicate the comparison quality. The comparison results can be stored in the mapped BAM file. The method for comparing sequence reads used with the teachings of the present invention can include one or more features described in U.S. Patent Application Publication No. 2012 / 0197623, published on August 2, 2012, which is incorporated herein by reference in its entirety.

[0104] The BAM file format structure is described in the "Sequence Alignment / Map Format Specification" dated September 12, 2014 (github.com / samtools / hts-specs). As described herein, a "BAM file" refers to a file compatible with the BAM format. As described herein, an "unaligned" BAM file refers to a BAM file that does not contain aligned sequence read information and mapping quality parameters, and a "mapped" BAM file refers to a BAM file that contains aligned sequence read information and mapping quality parameters.

[0105] The variant calling step can include detecting single nucleotide polymorphism (SNP), insertion and deletion (InDel), multinucleotide polymorphism (MNP) and complex block substitution events. In various embodiments, the variant calling device can be configured to transmit the variant called for the sample genome in the form of *.vcf, *.gff or *.hdf data files. The variant information called can use any file format communication, as long as the variant information called can be parsed and / or extracted for analysis. The variant detection method used together with the present teaching can include one or more features described in U.S. Patent Application Publication No. 2013 / 0345066, U.S. Patent Application Publication No. 2014 / 0296080, U.S. Patent Application Publication No. 2014 / 0052381, U.S. Patent Application Publication No. 2014 / 0052381, published on December 26, 2013, published on October 2, 2014, and published on February 20, 2014, each of which is incorporated herein by reference in its entirety.

[0106] According to various exemplary embodiments, appropriately configured and / or programmed hardware and / or software elements may be used to perform or implement one or more features of any one or more of the above teachings and / or exemplary embodiments. Determining whether to use hardware and / or software elements to implement an embodiment may be based on any number of factors, such as desired computing rate, power level, thermal tolerance, processing cycle budget, input data rate, output data rate, memory resources, data bus speed, etc., as well as other design or performance constraints.

[0107] Examples of hardware elements may include a processor, a microprocessor, one or more input devices, and / or one or more output devices (I / O) (or peripherals) coupled in a communicative manner by: a local interface circuit, a circuit element (e.g., a transistor, a resistor, a capacitor, an inductor, etc.), an integrated circuit, an application specific integrated circuit (ASIC), a programmable logic device (PLD), a digital signal processor (DSP), a field programmable gate array (FPGA), a logic gate, a register, a semiconductor device, a chip, a microchip, a chipset, etc. The local interface may include, for example, one or more buses or other wired or wireless connections, controllers, buffers (caches), drivers, repeaters, and receivers, etc., to allow appropriate communication between hardware components. A processor is a hardware device for executing software, especially software stored in a memory. A processor may be any custom or commercially available processor, a central processing unit (CPU), an auxiliary processor in several processors associated with a computer, a semiconductor-based microprocessor (e.g., in the form of a microchip or chipset), a macroprocessor, or any device generally used to execute software instructions. A processor may also represent a distributed processing architecture. I / O devices may include input devices, such as keyboards, mice, scanners, microphones, touch screens, interfaces for various medical devices and / or laboratory instruments, bar code readers, styluses, laser readers, radio frequency device readers, etc. In addition, I / O devices may also include output devices, such as printers, bar code printers, displays, etc. Finally, I / O devices may also include devices that communicate as both input and output, such as modulators / demodulators (modems; used to access another device, system, or network), radio frequency (RF) or other transceivers, telephone interfaces, bridges, routers, etc.

[0108] Examples of software may include software components, programs, applications, computer programs, applications, system programs, machine programs, operating system software, middleware, firmware, software modules, routines, subroutines, functions, methods, programs, software interfaces, application program interfaces (APIs), instruction sets, computing codes, computer codes, code segments, computer code segments, words, values, symbols, or any combination thereof. The software in the memory may include one or more independent programs, which may include an ordered list of executable instructions for performing logical functions. The software in the memory may include a system for identifying data flows according to the teachings of the present invention and any suitable custom or commercially available operating system (O / S) that may control the execution of other computer programs such as the system, and provide scheduling, input-output control, file and data management, memory management, communication control, etc.

[0109] According to various exemplary embodiments, a non-transitory machine-readable medium or object that is appropriately configured and / or programmed to store instructions or instruction sets can be used to perform or implement one or more of the above teachings and / or any one or more of the exemplary embodiments, which instructions or instruction sets, if executed by a machine, can cause the machine to perform the methods and / or operations according to the exemplary embodiments. Such machines may include, for example, any suitable processing platform, computing platform, computing device, processing device, computing system, processing system, computer, processor, scientific or laboratory instrument, etc., and may be implemented using any suitable combination of hardware and / or software. The machine-readable medium or article may include, for example, any suitable type of memory unit, memory device, memory article, memory medium, storage device, storage article, storage medium and / or storage unit, such as memory, removable or non-removable medium, erasable or non-erasable medium, writable or rewritable medium, digital or analog medium, hard disk, floppy disk, CD-ROM (CD-ROM), CD-R (CD-RW), CD-ROM, magnetic medium, magneto-optical medium, removable memory card or disk, various types of digital versatile disks (DVD), magnetic tape, cassette, etc., including any medium suitable for a computer. The memory may include any one or combination of volatile memory elements (such as random access memory (RAM, such as DRAM, SRAM, SDRAM, etc.) and non-volatile memory elements (such as ROM, EPROM, EEROM, flash memory, hard disk drive, magnetic tape, CDROM, etc.). In addition, the memory may incorporate electronic, magnetic, optical and / or other types of storage media. The memory may have a distributed structure in which various components are located away from each other but are still accessed by the processor. Instructions may include any suitable type of code implemented using any suitable high-level, low-level, object-oriented, visual, compiled and / or interpreted programming language, such as source code, compiled code, interpreted code, executable code, static code, dynamic code, encrypted code, etc.

[0110] According to various exemplary embodiments, one or more features of any one or more of the above teachings and / or exemplary embodiments may be performed or implemented, at least in part, using distributed, clustered, remote, or cloud computing resources.

[0111] According to various exemplary embodiments, one or more features of any one or more of the above teachings and / or exemplary embodiments can be executed or implemented using a source program, an executable program (target code), a script, or any other entity containing a set of instructions to be executed. When a source program, the program can be translated by a compiler, assembler, interpreter, etc., which may or may not be included in the memory, so as to operate correctly with the O / S. The instructions can be written using: (a) an object-oriented programming language with data classes and method classes; or (b) a procedural programming language with routines, subroutines, and / or functions, which may include, for example, C, C++, R, Pascal, Basic, Fortran, Cobol, Perl, Java, and Ada.

[0112] According to various exemplary embodiments, one or more of the exemplary embodiments described above may include sending, displaying, storing, printing or outputting information related to any information, signals, data and / or intermediate or final results that may be generated, accessed or used by such exemplary embodiments to a user interface device, a computer-readable storage medium, a local computer system or a remote computer system. For example, such information sent, displayed, stored, printed or outputted may be in the form of searchable and / or filterable runs and reports, pictures, tables, charts, graphs, spreadsheets, correlations, sequences and combinations thereof.

Claims

1. A method for analyzing genomic instability of a tumor sample genome, the method comprising: selectively amplifying nucleic acid sequences at targeted locations in the genome of the tumor sample by a targeted panel with low sample input from the tumor sample to generate a plurality of nucleic acid sequence reads; Dividing the genome into segments with homogeneous copy numbers using the log odds of heterozygous single nucleotide polymorphisms (SNPs) and the log ratios of copy number variations (CNVs) determined for the plurality of nucleic acid sequence reads, wherein the heterozygous SNPs are distributed across the genome; applying a threshold count to the square of the allele log odds for each segment in an autosome of the genome to identify unbalanced copy number (UCN) segments; adding the number of bases in the UCN segments to produce a sum of UCN bases; as well as The sum of the UCN bases is divided by the total number of bases in all of the segments identified in the autosomes of the genome to produce a ratio indicative of genomic instability.

2. The method of claim 1, wherein the ratio is expressed as a percentage to give a genomic instability (GI) score. 3 . The method of claim 1 , further comprising applying a maximum length threshold to each of the UCN segments before the adding step. 4 . The method according to claim 3 , further comprising excluding UCN segments that span the maximum length threshold or a greater length threshold of the entire chromosome.

5. The method according to claim 3, further comprising excluding UCN segments that span the maximum length threshold or greater length threshold of the p arm of the chromosome.

6. The method of claim 3, further comprising excluding UCN segments that span the maximum length threshold or greater that span the q arm of a chromosome.

7. The method of claim 3, wherein the maximum length threshold is a percentage of the entire chromosome length, a percentage of the p-arm length, or a percentage of the q-arm length. The method of claim 7 , wherein the maximum length threshold is 90%.

9. The method of claim 1, further comprising applying a minimum length threshold to the UCN segments, and excluding UCN segments that cross the minimum length threshold or less before the adding step.

10. The method of claim 9, wherein the minimum length threshold is 10 megabases (Mb).

11. The method of claim 1, further comprising dividing the number of bases in the UCN segment by the total number of bases in the chromosome containing the UCN segment to produce a normalized number of UCN bases per UCN segment.

12. The method of claim 11 , further comprising multiplying the normalized number of UCN bases per UCN segment by a weight value to give a weighted number of bases per UCN segment, wherein the adding step is applied to the weighted number of bases per UCN segment for all of the UCN segments to produce the sum of the UCN bases.

13. The method of claim 12, wherein the weight value is the total number of the UCN segments in the chromosome.

14. The method of claim 1, further comprising excluding segments of the segments having less than a minimum number of heterozygous SNPs.

15. The method of claim 1, wherein the ratio indicative of genomic instability is determined based solely on analyzing the tumor sample.

16. A system for analyzing genomic instability of a tumor sample genome, the system comprising a processor and a data repository, the data repository being communicatively connected to the processor, the processor being configured to execute instructions which, when executed by the processor, cause the system to perform a method comprising: receiving a plurality of nucleic acid sequence reads generated by selectively amplifying nucleic acid sequences at targeted locations in a genome of a tumor sample by a targeted panel having a low sample input from a tumor sample; Dividing the genome into segments with homogeneous copy numbers using the log odds of heterozygous single nucleotide polymorphisms (SNPs) and the log ratios of copy number variations (CNVs) determined for the plurality of nucleic acid sequence reads, wherein the heterozygous SNPs are distributed across the genome; applying a threshold count to the square of the allele log odds for each segment in an autosome of the genome to identify unbalanced copy number (UCN) segments; adding the number of bases in the UCN segments to produce a sum of UCN bases; as well as The sum of the UCN bases is divided by the total number of bases in all of the segments identified in the autosomes of the genome to produce a ratio indicative of genomic instability.

17. The system of claim 16, wherein the ratio is expressed as a percentage to give a genomic instability (GI) score.

18. The system of claim 16, wherein the ratio indicative of genomic instability is determined based solely on analyzing the tumor sample.

19. A non-transitory computer-readable medium storing instructions that, when executed by a computer, cause the computer to perform a method for analyzing a tumor sample genome for genomic instability, the method comprising: receiving a plurality of nucleic acid sequence reads generated by selectively amplifying nucleic acid sequences at targeted locations in a genome of a tumor sample by a targeted panel having a low sample input from a tumor sample; Dividing the genome into segments with homogeneous copy numbers using the log odds of heterozygous single nucleotide polymorphisms (SNPs) and the log ratios of copy number variations (CNVs) determined for the plurality of nucleic acid sequence reads, wherein the heterozygous SNPs are distributed across the genome; applying a threshold count to the square of the allele log odds for each segment in an autosome of the genome to identify unbalanced copy number (UCN) segments; adding the number of bases in the UCN segments to produce a sum of UCN bases; as well as The sum of the UCN bases is divided by the total number of bases in all of the segments identified in the autosomes of the genome to produce a ratio indicative of genomic instability.

20. The computer readable medium of claim 19, wherein the ratio is expressed as a percentage to give a genomic instability (GI) score.

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