Method for cancer prognosis by assessing tumor mutation diversity by establishing a diversity index

Abstract

The present invention is a method for predicting the response of colorectal cancer patients to therapy, the method comprising measuring tumor genetic heterogeneity by analyzing circulating tumor DNA from patient samples.

Description

Technical Field

[0001] The present invention relates to the field of oncology. More specifically, the present invention relates to the field of nucleic acid-based detection of cancer patients. Background Art

[0002] Many cancer patients are diagnosed at the metastatic stage or have progressed from early-stage disease to metastasis. At this point, the prognosis is poor and the selection of effective therapeutic therapies is critical. Modern diagnostic methods rely on mutations found in circulating tumor DNA (ctDNA) to predict tumor resistance and recurrence (see U.S. Patent Application Serial No. 14 / 774,518 and International Application No. PCT / US2015 / 049838, entitled "Identification and Use of Circulating Tumor Markers"). For example, an increase in the mutant allele frequency (AF) of a resistance mutation indicates resistance to a specific targeted therapy. However, a more comprehensive assessment of the evolution of the tumor from pre-treatment to post-treatment is necessary in order to select the appropriate therapy.

[0003] Mutation data obtained from analyzing circulating tumor DNA may contain information that cannot be easily interpreted using tools currently available in the art. Each patient has a range of circulating tumor variants from primary and metastatic tumors that have shed DNA into the circulation at a given time. The presence or absence of specific mutations or mutational load can be detected using techniques currently available in the art, but is not easily interpreted for use in patient care. It is necessary to interpret and translate these mutation data into clinically useful information. Summary of the invention

[0004] In one embodiment, the present invention provides a method for identifying the prognosis of a cancer patient, the method comprising the following steps: isolating nucleic acid from a cell-free blood sample of the patient; determining at least a portion of the sequence of each biomarker listed in Table 1 in the sample; determining the patient's tumor variation diversity index; if the tumor variation diversity is in the same quantile as the tumor variation diversity of patients with good treatment outcomes in a related population, the patient is identified as having a good prognosis; or, if the tumor variation diversity is in the same quantile as the tumor variation diversity of patients with poor treatment outcomes in a related population, the patient is identified as having a poor prognosis. The tumor variation diversity is selected from the Shannon diversity index, the Simpson diversity index, the Inverse Simpson diversity index, and the Gini-Simpson diversity index. In some embodiments, the cancer is selected from non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). The prognosis may be overall survival time (OS).

[0005] In some embodiments, the diversity index is determined using the proportion of species in a population determined according to Formula I. The Shannon diversity index may be determined according to Formula II. The Simpson diversity index may be determined according to Formula III. The Inverse Simpson diversity index may be determined according to Formula IV. The Gini-Simpson diversity index may be determined according to Formula V.

[0006] In some embodiments, the related population is a population of patients suffering from the same type of cancer.

[0007] The step of determining the sequence may include one or more of target enrichment, adaptor ligation, molecular barcoding, sequence alignment, error correction, and DNA amplification.

[0008] In another embodiment, the present invention provides a method for treating a patient with non-small cell lung cancer (NSCLC), the method comprising the following steps: isolating nucleic acid from a cell-free blood sample of the patient; determining at least a portion of the sequence of each biomarker listed in Table 1 in the sample; determining the patient's tumor variation diversity index; if the tumor variation diversity is low, determining that the patient is likely to respond positively to the chemotherapy regimen and administering the chemotherapy regimen; or if the tumor variation diversity is high, determining that the patient is unlikely to respond positively to the chemotherapy regimen and not administering the chemotherapy regimen. In some embodiments, if the tumor variation diversity is lower than the first tertile of the tumor variation diversity index in the relevant population, the tumor variation diversity is low; and if the tumor variation diversity is equal to or higher than the first tertile of the tumor variation diversity index in the relevant population, the tumor variation diversity is high.

[0009] In another embodiment, the present invention provides a method for treating a patient with small cell lung cancer (SCLC), the method comprising the following steps: isolating nucleic acid from a cell-free blood sample of the patient; determining at least a portion of the sequence of each biomarker listed in Table 1 in the sample; determining the patient's tumor variation diversity index; if the tumor variation diversity is high, determining that the patient is likely to respond positively to the chemotherapy regimen, and administering the chemotherapy regimen; or if the tumor variation diversity is low, determining that the patient is unlikely to respond positively to the chemotherapy regimen, and not administering the chemotherapy regimen. In some embodiments, if the tumor variation diversity is lower than the first tertile of the tumor variation diversity index in the relevant population, the tumor variation diversity is low; and if the tumor variation diversity is equal to or higher than the first tertile of the tumor variation diversity index in the relevant population, the tumor variation diversity is high.

[0010] In another embodiment, the present invention provides a computer system designed for detecting tumor variation diversity in patients, the computer system comprising a processor and a non-temporary computer-readable medium coupled to the processor, the medium comprising code executable by the processor, the code being used to execute a method comprising the following steps: analyzing sequencing data of the biomarkers in Table 1; performing sequence comparison and mutation detection and error correction; determining a tumor variation diversity index according to one or more formulas of Formula II, Formula III, Formula IV and Formula V, and determining whether the tumor variation diversity in the sample is above or below a predetermined threshold. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 The relationship between overall survival (OS) and Shannon diversity index in patients with stage IV small cell lung cancer (SCLC) is shown.

[0012] Figure 2 The relationship between overall survival (OS) and Gini-Simpson diversity index in patients with stage IV small cell lung cancer (SCLC) is shown.

[0013] Figure 3 The relationship between overall survival (OS) and Gini-Simpson diversity index in patients with stage IV lung adenocarcinoma (non-small cell lung cancer, NSCLC) is shown. DETAILED DESCRIPTION

[0014] definition

[0015] The following definitions are not limiting but merely helpful for understanding the present disclosure.

[0016] The term "PFS" is used herein to describe a patient's progression-free survival time.

[0017] The term "OS" is used herein to describe the overall survival time of a patient.

[0018] The term "circulating tumor DNA (ctDNA)" is used herein to describe a portion of cell-free DNA (cfDNA) derived from tumors found in human plasma or serum. Circulating tumor DNA is distinguished from non-tumor DNA by tumor-specific mutations.

[0019] The term "biomarker" is used herein to describe a nucleotide sequence that contains information related to a biological or clinical phenomenon. For example, the information may be the mutational state of the nucleotide sequence. The biomarker may be a gene (including a coding sequence, a regulatory sequence, an intron, or a splice site) or an intergenic region. The clinical phenomenon may be the presence of malignant cells, such as tumor cells, in a patient's sample.

[0020] The term "diversity index" is used herein to describe a quantitative measure that reflects how many different species are present in a data set while taking into account the even distribution of the underlying entities among these species. See Magurran, AE, Measuring Biological Diversity, 2003 Wiley-Blackwell. The terms "tumor diversity index" and "tumor mutation diversity index" are used interchangeably to refer to a diversity index applied to the mutation sequence variants (species) found in a tumor.

[0021] The present invention is a method for assessing the prognosis of a tumor patient based on the mutation content of the patient's circulating tumor DNA (ctDNA). Specifically, the mutation content is evaluated to determine the diversity of the patient's tumor cells.

[0022] The results of circulating tumor DNA analysis may contain information beyond the scope of what can be assessed with existing tools in the field. Each patient has a range of circulating tumor variants, including both primary and metastatic tumors, at a given time that have released DNA into the circulation. Because different subclones of tumors contribute to the production of circulating tumor DNA, there may be more circulating tumor variants than we can obtain from typical tissue assays. In addition, subclones of the original tumor cells evolve over time, thereby changing the mutation spectrum detectable in the patient's blood. The present invention is a new method for analyzing the richness and abundance of a patient's tumor cell population using an ecological diversity index in order to provide actionable results to the patient's physician.

[0023] Ecological diversity indices have been previously applied to tumors. Maley et al. (Genetic clonal diversity predicts progression to esophageal adenocarcinoma. Nat. Genet. 38, 468–473 (2006)) demonstrated the application of Shannon diversity in esophageal adenocarcinoma by measuring the genetically different clones of each sample using multiple tissue biopsies, cell sorting, and FISH probes for TP53 and chromosome 17 centromeres. It was found that patients with the top quartile of the Shannon diversity index had an increased chance of progression from Barrett's esophageal precancer to esophageal adenocarcinoma. Almendro V et al. (Inference of tumor evolution during chemotherapy by computation modeling and in situ analysis of genetic and phenotypic cellular diversity. Cell Reports. 6, 514-527 (2014)) used a similar approach to characterize the diversity of breast cancer, while Merlo LMF et al. (A comprehensive survey of clonal diversity measures in Barrett's esophagusas biomarkers of progression to esophageal adenocarcinoma. Cancer Prevention Research Nov; 3(11): 1388-97 (2010)) showed that Shannon and Simpson were similar in esophageal adenocarcinoma.

[0024] The present invention is a comprehensive method for applying an ecological diversity index to mutations detected in a patient's ctDNA. In some embodiments, the index is a Shannon index, a Simpson index, or a combination thereof. In some embodiments, mutations are assessed by applying next generation sequencing data of the patient's ctDNA. Assessment of ecological diversity can provide prognosis and suggested treatment methods for patients.

[0025] In some embodiments, the present invention uses a biomarker panel to identify somatic mutations and mutation loads in cancer-related genes by next generation sequencing (NGS). In some embodiments, the present invention utilizes blood or blood-derived samples from patients. The sample may include any part of the blood, such as serum or plasma, containing cell-free DNA, including circulating tumor DNA (cfDNA or ctDNA). In some embodiments, the sample is continuously obtained at different times during treatment, such as before and after surgery or before, during and after a chemotherapy regimen. In some embodiments, tumor samples (such as solid tumor samples) are used to compare with blood samples. Solid tissue or blood samples can be collected by appropriate methods for preserving DNA therein, including blood tissue collected in formalin-fixed paraffin embedding (FFPE), fresh frozen tissue, or a preservation medium.

[0026] In some embodiments, the present invention utilizes a biomarker group, including a genome or mutation group or a somatic variation group. Compared with existing methods such as whole genome sequencing (WGS) and whole exome sequencing (WES), the efficiency of using a biomarker group containing a small part of the genome (e.g., 1 megabase, 200 kilobases, 100 kilobases or less) is improved. The mutations evaluated in the biomarker group can include single nucleotide variations (SNVs), deletions, and insertions (in-dels), and if the single nucleotide variations (SNVs), deletions, and insertions (in-dels) occur in the coding region of the gene, they correspond to nonsense missense mutations and frameshift mutations. Other types of mutations include gene fusions and translocations. The selection, size, and content of such biomarker groups have been described in, for example, U.S. Patent Application Serial No. 14 / 774,518 and International Application No. PCT / US2015 / 049838 "Identification and Use of Circulating Tumor Markers". In some embodiments, the present invention includes determining the sequence of biomarkers (e.g., genes listed in Table 1) in the biomarker group. In some embodiments, the entire sequence of the gene is determined. In other embodiments, the entire coding sequence of a gene is determined. In other embodiments, only a portion of the sequence of a gene known to undergo mutation in cancer is determined. In other embodiments, the biomarker is not associated with a coding sequence, but rather with a regulatory sequence or a sequence of unknown function that is known to be mutated in human tumors.

[0027] In the context of the present invention, the sequence of the biomarker can be determined by any suitable method known in the art. The suitable method will have sufficient accuracy, such as sensitivity and specificity, to detect rare sequences with low error rates. In some embodiments, the sequencing method includes error correction steps, such as the use of molecular barcodes, other chemical or computational methods for error typing and error suppression, for example, see the patent application "Identification and Use of Circulating Tumor Markers", as described above. The sequencing method may include a massively parallel sequencing method, including array-based sequencing (Illumina, San Diego, Cal.), emulsion-based sequencing (Thermo Fisher, Waltham, Mass.), optical measurement-based sequencing (Pacific BioSciences, Menlo Park, Cal.), or nanopore-based sequencing (Roche Sequencing Solutions, Santa Clara, Cal.), or Oxford Nanopore (Oxford, UK), or any other available single-molecule-based sequencing method.

[0028] In some embodiments, the present invention utilizes biomarker panels such as ctDNA analysis kit (Roche Sequencing Solutions, Inc., Pleasanton, Cal.), which can analyze patient tissue and blood to identify and quantify tumor-specific mutations in samples. The composition of the biomarker panel is shown in Table 1 ctDNA analysis kit monitoring group.

[0029] Table 1. Composition of the monitoring biomarker panel

[0030]

[0031]

[0032] In some embodiments, the 197 gene sets listed in Table 1 are used. In some embodiments, the present invention further comprises the step of improving the biomarker panel based on the results obtained from clinical samples. In some embodiments, the present invention comprises the step of analyzing the correlation between the presence of biomarkers in cell-free DNA from a statistically large number of patients and A) RFS, B) TTR (or DFS), C) OS or any of the above responses to treatment. Other biomarkers showing predictive correlation will be included in the biomarker panel. Biomarkers that do not show statistically significant predictive correlation will be excluded from the biomarker panel.

[0033] In some embodiments, the step of determining the biomarker sequence includes a target enrichment step. The enrichment can be achieved by capturing the target sequence by one or more target-specific probes. The nucleic acids in the sample are mutated and contacted with single-stranded target-specific probes. The probes may include ligands for affinity capture moieties so that after forming a hybrid complex, they are captured by providing affinity capture moieties. In some embodiments, the affinity capture moiety is avidin or streptavidin, and the ligand is biotin. In some embodiments, the affinity capture moiety is bound to a solid support. As described in further detail below, the solid support may include superparamagnetic spherical polymer particles, such as DYNABEADS TM Magnetic beads or magnetic glass particles.

[0034] In some embodiments, the step of determining the biomarker sequence further includes an adapter connection step, wherein the adapter molecule is connected to the target nucleic acid. The connection can be a blunt end connection or a more efficient sticky end connection. The target nucleic acid can be blunt-ended by "end repair" including chain filling, that is, extending the 3'-end by DNA polymerase to eliminate the 5'-overhang. In some embodiments, the blunt-ended nucleic acid can be sticky by adding a single nucleotide to the 3'-end of the adapter and adding a single complementary nucleotide to the 3'-end of the target nucleic acid (for example, by DNA polymerase or terminal transferase). In other embodiments, the adapter and the target nucleic acid can obtain sticky ends (overhangs) by digestion with restriction endonucleases. The restriction endonuclease recognition site can be inherent or engineered into the sequence. In some embodiments, other enzyme steps may be required to complete the connection. In some embodiments, polynucleotide kinase can be used to add 5'-phosphate to the target nucleic acid molecule and the adapter molecule. In some embodiments, the adapter molecule is an artificial sequence synthesized in vitro. In other embodiments, the adapter molecule is a naturally occurring sequence synthesized in vitro. In other embodiments, the adaptor molecule is an isolated naturally occurring molecule.

[0035] In some embodiments, the step of determining the biomarker sequence further includes the step of amplifying the target nucleic acid. The amplification can be by exponential polymerase chain reaction (PCR), linear amplification of only one chain or any other method using oligonucleotide primers. In PCR Strategies (MAInnis, DHGelfand and JJSninsky, 1995, Academic Press, San Diego, CA) Chapter 14; PCR Protocols: A Guide to Methods and Applications (MAInnis, DHGelfand, JJSninsky and TJWhite, Academic Press, NY, 1990) describes a variety of PCR conditions. The amplification step may occur before or after the adapter is connected. Therefore, amplification utilizes a universal primer binding site, such as being connected to the target sequence by an adapter. In other embodiments, gene-specific (target-specific) primers or primer pairs are used before the adapter is connected, and the amplified target nucleic acid is connected to the adapter as described herein.

[0036] In some embodiments, the present invention includes introducing a barcode into the target nucleic acid. Sequencing a single molecule generally requires a molecular barcode, such as described in U.S. Patent Nos. 7,393,665, 8,168,385, 8,481,292, 8,685,678, and 8,722,368. A unique molecular barcode is a short artificial sequence that is usually added to each molecule in a sample (such as a patient's sample) in the initial steps of an in vitro operation. The barcode marks a molecule and its progeny. The unique molecular barcode (UID) has multiple uses. The barcode can track each single nucleic acid molecule in a sample to assess the presence and quantity of circulating tumor DNA (ctDNA) molecules in, for example, the patient's blood, so as to detect and monitor cancer without a biopsy. See U.S. Patent Application 14 / 774,518. Unique molecular barcodes can also be used for sequencing error correction. The entire progeny of a single target molecule are labeled with the same barcode and form a barcode family. Sequence variations that are not shared by all members of a barcoded family are discarded as artifacts rather than true mutations. Barcoding can also be used for positional deduplication and target quantification, since the entire family represents a single molecule in the original sample. See supra.

[0037] In some embodiments, the adapter includes one or more barcodes. In other embodiments, the amplification primers (e.g., those primers used for amplification before the adapter is connected) include a barcode of the 5'-part of the primer. The barcode can be a multiple sample ID (MID) for identifying the source of the sample when the sample is mixed (multiplexed). The barcode can also be used as a unique molecular ID (UID) for identifying each original molecule and its progeny. The barcode can also be a combination of UID and MID. In some embodiments, a single barcode is used as UID and MID. In some embodiments, each barcode includes a predefined sequence. In other embodiments, the barcode includes a random sequence. The barcode can be 1-20 nucleotides long.

[0038] In some embodiments, the step of determining the biomarker sequence further includes the step of sequence analysis. The steps include sequence alignment, error correction and determining sequence variation (mutation). In some embodiments, alignment is used to determine a consensus sequence from multiple sequences (e.g., multiple sequences with the same barcode (UID)). In some embodiments, a barcode (UID) is used to determine a consensus sequence from multiple sequences with the same barcode (UID). In other embodiments, a barcode (UID) is used to eliminate artifacts, i.e., variations present in some but not all sequences with the same barcode (UID). Such artifacts derived from PCR errors or sequencing errors can be eliminated.

[0039] In some embodiments, the quantity of each sequence in the sample can be quantified by the relative quantity of the sequence of each barcode (UID) in the quantitative sample. Each UID represents a single molecule in the original sample, and counting the different UIDs associated with each sequence variant can determine the proportion of each sequence in the original sample. Those skilled in the art will be able to determine the number of sequence reads necessary to determine the consensus sequence. In some embodiments, for accurate quantitative results, each UID ("sequence depth") needs to read the relevant number. In some embodiments, the desired depth is 5-50 reads per UID.

[0040] The invention includes the step of determining the diversity of tumor mutations in a patient sample by determining a diversity index, which is a quantitative measure that reflects how many different classes there are in a data set while taking into account how evenly elementary entities (such as individual mutant sequences) are distributed among those classes. See Magurran, AE, Measuring Biological Diversity, 2003 Wiley-Blackwell.

[0041] In some embodiments, the diversity index is expressed as (∑ i p i ln(pi )) of the Shannon diversity index, where p i is the proportion of species i in the population. The species is the variant of the sequence present in the patient sample. Since the proportions of all species in the sample should add up to 1 (i.e., ∑ i p i =1), so each p i By all p of each sample i Evaluate the number of species (i.e. variant (mutation) sequences) to determine p i In some embodiments, the number of species is assessed as VARDEPTH according to Formula 1. In other embodiments, the number of species is assessed as another quantitative measure, such as duplex depth (i.e., duplex molecules with variants measured) or allele frequency.

[0042] In some embodiments, p is calculated according to Formula I i , that is, the proportion of species i in the population.

[0043] Formula I

[0044] p i =VARDEPTH i / SVD

[0045] VARDEPTH = variant depth or molecular depth of deduplication, i.e., the number of unique molecules containing variants (mutations) detected in a sequencing run;

[0046] SVD=∑ i VARDEPTH i Represents the total number of molecules with detected variants (mutations)

[0047] Therefore, the Shannon diversity index is expressed as Formula II.

[0048] Formula II

[0049] Shannon=-∑ i (VARDEPTH i / SVD)ln(VARDEPTH i / SVD)

[0050] In some embodiments, the diversity index is represented by ∑ i p i 2 (Formula III) Simpson diversity index. In some embodiments, the diversity index is expressed as 1 / ∑ i p i 2Inverse Simpson index (Formula IV). In some embodiments, the diversity index is expressed as 1-∑ i p i 2 (Formula V) Gini-Simpson index. In all embodiments, p i is the proportion of species i in the population calculated as described above (eg, according to Formula I or using a quantitative measure in place of VARDEPTH used in Formula I instead of VARDEPTH).

[0051] In some embodiments, the tumor variation diversity determined according to the present invention is assessed as high or low. In some embodiments, the tumor variation diversity is assessed at a population level, wherein the relevant population includes cancer patients diagnosed with the same type of cancer. For example, if the tumor variation diversity is below a quantile in a population, it is defined as low; if the tumor variation diversity is equal to or above a quantile in a population, it is defined as high. The quantile may be a quartile, a tertile, or a median. In some embodiments, the quantile is a tertile, and if the tumor variation diversity is below the first tertile in a population, it is defined as low; if the tumor variation diversity is equal to or above the first tertile in a population, it is defined as high.

[0052] In some embodiments, the present invention includes a step of evaluating the cancer status of a patient using a tumor variation diversity index obtained from the patient's ctDNA. In some embodiments, the assessment includes identifying that the patient may or may not respond to anticancer therapy. In other embodiments, the assessment includes determining the prognosis of the patient represented by the predicted progression-free survival time (PFS) and overall survival time (OS). In some embodiments, the response to therapy is assessed as the predicted progression-free survival time (PFS) and overall survival time (OS) after the completion of therapy. In some embodiments, the therapy is first-line chemotherapy or chemoradiotherapy. The assessment is based on the tumor variation diversity determined as described herein. In some embodiments, the assessment step includes determining the patient's prognosis step can be completed using population data as described above. In some populations, a lower tumor diversity index indicates a poor prognosis and a poor response to therapy. For such populations, detecting a tumor diversity index lower than a set quantile (such as quartiles, tertiles, or medians) indicates an assessment of a poor prognosis and a poor response to therapy. In other populations, a higher tumor diversity index indicates a good prognosis and a positive response to therapy. For such a population, detecting a tumor multiplicity index at or above a set quantile (such as quartile, tertile or median) indicates an assessment of a good prognosis and a positive response to therapy.

[0053] The examples listed below show the use of the tumor diversity index in different populations (i.e., patients diagnosed with different types of cancer). In a first example, Figure 1 As shown, patients with stage IV small cell lung cancer (SCLC) with a lower Shannon diversity index had a poor prognosis compared with patients with a higher Shannon diversity index. The index was assessed at baseline (before chemotherapy). In the first tertile (≤1.17), patients with double deep mutations had a shorter overall survival (hazard ratio = 1.8; 95% CI 1-3.3; log-rank p = 0.034; median survival difference = 4.5 months).

[0054] In another embodiment, Figure 2 As shown, patients with stage IV small cell lung cancer (SCLC) with a lower Gini-Simpson diversity index had a poor prognosis compared to patients with a higher Gini-Simpson diversity index. The index was assessed at baseline (before chemotherapy). In the first tertile (≤0.64), patients with dual deep mutations had a shorter overall survival (hazard ratio = 1.8; 95% CI 1-3.3; log-rank p = 0.033; median survival difference = 4.5 months). Gini-Simpson and inverse Simpson values ​​gave the same survival analysis results.

[0055] In one embodiment of a different population, patients with stage IV lung adenocarcinoma (non-small cell lung cancer, NSCLC) with a lower Gini-Simpson diversity index had a better prognosis than patients with a higher Gini-Simpson diversity index ( Figure 3 ). The index was assessed at baseline (before chemotherapy). In the first quartile (≤0.65), patients with double deep variants had a longer progression-free survival after 6 months of chemotherapy treatment. Gini-Simpson and inverse Simpson values ​​gave the same survival analysis results.

[0056] In some embodiments, the present invention further includes a step of recommending or administering a therapy for a patient's cancer based on an assessment guided by a tumor variation diversity index obtained from the patient's ctDNA. In some embodiments, the present invention includes a step of determining whether a patient is likely to respond to therapy by predicting progression-free survival (PFS) and overall survival (OS) after completion of therapy (using tumor variation diversity determined as described herein), and if the patient is predicted to respond, recommending or administering the therapy to the patient. In some embodiments, the therapy is chemotherapy, chemoradiotherapy, or immunotherapy.

[0057] One aspect of the present invention includes a system for detecting the diversity of tumor variations in patients. The system includes a processor and a non-temporary computer-readable medium coupled to the processor, the medium including code executable by the processor, the code for executing a method comprising the following steps: analyzing the sequencing data of the biomarkers in Table 1; performing sequence comparison and mutation detection, error correction; determining the tumor variation diversity index according to formula II, III, IV or V and whether the tumor variation diversity in the sample is higher or lower than a predetermined threshold (e.g., a population-based threshold). In some embodiments, if the tumor variation diversity is equal to or higher than the threshold, the system classifies the patient as having high tumor variation diversity and optionally outputs a prognosis or therapy recommendation for high tumor variation diversity. At the same time, if the tumor variation diversity is lower than the threshold, the system classifies the patient as having low tumor variation diversity and optionally outputs a prognosis or therapy recommendation for low tumor variation diversity.

[0058] In some embodiments, a computer-readable medium includes a database including a list of available therapies that depend on the diversity of tumor mutations in a patient, and the computer-readable medium may include one or more storage devices. The computer-readable medium further includes program code having instructions for generating a report listing appropriate therapies.

[0059] The system may include various functional aspects, such as a server including a processor for processing digital data, a memory coupled to the processor for storing digital data, an input digitizer coupled to the processor for inputting digital data, program code stored in the memory and accessible by the processor, a display device coupled to the processor and the memory for displaying information obtained from the digital data, a data network, and one or more information databases. The databases may include patient data, patient sample data, clinical data including prior treatment data, lists of therapies and therapeutic agents, patient tracking data, and the like.

[0060] Example

[0061] Example 1. Assessment of tumor mutation diversity in patients with small cell lung cancer (SCLC)

[0062] In this example, pre-treatment plasma samples were obtained from 56 subjects with stage IV small cell lung cancer (SCLC). The subjects had previously been treated with first-line chemotherapy or chemoradiotherapy. The ctDNA monitoring kit (Roche Sequencing Solutions, Pleasanton, Cal.) (198 kilobase targeted next-generation sequencing panel) was used for analysis (Table 1). The samples were processed according to the manufacturer's recommendations. The sequencing data were analyzed according to the manufacturer's recommendations to determine variants in the sequence reads.

[0063] The Shannon diversity index (Formula I) and the Simpson diversity index (Formula II) were applied to the variant data by considering each somatic variant as a class and the number of detected doublets with that mutation as the abundance of that class. Samples were classified as having low tumor heterogeneity if their plasma variant diversity score was below the first tertile of the cohort.

[0064] Results showed that subjects with stage IV SCLC who had low tumor mutational diversity, as assessed by the Shannon diversity index, had shorter overall survival (hazard ratio = 1.8; 95% CI 1-3.3; log-rank p = 0.034; median survival difference = 4.5 months; Figure 1 Similarly, subjects with low tumor mutational diversity, as assessed by the Gini-Simpson index or inverse Simpson diversity index, had shorter overall survival (hazard ratio = 1.8; 95% CI 1-3.3; log-rank p = 0.033; median survival difference = 4.5 months; Figure 2 ).

[0065] Example 2. Evaluation of tumor mutation diversity in patients with non-small cell lung cancer (NSCLC)

[0066] We also evaluated the Simpson diversity index of pretreatment plasma samples from 41 patients with stage IV lung adenocarcinoma (non-small cell lung cancer (NSCLC)) from a prospective observational study. The subjects had been previously treated with first-line chemotherapy or chemoradiotherapy. The ctDNA monitoring kit (Roche Sequencing Solutions, Pleasanton, Cal.) (198 kilobase targeted next-generation sequencing panel) was used for analysis (Table 1). The samples were processed according to the manufacturer's recommendations. The sequencing data were analyzed according to the manufacturer's recommendations to determine variants in the sequence reads.

[0067] The Shannon diversity index (Formula I) and the Simpson diversity index (Formula II) were applied to the variant data. If the plasma variant diversity score of the sample was lower than the first tertile of the training cohort, it was classified as low tumor variant diversity. Subjects with stage IV adenocarcinoma with low tumor variant diversity, as assessed by the Gini-Simpson index or the inverse Simpson diversity index, had a longer progression-free survival time after 6 months of chemotherapy ( Figure 3 ).

[0068] Although the present invention has been described in detail with reference to specific embodiments, it will be apparent to those skilled in the art that various modifications may be made within the scope of the present invention. Therefore, the scope of the present invention should not be limited by the embodiments described herein, but by the claims presented below.

Claims

1. A computer system designed to detect tumor mutation diversity in a patient, the computer system comprising a processor and a non-transitory computer-readable medium coupled to the processor, the medium comprising code executable by the processor for performing a method for identifying a prognosis of a cancer patient, comprising the following steps: (a) analyzing sequencing data of the following biomarker panel, wherein the sequencing data is determined from nucleic acids isolated from a cell-free blood sample obtained from the patient, the biomarker panel consisting of the following biomarkers: (b) performing sequence comparison and mutation detection and error correction; determining the tumor variation diversity index of the patient according to one or more of Formula II, Formula III, Formula IV and Formula V; and (c) determining whether the tumor variation diversity in the sample is above or below a predetermined threshold, wherein if the tumor variation diversity is below the first tertile of the tumor variation diversity index in the related population, the tumor variation diversity is low; and if the tumor variation diversity is equal to or above the first tertile of the tumor variation diversity index in the related population, the tumor variation diversity is high, wherein the related population is composed of cancer patients diagnosed with the same type of cancer; wherein the cancer is selected from non-small cell lung cancer (NSCLC), small cell lung cancer (SCLC), and wherein when the cancer is SCLC, a low tumor multiplicity index indicates a poor prognosis, and when the cancer is NSCLC, a low tumor multiplicity index indicates a good prognosis; The tumor variation diversity is selected from the group consisting of the Shannon diversity index, the Simpson diversity index, the Inverse Simpson diversity index and the Gini-Simpson diversity index, and the diversity index is calculated using the formula I:p i =VARDEPTH i / SVD, determined by the proportion of species in the determined population, where VARDEPTH = deduplication variant depth and SVD = ∑ i VARDEPTH i represents the total number of molecules with detected variants; The Shannon diversity index is calculated according to Formula II: Shannon = -∑ i (VARDEPTH i / SVD)ln(VARDEPTH i / SVD); the Simpson diversity index is determined according to formula III: ∑ i p i 2 Determined; The Inverse Simpson diversity index is calculated according to Formula IV: 1 / ∑ i p i 2 Sure, The Gini-Simpson diversity index is calculated according to the formula V: 1-∑ i p i 2 Determined; and where p i is the proportion of species i in the population calculated as described above.

2. A computer system according to claim 1, wherein determining the sequencing data includes a target enrichment step.

3. The computer system of claim 1, wherein determining sequencing data comprises an adapter ligation step.

4. The computer system of claim 1, wherein determining sequencing data utilizes molecular barcodes.

5. The computer system of claim 1, wherein determining sequencing data comprises a sequence alignment step.

6. The computer system of claim 1, wherein determining the sequencing data comprises an error correction step.

Images