Tumor Mutational Burden Measurement Research Articles

Targeted 595-gene genomic profiling demonstrates low tumor mutational burden in olfactory neuroblastoma.

Olfactory neuroblastoma (ONB) is a rare skull-base malignancy associated with delayed local recurrence. Treatment options in recurrent disease are few and unreliable. We undertook analysis of the ONB exome and immune environment in order to identify potential future immunotherapy treatment options. Retrospective chart review and next-generation targeted 595-gene genomic profiling was performed on a cohort of 14 ONB cases utilizing Tempus proprietary DNA and RNA sequencing technology. Tempus analysis provided a measurement of tumor mutational burden (TMB) and composition of the immune cell infiltrate present in tumor samples. Clinically relevant genomic alterations and associated targeted therapies were identified using cancer.gov and clinicaltrials.gov. TMB was tested by univariate analysis against clinical stage, pathologic grade, recurrence risk, and immune cell infiltration. The mean age for the subjects was 50 years (range, 13 to 76 years) with a male:female ratio of 1:1. TMB for ONB samples ranged from 1.3 to 9.6 mutations/megabase (Mb) with mean of 3.8 mutations/Mb. Univariate analysis showed no association between TMB and tumor stage, pathologic grade, risk of recurrence, or immune cell infiltration. Genomic profile revealed that 6 of 13 tumors had genetic alterations with targeted therapies in clinical trials, whereas 1 tumor demonstrated KRAS Q61R mutation with U.S. Food and Drug Administration (FDA)-approved targeted therapies. TMB is a novel biomarker guiding the classification of neoplasms in the emerging era of immunotherapy. The characterization of ONB as a low-TMB pathology contributes to the overall taxonomy of all cancers and suggests limited utility of immunotherapy treatment.

EcTMB: a robust method to estimate and classify tumor mutational burden

Tumor Mutational Burden (TMB) is a measure of the abundance of somatic mutations in a tumor, which has been shown to be an emerging biomarker for both anti-PD-(L)1 treatment and prognosis; however, multiple challenges still hinder the adoption of TMB as a biomarker. The key challenges are the inconsistency of tumor mutational burden measurement among assays and the lack of a meaningful threshold for TMB classification. Here we describe a new method, ecTMB (Estimation and Classification of TMB), which uses an explicit background mutation model to predict TMB robustly and to classify samples into biologically meaningful subtypes defined by tumor mutational burden.

Harmonization and Standardization of Panel-Based Tumor Mutational Burden Measurement: Real-World Results and Recommendations oftheQuality in Pathology Study.

Tumor mutational burden (TMB) is a quantitative assessment of the number of somatic mutations within a tumor genome. Immunotherapy benefit has been associated with TMB assessed by whole-exome sequencing (wesTMB) and gene panel sequencing (psTMB). The initiatives of Quality in Pathology (QuIP) and Friends of Cancer Research have jointly addressed the need for harmonization among TMB testing options in tissues. This QuIP study identifies critical sources of variation in psTMB assessment. A total of 20 samples from three tumor types (lung adenocarcinoma, head and neck squamous cell carcinoma, and colon adenocarcinoma) with available WES data were analyzed for psTMB using six panels across 15 testing centers. Interlaboratory and interplatform variation, including agreement on variant calling and TMB classification, were investigated. Bridging factors to transform psTMB to wesTMB values were empirically derived. The impact of germline filtering was evaluated. Sixteen samples had low interlaboratory and interpanel psTMB variation, with 87.7% of pairwise comparisons revealing a Spearman's ρ greater than 0.6. A wesTMB cut point of 199 missense mutations projected to psTMB cut points between 7.8 and 12.6 mutations per megabase pair; the corresponding psTMB and wesTMB classifications agreed in 74.9% of cases. For three-tier classification with cut points of 100 and 300 mutations, agreement was observed in 76.7%, weak misclassification in 21.8%, and strong misclassification in 1.5% of cases. Confounders of psTMB estimation included fixation artifacts, DNA input, sequencing depth, genome coverage, and variant allele frequency cut points. This study provides real-world evidence that all evaluated panels can be used to estimate TMB in a routine diagnostic setting and identifies important parameters for reliable tissue TMB assessment that require careful control. As complex or composite biomarkers beyond TMB are likely playing an increasing role in therapy prediction, the efforts by QuIP and Friends of Cancer Research also delineate a general framework and blueprint for the evaluation of such assays.

Quantifying potential confounders of panel-based tumor mutational burden (TMB) measurement

ObjectivesRetrospective data including subgroup analyses in clinical studies have sparked strong interest in developing tumor mutational burden (TMB) as a predictive biomarker for immune checkpoint blockade. While individual factors influencing panel sequencing based measurement of TMB (psTMB) have been discussed in the recent literature, an integrative study quantifying, comparing and combining all potential confounders is still missing. Material and methodsWe separated different potential confounders of psTMB measurement including “panel size”, “germline mutation filtering”, “biological variance” and “technical variance” and developed a specific error model for each of these factors. Published experimental psTMB data were fitted to the error models to quantify the contribution of each of the confounders. The total psTMB variance was obtained as sum over the variance contributions of each of the confounders. ResultsUsing a typical large panel (size 1–1.5 Mbp) total errors of 57 %, 42 %, 34 % and 28 % were observed for tumors with psTMB of 5, 10, 20 and 40 muts/Mbp. Even for large panels, the stochastic error connected to the panel size represented the largest of all contributions to the total psTMB variance, especially for tumors with TMB up to 20 muts/Mbp. Other sources of psTMB variability could be kept under control, but rigorous quality control, best practice laboratory workflows and optimized bioinformatics pipelines are essential. ConclusionA statistical framework for the analysis of complex, genomic biomarkers was developed and applied to the analysis of psTMB variability. The methods developed here can support the analysis of other quantitative biomarkers and their implementation in clinical practice.

Tumor mutational burden.

Tumor mutational burden.

Abstract 4125: Precise and reproducible measurement of tumor mutational burden using panel sequencing: the role of panel size and of mutation types

Abstract Tumor mutational burden (TMB) is an emerging biomarker for patient stratification for immune checkpoint inhibition. Implementing TMB measurement by panel sequencing, experimental setup and bioinformatics pipelines need to be optimized to minimize sources of error and variance. To gain insight into contributors to TMB variance, combinatorial calculations were performed based on the hypothesis that each base in the coding sequence was mutated with the same probability. Simulations of sequencing panels in the TCGA data were performed to check for additional contributors to the variance. A narrow definition of TMB including only missense mutations was compared to a broader definition additionally including other mutation types. The quantity under investigation was TMB measured in relatively to the size of the panel (per Mbp). The combinatorial contribution to the coefficient of variation (CV) of TMB turned out to be inversely proportional to the square root of the panel size and inversely proportional to the square root of the TMB. For a tumor with TMB of 10 muts/Mbp, the combinatorial CV increased drastically for panels sizes smaller than 1.5 Mbp. The combinatorial variance represents a persistent contribution to the error of TMB calculation that cannot be avoided. Simulations in WES data confirmed the combinatorial variance as main contributor to the imprecision of TMB estimation. In many instances, the fold change between the number of all mutations in the coding sequence and the number of missense mutations was similar for tumors of the same cancer type. However, the fold changes were different in specific cancer types for example significantly higher in melanoma (1.66) compared lung adenocarcinoma (1.48). In particular, the former tumors were observed to include a higher proportion of synonymous mutations than the latter. The combinatorial source of error implies that a panel of least 1.5 Mbp is needed to precisely determine TMB for tumors ranging around typical currently used diagnostic thresholds (10 muts/Mbp). Additional sources of error might even favor larger panel sizes such as 3 Mbp. The combinatorial error can be reduced by including all mutations types instead of only missense mutations and simultaneously adjusting TMB with a cancer type specific factor. Citation Format: Jan Budczies, Eugen Rempel, Michael Allgäuer, Daniel Kazdal, Volker Endris, Peter Schirmacher, Albrecht Stenzinger. Precise and reproducible measurement of tumor mutational burden using panel sequencing: the role of panel size and of mutation types [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4125.

Abstract 3167: Improving and standardizing TMB assay performance

Abstract Determining and using the most effective and safest treatment is of great importance in cancer disease management. Checkpoint inhibitors that target immune regulatory molecules such as PD-1, PD-L1, and CTLA-4, have successfully improved PFS and OS - but only in some patients and for some cancers. In the case of PD-1 and PD-L1 inhibitors, it is believed that PD-L1 expression by a solid tumor allows it to escape attack from the immune system and that by inhibiting the PD-L1/PD-1 interaction immune evasion is no longer possible. This may then cause some of the mutations that give rise to expressed neoantigens to act as targets for T cells and allow for the gradual elimination of the tumor. Since the risk for developing autoimmune adverse events is not insignificant with the use of checkpoint inhibitors, determining which patients are likely to respond favorably to their use is imperative. Similarly, expanding approved uses to new indications also benefits from the identification of populations that are most likely to show improvement in PFS and OS - generally, through clinically-validated biomarkers. Current indications for the use of PD-1 and PD-L1 inhibitors rely on cancer type and several biomarkers. One of these is the expression (or level of expression) of PD-L1 on the tumor. Another - usually in colorectal cancer - is the presence of microsatellite instability (MSI), which indicates that DNA is not being copied with high fidelity and resulting mutations may lead to the emergence of potential neoantigens. While MSI can be assessed by the PCR amplification of several loci, the presence or absence of neoantigens cannot, and they can also be created in the absence of MSI. This has given rise to a potential biomarker - tumor mutational burden (TMB) - that is an assessment of the number of relevant mutations in a tumor. TMB measurement is challenging since different targeted next generation sequencing (NGS) panels look at different regions and percentages of the genome and use different criteria as to what constitutes a relevant mutation. Presently, there is poor correlation between different TMB assays at mutation levels that may be relevant for a companion diagnostic where patients may be denied treatment. Here, we describe the characterization of a panel of reference materials for the assessment, harmonization, and improvement of TMB measurements by NGS assays. The panel is comprised of cancer cell lines and their SNP-matched normals. DNA from unfixed cells was used to establish a baseline truth set of somatic mutations and germline SNPs, and DNA from FFPE cells was used to determine how TMB assessment is affected by fixation artifacts - especially, at lower variant allele frequencies. We present data from high coverage whole exome sequencing - the current gold standard in TMB assessment - and from targeted NGS panels that are more typical of what may be used clinically. We show that accurate measurements at what may be clinically relevant TMB cutoffs remain a challenge. Citation Format: Matthew G. Butler, Yves Konigshofer, Lequan Nguyen, Shikha Kalotra, Omo Clement, Russell K. Garlick, Bharathi Anekella. Improving and standardizing TMB assay performance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3167.

Bioinformatic Methods and Bridging of Assay Results for Reliable Tumor Mutational Burden Assessment in Non-Small-Cell Lung Cancer.

IntroductionTumor mutational burden (TMB) has emerged as a clinically relevant biomarker that may be associated with immune checkpoint inhibitor efficacy. Standardization of TMB measurement is essential for implementing diagnostic tools to guide treatment.ObjectiveHere we describe the in-depth evaluation of bioinformatic TMB analysis by whole exome sequencing (WES) in formalin-fixed, paraffin-embedded samples from a phase III clinical trial.MethodsIn the CheckMate 026 clinical trial, TMB was retrospectively assessed in 312 patients with non-small-cell lung cancer (58% of the intent-to-treat population) who received first-line nivolumab treatment or standard-of-care chemotherapy. We examined the sensitivity of TMB assessment to bioinformatic filtering methods and assessed concordance between TMB data derived by WES and the FoundationOne® CDx assay.ResultsTMB scores comprising synonymous, indel, frameshift, and nonsense mutations (all mutations) were 3.1-fold higher than data including missense mutations only, but values were highly correlated (Spearman’s r = 0.99). Scores from CheckMate 026 samples including missense mutations only were similar to those generated from data in The Cancer Genome Atlas, but those including all mutations were generally higher. Using databases for germline subtraction (instead of matched controls) showed a trend for race-dependent increases in TMB scores. WES and FoundationOne CDx outputs were highly correlated (Spearman’s r = 0.90).ConclusionsParameter variation can impact TMB calculations, highlighting the need for standardization. Encouragingly, differences between assays could be accounted for by empirical calibration, suggesting that reliable TMB assessment across assays, platforms, and centers is achievable.

Tumor mutational burden reference materials for assay standardization.

e14746 Background: Next Generation Sequencing based assays are designed to detect genomic aberrations in a limited number of target regions. However, there is a need for accurate measurement of tumor mutational burden (TMB) as low as 4 to as high as 50. As TMB assessment is added to various targeted panels, consistent results between panels are required to advance the broad use of this biomarker. Properly designed reference materials aid measurement standardization and are required to demonstrate assay concordance. We developed reference materials that vary in TMB score, tumor content 5-50% and are prepared in FFPE format. Methods: Seven lung and two breast tumor cell lines as well as matched “normal” lymphoblastoid cell lines were expanded in cell culture. Genomic DNA (gDNA) from each cell line was extracted. Tumor/normal mixes were made by mixing DNA and by embedding cells in FFPE blocks. Whole exome sequencing (WES) results were obtained using Agilent SureSelectXT for library construction and an Illumina Novaseq for sequencing. The Friends of Cancer Research TMB consensus method for analyzing WES data was used to filter variants and calculate TMB scores. Results: The cell lines were grown at large scale to produce extractable gDNA. 100% gDNA tumor, 30% gDNA tumor mixes and 30% FFPE cell line mixes were prepared. Preliminary results show that a clinically-relevant range of TMB values ranging from 4 to 35 mutations per million bases. The several thousand mutations that were observed across the lines were found in a variety of genes, which may explain why TMB in targeted panels is influenced by the specific target regions. Also, the initial results show that 30% cell line mixes showed similar TMB results to 100% gDNA. Conclusions: Our approach with wide ranging TMB values as tumor normal mixes is flexible and can be used to test different tumors and assays. For this study we established WES as the ground truth measurement for comparison to other assay formats and obtained comparison data from other panels. This approach also allows laboratories to test additional variables including formalin fixation, sample extraction, gene panel size, target regions, sequencing depth, filtering and limits of detection.

Defining tissue requirements for a reliable detection of tumor mutational burden (TMB) in clinical FFPE samples.

e14265 Background: In the context of immuno-oncology related cancer treatment, tumor mutational burden (TMB) is currently explored as a predictive biomarker for several human malignancies. Several sequencing assays are increasingly commercially available. Established methodologies require rather large amounts of DNA input (in the range of 100 ng) which, however, are frequently not available from small biopsies. We aim to investigate how tissue size and DNA input influence TMB scores. Methods: DNA from 20 specimens (12 biopsies of non-small cell lung cancer (NSCLC); 8 surgical resection specimens from NSCLC, colorectal cancer and endometrial carcinomas) was manually extracted by using the Qiagen GeneRead DNA FFPE kit. Cases were selected to provide a wide range of relative tumor cell content (from < 10% to > 50%) and to include microsatellite-stable and –instable (MSI) cases. Samples were analyzed in triplicates from predefined numbers of unstained sections of a standardized tissue size. DNA quantification was done fluorometrically and by qPCR. Up to 40 ng of DNA were analyzed with the QIASeq TMB Panel (incl. MSI primer boosters; Qiagen). Sequencing was done on an Illumina NextSeq platform, TMB scores and MSI status were determined by using the CLC workbench 5.0.1 (Qiagen). Results: Biopsy samples generated less numbers of DNA molecules (as detected by unique molecular identifiers, UMIs) and less overall reads compared to resection samples. UMI coverage was > 500x in all samples with > 15 ng DNA input. TMB scores were highly reproducible among all samples with > 15 ng DNA but differed significantly among samples with limited DNA input. Interestingly, TMB scores were inversely correlated with DNA input among those samples with < 15ng. Thus, valid TMB scores could also be obtained from only one slice of 1 cm2 tissue from tumor resections or 3 slices of an endoscopic biopsy (each of 5µm thickness). All pre-characterized MSI carcinomas could be detected correctly. Conclusions: We provide first evidence that TMB can be reliably determined also in small biopsies yielding limited DNA content. However, false high TMB scores can occur in samples with < 15ng DNA input. Our results contribute to the definition of minimum requirements (tissue size and DNA input) for valid TMB measurement on clinical FFPE samples.

Measurement of tumor mutational burden (TMB) in routine molecular diagnostics: in silico and real-life analysis of three larger gene panels.

Assessment of Tumor Mutational Burden (TMB) for response stratification of cancer patients treated with immune checkpoint inhibitors is emerging as a new biomarker. Commonly defined as the total number of exonic somatic mutations, TMB approximates the amount of neoantigens that potentially are recognized by the immune system. While whole exome sequencing (WES) is an unbiased approach to quantify TMB, implementation in diagnostics is hampered by tissue availability as well as time and cost constrains. Conversely, panel-based targeted sequencing is nowadays widely used in routine molecular diagnostics, but only very limited data are available on its performance for TMB estimation. Here, we evaluated three commercially available larger gene panels with covered genomic regions of 0.39 Megabase pairs (Mbp), 0.53 Mbp and 1.7 Mbp using i) in silico analysis of TCGA (The Cancer Genome Atlas) data and ii) wet-lab sequencing of a total of 92 formalin-fixed and paraffin-embedded (FFPE) cancer samples grouped in three independent cohorts (non-small cell lung cancer, NSCLC; colorectal cancer, CRC; and mixed cancer types) for which matching WES data were available. We observed a strong correlation of the panel data with WES mutation counts especially for the gene panel >1Mbp. Sensitivity and specificity related to TMB cutpoints for checkpoint inhibitor response in NSCLC determined by wet-lab experiments well reflected the in silico data. Additionally, we highlight potential pitfalls in bioinformatics pipelines and provide recommendations for variant filtering. In summary, our study is a valuable data source for researchers working in the field of immuno-oncology as well as for diagnostic laboratories planning TMB testing.

Approach to evaluating tumor mutational burden in routine clinical practice.

Immune checkpoint inhibition with monoclonal antibodies has emerged as a promising therapeutic approach but in most tumor types responses are unpredictable and observed in a minority of treated patients. Positive and negative predictive biomarkers for efficacy of these costly drugs are desperately needed. Immunohistochemistry (IHC) for programmed death ligand (PD-L1) expression in tumor and inflammatory infiltrate has emerged as one predictive biomarker of some value. However, multiple confounders including those inherent to any IHC and the unique complexities of the biology of the immune response have limited its utility. Tumor mutational burden (TMB) has emerged as a seemingly more promising predictive biomarker for immunotherapy with checkpoint inhibitors in several tumor types and is likely to be incorporated into future treatment algorithms for these agents. Given this, the need to define and standardize key parameters of the most promising biomarkers becomes essential to allow all stakeholders to make meaningful observations and inferences as to the efficacy of ostensibly similar agents and combinations in various settings. This review briefly summarizes approaches to measurement of TMB and ongoing efforts to achieve harmonization of this key biomarker.

Implementing tumor mutational burden (TMB) analysis in routine diagnostics-a primer for molecular pathologists and clinicians.

Tumor mutational burden (TMB) is a new biomarker for prediction of response to PD-(L)1 treatment. Comprehensive sequencing approaches (i.e., whole exome and whole genome sequencing) are ideally suited to measure TMB directly. However, as their applicability in routine diagnostics is currently limited by high costs, long turnaround times and poor availability of fresh tissue, targeted next-generation sequencing (NGS) of formalin-fixed and paraffin-embedded (FFPE) samples appears to be a more feasible and straight-forward approach for TMB approximation, which can be seamlessly integrated in already existing diagnostic workflows and pipelines. In this work, we provide an overview of the clinical implications of TMB testing and highlight key parameters including pre-analysis, analysis and post-analytical steps that influence and shape TMB approximation by panel sequencing. Collectively, the data will not only serve as a field guide and state of the art knowledge source for molecular pathologists who consider implementation of TMB measurement in their lab, but also enable clinicians in understanding the specific parameters influencing TMB test results and reporting.

141P - Tumor mutational burden (TMB) standardization initiative: Establishing a consistent methodology for TMB measurement in clinical samples

141P - Tumor mutational burden (TMB) standardization initiative: Establishing a consistent methodology for TMB measurement in clinical samples

Abstract A41: Analytic validation and clinical feasibility of a next-generation sequencing assay to assess tumor mutational burden from blood (bTMB) as a biomarker for anti-PD-L1 response in NSCLC

Abstract Background: The need to identify biomarkers that predict benefit to checkpoint inhibitor therapies has led to the discovery and development of tumor mutational burden (TMB), a measure of potential tumor neoantigenicity derived from tissue biopsies that has shown clinical utility across a range of tumor types. A significant fraction of patients, however, are not candidates for tissue biopsies, presenting the need for blood-based methods to determine TMB. Here we describe the development of an assay to identify TMB from cell-free DNA derived from blood (bTMB). We present the analytic validation and clinical feasibility data that support the application of bTMB in a prospective clinical trial, BFAST (NCT03178552), evaluating the anti-PD-L1 agent atezolizumab in patients with non-small cell lung cancer (NSCLC). Methods: The bTMB assay surveys somatic base substitutions down to 0.5% allele frequency across 394 genes from as little as 1% tumor content in a cell free DNA (cfDNA) sample derived from blood. Analytic validation was focused on establishing accuracy and precision of the bTMB measurement, as well as the minimum amount of cell-free and circulating tumor DNA required to make precise and reliable bTMB calls. The accuracy of two bTMB cutoffs was established against TMB derived from FoundationOne, an analytically validated TMB platform. Precision was evaluated by comparing the reproducibility of bTMB calls across replicate samples. We also retrospectively analyzed plasma samples from the OAK (NCT02008227) and POPLAR (NCT01903993) trials with the bTMB assay to determine the association of bTMB with atezolizumab clinical activity. The biomarker evaluable population (BEP) included 211 patients in POPLAR (intention-to-treat [ITT] =287) and 583 patients in OAK (excludes patients with known EGFR/ALK mutations; ITT=850), with blood samples available for targeted genomic sequencing. Assay positivity was defined as the presence of a number of somatic base substitutions greater than or equal to the bTMB cutoffs. Results: The average positive percent agreement (PPA), negative percent agreement (NPA) and positive predictive value (PPV) across the bTMB cutoffs were 95%, 100% and 100%, respectively. The average precision was 96%, with a coefficient of variation of 7%. The assay limit of detection was defined as 1% tumor content in at least 20 ng of cfDNA. In POPLAR, improved progression-free survival (PFS) and overall survival (OS) hazard ratios (HRs) with atezolizumab vs docetaxel were observed for patients with bTMB at or above a range of bTMB thresholds compared with the ITT and BEP populations. In OAK, PFS benefit with atezolizumab vs docetaxel was observed at bTMB thresholds ≥10 (cut point ≥10: HR 0.73; n=251) compared with BEP (HR 0.87, 95% CI 0.73-1.04; n=585). bTMB did not correlate with PD-L1 expression as measured by VENTANA SP142 immunohistochemistry. Conclusions: We have developed and analytically validated a blood-based assay to determine TMB with high accuracy and precision, using as little as 1% tumor content in a sample with 20 ng of cfDNA. Retrospective analyses from POPLAR and OAK data provide the first demonstrations that blood-based measurement of TMB may be associated with atezolizumab clinical efficacy in second-line NSCLC. Thus, the bTMB assay may provide a non-invasive biomarker to identify patients who derive clinical benefit from single agent PD-1/PD-L1 inhibition. Prospective studies using bTMB are currently ongoing in patients with first-line NSCLC, including BFAST and B-F1RST (NCT02848651). Citation Format: Daniel S. Lieber, Emily White, Jacob Silterra, Shan Zhong, Tina Brennan, Michael Coyne, Mark Kennedy, David R. Gandara, Marcin Kowanetz, Sarah M. Paul, Erica Schleifman, Yan Li, Achim Rittmeyer, Louis Fehrenbacher, Lukas Amler, Todd Riehl, Craig Cummings, Priti S. Hegde, Wei Zou, Alan Sandler, Marcus Ballinger, Tony Mok, David S. Shames, Doron Lipson, Christine Malboeuf, David Fabrizio. Analytic validation and clinical feasibility of a next-generation sequencing assay to assess tumor mutational burden from blood (bTMB) as a biomarker for anti-PD-L1 response in NSCLC [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2017 Oct 1-4; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2018;6(9 Suppl):Abstract nr A41.

Identification of DNA repair/replication genes mutations in determining response to immune checkpoint inhibitors in non-small cell lung cancer patients.

e14582 Background: Tumor mutational burden (TMB) is considered a promising predictive biomarker for immune checkpoint blockade (ICB) therapy response . However, the measurement of TMB relies on the whole-exome sequencing (WES), which is technically limited by sample volume and freshness, thus restricts its use in clinical practice. Since TMB is controlled by the balance between DNA damage and the repair, it is a rational approach to identify a subset of DNA repair/replication gene mutations associated with increased TMB as a surrogate, which could be monitored in clinic to predict ICB efficacy even busing limited archived tissues. Methods: The mutation status of 471 genes associated with DNA repair and replication were analyzed for the correlation with TMB and/or ICB therapeutic responses in three cohorts of NSCLC patients, which are: Cohort A, WES data of 100 Chinese NSCLC patients; Cohort B, WES data of NSCLC patients from TCGA database; Cohort C, WES data and therapeutic response to PD-1 mAb from a Science paper (Rizvi et al. 2015, Science). Results: We identified somatic mutations in 32 DNA repair/replication genes associated with high TMB in both Cohort A and Cohort B. In Cohort C, mutations were found in 20 out of these 32 genes, and the mutation status of 17/20 genes were associated with the clinical benefit of ICB therapy. Noteworthy, mutations of TP53, POLQ, MMS22L, HERC2, CEP164, BRCA1/2, FANCM and MSH4 were mostly common mutations associated with TMB in all 3 cohorts, and ERCC2 and ERCC6 were uniquely high mutated and associated with high TMB in Chinese Han people from Cohort A. Interestingly, EGFR activating mutations were inversely correlated with both high TMB and good ICB response. Conclusions: This retrospective study identified 17 DNA repair/replication-related genes, whose mutation status was associated with high TMB and/or ICB response. Mutational patterns of DNA repair genes are likely different between western and Chinese NSCLC patient population. The data also suggested that NSCLC patients carrying EGFR activating mutations are not preferential population for ICB therapy. Further data is needed to verify the validity of these observations.