Methods and genetic mutation patterns for detecting increased tumor mutation burden

Abstract

The field of the present invention generally relates to cancer, including methods for cancer diagnosis, prognosis, and treatment. In particular, the field of the present invention relates to novel mutation patterns of a unique set of point mutations containing cytosine or guanidine changes, as well as methods, systems, and components thereof based on the novel mutation patterns for identifying tumor samples with increased tumor mutation burden (TMB). Both the mutation patterns and the methods, systems, and components thereof can be used to identify cancer patients, particularly those with microsatellite-stable cancers, who will effectively respond to immune checkpoint inhibitor therapy.

Description

[Technical Field]

[0001] The field of the present invention relates generally to cancer, including methods for cancer diagnosis, prognosis, and treatment. In particular, the field of the present invention relates to novel mutational signatures of unique sets of point mutations containing cytosine or guanidine changes, as well as methods, systems, and components thereof based on the novel mutational signatures for identifying tumor samples with increased tumor mutational burden (TMB). Both the mutational signatures and the methods, systems, and components thereof can be used to identify cancer patients, particularly microsatellite stable cancer patients, who will effectively respond to immune checkpoint inhibitor therapy. [Background technology]

[0002] Treatment with immune checkpoint blockade (ICB) therapy antibodies, such as those targeting programmed cell death protein 1 (PD-1), its ligand (PD-L1), and / or cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), has shown impressive response rates and the potential for durable disease remission, but unfortunately, only in a subset of cancer patients. Furthermore, many patients who effectively respond to ICB may experience toxicity (Non-Patent Document 1). Thus, despite the remarkable success of ICB in improving overall survival in patients with various types of cancer, including metastatic melanoma (Non-Patent Document 2), non-small cell lung cancer (NSCLC) (Non-Patent Document 3), urothelial carcinoma (Non-Patent Document 4), renal cell carcinoma (Non-Patent Document 5), and many others, its potentially high toxicity and severe side effects pose a growing need for approaches that can predict which patients will respond effectively. Currently, this need is further underscored by the high cost of immunotherapy drugs and the reluctance of many health insurance companies to prepay or reimburse prescriptions.For these reasons, various tests and predictive algorithms have been proposed to identify responders to ICB.

[0003] Detection of PD-L1 by immunohistochemistry (IHC) has been widely studied as a predictor of anti-PD(L)-1 therapy and is considered a useful biomarker in certain circumstances, as witnessed by the Food and Drug Administration (FDA)-approved companion diagnostic test for pembrolizumab in NSCLC, gastric / gastroesophageal junction adenocarcinoma, cervical cancer, and urothelial carcinoma, and has also shown some predictive ability in other cancer types, such as head and neck cancer and small cell lung cancer. However, PD-L1 IHC is an imperfect marker and has been considered inconclusive for predicting immunotherapy response in many situations (NPL 6 and references therein). Therefore, alternative biomarkers have been evaluated, including the presence of tumor-infiltrating lymphocytes (TILs) (NPL 7), inflammatory gene expression profiles in T cells (NPL 8), immune gene expression signatures, or even assessment of the gut microbiota (NPL 9).

[0004] Cancer is now known to be a genetic disease in which the accumulation and selection of somatic mutations drives tumor growth and evolution (Non-Patent Document 10). The problem is that every type of cancer, and even every individual cancer, has a unique genetic profile (Non-Patent Document 11). And although detectable driver mutations, such as those in KRAS, BRAF, or EGFR genes, which can themselves be targeted by specific approaches, are often prevalent, their detection does not usually predict how effectively a cancer will respond to the activation of a patient's immune system by ICB.

[0005] Accumulating evidence indicates that a particularly potent class of antigens, enabling the immune system to distinguish between normal and transformed cancer cells and effectively target the latter, is formed by peptides completely absent from the normal human genome; these antigens are commonly referred to as "neoantigens." For a large group of human tumors without viral etiology, such neoantigens result solely from the expression of tumor-specific genetic alterations (NPL 12). However, it is believed that only a small fraction of somatic mutations in tumor DNA can be translated and processed, loaded onto major histocompatibility complex (MHC) molecules, and presented on the cancer cell surface, and even fewer somatic mutations are likely to be recognized by T cells (NPL 13). Thus, not all neopeptides are effectively immunogenic (NPL 14). Furthermore, at least in melanoma, the majority of neoantigen-specific T cell responses are directed against peptides that are essentially unique to a given single specific tumor and, moreover, appear unlikely to play a major role in cellular transformation (NPL 15). In conclusion, due to the unique nature of this situation, it is extremely difficult to establish markers that predict response to ICB based on neoantigen profiling. However, the collected data are consistent in confirming the concept that the more somatic mutations a tumor typically accumulates, the more T cell-inducing antigens it is likely to form and present to the immune system. As a result, a general estimate of the number of somatic genetic errors accumulated in the tumor genome is now widely recognized as a useful estimate of tumor neoantigen burden.

[0006] In 2018, the importance of this tumor-specific accumulation of genetic errors, manifested either as the presence of microsatellite instability (MSI) or an increased tumor mutation burden (TMB, also known as tumor mutation load or TML), was recognized by the FDA by marking them as excellent indicators of immunotherapy in several cancers (16). Importantly, the FDA approval of anti-PD-1 therapy in patients with so-called microsatellite instability-high (MSI-H) cancers was the first tissue-agnostic drug approval and the first FDA-approved companion biomarker assay for pan-cancer therapy. This represents a particularly important paradigm shift in the oncology field from a focus on tissue-specific treatments to a more global approach that relies on personalized genetic indications and can be applied to virtually all cancers for which an indication exists.

[0007] MSI is the genome-wide accumulation of numerous DNA replication errors resulting from impaired DNA mismatch repair (MMR) mechanisms. These errors are specifically observed as changes in the number of nucleotides within single and dinucleotide repeat sequences, e.g., (A)n or (CA)n, due to deletions or insertions of repeat units (also known as "indels"). It is observed in a substantial subset of colorectal cancer (CRC) cases, and defects in MMR genes are known to be crucial for tumorigenesis and disease progression. In fact, the discovery of a single super-responder with MSI-H CRC quickly led to the successful clinical trial of pembrolizumab in patients with MSI-H or MMR-deficient solid tumors and its rapid approval in this biomarker-defined (rather than tissue-defined, as was previously the case) patient group (NPL 17).

[0008] The reliability of MSI-H as an indicator of effective immunotherapy is further supported by the finding that the increased accumulation of MSI-specific genomic indel mutations correlates with the generation of novel open reading frames encoding neoantigen sequences (NPL 18). The latter may explain why MSI-H tumors naturally exhibit high lymphocytic infiltration and consequently select for increased expression of at least five immune checkpoint molecules (NPL 19), which are precise targets for therapeutic checkpoint inhibitors. This, combined with the fact that available tests and diagnostic standards exist for detecting MSI in tumors, including the original Bethesda panel and its derivatives, or the more recent highly sensitive and rapid DNA-based Idylla™ MSI assay from Biocartis NV, based on novel short homopolymer markers (described in Patent Literature 1 and Patent Literature 2), has made MSI a recommended primary screening tool not only for colorectal and endometrial cancers, where MSI-H tumors occur relatively frequently, but also for many other cancer types.

[0009] Another histopathological hallmark of many MSI-H tumors is generally an increased tumor mutation burden or load (TMB or TML). TMB is a highly intriguing phenomenon resulting from tumor selection that disables DNA surveillance pathways that may be distinct from MMR. Consequently, it has been observed in many cancers, particularly melanoma and non-small cell lung cancer (NSCLC), that are microsatellite stable (MSS). For example, although the majority of patients with MSI-H solid tumors also have high TMB, only 16% of patients with high TMB were estimated to be MSI-H (NPL 20). Importantly, TMB is considered highly useful for estimating neoantigen burden and therefore holds great potential for identifying patients who will effectively benefit from immunotherapy, especially those with MSS tumors with high TMB that cannot be identified by MSI testing (NPL 21).

[0010] For example, MSI-H is very rare in NSCLC, and elevated TMB is observed relatively frequently, although not as high as the median number of mutations in MSI-H tumors, which often reaches several thousand per exome (NPL 22). Comparison of findings in small cell lung cancer (SCLC), NSCLS, and urothelial carcinoma indicates that the TMB threshold for selecting good ICB responders is approximately 200 missense mutations, corresponding to ≥10 mutations per megabase (mut / Mb) in the Foundation One study or ≥7 mut / Mb in the MSK-IMPACT study (NPL 23; NPL 24; NPL 25). Interestingly, applying a higher threshold of TMB equal to 16.2 mut / Mb for atezolizumab treatment in NSCLC (26) or 15 mut / Mb for ipilimumab / nivolumab treatment (27) did not increase efficacy, suggesting a functional basis for the selection of ICB-responsive antigens in tumors. Given the above, there are indications that increased TMB in MSS tumors does not need to be large to identify favorable responders, and that higher TMB is more likely to display immune-effective neoantigens (28).

[0011] One of the current major challenges in the oncology field for setting an accurate TMB threshold to define ICB responders is the significant variation in TMB counts depending on the service provider and the TMB estimation method used. Initially, TMB was determined by whole-exome sequencing (WES) of tumor DNA matched with normal DNA to exclude germline mutations and capture only tumor-acquired somatic mutations (29). Results are reported as the total number of somatic mutations, which may or may not include indels. While WES is still believed to be the best method for measuring exonic TMB, unfortunately, due to its cost and complexity, it has been replaced in clinical practice by more or less strict approximate approaches and remains a research-only investigative tool. For example, common clinical approaches include the use of targeted NGS panels, such as Foundation Medicine's F1CDx panel and MSKCC's MSK-IMPACT panel, both of which have demonstrated predictive capabilities for ICB in various published studies and have consequently been approved by the US FDA. F1CDx defines TMB as the total number of synonymous and nonsynonymous mutations per megabase (mut / Mb) based on the number of substitutions captured in the coding portions of panel genes after applying various filters and other mathematical functions, including excluding germline events by comparison with public and private mutation databases. MSK-IMPACT focuses on nonsynonymous mutations using data from sequencing panel genes from both tumor and germline DNA. Many approaches exist, all of which differ in variables such as genome size covered by the NGS-targeted gene panel, sequencing depth, mutation types covered, read length, cutpoints or filters, and other mathematical functions applied during variant calling, as well as the choice of aligner. As a result of this variability, the final reported TMB level inevitably varies, and often very significantly, depending on the estimation method used.

[0012] Due to the above and several additional preanalytical factors (including sample fixation artifacts and NGS library preparation strategies) that can affect the final reported TMB count, significant inconsistency currently exists in TMB assessment, especially at the lower TMB ranges that may be clinically relevant. As a result, establishing a uniform, generally applicable, and meaningful threshold for TMB classification is currently nearly impossible. A desirable alternative would be to directly test for the presence of mutations in genes that directly cause the TMB phenotype. Unfortunately, the current state of knowledge regarding all possible underlying mechanisms may be insufficient to define all genes that may be involved in the process, let alone the genes we believe are involved, and much information is still missing regarding the precise mutations that cause the phenotype. In addition to mechanisms involved in maintaining the fidelity of DNA replication, including the p53 pathway, polymerases ε and δ (Non-Patent Document 30; Non-Patent Document 31), DNA proofreading mechanisms, and the aforementioned MMR, numerous other factors have been reported to contribute to TMB, ranging from UV light in melanoma to tobacco carcinogens in NSCLC (Non-Patent Document 32), mutations associated with the APOBEC cytidine deaminase family (Non-Patent Document 33), or those arising after cytotoxic chemotherapy in resistant emergent tumor subclones (Non-Patent Document 34). Consequently, given the expected multicomponent nature and complexity of the underlying TMB-related pathways and the precise causative mutations involved (Non-Patent Document 35), the field would greatly benefit from the provision of more specific and defined "hotspot" mutational patterns to capture the small subset of immunotherapy responders affected by TMB, similar to the principles of existing tests for MSI. [Prior art documents] [Patent documents]

[0013] [Patent Document 1] PCT / EP2013 / 057516

Patent document 2

Non-licensed literature

[0014] [Non-licensed document 1] Yuan et al., 2016, J ImmunoTher of Canc [Non-licensed document 2] Hodi et al., 2010, N Eng J Med [Non-licensed document 3] Borghaei et al., 2015, N Eng J Med

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[0015] To address the above-discussed shortcomings, we present here, for the first time, a panel and a method based on it for capturing at least a portion of patients with increased tumor mutation burden who may benefit from ICB or other immunotherapy approaches. The advantage of the method proposed herein is that it captures tumor samples that exhibit a genomic scarring signature reminiscent of defects in POLE gene function (encoding the catalytic subunit of polymerase ε) in microsatellite-stable (MMS) patients, which may be missed by existing standard assays such as MSI / MMR deficiency assays or complementary tests targeting specific hotspot POLE / POLD1 mutations. Furthermore, the mutation pattern presented herein also captures cases with increased TMB, which may result from perturbations of other repair mechanisms, such as mutations in the EXO1 and MUTYH genes. Furthermore, cases with elevated TMB have been detected without demonstrating a clear underlying mechanism for the repair defect. These and other features and advantages are further described herein. [Means for solving the problem]

[0016] Disclosed herein are methods, systems, and components thereof for analyzing the presence of increased tumor mutation burden (TMB) in a sample obtained from a patient. The disclosed methods and systems are typically utilized to test at least four different genomic sites that map to the GRC37 human genome assembly of Table 1 for the presence of alterations of cytosine or guanine to any other nucleic acid base, wherein detection of the presence of at least one of the alterations indicates the presence of increased tumor mutation burden (TMB).

[0017] The disclosed methods, systems, and components may further be utilized to treat patients, such as cancer patients, with an increased tumor mutational burden, as defined herein, which may include administering immunotherapy, such as anti-PD1, anti-PD-L1, and / or anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) therapy, administering chemotherapy, administering radiation therapy, and / or performing surgery or resection of the patient's tumor tissue.

[0018] As an example, methods, systems, and components are presented for analyzing the presence of increased tumor mutation burden (TMB) in samples obtained from patients.The methods, systems, and components include testing the sample for the presence of cytosine or guanine to other nucleic acid bases, such as adenine or thymine, at genomic test sites.In some embodiments, the disclosed methods, systems, and components are used to analyze the presence of at least four different genomic sites, such as those listed in Table 1, mapped to the GRC37 human genome assembly. chr10 89720744, located within the PTEN gene; chr7 112461939, located within the BMT2 gene; chr12 89985005, located within the ATP2B1 gene; and chr17 29677227, located within the NF1 gene; wherein detecting the presence of at least one cytosine or guanine alteration indicates the presence of an increased tumor mutational burden (TMB).

[0019] A sample can be tested for the presence of a mutation in at least one of the different genomic sites by reacting the sample with a reagent that determines the identity of the nucleotide at the different genomic sites. Suitable reagents can include, but are not limited to, primers that hybridize with sequences adjacent to the site of the mutation and can be used to amplify and prepare a polynucleotide sample containing the mutation. In some embodiments, the primers can be used to prepare an amplicon that contains the mutation site and has a size of at least about 50, 100, 150, 200, or 250 nucleotides in length (or a size within a range limited by any of these values, such as 50 to 150 nucleotides in length).

[0020] Suitable reagents may include primers for sequencing a nucleotide sample and identifying nucleotides at different genomic sites. Suitable primers may hybridize adjacent to the site of a cytosine or guanine variation, for example, at about 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 nucleotides upstream (or downstream) of the variation (or within a range bounded by any of these values, such as 10-50 nucleotides upstream or downstream of the variation).

[0021] In further examples, the disclosed methods, systems, and components include testing samples for the presence of cytosine or guanine changes at additional genomic sites disclosed herein that may indicate increased TMB.

[0022] In further examples, the disclosed methods, systems, and components include testing a tumor sample to determine the MSI status of the tumor sample as a microsatellite stability (MSS) test. In further embodiments, the disclosed methods, samples, and components include testing a tumor sample and determining whether the tumor sample contains or lacks a POLE hotspot mutation selected from P286R and V411L.

[0023] The systems disclosed herein may include automated systems that include components for carrying out the methods disclosed herein. Optionally, the disclosed systems include instruments and cartridges that are adapted for and / or contain appropriate structures and / or reagents for carrying out the methods disclosed herein. Similarly, cartridges that contain reagents for carrying out the disclosed methods and that are operable as part of such automated systems are further provided.

[0024] In a further aspect, further disclosed is the use of the disclosed methods, cartridges, and systems in detecting TMB.

[0025] In yet another non-limiting embodiment, the methods, cartridges, and systems provided herein provide an additional use in determining whether a patient from whom a tumor sample was obtained should receive cancer immunotherapy treatment. An example of the latter may be immune checkpoint blockade (ICB) therapy, which includes an antibody specific for at least one of the following targets: PD-1, PD-L1, CTLA4, TIM-3, or LAG3. That is, the disclosed methods, systems, and components may include administering cancer immunotherapy treatment to a patient in need thereof. [Brief explanation of the drawings]

[0026] For a more complete understanding, please refer to the following detailed description taken in conjunction with the accompanying drawings.

[0027] [Figure 1] Figure 1 shows the TMB of different categories of TCGA-UCEC tumors. Red circles indicate three samples with POLD1 mutations but no POLE mutations. [Figure 2] Figure 2 shows the TMBs of different categories of TCGA-COAD tumors. Three POLD1-mutated samples have baseline TMBs. [Figure 3] Figure 3 shows the TMB of different categories of TCGA-COAD tumors. Three POLD1-mutated samples have baseline TMB. [Figure 4] Figure 4 shows the TMB of different categories of TCGA-non-UCEC and non-COAD tumors. [Figure 5] Figure 5 shows the TMBs of various categories of TCGA-UCEC tumors. Circles indicate coverage of the eight MSS POLE non-hotspot mutation samples identified by retrospective application of the initial 34-marker panel to all UCEC samples in TCGA. [Figure 6] FIG. 6 shows the co-occurrence between the 34 markers originally identified, for example, RB1CC1 and BRWD3 have one co-occurrence. [Figure 7] Figure 7 shows a distribution histogram of 10,000 randomly selected subsets of four markers as a function of sample acquisition in the dataset. For a randomly selected four marker panel, the maximum number of samples observed at any one time is 43, with a median of 30. DETAILED DESCRIPTION OF THE INVENTION

[0028] The practical application described herein is based on the identification of a marker panel for detecting mutational patterns of POLE function loss that can identify tumor samples with increased tumor mutation burden (TMB), thus also providing an indication of whether the patient from whom the tumor sample was derived is likely to respond effectively to cancer immunotherapy, such as immune checkpoint blockade (ICB) immunotherapy. The advantage of the marker panel and method presented herein derives from the fact that it appears to effectively identify samples with increased TMB, even if they are microsatellite stable (MSS) and / or lack hotspot POLE mutations. Consequently, the panel and method presented herein can be considered a gateway for identifying at least some patients who could benefit from ICB but who are overlooked by other currently available screening tests.

[0029] The panel presented herein is based on the initial identification of 34 highly recurrent genetic variants from UCEC records with confirmed MSS POLE hotspots available from whole-exome sequencing (WES) results listed in the TCGA database. The 34 recurrent variants comprise changes (i.e., mutations) of cytosine or guanine to thymine or adenine, or possibly any other nucleic acid base, and are listed in Table 1 below. They are defined by their location ("site," as further used herein) with reference to the GRCh37 / hg19 human genome assembly (currently accessible, for example, via the UCSC Genome Browser at https: / / genome.ucsc.edu / ). For clarity, different synonyms may be used herein, consistent with their standard meanings in the fields of molecular biology and biotechnology, when referring to a group or panel, or at least one or more of the 34 recurrent variants (or simply "variants") disclosed herein. These synonyms include reference to any one "mutation" or "mutations" (the latter possibly with a description, e.g., "recurrent mutation," "newly identified mutation," "mutation disclosed herein," etc.), "marker" or "markers" (the latter possibly with a description), "site of a cytosine or guanine change" or "sites of a cytosine or guanine change" (the latter possibly with a description), "cytosine or guanine change" or "cytosine or guanine changes" (the latter possibly with a description), or simply "change" or "changes" (the latter possibly with a description). To more clearly define these newly identified mutations, Table 1 also provides the name of the gene in which the change site defining the variant is located and the type of mutation the change causes in the gene product.For example, "stopgain" refers to a type of mutation that results in a premature stop codon, i.e., "a stop was gained," intended to terminate translation. Thus, the type of mutation labeled "nonsynonymous SNV" refers to a missense mutation, i.e., a single nucleotide variant (SNV) caused by a nucleobase mutation that alters a codon to produce a different amino acid in the product protein. Furthermore, Table 1 shows the exact nucleobase or nucleotide (nt) mutational changes in the coding sequence (CDS) of a gene (starting from the start codon of the most common mRNA variant), the amino acid (aa) mutation in the gene's protein product (where "X" indicates truncation), and, in the last column, the wild-type (WT) genomic sequence adjacent to the site where the mutation occurred (the nt at the site of change is marked in bold). As used herein, the terms nucleobase and nucleotide can be considered largely synonymous and refer to biochemical units within nucleic acids that can undergo mutational change. Their meaning is a subtle distinction from a purely biochemical perspective. Nucleobases are nitrogenous heterocyclic bases in nucleic acids, either double-ring purines such as adenine (A) or guanine (G), or single-ring pyrimidines such as thymine (T), uracil (U), or cytosine (C). Conversely, nucleotides are the actual monomers that make up the chains of nucleic acid biopolymer molecules, such as DNA or RNA. Each nucleotide consists of a nucleobase, a five-carbon pentose sugar (deoxyribose in DNA or ribose in RNA), and a phosphate group. In the last column of Table 1, the WT base at the mutated variant position is always indicated by 20 nucleotides, i.e., 19 nt (nucleotides) upstream and 20 nt downstream of the altered site. Notably, as can be seen from the column detailing the nt changes in the CDS, the affected nucleobase is always cytosine (C) or its complementary pairing nucleobase, guanine (G). Even more unusual, the recurrent variants all consist of C or G mutations in very similar sequence contexts.Namely, 33 of the 34 recurrent variants identified contain the trinucleotide sequence TT. C or its complement G (The sequence is always provided in the 5'->3' direction, and the nucleotide mutated in the recurrent variant is underlined.) Furthermore, 23 of them are TT C GA or its complementary strand TC G The mutations occur within the same 5-nt strip of AA (the site of the mutation is underlined). This finding is consistent with previous reports on mutation patterns in POLE deficiency (Shinbrot et al., 2014, Genome Res) and highlights the specificity of the POLE scar mutation pattern variants identified herein. Interestingly, 79.4% of the mutations involve cytosine to thymine changes (C to T, which in DNA is equivalent to a guanine to adenine (G to A) change, depending on which DNA strand the mutation is read on), while the remaining 20.6% involve either C to A or G to T, depending on which DNA strand the mutation is read on. [Table 1] JPEG0007737315000002.jpg193142JPEG0007737315000003.jpg200143JPEG0007737315000004.jpg59142

[0030] The 34 recurrent cytosine or guanine alterations initially identified in the TCGA-MSS-UCEC samples were then tested against all tumor records in the TCGA database, as detailed in the Examples section. This analysis resulted in 82 samples from a variety of tumors, the details of which are provided in Table 2 (where "MSS" = microsatellite stable; "MSI-L" or "MSI-H" = MSI positive; "Hotspot" = presence of a POLE hotspot mutation; "POLE" = presence of a POLE non-hotspot mutation; "EXO1" = presence of an EXO1 mutation; "MUTYH" = presence of a MUTYH mutation; "NA" = not available, i.e., presence of a mutation of interest not represented in TCGA; TMB expressed as substitutions / Mb, not including indels).

[0031] Interestingly, 56 of these samples were annotated in TCGA as having TMB > 300 substitutions / megabase (subst / Mb), labeling them as having a hypermutator phenotype or hyperTMB ("HYPER"). Additionally, 64 had TMB > 200 subst / Mb (upper-end high TMB or "high+" or higher), 72 had TMB > 100 subst / Mb (medium-range high or "high" or higher), and 7 had TMB < 50 subst / Mb (classified as medium and low increments of TMB; "med incr" and "low incr"). Of the samples, 55 were MSS, 66 had mutations in the POLE gene (44 of which had POLE hotspot mutations), and 6 were positive for EXO1 mutations, while 4 were positive for MUTYH mutations. All of the above suggests promising specificity for detecting samples with perturbations in any of the DNA surveillance mechanisms, particularly those not detectable by MSI or hotspot POLE mutation testing. Notably, the panel's markers, often MSS samples, appear surprisingly efficient in identifying high, especially hyperTMB-affected samples, offering great potential for identifying a proportion of effective responders to ICB that would be missed by current screening tests. Notably, there appears to be no correlation between the number of mutated variants and TMB levels (see Figure 2 below), meaning that each mutational marker, by itself, can already predict increased TMB in a sample. [Table 2] JPEG0007737315000006.jpg181132

[0032] The discovery of 34 single nucleotide polymorphisms specifically associated with increased TMB is unexpected. Increased TMB is expected to be caused by defects in DNA replication and repair, with mutations expected to spread randomly and be scattered throughout the genome of cancer cells. Today, increased TMB must be assessed by sequencing hundreds of amplicons with approximately 1 Mb of coverage (Buettner et al., 2019, ESMO Open Cancer Horiz), requiring large sequencing capacity. Therefore, the finding that each of the 34 SNVs predicts elevated TMB on its own is surprising and points to the 34 loci, e.g., POLE, EXO1, MUTYH, and other previously unidentified mechanisms, as preferred targets of replication and repair defects. Because the mutation pattern is observed in MSS samples, it is unrelated to MSI or defective MMR. Notably, the median TMB level found for the 34 SNVs was equivalent to 612 mutations / Mb, significantly higher than the median TMB level in MSI samples, which averaged approximately 47 mutations / Mb (Fabrizio et al., 2018, JGastrointest Oncol). Furthermore, the number of samples with TMB <10 in TCGA was 3,529, of which two were positive for one of the 34 SNVs listed in Table 1. This suggests that each of the 34 markers has a strong association with elevated TMB and very high specificity, thereby advocating for further clinical use. Due to the small number of targets, the markers identified herein can be efficiently used to detect elevated TMB in various diagnostic applications. These include PCR-based detection or the addition of the 34 loci to existing NGS pipelines, without requiring much higher NGS capacity, to identify cancer patients with elevated TMB, who are expected to be prime candidates for immunotherapy response.

[0033] In view of the above, methods, systems, and components are provided for analyzing the presence of increased tumor mutation burden (TMB) in a sample obtained from a patient, and the method, system, and components include: if at least one of the genomic sites in Table 1 mapped to the GRC37 human genome assembly contains a change from cytosine or guanine to other nucleic acid bases (for example, thymine or adenine), and detecting the presence of at least one such change indicates increased tumor mutation burden (TMB), the method, system, and components classify the sample as having tumor mutation burden (TMB).In possible embodiments, the change from cytosine or guanine to other nucleic acid bases is selected from the following: a change from cytosine to thymine or adenine, and a change from guanine to adenine or thymine.In a further embodiment, the change from cytosine or guanine to other nucleic acid bases is selected from the following: a change from cytosine to thymine, and a change from guanine to adenine.

[0034] For example, the disclosed methods, systems, and components may include analyzing for the presence of increased tumor mutation burden (TMB) in a sample obtained from a patient. In some embodiments, the methods, systems, and components may include examining at least four different genomic sites mapped to the GRC37 human genome assembly in Table 1 for the presence of a cytosine or guanine to other nucleic acid base (e.g., thymine or adenine) change, wherein detecting the presence of at least one of the mutations indicates an increased tumor mutation burden (TMB). In possible embodiments, the change from cytosine or guanine to other nucleic acid base is selected from a cytosine to thymine or adenine change, and a guanine to adenine or thymine change. In further embodiments, the change from cytosine or guanine to other nucleic acid base is selected from a cytosine to thymine change and a guanine to adenine change.

[0035] As used herein, the term "increased TMB" should be interpreted as an increase in tumor mutation burden or tumor mutation load (TMB or TML, respectively) in reference to a normal, i.e., non-tumor, sample, usually a normal histomatched sample from the same patient. Because TMB values ​​are highly dependent on the estimation method used (WES or targeted NGS also depend on the mutations and functions included in the estimation), the exemplary values ​​provided herein are consistent with annotations obtained from TCGA and include synonymous and nonsynonymous substitutions / Mb, but do not include indels. With respect to TMB as defined by TCGA, it can be assumed that the methods presented herein can indicate the presence of an increased TMB, defined as exhibiting more than 4.5 substitutions / Mb. However, depending on the variants selected from Table 1 and the context-dependent application of various screening thresholds, in possible embodiments, an increased TMB can be defined as exhibiting more than 10 substitutions / Mb, more than 50 substitutions / Mb, or perhaps more than 100 substitutions / Mb. In one embodiment, it can be defined as exhibiting more than 200 or more than 300 substitutions / Mb.

[0036] The exemplary selection of four markers from Table 1 allows the following coverage of all samples from Table 2: PTEN(i), BMT2, ATP2B1, and GRM5, covering approximately 54% of 44 / 82 glioblastoma samples, with 7 high, 36 hyper, and 1 low increment TMB. 65% of UCEC samples are covered. PTEN(i), BMT2, ATP2B1, and NF1, covering 43 / 82 samples (9 high, 34 hyper), also covering 65% of UCEC samples. In line with the above, and based on an estimate of the individual strength of all mutant markers, four exemplary markers that perform very well together were found to be those located in the BMT2 gene, the ATP2B1 gene, the NF1 gene, and the PTEN gene located at chr10 89720744 (termed PTEN(i) because two recurrent variants were identified in PTEN).

[0037] Thus, in some embodiments, the disclosed methods, systems, and components may include detecting alterations at four or more different genomic sites in Table 1, optionally wherein the at least four different genomic sites in Table 1 are: chr10 89720744, located within the PTEN gene; chr7 112461939, located within the BMT2 gene; chr12 89985005, located within the ATP2B1 gene, and . chr17 29677227, located within the NF1 gene.

[0038] An exemplary selection of a five-marker panel made with PTEN(i), BMT2, ATP2B1, NF1, and either GRM5 or UGT8 yields 50 / 82 samples (approximately 61%) from Table 2. Specifically, the PTEN(i), BMT2, ATP2B1, NF1, and GRM5 panel provides 50 / 82 coverage, including 9 high, 40 hyper, and 1 low increment (glioblastoma). UCEC coverage for this combination is 72%. For PTEN(i), BMT2, ATP2B1, NF1, and UGT8, the total coverage is 50 / 82, with 11 high, 39 hyper, and 70% coverage of UCEC. Thus, in another possible embodiment, the disclosed methods, systems, and components further include testing for the presence of an alteration at the following site in Table 1: chr11 88338063, located within the GRM5 gene.

[0039] Next, the performance of a six-marker panel, e.g., PTEN(i), BMT2, ATP2B1, NF1 plus GRM5, UTG8, HTR2A, or ZNF678, is as follows: for PTEN(i), BMT2, ATP2B1, NF1, GRM5, and UGT8, it is 55 / 82, with 11 high, 43 hyper, and 1 low increment, for a UCEC coverage of 74%; for PTEN(i), BMT2, ATP2B1, NF1, GRM5, and HTR2A, it is 55 / 82, with 10 high, 42 hyper, 2 low, and 1 med, for a UCEC coverage of 78%. For PTEN(i), BMT2, ATP2B1, NF1, UGT8 and HTR2A, 56 / 82, high 12, hyper 42, low 1, med 1, and UCEC coverage of 78%. Thus, in another possible embodiment, the disclosed methods, systems, and components further include testing for the presence of an alteration at the following site in Table 1: chr4 115544340, located within the UGT8 gene.

[0040] In further embodiments, the disclosed methods, systems, and components include further testing for the presence of alterations at the following sites in Table 1: chr13 47409732, located within the HTR2A gene; chr1 227843477, located within the ZNF678 gene. These and other exemplary seven marker panels have the following coverage: (An additional variant, designated PTEN(ii), represents a mutation at site: chr10 89624245, within the PTEN gene.) NF1 BMT2 ATP2B1 PTEN(i) GRM5 UGT8 HTR2A, 60 / 82, high 12, hyper 45, low 2, med 1, and 80% of all UCEC. NF1 BMT2 ATP2B1 PTEN(i) GRM5 UGT8PTEN(ii), 60 / 82, high 11, hyper 47, low 1, med 1, and 78% of UCEC. NF1 BMT2ATP2B1 PTEN(i) GRM5 UGT8 ZNF678, 59 / 82, high 12, hyper 46, low 1, UCEC 80%. NF1 BMT2 ATP2B1 PTEN(i) GRM5 HTR2APTEN(ii), 59 / 82, high 10, hyper 45, low 2, medium 2, UCEC 80%. NF1 BMT2ATP2B1 PTEN(i) GRM5 HTR2A ZNF678, 59 / 82, high 11, hyper 45, low 2, med 1, UCEC 83%. NF1 BMT2 ATP2B1 PTEN(i) GRM5 PTEN(ii) ZNF678, 60 / 82, high 10, hyper 48, low 1, medium 1, UCEC 83%. NF1 BMT2 ATP2B1 PTEN(i) UGT8 HTR2APTEN(ii), 60 / 82, high 12, hyper 45, low 1, medium 2, UCEC 80%. NF1 BMT2ATP2B1 PTEN(i) UGT8 HTR2A ZNF678, 59 / 82, high 13, hyper 44, low 1, medium 1, UCEC 83%. NF1 BMT2 ATP2B1 PTEN(i) UGT8 PTEN(ii) ZNF678, 60 / 82, high 12, hyper 47, med 1, UCEC 83%.NF1 BMT2ATP2B1 PTEN(i) HTR2A PTEN(ii) ZNF678, 58 / 82, 11 high, 43 hyper, 1 low, 2 med, 80% of all UCEC.

[0041] In another embodiment, the disclosed methods, systems, and components further include testing for the presence of an alteration at the following site from Table 1: chr10 89624245, located within the PTEN gene (further referred to as PTEN(ii) in the above variant).

[0042] As can be seen from the above calculations, each time one marker is added, coverage of the samples in Table 2 improves. It was found that 19 markers, rather than the initially identified 34 markers, were sufficient to cover all samples in Table 2. Based on this finding, alternative panels (each one more marker than the exemplary panel directly above) can be provided as further exemplary embodiments of the present invention until a panel of 19 or more markers covering all samples in Table 2 is achieved.

[0043] In one further embodiment, the disclosed methods, systems, and components further include testing for the presence of an alteration at the following site in Table 1: chr19 47424921, located within the ARHGAP35 gene.

[0044] In another embodiment, the disclosed methods, systems, and components further include testing for the presence of an alteration at the following site in Table 1: chr8 121228689, located within the COL14A1 gene.

[0045] In another embodiment, the disclosed methods, systems, and components further include testing for the presence of alterations at the following sites in Table 1: chr10 89720744, located within the PTEN gene; chr7 112461939, located within the BMT2 gene; chr12 89985005, located within the ATP2B1 gene; chr17 29677227, located within the NF1 gene; chr11 88338063, located within the GRM5 gene; chr10 89624245, located within the PTEN gene; chr4 115544340, located within the UGT8 gene; chr13 47409732, located within the HTR2A gene; chr1 227843477, located within the ZNF678 gene; chr19 47424921, located within the ARHGAP35 gene; chr8 121228689, located within the COL14A1 gene.

[0046] In another embodiment, the disclosed methods, systems, and components further include testing for the presence of one or more alterations at the following sites in Table 1: chr18 50832017, located within the DCC gene; chr7 39745749, located within the RALA gene; chr11 60468341, located within the MS4A8 gene; chrX 110970087, located within the ALG13 gene; chr18 74635035, located within the ZNF236 gene; chrX 79942391, located within the BRWD3 gene; chr2 113417110, located within the SLC20A1 gene; chrX 99662008, located within the PCDH19 gene; chr9 5968511, located within the KIAA2026 gene; chrX 74519615, located within the UPRT gene; chr6 31779382, located within the HSPA1L gene; chr19 52825339, located within the ZNF480 gene; chr3 370022, located within the CHL1 gene; chr18 53017619, located within the TCF4 gene; chr6 101296418, located within the ASCC3 gene; chr2 9098719, located within the MBOAT2 gene; chr19 12501557, located within the ZNF799 gene; chr18 54281690, located within the TXNL1 gene; chr16 68598492, located within the ZFP90 gene; chr10 128908585, located within the DOCK1 gene; chr1 78428511, located within the FUBP1 gene; chrX 119678368, located within the CUL4B gene; chr8 53558288, located within the RB1CC1 gene.

[0047] In the next possible embodiment, a 19-marker panel is used that covers all samples listed in Table 2. According to this embodiment, the disclosed methods, systems, and components include testing for the presence of alterations at the following sites in Table 1: chr10 89720744, located within the PTEN gene; chr7 112461939, located within the BMT2 gene; chr12 89985005, located within the ATP2B1 gene; chr17 29677227, located within the NF1 gene; chr11 88338063, located within the GRM5 gene; chr10 89624245, located within the PTEN gene; chr4 115544340, located within the UGT8 gene; chr13 47409732, located within the HTR2A gene; chr1 227843477, located within the ZNF678 gene; chr19 47424921, located within the ARHGAP35 gene; chr8 121228689, located within the COL14A1 gene; chr18 50832017, located within the DCC gene; chr7 39745749, located within the RALA gene; chr11 60468341, located within the MS4A8 gene; chrX 110970087, located within the ALG13 gene; chr18 74635035, located within the ZNF236 gene; chrX 79942391, located within the BRWD3 gene; chr2 113417110, located within the SLC20A1 gene; chrX 99662008, located within the PCDH19 gene.

[0048] In another embodiment, the disclosed methods, systems, and components include testing for the presence of the POLE hotspot P286R mutation or the hotspot V411L mutation.

[0049] In yet another embodiment, the disclosed methods, systems, and components include testing for POLE hotspot mutations. Thus, in one possible embodiment, the disclosed methods, systems, and components include analyzing a sample obtained from a patient for the presence or absence of an elevated tumor mutation burden (TMB). The disclosed methods, systems, and components may include testing the sample for the presence of a POLE hotspot P286R mutation or a hotspot V411L mutation, and the presence of a change of cytosine or guanine to other nucleobases, at at least four of the following distinct genomic sites mapped to the GRC37 human genome assembly in Table 1: chr10 89720744, located within the PTEN gene (PTEN(i) mutant); chr7 112461939, located within the BMT2 gene; chr11 88338063, located within the GRM5 gene; chr4 115544340, located within the UGT8 gene; chr12 89985005, located within the ATP2B1 gene; and chr17 29677227, located within the NF1 gene; where detection of the presence of at least one of the alterations at any of the genomic sites in Table 1 or any of the hotspot POLE mutations indicates increased tumor mutational burden (TMB).

[0050] In another embodiment, the disclosed methods, systems, and components may include testing for the presence of one or more alterations at the following sites in Table 1: chr12 89985005, located within the ATP2B1 gene; chr10 89624245, located within the PTEN gene; chr13 47409732, located within the HTR2A gene; chr1 227843477, located within the ZNF678 gene; chr19 47424921, located within the ARHGAP35 gene; chr8 121228689, located within the COL14A1 gene; chr18 50832017, located within the DCC gene; chr7 39745749, located within the RALA gene; chr11 60468341, located within the MS4A8 gene; chrX 110970087, located within the ALG13 gene; chr18 74635035, located within the ZNF236 gene; chrX 79942391, located within the BRWD3 gene; chr2 113417110, located within the SLC20A1 gene; chrX 99662008, located within the PCDH19 gene.

[0051] In alternative embodiments, the disclosed methods, systems, and components include testing for any one of the two POLE hotspot mutations P286R or V411L using any of the following combinations of markers from Table 1. Respective coverage results are also provided: BMT2 + SLC20A1 + PTEN(i) + 2 POLE hotspots: high 10, hyper 47 (>73%), 57 (75%) >15 and 85% UCEC. BMT2 + NF1 + ATP2B1 + PTEN(i) + 2 POLE hotspots: 12 high, 47 hyper (>76%), 59 (78%) >15, 89% UCEC. NF1 + BMT2 + UGT8 + PTEN(i) + 2 POLE Hotspots: High 14, Hyper 46 (over 77%), 60 (79%) over 15, 85% UCEC NF1 + BMT2 + GRM5 + PTEN(i) + 2 POLE Hotspots: High 12, Hyper 47, Low 1 (over 76%), 59 (78%) over 15, 85% UCEC BMT2 + NF1 + SLC20A1 + PTEN(i) + 2 POLE hotspots: high 12, hyper 48 (over 77%), 60 (79%) 15, 87% UCEC BMT2 + ALG13 + SLC20A1 + PTEN(i) + 2 POLE Hotspots: High 11, Hyper 48, Med 1 (over 77%), 60 (79%) over 15, 85% UCEC BMT2 + GRM5 + SLC20A1 + PTEN(i) + 2 POLE Hotspot: High 10, Hyper 49, Low 1 (over 76%), 59 (78%) over 15, 85% UCEC BMT2 + BRWD3 + SLC20A1 + PTEN(i) + 2 POLE Hotspot: High 12, Hyper 48 (over 77%), 60 (79%) over 15, 85% UCEC BMT2 + RB1CC1 + SLC20A1 + PTEN(i) + 2 POLE Hotspots: 12 high, 48 hyper (over 77%), 60 (79%) over 15, 85% UCEC

[0052] In another embodiment, the disclosed methods, systems, and components include testing the sample for the presence of additional mutations in POLE, and / or for the presence of mutations in EXO1 and / or MUTYH.

[0053] In another embodiment, the disclosed methods, systems, and components include testing for an additional mutation in POLE, where the additional mutation in POLE is one or more of the following: T1104M, A1967V, H144Q, S1644L, A456P, R1233, T2202M, P436R, R705W, S459F, S297F, A189T, P436R, L1235I, R1371, D213A, P135S, A456P, K777N, F367S.

[0054] In some embodiments, the disclosed methods, systems, and components include testing for any of these other POLE mutations, including T1104M, A1967V, H144Q, S1644L, A456P, R1233, T2202M, P436R, R705W, S459F, S297F, A189T, P436R, L1235I, R1371, D213A, P135S, A456P, K777N, F367S, and the presence of the detected mutation indicates increased TMB.

[0055] In some embodiments, the disclosed methods, systems, and / or components include and / or utilize oligonucleotide reagents for testing a sample and identifying nucleotides at genomic sites within the sample. Suitable oligonucleotide reagents can include primers or primer pairs for amplifying a polynucleotide sample containing the genomic site being tested.

[0056] In some embodiments, the oligonucleotide reagent comprises a primer pair that hybridizes to a polynucleotide sequence that flanks a genomic site in a polynucleotide sample and can be utilized to amplify the polynucleotide sample and prepare an amplicon that includes the genomic site (e.g., a genomic site in Table 1). The primer pair can hybridize to a polynucleotide sequence that flanks the genomic site at selected flanking sites to prepare an amplicon that includes the genomic site and has an appropriate size, e.g., at least about 50, 100, 150, 200, or 250 nucleotides, or a size range bounded by any of these values, such as 50 to 150 nucleotides. A suitable oligonucleotide reagent can comprise a set of primer pairs for amplifying multiple genomic sites in Table 1, e.g., four or more primer pairs for amplifying four or more genomic sites in Table 1 in a polynucleotide sample.

[0057] In some embodiments, the oligonucleotide reagent comprises primers for sequencing a polynucleotide sample containing a genomic site (e.g., a genomic site in Table 1). Thus, the primers may hybridize to a polynucleotide sequence upstream of the genomic site, e.g., at least about 10, 20, 30, 40, or 50 nucleotides upstream of the genomic site, or within a range bounded by any of these values, such as a sequence at least about 10, 20, 30, 40, or 50 nucleotides upstream of the genomic site, or a sequence 30-50 nucleotides upstream of the genomic site. The primers can then be utilized to sequence the polynucleotide sample and determine the identity of the nucleotide at the genomic site. A suitable oligonucleotide reagent may comprise a set of primers for sequencing multiple genomic sites in Table 1, e.g., four or more primers for sequencing four or more genomic sites in Table 1 in a polynucleotide sample.

[0058] In some embodiments, the oligonucleotide reagent comprises a probe that hybridizes to a genomic site (e.g., a genomic site in Table 1). Suitable probes may include a probe that hybridizes to a mutation at the genomic site and / or a probe that hybridizes to a wild-type or control sequence at the genomic site. Alternatively, suitable probes may include a probe that hybridizes to a mutation at the genomic site, possibly provided together with a probe that hybridizes to a wild-type or control sequence at the genomic site. A suitable oligonucleotide reagent may comprise a set of probes for hybridizing to multiple genomic sites in Table 1, for example, four or more probes for hybridizing to four or more genomic sites in Table 1 in a polynucleotide sample.

[0059] In another embodiment, the disclosed methods, systems, and components include testing the sample for the presence of one or more mutations using at least one oligonucleotide specific for hybridizing with the at least one or more mutations. The oligonucleotide can be a primer or a probe. Because the advantage of the methods provided herein over NGS alternatives is the limited number of markers, the methods of the present invention can potentially be performed using PCR-based assays that include mutation-specific oligonucleotides, such as primers (e.g., TaqMan primers) and detection probes. In another embodiment, the disclosed methods, systems, and components include oligonucleotides (e.g., primers or primers and probes) for performing multiplex PCR. According to this embodiment, such methods can include performing multiplex PCR in one or more reaction tubes or chambers, such as the chambers of an integrated detection cartridge.

[0060] In some embodiments, the disclosed methods include detecting alterations of cytosine or guanine to other nucleic acid bases (presumably adenine or thymine) in a polynucleotide sample (e.g., a genomic DNA sample) at four or more genomic sites from Table 1 that map to the GRC37 human genome assembly, wherein the detecting comprises amplifying at least a portion of the DNA sample and sequencing the amplified portion to detect the alterations. In some embodiments, the disclosed methods may include detecting alterations at the following four genomic sites: chr10 89720744, located in the PTEN gene; chr7 112461939, located in the BMT2 gene; chr12 89985005, located in the ATP2B1 gene; and chr17 29677227, located in the NF1 gene. Optionally, the disclosed method may include: (a) amplifying a DNA sample to prepare a DNA amplicon comprising the following four genomic sites: chr10 89720744, located in the PTEN gene; chr7 112461939, located in the BMT2 gene; chr12 89985005, located in the ATP2B1 gene; and chr17 29677227, located in the NF1 gene; and (b) sequencing the DNA amplicon to detect alterations. In further embodiments, the method of the present invention may include detecting one or more additional alterations at the sites listed in Table 1, as described above. Optionally, the DNA sample is obtained from a cancer patient, and the method of the present invention further includes administering the DNA sample to the patient for cancer treatment (optionally including administering immunotherapy and / or non-immunotherapy to the patient, such as, for example, chemotherapy, radiation therapy, and / or surgery (e.g., tumor resection)).

[0061] In some embodiments, the disclosed system comprises a reagent for detecting cytosine or guanine to any other nucleic acid base alterations at four or more genomic sites from Table 1 mapped to the GRC37 human genome assembly in a DNA sample, and optionally, the reagent comprises a component for amplifying at least a portion of the DNA sample and a reagent for sequencing the amplified portion to detect alterations. In further possible embodiments, the system may comprise a reagent for detecting one or more additional alterations at the sites listed in Table 1, as described above. In some embodiments, the reagent comprises a component for amplifying at least a portion of a DNA sample comprising the following four genomic sites: chr10 89720744, located in the PTEN gene; chr7 112461939, located in the BMT2 gene; chr12 89985005, located in the ATP2B1 gene; and chr17 29677227, located in the NF1 gene; and a component for sequencing the genomic sites. Optionally, the system may be at least partially automated and / or include a hardware processor programmed to execute and / or operate the mechanical components of the system to perform one or more tasks selected from (i) receiving and / or transporting the sample to the system, (ii) adding one or more components, reagents, and / or tools to the sample (e.g., one or more components, reagents, and / or tools for performing PCR and / or sequencing of four or more genomic sites listed in Table 1), (iii) performing PCR of the sample, (iv) detecting PCR products (e.g., PCR products of four or more genomic sites listed in Table 1), (v) sequencing at least four or more genomic sites listed in Table 1, and (vi) generating a report indicating the nucleotides of four or more genomic sites listed in Table 1.

[0062] The disclosed systems and components may include one or more cartridges. The term "cartridge" as used herein should be understood as a self-contained assembly of chambers and / or channels formed as a single object that can be transferred or moved as a single fitting inside or outside a larger instrument suitable for receiving or connecting such a cartridge. The cartridge and its instrument can be considered to form an automated platform, or even an automated platform. Some components contained in the cartridge may be rigidly connected, while others may be flexibly connected and movable relative to other components of the cartridge. Similarly, the term "fluidic cartridge" as used herein should be understood as a cartridge containing at least one chamber or channel suitable for handling, processing, discharging, or analyzing a fluid, preferably a liquid. One example of such a cartridge is described in WO2007004103. Advantageously, the fluidic cartridge may be a microfluidic cartridge. Generally, the terms "fluidic" or sometimes "microfluidic" as used herein refer to systems and arrangements dealing with the behavior, control, and manipulation of fluids that are geometrically constrained in at least one or two dimensions (e.g., width and height or channel) at small, typically submillimeter scales. Such small volumes of fluid are moved, mixed, separated, or otherwise processed at the microscale, requiring small size and low energy consumption. Microfluidic systems include structures such as micropneumatic systems (e.g., pressure sources, liquid pumps, microvalves, etc.) and microfluidic structures for processing micro-, nano-, and picoliter volumes (e.g., microfluidic channels, etc.). Exemplary and highly suitable fluidic systems in connection with the present invention were described in EP 1896180, EP 1904234, and EP 2419705.In line with the above, the term "chamber" should be understood as any functionally characterized compartment of any geometric shape within a fluidic or microfluidic assembly, characterized by at least one wall, and including the means necessary to perform the function attributed to said compartment. In this context, an "amplification chamber" should be understood as a compartment within a (micro)fluidic assembly, purposefully provided therein and suitable for performing nucleic acid amplification. Examples of amplification chambers include PCR chambers and qPCR chambers. In accordance with the above, in alternative embodiments, such cartridges and / or integrated systems are provided that contain one or more oligonucleotides specific for hybridizing to sequences containing at least one cytosine or guanine change at four or more genomic sites from Table 1 mapped to the GRC37 human genome assembly. Optionally, the disclosed cartridges may contain oligonucleotide primers for amplifying and / or sequencing one or more genomic sites listed in Table 1. Such primers can be designed within appropriate nucleotide ranges upstream or downstream to bracket cytosine or guanine changes at four or more genomic sites from Table 1 (exemplary ranges of nucleotides are provided above), or primers can be designed to cover cytosine or guanine changes from Table 1, for example, if an ARMS primer approach is desired.

[0063] In further embodiments, the disclosed methods, systems, and components include identifying samples affected by TMB regardless of MSI status. The disclosed methods, systems, and components may include analyzing for the presence of microsatellite instability (MSI) in a sample.

[0064] In another embodiment, the disclosed methods, systems, and components include evaluating a test sample to determine whether the test sample is microsatellite stable. According to this embodiment, the disclosed methods, systems, and components may include determining that the sample is microsatellite stable (MSS).

[0065] In another embodiment, in light of the paradigm shift in the cancer field that focuses on a generalized approach to cancer rather than limiting marker screening methods to tumors of a specific tissue of origin, the disclosed methods, systems, and components can be utilized to evaluate any type of cancer sample, i.e., cancer samples derived from any tissue type. This is particularly consistent with the fact that ICB is considered a generalized cancer treatment that is not limited to a specific cancer tissue type, and therefore, the methods of the present invention may identify ICB responders that cannot be identified by most commercially available methods. In an alternative embodiment, the disclosed methods, systems, and components can be utilized to evaluate any tumor sample derived from the tissues listed in Table 2, and are optionally performed on endometrial cancer (UCEC) samples and / or colorectal cancer (COAD) samples.

[0066] As already mentioned throughout this specification, a major advantage of the methods presented herein is their promising potential for identifying ICB responders that may be missed by other, more commonly available methods, such as testing for MSI. Accordingly, in advantageous embodiments, methods are provided that further comprise classifying the patient from whom the sample was obtained as a responder to immunotherapy, preferably immunotherapy comprising treatment with an antibody specific for at least one selected from PD-1, PD-L1, CTLA4, TIM-3, and / or LAG3. Accordingly, the disclosed methods may include administering to the patient in need thereof immunotherapy against a target selected from PD-1, PD-L1, CTLA4, TIM-3, and / or LAG3 (e.g., antibody therapy against PD-1, PD-L1, CTLA4, TIM-3, and / or LAG3).

[0067] In line with the above, one can also envisage the use of the methods, cartridges and systems described herein in the stratification of patients for TMB testing and immunotherapy, said therapy comprising ICB treatment, most preferably with any antibody specific for PD-1, PD-L1, CTLA4, TIM-3, and / or LAG3. [Example]

[0068] 1. Identification of polymerase epsilon (POLE) scar mutation patterns in uterine endometrial tumors (UCEC) from TCGA

[0069] Maintaining the fidelity of DNA replication is thought to depend on a delicate balance between intrinsic errors by polymerases δ and ε (Korona et al., 2011, Nucl Acids Res), the equilibrium between proofreading and MMR, and differential nucleotide processing during lagging and leading strand synthesis (Lujan et al., 2016, Crit Rev in Biochem and Molec Biol). Extensive studies in yeast models have shown that mutations in the exonuclease domains of Polδ and Polε homologs can cause a mutator phenotype (Skoneczna et al. 2015, FEMS Microbiol Rev).

[0070] Based on the above, we decided to define a discovery dataset using The Cancer Genome Atlas (TCGA) database to identify a set of potential markers for detecting defects in the POLE and POLD1 genes (encoding the catalytic subunits of polymerase ε and δ, respectively). We chose to focus on UCEC (Unionized Cervical Cancer) samples, which harbor relatively frequent POLE and POLD1 mutations, as previously reported by The Cancer Genome Atlas Research Network (Levine et al., 2013, Nature). At the time of analysis, TCGA contained a total of 524 UCEC samples. Based on the microsatellite instability (MSI) annotations provided by TCGA, 165 samples were MSI-positive (annotated as MSI-L or MSI-H, i.e., MSI-low or MSI-high). In our findings, MSI-positive tumors are thought to share distinct characteristics from MSSPOLE-deficient tumors, and due to the fact that efficient methods currently exist for detecting MSI-positive samples, we focused only on the remaining 359 microsatellite-stable (annotated as MSS) TCGA-UCEC samples.

[0071] Among 359 TCGA-UCEC-MSS samples, we identified 32 samples containing one of two POLE hotspot mutations (P286R and V411L), 13 samples containing other POLE mutations, and 12 samples containing POLD1 mutations. Nine of the 12 samples with POLD1 mutations also contained POLE mutations. We then plotted tumor mutation burden (TMB) values, defined as the number of somatic substitutions per coding Mb (tumor vs. matched normal samples, WES variant calling, including both synonymous and nonsynonymous mutations but excluding indels). Results for the following sample groups are shown in Figure 1: MSI-positive UCEC samples ("MSI," including both MSI-L and MSI-H), MSS UCEC samples containing POLE P286R or V411L mutations ("POLE hotspot"), MSS UCEC samples containing POLE non-hotspot mutations ("POLE other"), MSS UCEC samples containing POLD1 mutations ("POLD1"), and MSS UCEC samples lacking either POLE or POLD1 mutations.

[0072] As can be seen in Figure 1, the three POLD1-mutated POLE-unmutated samples (marked inside the added circles) had similar TMBs to samples without POLE or POLD1 mutations, indicating that POLD1 mutations alone do not cause the hypermutator phenotype. Consequently, the remaining marker analysis was performed using the 32 UCEC-MSS samples containing POLE hotspot mutations.

[0073] To detect recurrent marker variants, we downloaded the somatic variant lists from exome sequencing of 32 TCGA-UCEC-MSS samples harboring POLE hotspot mutations. For all these variants, we performed the following analytical steps to detect recurrent variants. First, we pooled all variants from the 32 samples. Subsequently, we excluded variants present in any of the 314 non-POLE-mutated samples. Next, we excluded known variants from public databases, including the 1000 Genomes Database (v.2015 Aug), dbsnp (v.138), Kaviar Database (v.20150923), and hrcr1 Database (first release). Then, we annotated nonsynonymous / gain-of-stop exonic mutations. Finally, we selected recurrent variants occurring in ≥6 of the 32 samples (frequency >0.18).

[0074] As a result, 34 recurrent mutant markers were identified, which are listed in Table 3 [Table 3] JPEG0007737315000008.jpg93129

[0075] For the 40 POLE-deficient TCGA-UCEC-MSS samples detected (including 32 with hotspot mutations and 8 with other mutations), the number of positive markers scored was correlated with TMB levels per sample using Pearson correlation. The correlation coefficient was 0.31, indicating that the correlation was not significant. Although no correlation was found, the experimental results are interesting because they indicate that every single mutation in the identified set is specifically associated with increased TMB by itself.

[0076] 2. Search for POLE scar mutation patterns in colorectal tumors (COAD) from TCGA and additional other MSS-POLE-hotspot tumors from TCGA

[0077] Next, we performed the same analysis using 428 colorectal samples from TCGA (COAD) available at TCGA. Among these samples, 72 samples were annotated as MSI-H and 356 samples were annotated as MSS. Of the 356 TCGA-COAD-MSS samples, four samples contained a POLE hotspot mutation, seven samples contained at least one other POLE mutation (non-hotspot), and three samples contained a POLD1 mutation. We then plotted TMB levels across the various categories of samples, as we did for the UCEC samples, as described above. The results are shown in Figure 3.

[0078] Similar to the UCEC MSS sample analysis, POLD1-mutated POLD1-unmutated COAD samples did not show elevated TMB, confirming previous observations that POLD1 mutations alone do not cause a hypermutator phenotype.

[0079] A recurrent variant search was performed as described above using the four identified TCGA-COAD-MSS samples containing POLE hotspot mutations. No recurrent mutations were found in these samples, although this may be due to the very small number of samples used in the analysis.

[0080] Since we did not identify any recurrent mutations in the TCGA-COAD-MSS-POLE-hotspot samples, we searched for other MSS tumor samples containing POLE-hotspot mutations among all other cancer types in the TCGA database (i.e., excluding TCGA-UCEC and TCGA-COAD). We found that TCGA lists eight of them, as shown in the "POLE Hotspot" group in Figure 4. Among them, four samples harbored the P286R hotspot mutation, including one from rectal cancer (READ), one from pancreatic cancer (PAAD), one from bladder cancer (BLCA), and one from breast cancer (BRCA). The remaining four harbored the V411L hotspot, including one from READ, one from gastric cancer (STAD), one from glioblastoma (GBM), and one from cervical cancer (CESC). In addition, TCGA included 140 MSS non-UCEC and non-COAD cancer samples with other non-hotspot POLE mutations, some of which had elevated TMB, shown in the “POLE Other” group in Figure 4 .

[0081] We applied the above discovery approach to all eight TCGA non-UCEC and TCGA non-COAD MSSPOLE hotspot samples, but were unable to identify any recurrent mutations.

[0082] 3. Retrospective application of the POLE mutation pattern marker panel identified in UCEC-POLE hotspot mutation samples of all UCEC TCGA records

[0083] Given the absence of recurrent mutations in non-UCEC samples from COAD or other cancers, we defined the 34 recurrent mutations identified in UCEC tumors as the initial 34 POLE mutation pattern marker panel for detecting POLE-deficient tumors in TCGA records.

[0084] We first applied the initial 34-marker panel to all 524 TCGA-UCEC samples to estimate its sensitivity and specificity. For each sample, we overlapped the 34-marker panel with its variant list to determine how many variants of the 34 potential markers could be detected per sample. If one variant (i.e., one marker) was detected in a particular sample, the sample was considered positive for this variant.

[0085] As a result, we identified 47 TCGA-UCEC samples with at least one positive marker. These samples were defined as POLE-deficient samples. The 47 POLE-deficient samples included: (i) all 32 samples with POLE hotspot mutations used to define the initial 34-marker panel, (ii) one MSI-H sample with a POLE hotspot mutation, (iii) six MSI-H samples with other POLE mutations, and (iv) eight MSS samples with other POLE mutations. Because the MSI-H samples were not of interest in this analysis, we further investigated the eight MSS samples with other POLE mutations. Sample details are shown in Table 4 below (where "MSS" = microsatellite stable; "MSI-L" or "MSI-H" = MSI positive; "Hotspot" - presence of POLE hotspot mutation; "POLE" = presence of POLE non-hotspot mutation; "EXO1" = presence of EXO1 mutation; "MUTYH" = presence of MUTYH mutation; "NA" = presence of mutation of interest not represented in TCGA; TMB expressed as substitutions / Mb, not including indels). [Table 4] JPEG0007737315000010.jpg46127

[0086] As further shown in Figure 5, all eight identified UCEC MSS samples (circled in Figure 5) had elevated TMB, with the lowest TMB observed being 188.4 substitutions / Mb. Further details of these samples and a list of the exact POLE non-hotspot mutations found in them are provided in Table 5 below. Notably, the above lowest TMB of 188.4 substitutions / Mb observed in sample TCGA-DF-A2KV is even higher than the TMB observed in MSS samples TCGA-EY-A1GD and TCGA-QS-A5YQ (see Table 4 above), which contain POLE hotspots, strongly suggesting that the POLE non-hotspot mutations listed herein can effectively disable the proper function of polymerase ε. [Table 5]

[0087] The above results indicate that the initial 34-marker panel can detect not only the detected set of UCEC samples with POLE hotspot mutations, but also other POLE-deficient samples with substantially elevated TMB levels (at least 188.4 substitutions / MB). This is further supported by Table 6, which shows the amount of MSS-UCEC samples detected by the 34-marker panel from all MSS-UCEC samples in TCGA by different TMB level ranges (i.e., when at least one variant was detected). [Table 6]

[0088] 4. Application of the POLE mutation pattern marker panel identified in UCEC-POLE hotspot mutation samples to all cancer types in the TCGA record except for UCEC samples

[0089] Next, we applied the 34-marker panel to all 7,346 TCGA sample records, including both MSI-positive and MSS samples belonging to 14 different cancer types, excluding the TCGA-UCEC sample analyzed above. To test the number of positive markers that could be identified per sample, we screened the variant lists of all samples using the initial 34-marker panel. If a sample contained at least one (>0) positive marker, it was considered to contain a mutation pattern unique to POLE-deficient samples.

[0090] In total, we identified 35 samples across 10 different cancer types. Of these 35 samples, three were MSI-H, 11 contained one of the POLE hotspot mutations, and eight contained at least one other POLE mutation (one of eight was MSI-H, and the remaining seven were MSS samples with a high TMB range of 262.1 to 1846.8 substitutions / MB); six samples had EXO1 somatic mutations (two of six were EXO1 mutations but not POLE mutations); and finally, four samples had MUYTH somatic mutations (notably, all also had POLE mutations, and three contained POLE hotspot mutations). Detailed information on the detected samples is shown in Table 7 (in the table, "MSS" = microsatellite stable; "MSI-L" or "MSI-H" = MSI positive; "Hotspot" - presence of POLE hotspot mutation; "POLE" = presence of POLE non-hotspot mutation; "EXO1" = presence of EXO1 mutation; "MUTYH" = presence of MUTYH mutation; "NA" = presence of mutation of interest not represented in TCGA; TMB expressed as substitution / Mb, not including indels). [Table 7]

[0091] Table 7 above also shows that the 34 panel identified 12 non-UCEC tumor samples (marked in bold) with lower TMBs than the lowest TMB observed among the MSS UCEC POLE hotspot, including the samples used to construct the discovery panel (i.e., sample TCGA-QS-A5YQ TMB = 132.4 substitutions / Mb, see Table 4). Two of these samples were MSI-H (gastric adenocarcinoma or STAD samples TCGA-VQ-A8PB and TCGA-VQ-A91E), which could explain the lower TMB values ​​assigned, as the values ​​shown here do not include indels. The remaining 10 samples were annotated with MSS and, based on TCGA records, contained no mutations in POLE, EXO1, or MUTYH. However, with the exception of melanoma (i.e., SKCM samples TCGA-WE-A8K5, TCGA-D3-A51G, TCGA-FR-A3YO, and TCGA-FS-A4F2), all were derived from primary, i.e., likely early-stage, tumors. Despite their low TMB values ​​and lack of key driver mutations, the detection of these samples by the 34 panel is considered valuable and may suggest favorable ICB responder status. Notably, as explained above, TMB values ​​by themselves are highly unreliable and vary depending on the test used.For example, in SCLC, NSCLS, and urothelial carcinoma, the TMB threshold for selecting good responders to ICB corresponded to ≥10 mutations per megabase (mut / Mb) in the Foundation One trial and ≥7 mut / Mb in the MSK-IMPACT trial (Antonia et al., 2017, World Conf on Lung Cancer; Abstract OA 07.03a; Kowanetz et al., 2016, Ann Oncol; Powleset et al., 2018, Genitourinary Cancer Symptoms); and applying a higher threshold of ≥16.2 mut / Mb (Kowanetz et al., J Thoracic Oncol) or 15 mut / Mb (Ramalingam et al., 2018, AACR Ann Meeting, Abstract #1137) did not increase the efficacy of different treatments. Therefore, we hypothesize that these samples may still be derived from good responders, and that the tumors are either still in their early stages or are simply affected by a DNA surveillance mechanism other than that associated with POLE deficiency. The latter may be further supported by the fact that more than one-third of these MSS samples are melanomas (SKCM samples), whose mutation acquisition mechanism is known to be driven by UV damage, and therefore does not require highly elevated TMB to generate immunoreactive neoantigens (Gubin et al., 2014, Nature).

[0092] Next, as shown in Figures 4 and 5, by applying the initial panel proposed herein, 12 non-UCEC MSS samples containing POLE hotspot mutations were also notably identified in the TCGA database. Specifically, these included the four MSS POLE hotspot COAD samples shown in Figure 3 and the eight MSS POLE hotspot non-COAD / non-UCEC samples shown in Figure 4. Eleven of these 12 samples were then confirmed to be positive for at least one of the 34 initial mutation pattern markers. The 12th sample could not be confirmed, likely due to incomplete TCGA annotation.

[0093] More notably, the seven MSS non-UCEC samples containing POLE non-hotspot mutations extracted from all TCGA records by applying the initial POLE scar mutation pattern panel of 34 identified markers all showed very high TMB, i.e., ranging from 262.1 to 1846.8 substitutions / MB.

[0094] This finding is consistent with the results obtained when the 34-marker panel was applied to all TCGA-UCEC samples, and when TCGW-UCEC-MSS samples containing POLE other than hotspot mutations were extracted, TMB significantly increased, ranging from a minimum of 188.4 substitutions / MB to 1478.9 substitutions / MB.

[0095] The above results indicate that the first 34 markers for identifying POLE-dependent scars are highly sensitive to samples with POLE mutations, either POLE hotspot mutations or other POLE mutations affecting the proper function of the enzyme, all of which have a very high tumor mutation burden.

[0096] The POLE non-hotspot mutations selected in MSS samples by the initial 34-marker panel identified herein are shown in Table 8 below (showing TCGA non-UCEC samples) and Table 5 above (showing TCGA-UCEC samples). [Table 8]

[0097] Comparing the POLE non-hotspot mutations listed in Table 8 with those in Table 5, we find that some of these mutations reoccur across different samples and cancer types. For example, four samples (two READ, two UCEC) harbor the POLE S459F mutation, four samples (one GBM, one COAD, and two UCEC) harbor the A456P mutation, and two samples exhibit the S297F mutation (one CESC and one UCEC). This may indicate the functional relevance of these and other above-listed POLE non-hotspot mutations and their causative involvement in the increased TMB phenotype.

[0098] The records shown in Tables 8 and 5 suggest that the initially identified 34-marker panel can be used to identify samples with deficient or impaired POLE function and significantly elevated TMB. To further support this, we pooled data for COAD, PAAD, STAD, and READ MSS samples that reliably demonstrated MSI status in TCGA and compared the number of these samples detected by the 34-marker panel (i.e., with at least one variant detected) for different TMB level categories. Data for COAD, PAAD, STAD, and READ MSS samples are shown in Table 9, and data for these samples combined with UCEC samples is shown below in Table 10. [Table 9] [Table 10]

[0099] 5. Further analysis of the strength and redundancy of individual markers using the initially identified 34-marker panel

[0100] A detailed computational analysis was initiated to investigate which markers showed the strongest performance in recovering samples with elevated TMB levels. For this purpose, all combinations of markers were thoroughly screened for their combined performance. The best-performing combinations were withheld. At the same time, markers showing a high level of redundancy were identified through calculation of biomarker co-occurrence. Co-occurrence between markers is shown in Figure 6. Markers for the genes RB1CC1 and BRWD3 have 1 co-occurrence. Other strongly correlated markers are shown in Table 11. [Table 11]

[0101] This allowed us to create a minimal experimental panel of 19 markers that covered all samples. Further reducing the number of markers per subsampled panel, we obtained a minimal panel that could obtain a sample set superior to that obtained by random sampling of markers. To perform random sampling, we tested a randomly selected subset of 10,000 markers and assessed their ability to capture samples within the dataset. The results are shown in Figure 7. They show that for a four-marker panel, the maximum number of samples per observation was 43, with a median of 30. We then stepwise selected the best-performing biomarkers among the 19-marker panels, starting with the minimal four-marker panel. The best-performing panels we identified are described in the "Detailed Description" section above. We found two best-performing four-marker panels, including markers for the PTEN(i), BMT2, and ATPB1 genes and one additional marker for either NF1 or GRM5, which retrieved 43 or 44 of the 82 identified samples, depending on whether they included GRM5. Sampling simulation results showed that even this minimal subset of biomarkers very rarely (1 / 10000) yielded a similarly good score with random sampling, highlighting the predictive power calculated with a minimum panel of four or more biomarkers for picking samples with elevated TMB.

[0102] In addition to establishing a biomarker-based panel, a minimal panel based on biomarker and POLE hotspot mutation prevalence was also generated using the same methods described above. The results of these calculations are also described in the “Detailed Description” section above.

[0103] 6. Experimental testing of endometrial cancer samples

[0104] In further experiments, a series of tumor samples from endometrial cancer patients were analyzed for the presence of at least one mutation to determine the presence of increased tumor mutation burden (TMB) using a method involving sequencing various genomic sites that map to the GRC37 human genome assembly in Table 1. The results were compared to the total number of mutations present in the sequenced regions, including the number of nucleotide variants found in a standard somatic cancer panel used in routine clinical sequencing panels, consisting of a panel of 75 amplicons covering hotspot regions of the 21 most common cancer genes and an additional 25 MSI markers.

[0105] To achieve this goal, we sequenced 36 formalin-fixed, paraffin-embedded endometrial cancer samples using 34 amplicons covering the 34 variations in Table 1. DNA was extracted from the samples using the Invitrogen PureLink™ Genomic DNA Mini Kit according to the manufacturer's instructions (Invitrogen™ K182002), with DNA extracted from pathologically annotated neoplastic regions of the tumors. Targeted sequencing was performed using a custom panel (134 amplicons total) using the Ion PGM™ for next-generation sequencing, and analysis was performed using Torrent Suite Software (ThermoFisher Scientific) for sequencing and data analysis according to the manufacturer's instructions. The results are shown in Table 12. In this randomly selected series of endometrial cancers, 10 / 36 (27.8%; samples 1, 2, 3, 5, 6, 7, 15, 17, 18, and 34) were positive for at least one marker. The geometric mean number of nucleotide variants detected by sequencing was 216 in samples containing one or more of the Table 1 markers, compared with a geometric mean of 32 variants in samples where no variants were detected. Groups containing any of the markers had a mean elevated TMB of 6.75-fold compared with the control group, confirming that this mutation pattern captures elevated TMB.

[0106] As further shown in Table 12, samples 2, 3, 6, 17, 18, and 34 contained two to seven markers. As explained above, the likelihood of two or more markers randomly selected from a set of 34 markers appearing in a genome is virtually nonexistent. Therefore, this provides further evidence, in an independent, real-world sample set, that markers are associated with DNA repair failure mechanisms and may be part of the scar mutation patterns resulting from certain cancers. Samples with a single marker detected (samples 1, 5, 7, and 15) showed a geometric mean number of variants of 166, while those with two or more markers showed a geometric mean of 257. However, samples positive for only one of the markers also showed a significantly increased number of variants compared to samples without the marker.

[0107] Furthermore, Table 12 shows that 16 / 34 markers in Table 1 were detected in 10 endometrial cancer samples, for a total of 26 markers. Some markers in Table 1 were present in two samples (UPRT, ARHGAP35) or three samples (ASCC3, GRM5, HTR2A, MS4A8) and therefore may be promising markers for detecting elevated TMB in endometrial cancer. As reported in Table 11, some markers may frequently occur together, and in this experiment, ASCC3 and FUBP1 occurred together in sample number 3. [Table 12] JPEG0007737315000019.jpg89135

[0108] Drawing Terminology substitutions / Mb POLE hotspot POLE others POLE others None Nr of positive markers Pearson Correlation Histogram of results Frequency results results

Claims

1. 1. A method for analyzing the presence of increased tumor mutational burden (TMB) in a sample obtained from a patient, the method comprising testing at least four different genomic sites from Table 1, (i) the presence of a cytosine or guanine to other nucleobase change mapped to the GRC37 human genome assembly, as identified in column 2 of Table 1; (ii) detecting the presence of at least one of the alterations is indicative of increased tumor mutational burden (TMB); (iii) at least four different genomic sites from Table 1 chr10 89720744, located within the PTEN gene; chr7 112461939, located within the BMT2 gene; chr12 89985005, located within the ATP2B1 gene; and chr17 29677227, located within the NF1 gene and (iv) the sample is an endometrial sample; method. 【Table 1】

2. 2. The method of claim 1, further comprising testing for the presence of a variation at position chr11 88338063 from Table 1 within the GRM5 gene.

3. 3. The method of claim 1 or 2, further comprising testing for the presence of an alteration at position chr4 115544340 from Table 1 within the UGT8 gene.

4. chr13 47409732 from Table 1, located within the HTR2A gene; or 4. The method of any one of claims 1 to 3, further comprising testing for the presence of an alteration at at least one of two sites located within chr1 227843477, the ZNF678 gene.

5. chr13 47409732 from Table 1, located within the HTR2A gene; and 5. The method of any one of claims 1 to 4, further comprising testing for the presence of an alteration at both of the two sites located within chr1 227843477, the ZNF678 gene.

6. 6. The method of any one of claims 1 to 5, further comprising testing for the presence of an alteration at position chr10 89624245 from Table 1 within the PTEN gene.

7. 7. The method of any one of claims 1 to 6, further comprising testing for the presence of an alteration at position chr19 47424921 from Table 1 within the ARHGAP35 gene.

8. 8. The method of any one of claims 1 to 7, further comprising testing for the presence of an alteration at chr8 121228689 from Table 1, position within the COL14A1 gene.

9. The following sites from Table 1: chr10 89720744, located within the PTEN gene; chr7 112461939, located within the BMT2 gene; chr12 89985005, located within the ATP2B1 gene; chr17 29677227, located within the NF1 gene; chr11 88338063, located within the GRM5 gene; chr10 89624245, located within the PTEN gene; chr4 115544340, located within the UGT8 gene; chr13 47409732, located within the HTR2A gene; chr1 227843477, located within the ZNF678 gene; chr19 47424921, located within the ARHGAP35 gene; chr8 121228689, located within the COL14A1 gene; 9. The method of claim 1, comprising testing for the presence of an alteration in

10. The following sites from Table 1: chr18 50832017, located within the DCC gene; chr7 39745749, located within the RALA gene; chr11 60468341, located within the MS4A8 gene; chrX 110970087, located within the ALG13 gene; chr18 74635035, located within the ZNF236 gene; chrX 79942391, located within the BRWD3 gene; chr2 113417110, located within the SLC20A1 gene; chrX 99662008, located within the PCDH19 gene; chr9 5968511, located within the KIAA2026 gene; chrX 74519615, located within the UPRT gene; chr6 31779382, located within the HSPA1L gene; chr19 52825339, located within the ZNF480 gene; chr3 370022, located within the CHL1 gene; chr18 53017619, located within the TCF4 gene; chr6 101296418, located within the ASCC3 gene; chr2 9098719, located within the MBOAT2 gene; chr19 12501557, located within the ZNF799 gene; chr18 54281690, located within the TXNL1 gene; chr16 68598492, located within the ZFP90 gene; chr10 128908585, located within the DOCK1 gene; chr1 78428511, located within the FUBP1 gene; chrX 119678368, located within the CUL4B gene; chr8 53558288, located within the RB1CC1 gene; The method of any one of claims 1 to 9, further comprising testing for the presence of one or more alterations in

11. The following sites from Table 1: chr10 89720744, located within the PTEN gene; chr7 112461939, located within the BMT2 gene; chr12 89985005, located within the ATP2B1 gene; chr17 29677227, located within the NF1 gene; chr11 88338063, located within the GRM5 gene; chr10 89624245, located within the PTEN gene; chr4 115544340, located within the UGT8 gene; chr13 47409732, located within the HTR2A gene; chr1 227843477, located within the ZNF678 gene; chr19 47424921, located within the ARHGAP35 gene; chr8 121228689, located within the COL14A1 gene; chr18 50832017, located within the DCC gene; chr7 39745749, located within the RALA gene; chr11 60468341, located within the MS4A8 gene; chrX 110970087, located within the ALG13 gene; chr18 74635035, located within the ZNF236 gene; chrX 79942391, located within the BRWD3 gene; chr2 113417110, located within the SLC20A1 gene; chrX 99662008, located within the PCDH19 gene; The method of any one of claims 1 to 10, comprising testing for the presence of an alteration in

12. 12. The method of any one of claims 1 to 11, further comprising testing for the presence of a POLE hotspot P286R mutation or a POLE hotspot V411L mutation.

13. 1. A method for analyzing the presence of increased tumor mutation burden (TMB) in an endometrial sample obtained from a patient, the method comprising: chr10 89720744, located within the PTEN gene; chr7 112461939, located within the BMT2 gene; chr17 29677227, located within the NF1 gene; chr11 88338063, located within the GRM5 gene; chr4 115544340, located within the UGT8 gene; chr12 89985005, located within the ATP2B1 gene; and testing the sample for the presence of a POLE hotspot P286R mutation or a POLE hotspot V411L mutation and for the presence of a cytosine or guanine to other nucleobase changes, in at least four of The method, wherein detecting the presence of at least one of said alterations, or detecting the presence of any POLE hotspot mutation, indicates an increased tumor mutational burden (TMB).

14. The following sites from Table 1: chr10 89624245, located within the PTEN gene; chr13 47409732, located within the HTR2A gene; chr1 227843477, located within the ZNF678 gene; chr19 47424921, located within the ARHGAP35 gene; chr8 121228689, located within the COL14A1 gene; chr18 50832017, located within the DCC gene; chr7 39745749, located within the RALA gene; chr11 60468341, located within the MS4A8 gene; chrX 110970087, located within the ALG13 gene; chr18 74635035, located within the ZNF236 gene; chrX 79942391, located within the BRWD3 gene; chr2 113417110, located within the SLC20A1 gene; chrX 99662008, located within the PCDH19 gene; 14. The method of claim 13, further comprising testing for the presence of one or more alterations in

15. The method of any one of claims 1 to 14, further comprising testing the sample for the presence of additional mutations in POLE and / or for the presence of mutations in EXO1 and / or MUTYH.

16. 16. The method of claim 15, wherein the additional mutation in POLE is one or more of the following: T1104M, A1967V, H144Q, S1644L, A456P, R1233, T2202M, P436R, R705W, S459F, S297F, A189T, P436R, L1235I, R1371, D213A, P135S, A456P, K777N, F367S.

17. The method of any one of claims 1 to 16, further comprising analyzing the presence of microsatellite instability (MSI) in the sample.

18. The method of any one of claims 1 to 17, wherein the sample is microsatellite stable (MSS).

19. 19. The method of any one of claims 1 to 18, further comprising classifying the patient from whom the sample was obtained as a responder to immunotherapy; or 19. The method of any one of claims 1 to 18, further comprising classifying the patient from whom the sample was obtained as a responder to immunotherapy, wherein the immunotherapy comprises treatment with an antibody specific for at least one selected from PD-1, PD-L1, CTLA4, TIM-3, and / or LAG3.

20. A system for analyzing the presence of increased tumor mutational burden (TMB) in a DNA sample obtained from a patient, the system comprising reagents for testing at least four different genomic sites from Table 1, The presence of cytosine or guanine to other nucleobase changes mapped to the GRC37 human genome assembly as identified in column 2 of Table 1; the reagents include components for amplifying at least a portion of the DNA sample and reagents for sequencing the amplified portion to detect alterations; detecting the presence of at least one of the alterations is indicative of increased tumor mutational burden (TMB); The at least four different genomic sites are: chr10 89720744, located within the PTEN gene; chr7 112461939, located within the BMT2 gene; chr12 89985005, located within the ATP2B1 gene; and chr17 29677227, located within the NF1 gene; and The DNA sample is a sample obtained from an endometrial cancer patient. system. 【Table 1】

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