Tumor Mutational Burden Estimation Research Articles

Abstract 5671: Alignment of TMB measured on clinical samples: Phase IIB of the Friends of Cancer Research TMB Harmonization Project

Abstract Introduction: Tumor mutational burden (TMB) is the number of somatic mutations per megabase in a tumor's genome and has shown promise as a predictive biomarker of response to immune checkpoint inhibitors across several cancers. TMB is typically measured by whole exome sequencing (WES TMB) or by targeted next-generation sequencing gene panels (panel TMB). As more assays are developed to estimate TMB, harmonization is emerging as an unmet need and is a key goal of the Friends of Cancer Research (Friends) TMB Harmonization Project. Phase I of the Harmonization Project demonstrated correlation between panel TMB and WES TMB using TCGA data and defined theoretical sources of variability across panels. In phase IIA, sustainable TMB reference standard materials generated from human derived cell lines were used to characterize variability in TMB measurements across panels and assessed for utility in TMB alignment. Phase IIB aims to characterize variability in TMB measurements in clinical samples and to establish best practices for estimating and aligning TMB in order to improve consistency across panels. Methods: Fifteen laboratories (16 targeted gene panels) at different stages of development participated in phase IIB. Thirty formalin-fixed paraffin-embedded (FFPE) samples with >30% tumor content were acquired; tumor DNA was isolated by a single reference lab. TMB values were calculated for DNA extracted from lung (N=10), bladder (N=10), and gastric tumors (N=10) using WES and a uniform bioinformatics pipeline agreed upon by all Consortium members. DNA samples were also sent to all laboratories, and each used their own sequencing and bioinformatics pipelines to estimate TMB from the genes represented in their respective panels. For each tumor sample, a median across panel TMB estimates was calculated; individual panel TMB estimates were translated to fold-changes relative to the sample median to quantify variability. Association between WES TMB (reference) and panel TMB will be assessed by regression analysis; dependence of association on cancer type was investigated. Results: A subset of tumor samples (9 bladder, 7 lung, and 5 gastric) was analyzed using 11 panels at the time of abstract submission. Median panel TMB values ranged 0.60 - 40.26 across samples, with median of median values of 5.35. Fold-change from sample-level medians ranged 0x - 6.67x. Assessment of these clinical samples by WES and all 16 gene panels, as well as regression analysis results, are forthcoming. Conclusions: The Friends TMB Harmonization Project has made substantial progress in characterization of TMB measurement variability and association between WES TMB and panel TMB. These are important steps toward alignment of TMB estimates generated by different gene panels which may improve the interpretation of findings within clinical development programs and ultimately enhance the usefulness of this predictive biomarker in clinical decision making. Citation Format: Diana M. MERINO, Laura M. Yee, Lisa M. McShane, P. Mickey Williams, Tomas Vilimas, Rajesh Patidar, J. Carl Barrett, Shu-Jen Chen, Jen-Hao Cheng, Jeffrey M. Conroy, Dinesh Cyanam, Kenneth R. Eyring, David A. Fabrizio, Vincent Funari, Elizabeth P. Garcia, Sean T. Glenn, Christopher D. Gocke, Vikas Gupta, Lisa M. Haley, Matthew D. Hellmann, Laurel Keefer, Lauryn R. Keeler, Brett Kennedy, Alexander J. Lazar, Laura E. MacConaill, Kristen L. Meier, Arnaud Papin, Naiyer A. Rizvi, Ethan Sokol, Phillip Stafford, John F. Thompson, Warren Tom, Victor J. Weigman, Mingchao Xie, Chen Zhao, Mark D. Stewart, Jeff Allen. Alignment of TMB measured on clinical samples: Phase IIB of the Friends of Cancer Research TMB Harmonization Project [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5671.

Comprehensive next-generation profiling of clonal hematopoiesis in cancer patients using paired tumor-blood sequencing for guiding personalized therapies.

e13662 Background: Clonal hematopoiesis (CH) is commonly observed during genomic profiling of cancer patients. However, the relevance of CH in Chinese patients has yet to be characterized. Methods: We analyzed next-generation sequencing (NGS) data from blood and matched solid tumors of 4,544 cancer patients. For delineating the impact of CH on panel-based estimation of tumor mutational burden (TMB), its correlation with whole-exome-based TMB calculation was analyzed in an independent cohort of 175 patients. Results: A total of 1,749 CH mutations in 237 genes were identified in the blood of 1,301 (28.6%) patients, among which 26% carried more than one CH mutations. DMNT3A (35.8%), TET2 (13.2%), NOTCH1 (4.8%), ASXL1 (3.8%) and TP53 (3.7%) were among the top altered CH genes, while the prevalence of several other known CH genes, including JAK2 and SF3B1, were below 1%. The predominant hot-spot mutation in DNMT3A at R882 showed varying frequencies of different amino acid changes. An age-associated accumulation of CH events was observed, with alterations in DNMT3A, TET2, ASXL1 and TP53 showing evidence of early changes. A large proportion (74.1%, 1296 mutations) of CH was detectable in the matched tumors. Of these, 453 (25.9%) were functionally annotated as oncogenic or likely oncogenic, including two EGFR kinase domain mutations (V742I and R776H), one KRAS mutation (F156L) and a loss-of-function BRCA2 mutation. With respect to immunotherapy, while CH was not associated with microsatellite instability, it had a negative impact on accurate TMB determination. Consequently, we developed a novel panel-based TMB estimation algorithm with CH filters that showed optimal concordance with exome sequencing in an independent cohort of 175 patients. Conclusions: Our findings highlight the clinical significance of CH in fulfilling the promise of precision medicine and emphasize the need for matched tumor-blood sequencing.

Establishing guidelines to harmonize tumor mutational burden (TMB): in silico assessment of variation in TMB quantification across diagnostic platforms: phase I of the Friends of Cancer Research TMB Harmonization Project

BackgroundTumor mutational burden (TMB), defined as the number of somatic mutations per megabase of interrogated genomic sequence, demonstrates predictive biomarker potential for the identification of patients with cancer most likely...

Abstract P4-05-09: Molecular differences between high and low tumor mutational burden (TMB) across breast cancer (BC) subtypes

Abstract Background. High-TMB (HTMB) is an emerging promising agnostic biomarker for predicting benefit from immune-checkpoint inhibitors, independently of tumor type. At ASCO 2019, the TAPUR trial reported an interesting 21% ORR in heavily pretreated metastatic BC patients with very HTMB [vHTMB, ≥9 mutations/megabase (Muts/Mb)]. We aimed to define the differential gene expression and methylation landscape between low and high TMB in each BC subtype. Methods. In TCGA, we identified 848 patients with WES data available for TMB estimation. [ER+/HER2- (LumA by PAM50)) n = 364; ER+/HER2- (LumB by PAM50), n = 147; HER2+, n = 158; and TN, n = 179]. High TMB was defined according to two different cut-offs: ≥9 (vHTMB) and≥5 Muts/Mb (HTMB). The second arbitrary cut-off was used to define a larger group allowing to better characterize the different molecular landscapes associated with high and low TMB in each BC subtype. The HTMB group was compared with an equal number of tumors with low TMB. We assessed the differential RNA expression and methylation of single genes and pathways (defined using Gene Ontology - GO). “Common” genes and pathways were defined as recurrently associated with TMB (p<0.05) in all subtypes and with a combined p value ≤0.00001. Results. The overall prevalence of vHTMB (≥9) was 4.5%, with no substantial differences across subtypes (4.4%, LumA; 4.8% LumB; 5.7% HER2+; 3.3% TN). The prevalence of HTMB (≥5) was 13.7%, but it was different across BC subtypes (p=8.0E-07) (8.2%, LumA; 12.9% LumB; 13.3% HER2+; 25.7% TN). We found more “common” genes down-regulated (n=70) than up-regulated (n=3) in HTMB group. Two of these three genes (HSPE1 and FEZF1.AS1) have been associated with poor prognosis in BC. When we considered the “common” pathways, only 3 were up-regulated in HTMB, all implicated in post-transcriptional repression of gene expression (gene silencing by miRNA and mRNA binding involved in post-transcriptional gene silencing). Conversely, 66 were significantly down-regulated (including transcription coregulator and coactivator activity, protein serine/threonine kinase activity and ubiquitin-protein transferase activity and binding). Some genes and pathways were associated with TMB only in a specific BC subtype (p ≤0.00001). For instance, 16 pathways were down-regulated in the HTMB group of TNBC. These inlcuded 12 pathways implicated in immune response. Conversely, in LumB, 11 pathways were up-regulated in HTMB group and implicated in immune response. Intrestingly, these pathways were all significantly down-regulated in the HTMB group of LumA and TN. In HTMB group, we found 7 and 4 “common” genes hypermetylated and hypomethylated, respectively. Four pathways were commonly hypermethylated (chromatin silencing at rDNA, telomere organization and positive regulation of G1/S transition of mitotic cell cycle) and five were hypomethilated (including mitotic sister chromatid segregation). Considering private alterations, in TNBC, 23 of 27 significant pathways were hypermetylated in HTMB group including double-strand break repair via nonhomologous end joining, epigenetic negative regulation of gene expression, and regulation of gene silencing by miRNA. Conclusions. Very-high TMB which is considered potentially druggable (≥9 Muts/Mb) is rare in BC, and equally frequent in all subtypes. Instead, HTMB (≥5 Muts/Mb) is more frequent in TNBC. BCs with HTMB had a different molecular landscape. Overall, several genes are recurrently down-regulated in HTMB group, and this is at least partly due to miRNA regulated post-trascriptional silencing, which might rapresents a new mechanism of immune escape. The positive association between TMB and immune genes in LumB, as well as the negative association in TN and LumA, suggest that immune editing and surveillance might be dependent on the molecular context. Citation Format: Luca Licata, Barbara Galbardi, Balázs Győrffy, Thomas Karn, Lorenzo Sica, Alessia Rognone, Patrizia Zucchinelli, Daniela Aldrighetti, Stefania Zambelli, Luca Gianni, Giampaolo Bianchini. Molecular differences between high and low tumor mutational burden (TMB) across breast cancer (BC) subtypes [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P4-05-09.

Assessment of Tumor Mutational Burden in Pediatric Tumors by Real-Life Whole-Exome Sequencing and In Silico Simulation of Targeted Gene Panels: How the Choice of Method Could Affect the Clinical Decision?

Background: Tumor mutational burden (TMB) is an emerging genomic biomarker in cancer that has been associated with improved response to immune checkpoint inhibitors (ICIs) in adult cancers. It was described that variability in TMB assessment is introduced by different laboratory techniques and various settings of bioinformatic pipelines. In pediatric oncology, no study has been published describing this variability so far. Methods: In our study, we performed whole exome sequencing (WES, both germline and somatic) and calculated TMB in 106 patients with high-risk/recurrent pediatric solid tumors of 28 distinct cancer types. Subsequently, we used WES data for TMB calculation using an in silico approach simulating two The Food and Drug Administration (FDA)-approved/authorized comprehensive genomic panels for cancer. Results: We describe a strong correlation between WES-based and panel-based TMBs; however, we show that this high correlation is significantly affected by inclusion of only a few hypermutated cases. In the series of nine cases, we determined TMB in two sequentially collected tumor tissue specimens and observed an increase in TMB along with tumor progression. Furthermore, we evaluated the extent to which potential ICI indication could be affected by variability in techniques and bioinformatic pipelines used for TMB assessment. We confirmed that this technological variability could significantly affect ICI indication in pediatric cancer patients; however, this significance decreases with the increasing cut-off values. Conclusions: For the first time in pediatric oncology, we assessed the reliability of TMB estimation across multiple pediatric cancer types using real-life WES and in silico analysis of two major targeted gene panels and confirmed a significant technological variability to be introduced by different laboratory techniques and various settings of bioinformatic pipelines.

An Exon Signature to Estimate the Tumor Mutational Burden of Right-sided Colon Cancer Patients

The clinical applicability of the whole-exome sequencing (WES) in estimating tumor mutational burden (TMB) is currently limited by high cost, time-consuming and tissue availability. And given to the differences in the mutational landscapes among different types of cancer, we aimed to develop a cancer-specific signature to estimate TMB for right-sided colon cancer patients (RCC). Using WES data of 315 RCC patients, we identified the exons in which the number of mutational sites of the coding DNA sequences associated with TMB through linear regression analysis. Then, among these exons, we extracted a signature composed by 102 exons (~0.13 Mbp) through a heuristic selection procedure. The TMB estimated by the signature was highly correlated with those calculated by WES in the discovery dataset (R2=0.9869) and three independent validation datasets (R2=0.9351, R2=0.8063 and R2=0.9527, respectively). And the performance of the signature was superior to a colorectal-specific TMB estimation model contained 22 genes (~0.24 Mbp). Moreover, between TMB-high and TMB-low RCC patients, there were significantly differences in the frequencies of microsatellite instability status, CpG island methylator phenotype, BRAF, KRAS and POLE/POLD1 mutation status (p<0.01). However, the performances of the signature in other types of cancer were dramatically degraded (left-sided colon cancer, R2=0.7849 and 0.9407, respectively; rectum, R2=0.5955 and R2=0.965, respectively; breast cancer, R2=0.8444; lung cancer, R2=0.5963), suggesting that it was necessary to develop cancer-specific TMB estimated signatures to estimate precisely the TMB in different types of cancer. In summary, we developed an exon signature that can accurately estimate TMB in RCC patients, and the cost and time required for the assessment of TMB can be considerably decreased, making it more suitable for blood and/or biopsy samples.

Optimizing panel-based tumor mutational burden (TMB) measurement

BackgroundPanel sequencing based estimates of tumor mutational burden (psTMB) are increasingly replacing whole exome sequencing (WES) tumor mutational burden as predictive biomarker of immune checkpoint blockade (ICB). DesignA mathematical law describing psTMB variability was derived using a random mutation model and complemented by the contributions of non-randomly mutated real-world cancer genomes and intratumoral heterogeneity through simulations in publicly available datasets. ResultsThe coefficient of variation (CV) of psTMB decreased inversely proportional with the square root of the panel size and the square root of the TMB level. In silico simulations of all major commercially available panels in the TCGA pan-cancer cohort confirmed the validity of this mathematical law and demonstrated that the CV was 35% for TMB = 10 muts/Mbp for the largest panels of size 1.1–1.4 Mbp. Accordingly, misclassification rates (gold standard: WES) to separate ‘TMBhigh’ from ‘TMBlow’ using a cut-point of 199 mutations were 10%–12% in TCGA-LUAD and 17%–19% in TCGA-LUSC. A novel three-tier psTMB classification scheme which accounts for the likelihood of misclassification is proposed. Simulations in two WES datasets of immunotherapy treated patients revealed that small gene panels were poor predictors of ICB response. Moreover, we noted substantial intratumoral variance of psTMB scores in the TRACERx 100 cohort and identified indel burden as independent marker complementing missense mutation burden. ConclusionsA universal mathematical law describes accuracy limitations inherent to psTMB, which result in substantial misclassification rates. This scenario can be controlled by two measures: (i) a panel design that is based on the mathematical law described in this article: halving the CV requires a fourfold increase in panel size, (ii) a novel three-tier TMB classification scheme. Moreover, inclusion of indel burden can complement TMB reports. This work has substantial implications for panel design, TMB testing, clinical trials and patient management.

TMB standardization by alignment to reference standards: Phase II of the Friends of Cancer Research TMB Harmonization Project.

2624 Background: Tumor mutational burden (TMB) is a predictive biomarker of response to immune checkpoint inhibitors across multiple cancers. In Phase 1 of the Friends of Cancer Research Harmonization Project, we demonstrated a robust correlation between TMB estimated using targeted next-generation sequencing (NGS) gene panels and whole exome sequencing (WES) applied to MC3-TCGA data. These findings demonstrated variability in TMB estimates across different panels. Phase 2 evaluates sustainable TMB reference standard materials for TMB alignment to assess this variability. The goal of this effort is to establish best practices for estimating TMB in order to improve consistency across panels, for the sake of optimizing clinical application and facilitating integration of datasets generated from multiple assays. Methods: Fifteen laboratories with targeted panels at different stages of development participated. We identified a set of reference standards consisting of 10 well-characterized human-derived lung and breast tumor-normal matched cell lines. WES was performed using a uniform bioinformatics pipeline agreed upon by all team members (WES-TMB). Each laboratory used their own sequencing and bioinformatics pipelines (tumor-only and tumor-normal) to estimate TMB according to genes represented in their respective panels (panel-TMB). The association between WES-TMB and each panel-TMB was investigated using regression analyses. Bias (relative to WES-TMB) and variability in TMB estimates across panels were rigorously assessed. All analyses were blinded. Results: The set of reference standards spanned a clinically meaningful TMB range (4.3 to 31.4 mut/Mb). Preliminary data from 12 laboratories shows a good correlation between panel-TMB and WES-TMB in this empirical analysis. Across panels, regression R2 values range 0.77-0.96 with slopes ranging 0.60-1.26. Calibration analyses that seek to minimize variability of TMB estimates across panels using the established set of reference standards are ongoing, as well as investigating cancer type dependence on the relationship between panel-TMB vs. WES-TMB, which will be available at the time of presentation. Conclusions: Preliminary findings demonstrate feasibility of using sustainable reference control cell lines to standardize and align estimation of TMB across different targeted NGS assays. Future studies aim to validate reference standard material as a reliable alignment tool by using formalin-fixed paraffin-embedded human tumor samples.

Evaluation of TMB estimates for the prediction of response to immune checkpoint blockage.

2632 Background: Tumor mutational burden (TMB) is an emerging biomarker in immuno-oncology (IO). Panel sequencing (PS) is a promising approach for its implementation in clinical practice. Methods: We analyzed TMB as predictor of IO response in a multi-cancer cohort published by Miao et. al. The performance of three large panels (Illumina TSO500, Qiagen TMB [QIAseq] and Oncomine TMB [OTMB]) and two small panels (Illumina TST170 and Oncomine Comprehensive Assay [OCAv3]) was compared to WES by in silico simulations. Separation of responders (PR/CR) from non-responders (PD) was analyzed in the multi-cancer cohort (n = 193), in the lung cancer (n = 36) and in the melanoma (n = 125) subcohort. We also simulated PS in the TCGA pan-cancer cohort. Results: In lung cancer, TMB was strongly predictive for IO response (area under ROC curve [AUC] 0.78-0.94). WES performed (borderline)-significantly better than PS for all five panels (OCAv3: p = 0.011, TST170: p = 0.01, QIAseq: p = 0.048, OTMB: p = 0.063, TSO500: p = 0.11). For the cut-point of 199 mutations, misclassification rates compared to WES (16.7%) were borderline-significantly higher for the small panels OCAv3 (33.3%, p = 0.087) and TST170 (36.1%, p = 0.054), but not for the large panels. In melanoma, TMB was moderately predictive (AUC 0.58-0.63) and WES performed (borderline)-significantly better than the OCAv3, TST170, QIAseq and TSO500 panels. In the multi-cancer cohort, WES did not perform better than PS. TBM estimates from PS include an inherent fuzziness originating from restriction to a limited part of the coding sequence. Based on a random mutation model, we derived a mathematical formula for the coefficient of variation (CV) of TMB: The CV decreases inversely proportional with both the square root of the TMB level and with the square root of the panel size. We showed that the mathematical law is valid for real-word mutation data. Conclusions: Small panels (size &lt; 1 Mpb) performed imprecise in diagnostic TMB estimation. Even using the largest commercially available panels it can be challenging to capture the full predictive information of TMB. The detrimental effect of small panel size can be addressed by using larger panels, but halving the CV of TMB necessitates quadruplication of the panel size.

Overestimation of tumor mutational burden (TMB) using algorithms compared to germline subtraction.

2621 Background: TMB is an emerging predictor of survival with immunotherapy. TMB is determined by taking the difference between somatic and germline datasets when tumor-normal pairs are available. In the case of commonly utilized tumor-only sequencing, additional steps are needed to estimate the somatic alterations. Computational tools have been developed that determine germline contribution based on sample copy state, purity estimates and occurrence of the variant in population databases. Given the potential bias of population datasets, we hypothesized that tumor-only filtering approaches may overestimate the actual TMB. Methods: We assessed the TMB from 50 tumors in 10 diseases including all missense, indels, and frameshift variants with an allelic fraction (AF) ≥5% and Coverage ≥100X within the tumor. Tumor-only TMB was evaluated against the gold standard of matched germline subtracted TMB at three levels. Level 1 removed all the tumor-only variants with AF in the non-TCGA ExAC database ≥1%. Level 2 removed all variants observed in population databases simulating a naive approach of removing germline variation. Level 3 used an internal tumor-only pipeline for calculating TMB. Results: There were significantly higher estimates of TMB with Level 1, Level 2 and Level 3 tumor-only filtering approaches than that determined by germline subtraction, resulting in significant bias. Whereas there was no correlation between TMB estimates and tumor-germline TMB for Level 1 filtering, there were improvements in correlations for Level 2 and Level 3. Conclusions: The tumor-only approaches that filter variants in population databases overestimate TMB compared to that determined by germline subtraction. Despite improved correlations with more stringent filtering approaches, these falsely elevated estimates may result in the inappropriate categorization of tumor specimens and negatively impact clinical trial results and patient outcomes. [Table: see text]

Assessment of Blood Tumor Mutational Burden as a Potential Biomarker for Immunotherapy in Patients With Non–Small Cell Lung Cancer With Use of a Next-Generation Sequencing Cancer Gene Panel

Tumor mutational burden (TMB), as measured by whole-exome sequencing (WES) or a cancer gene panel (CGP), is associated with immunotherapy responses. However, whether TMB estimated by circulating tumor DNA in blood (bTMB) is associated with clinical outcomes of immunotherapy remains to be explored. To explore the optimal gene panel size and algorithm to design a CGP for TMB estimation, evaluate the panel reliability, and further validate the feasibility of bTMB as a clinical actionable biomarker for immunotherapy. In this cohort study, a CGP named NCC-GP150 was designed and virtually validated using The Cancer Genome Atlas database. The correlation between bTMB estimated by NCC-GP150 and tissue TMB (tTMB) measured by WES was evaluated in matched blood and tissue samples from 48 patients with advanced NSCLC. An independent cohort of 50 patients with advanced NSCLC was used to identify the utility of bTMB estimated by NCC-GP150 in distinguishing patients who would benefit from anti-programmed cell death 1 (anti-PD-1) and anti-programmed cell death ligand 1 (anti-PD-L1) therapy. The study was performed from July 19, 2016, to April 20, 2018. Assessment of the Spearman correlation coefficient between bTMB estimated by NCC-GP150 and tTMB calculated by WES. Evaluation of the association of bTMB level with progression-free survival and response to anti-PD-1 and anti-PD-L1 therapy. This study used 2 independent cohorts of patients with NSCLC (cohort 1: 48 patients; mean [SD] age, 60 [13] years; 15 [31.2%] female; cohort 2: 50 patients; mean [SD] age, 58 [8] years; 15 [30.0%] female). A CGP, including 150 genes, demonstrated stable correlations with WES for TMB estimation (median r2 = 0.91; interquartile range, 0.89-0.92), especially when synonymous mutations were included (median r2 = 0.92; interquartile range, 0.91-0.93), whereas TMB estimated by the NCC-GP150 panel found higher correlations with TMB estimated by WES than most of the randomly sampled 150-gene panels. Blood TMB estimated by NCC-GP150 correlated well with the matched tTMB calculated by WES (Spearman correlation = 0.62). In the anti-PD-1 and anti-PD-L1 treatment cohort, a bTMB of 6 or higher was associated with superior progression-free survival (hazard ratio, 0.39; 95% CI, 0.18-0.84; log-rank P = .01) and objective response rates (bTMB ≥6: 39.3%; 95% CI, 23.9%-56.5%; bTMB <6: 9.1%; 95% CI, 1.6%-25.9%; P = .02). The findings suggest that established NCC-GP150 with an optimized gene panel size and algorithm is feasible for bTMB estimation, which may serve as a potential biomarker of clinical benefit in patients with NSCLC treated with anti-PD-1 and anti-PD-L1 agents.

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.

Size matters: Dissecting key parameters for panel-based tumor mutational burden analysis.

Tumor mutational burden (TMB) represents a new determinant of clinical benefit from immune checkpoint blockade that identifies responders independent of PD-L1 expression levels and is currently being explored in clinical trials. Although TMB can be measured directly by comprehensive genomic approaches such as whole-genome and exome sequencing, broad availability, short turnaround times, costs and amenability to formalin-fixed and paraffin-embedded tissue support the use of gene panel sequencing for approximating TMB in routine diagnostics. However, data on the parameters influencing panel-based TMB estimation are limited. Here, we report an extensive in silico analysis of the TCGA data set that simulates various panel sizes and compositions. We demonstrate that panel size is a critical parameter that influences confidence intervals (CIs) and cutoff values as well as important test parameters including sensitivity, specificity, and positive predictive value. Moreover, we evaluate the Illumina TSO500 panel, which will be made available for TMB estimation, and propose dynamic, entity-specific cutoff values based on current clinical trial data. Optimizing the cost-benefit ratio, our data suggest that panels between 1.5 and 3 Mbp are ideally suited to estimate TMB with small CIs, whereas smaller panels tend to deliver imprecise TMB estimates for low to moderate TMB (0-30 muts/Mbp), connected with insufficient separation of hypermutated tumors from non-hypermutated tumors.

A scalable solution for tumor mutational burden from formalin-fixed, paraffin-embedded samples using the Oncomine Tumor Mutation Load Assay.

Tumor mutational burden (TMB) is an increasingly important biomarker for immune checkpoint inhibitors. Recent publications have described strong association between high TMB and objective response to mono- and combination immunotherapies in several cancer types. Existing methods to estimate TMB require large amount of input DNA, which may not always be available. In this study, we develop a method to estimate TMB using the Oncomine Tumor Mutation Load (TML) Assay with 20 ng of DNA, and we characterize the performance of this method on various formalin-fixed, paraffin-embedded (FFPE) research samples of several cancer types. We measure the analytical performance of TML workflow through comparison with control samples with known truth, and we compare performance with an orthogonal method which uses matched normal sample to remove germline variants. We perform whole exome sequencing (WES) on a batch of FFPE samples and compare the WES TMB values with TMB estimates by the TML assay. In-silico analyses demonstrated the Oncomine TML panel has sufficient genomic coverage to estimate somatic mutations with a strong correlation (r2=0.986) to WES. Further, in silico prediction using WES data from three separate cohorts and comparing with a subset of the WES overlapping with the TML panel, confirmed the ability to stratify responders and non-responders to immune checkpoint inhibitors with high statistical significance. We found the rate of somatic mutations with the TML assay on cell lines and control samples were similar to the known truth. We verified the performance of germline filtering using only a tumor sample in comparison to a matched tumor-normal experimental design to remove germline variants. We compared TMB estimates by the TML assay with that from WES on a batch of FFPE research samples and found high correlation (r2=0.83). We found biologically interesting tumorigenesis signatures on FFPE research samples of colorectal cancer (CRC), lung, and melanoma origin. Further, we assessed TMB on a cohort of FFPE research samples including lung, colon, and melanoma tumors to discover the biologically relevant range of TMB values. These results show that the TML assay targeting a 1.7-Mb genomic footprint can accurately predict TMB values that are comparable to the WES. The TML assay workflow incorporates a simple workflow using the Ion GeneStudio S5 System. Further, the AmpliSeq chemistry allows the use of low input DNA to estimate mutational burden from FFPE samples. This TMB assay enables scalable, robust research into immuno-oncology biomarkers with scarce samples.

Abstract LB-381: Effects of sequencing parameters and panel size on mutational burden calculations

Abstract Immunotherapies are an emerging approach for cancer treatment that leverage the power of the immune system to attack cancer cells. By targeting immune check-point pathways such as PD-1, these treatments have been highly effective for some patients diagnosed with skin, bladder, lung, or kidney cancer. However, response rates for these treatments vary, making it beneficial to identify patients who are likely to respond to the treatment. Qualitative methods for predicting immunotherapy response are available but are technically challenging and subjective. An emerging quantitative predictor of immunotherapy response, tumor mutational burden (TMB), measures the number of somatic mutations per Mb of the genome. TMB calculations using whole exome data have been shown to correlate with patient response to immunotherapies. However, derivation of this score is not well characterized, nor is the score universally performed across laboratories. Here we describe the results from calculating TMB scores varying cancer panel size and amount of sequencing generated. First, variants are called in matched tumor and normal samples within Exomes. Then low coverage variants are removed along with variants outside the targeted, high-confidence regions in each panel. The remaining variants in the normal samples constitute the germline variants and are subtracted from the number of variants identified in tumor samples to yield the TMB score. We then repeated this across multiple indications across TCGA to review robustness of score in indications across the spectra of burden. TMB scores from the our comprehensive cancer closely resembles scores calculated using whole exome data across indications. We propose rules for gene content and sequencing depth for providing a range of burden scores that could be used for determining thresholds needed for determining therapeutic implications. We suggest that only a small subset of genes are required for accurate TMB estimation, thus reducing costs associated with predicting patient response to immunotherapy. Citation Format: Scott Yourstone, Natalie Mola, Victor J. Weigman. Effects of sequencing parameters and panel size on mutational burden calculations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr LB-381.

Data Interoperability of Whole Exome Sequencing (WES) Based Mutational Burden Estimates from Different Laboratories.

Immune checkpoint inhibitors, which unleash a patient’s own T cells to kill tumors, are revolutionizing cancer treatment. Several independent studies suggest that higher non-synonymous mutational burden assessed by whole exome sequencing (WES) in tumors is associated with improved objective response, durable clinical benefit, and progression-free survival in immune checkpoint inhibitors treatment. Next-generation sequencing (NGS) is a promising technology being used in the clinic to direct patient treatment. Cancer genome WES poses a unique challenge due to tumor heterogeneity and sequencing artifacts introduced by formalin-fixed, paraffin-embedded (FFPE) tissue. In order to evaluate the data interoperability of WES data from different sources to survey tumor mutational landscape, we compared WES data of several tumor/normal matched samples from five commercial vendors. A large data discrepancy was observed from vendors’ self-reported data. Independent data analysis from vendors’ raw NGS data shows that whole exome sequencing data from qualified vendors can be combined and analyzed uniformly to derive comparable quantitative estimates of tumor mutational burden.