Panel-based Tumor Mutational Burden Research Articles

Open the Technical Black Box of Tumor Mutational Burden (TMB): Factors Affecting Harmonization and Standardization of Panel-Based TMB.

As tumor mutational burden (TMB) has been approved as a predictive biomarker for immune checkpoint inhibitors (ICIs), next-generation sequencing (NGS) TMB panels are being increasingly used clinically. However, only a few of them have been validated in clinical trials or authorized by administration. The harmonization and standardization of TMB panels are thus essential for clinical implementation. In this review, preanalytic, sequencing, bioinformatics and interpretative factors are summarized to provide a comprehensive picture of how the different factors affect the estimation of panel-based TMB. Among the factors, poor DNA quality, improper formalin fixation and residual germline variants after filtration may overestimate TMB, while low tumor purity may decrease the sensitivity of the TMB panel. In addition, a small panel size leads to more variability when comparing with true TMB values detected by whole-exome sequencing (WES). A panel covering a genomic region of more than 1Mb is more stable for harmonization and standardization. Because the TMB estimate reflects the sum of effects from multiple factors, deliberation based on laboratory and specimen quality, as well as clinical information, is essential for decision making.

Optimizing the evaluation of gene-targeted panels for tumor mutational burden estimation

Though whole exome sequencing (WES) is the gold-standard for measuring tumor mutational burden (TMB), the development of gene-targeted panels enables cost-effective TMB estimation. With the growing number of panels in clinical trials, developing a statistical method to effectively evaluate and compare the performance of different panels is necessary. The mainstream method uses R-squared value to measure the correlation between the panel-based TMB and WES-based TMB. However, the performance of a panel is usually overestimated via R-squared value based on the long-tailed TMB distribution of the dataset. Herein, we propose angular distance, a measurement used to compute the extent of the estimated bias. Our extensive in silico analysis indicates that the R-squared value reaches a plateau after the panel size reaches 0.5 Mb, which does not adequately characterize the performance of the panels. In contrast, the angular distance is still sensitive to the changes in panel sizes when the panel size reaches 6 Mb. In particular, R-squared values between the hypermutation-included dataset and the non-hypermutation dataset differ widely across many cancer types, whereas the angular distances are highly consistent. Therefore, the angular distance is more objective and logical than R-squared value for evaluating the accuracy of TMB estimation for gene-targeted panels.

A comparative genomics analysis of lung adenocarcinoma for Chinese population by using panel of recurrent mutations.

Previous studies have demonstrated that Chinese lung adenocarcinoma (LUAD) patients have unique genetic characteristics, however, the specific genomic features relating to the development and treatment of LUAD in the Chinese population are not fully understood. Here, we applied the ultra-deep targeted sequencing to 66 Chinese LUAD samples, accompanied by comparative analysis with 162 Caucasian LUAD in The Cancer Genome Atlas. We focused on the 68 recurrently mutated genes and results revealed that the panel-based tumor mutational burden (pTMB) is significantly higher in the Chinese LUAD (P=0.0017). Additionally, the percentage of smoking-associated C>A transversion is significantly lower in Chinese LUAD (15.5%vs. 39.7%, P=5.69×10−27), while C>T transition is more frequent in Chinese LUAD (35.8%vs. 25.7%, P=2.67×10−5), which indicated the ethnic difference in mutation types. Notably, novel driver genes (GNAS and JAK1) that are peculiar to Chinese LUAD were identified, and a more convergent distribution of mutations was observed in the Chinese cohort (P=0.012) compared with scattered mutations in Caucasian LUAD. Our results present a distinct genomic profile of Chinese LUAD compared to Caucasians LUAD and elucidate the ethnic difference in mutation distribution besides the type and rate.

Tumor Mutational Burden as a Predictive Biomarker in Solid Tumors.

Tumor mutational burden (TMB), defined as the number of somatic mutations per megabase of interrogated genomic sequence, varies across malignancies. Panel sequencing-based estimates of TMB have largely replaced whole-exome sequencing-derived TMB in the clinic. Retrospective evidence suggests that TMB can predict the efficacy of immune checkpoint inhibitors, and data from KEYNOTE-158 led to the recent FDA approval of pembrolizumab for the TMB-high tumor subgroup. Unmet needs include prospective validation of TMB cutoffs in relationship to tumor type and patient outcomes. Furthermore, standardization and harmonization of TMB measurement across test platforms are important to the successful implementation of TMB in clinical practice. SIGNIFICANCE: Evaluation of TMB as a predictive biomarker creates the need to harmonize panel-based TMB estimation and standardize its reporting. TMB can improve the predictive accuracy for immunotherapy outcomes, and has the potential to expand the candidate pool of patients for treatment with immune checkpoint inhibitors.

Abstract 3093: Quantifying the confounders of panel-based tumor mutational burden (TMB) measurement

Abstract Retrospective 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 interfering with panel sequencing based measurement of TMB (psTMB) have been discussed in recent studies, an integrative study quantifying, comparing and combining all potential confounders is still missing. We 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 different variance contributions. Using a typical large panel (size 1 to 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. We developed an integrated framework for the analysis of psTMB variability and provide a set of recommendations for implementation of psTMB as a laboratory developed test. Our approach highlights the stochastic portion of psTMB variability. The stochastic error connected inherently with psTMB could in part explain the suboptimal performance of the biomarker in some of the recent lung cancer studies. Citation Format: Jan Budczies, Daniel Kazdal, Michael Allgäuer, Petros Christopoulos, Eugen Rempel, Nicole Pfarr, Wilko Weichert, Stefan Fröhling, Michael Thomas, Solange Peters, Volker Endris, Peter Schirmacher, Albrecht Stenzinger. Quantifying the confounders of panel-based tumor mutational burden (TMB) measurement [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 3093.

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.

Tumor Mutational Burden Determined by Panel Sequencing Predicts Survival After Immunotherapy in Patients With Advanced Gastric Cancer.

Objective: Panel-based sequencing is widely used to measure tumor mutational burden (TMB) in clinical trials and is ready to enter routine diagnostics. However, cut-off points to distinguish “TMB-high” from “TMB-low” tumors are not consistent and the clinical implications of TMB in predicting responses to immune checkpoint blockade (ICB) in gastric cancer are not clearly defined. We aimed to assess whether TMB is associated with the response to immunotherapy and to examine its relation with other biomarkers of immunotherapy response in advanced gastric cancer.Design: In total, 63 patients with advanced gastric cancer treated with ICB were included in the study. Panel-based TMB in gastric tumor samples, treatment responses to ICB, clinicopathological data, and time to progression were retrospectively analyzed. Microsatellite instability (MSI) status, Epstein–Barr virus (EBV) positivity, and programmed death-ligand 1 (PD-L1) combined positive score (CPS) were also analyzed.Results: TMB ranged from 0 to 446 mutations/megabase (mt/mb) and was significantly associated with MSI (P < 0.001), PD-L1 CPS (P = 0.022), response to ICB (P = 0.04), chemotherapy (P = 0.02) and older patient age (≥65 years; P = 0.0014). The cut-off point of 14.31 mt/mb determined by log-rank statistics for progression-free survival divided the tumors into eight (12.7%) TMB-high and 55 (87.3%) TMB-low tumors. The median TMB of the chemo-refractory group was significantly higher (8.43 mt/mb) compared to that of chemo-naïve group (3.42 mt/mb) (P = 0.02). Patients with TMB-high tumors showed prolonged progression-free survival in univariate [HR, 0.32; 95% confidence interval (CI), 0.12–0.90] and multivariate (HR, 0.21; 95% CI, 0.07–0.69) analyses. In area under the receiver operating curve (AUC) analysis of TMB, PD-L1, EBV, MSI, and their combination, the AUC value was the highest for EBV (0.97), followed by MSI (0.96), PD-L1 (0.81), the combination (0.78), and TMB (0.56).Conclusion: In addition to EBV, MSI, and PD-L1 CPS, TMB could be used as a predictive biomarker in patients with advanced gastric cancer treated with ICB and may aid clinical decision making.

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...

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.

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.

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.

Spatial and Temporal Heterogeneity of Panel-Based Tumor Mutational Burden in Pulmonary Adenocarcinoma: Separating Biology From Technical Artifacts

BackgroundTumor mutational burden (TMB) is an emerging biomarker used to identify patients who are more likely to benefit from immuno-oncology therapy. Aside from various unsettled technical aspects, biological variables such as tumor cell content and intratumor heterogeneity may play an important role in determining TMB. MethodsTMB estimates were determined applying the TruSight Oncology 500 targeted sequencing panel. Spatial and temporal heterogeneity was analyzed by multiregion sequencing (two to six samples) of 24 pulmonary adenocarcinomas and by sequencing a set of matched primary tumors, locoregional lymph node metastases, and distant metastases in five patients. ResultsOn average, a coding region of 1.28 Mbp was covered with a mean read depth of 609x. Manual validation of the mutation-calls confirmed a good performance, but revealed noticeable misclassification during germline filtering. Different regions within a tumor showed considerable spatial TMB variance in 30% (7 of 24) of the cases (maximum difference, 14.13 mut/Mbp). Lymph node–derived TMB was significantly lower (p = 0.016). In 13 cases, distinct mutational profiles were exclusive to different regions of a tumor, leading to higher values for simulated aggregated TMB. Combined, intratumor heterogeneity and the aggregated TMB could result in divergent TMB designation in 17% of the analyzed patients. TMB variation between primary tumor and distant metastases existed but was not profound. ConclusionsOur data show that, in addition to technical aspects such as germline filtering, the tumor content and spatially divergent mutational profiles within a tumor are relevant factors influencing TMB estimation, revealing limitations of single-sample–based TMB estimations in a clinical context.

Abstract 1346: Panel-based tumor mutational burden (TMB) analysis of matched tumor and plasma specimens using Illumina’s TruSight Oncology 500 next-generation sequencing assay

Abstract Checkpoint inhibitor (CPI) therapy demonstrates a remarkable clinical benefit in many cancer types. However, the ability to successfully select patients who will benefit from CPIs is still limited. Tumor mutational burden (TMB), a measure of the number of somatic mutations per coding area of tumor genome, is a putative biomarker of response showing great promise in CPI and immunotherapy combination trials. The ability to measure TMB from tumor biopsies or plasma samples will be important for clinical adoption of this biomarker. Herein we report on initial performance evaluation of Illumina’s TruSight™ Oncology 500 gene (TSO500) NGS assay for the analysis of TMB in FFPE tissue and plasma cell-free (cf)DNA specimens. Illumina’s TSO500 assay employs hybrid-capture based approach for target enrichment coupled with unique molecular indices to enable low frequency variant detection of single nucleotide variants and indels. This comprehensive cancer panel interrogates relevant cancer biomarkers in &amp;gt;500 cancer genes (~2 Mb) from as little as 40 ng of FFPE DNA or 30 ng of cfDNA. In addition to variant calls, Illumina’s analysis pipeline reports a TMB score and microsatellite instability (MSI) status. Results obtained with the TSO500 TMB assay were compared to our validated whole exome sequencing (WES) TMB assessment for FFPE tissue specimens, with and without matched normal. As previously reported, the WES TMB tumor-only pipeline uses somatic variant classifications determined using a random forest model to generate TMB scores from analysis of tumor FFPE specimens. TSO500 TMB and MSI statues were evaluated in a set of 50 tumor FFPE specimens obtained from late-stage (III+) colorectal and bladder cancer patients. TSO500 TMB results correlated well with the WES-derived TMB results, although some differences were observed in the magnitude of high TMB scores. TSO500 TMB levels correlated strongly with the MSI status, with 100% of MSI-high tumors demonstrating high TMB scores (&amp;gt; 20), and greater than 85% of MSI-low tumors characterized as TMB-low (&amp;lt;20%). Additionally, correlations between tissue and blood-based TMB assessments in matched FFPE and plasma specimens are reported. Overall, our results demonstrate a strong correlation between TSO500 panel-based and whole exome sequencing-based TMB results in tissue and report comparisons to TMB calling in liquid biopsies. Our data further confirm that tumor mutational load is strongly connected to DNA damage repair deficiency in colorectal and bladder cancer patients. Citation Format: Stephanie B. Hastings, Gunjan Hariani, Victor J. Weigman, Tingting Jiang, Chen Zhao, Rajiv Raja, Traci Pawlowski, Philip Brohawn, Patrick Hurban. Panel-based tumor mutational burden (TMB) analysis of matched tumor and plasma specimens using Illumina’s TruSight Oncology 500 next-generation sequencing assay [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 1346.

Abstract 3404: Landscape and genomic correlates of ctDNA-based tumor mutational burden across six solid tumor types

Abstract Background: Tumor mutational burden (TMB) has emerged as a predictive biomarker of response to immune checkpoint inhibitor (ICI) therapy. Current panel-based TMB algorithms aggregate signal from certain types of somatic variants (e.g. non-synonymous coding SNVs); however, delineating the contributions of these and other types of mutations may refine TMB calculation from gene panels. Moreover, early studies suggest other possible genomic correlates of patient outcome to ICI which may be complementary to TMB. Here, we explore the landscape of mutations comprising TMB and other genomic features correlating with TMB on a subset of several thousand late-stage plasma samples run on GuardantOMNITM (OMNI), a highly sensitive 500-gene cfDNA sequencing platform. Methods: We developed a cfDNA-based TMB algorithm which is robust to variable tumor shedding levels and presence of clonal hematopoiesis. We assessed cfDNA-based TMB in over 1,000 plasma samples across six tumor types, including lung and prostate. We examine the contribution of nonsynonymous, synonymous, intronic SNVs, and indels to TMB score. We investigate correlations between TMB and additional genomic features, including chromosomal instability, loss of HLA-bearing chromosome 6p, microsatellite instability (MSI), and common oncogenic and resistance mutations. Results: We found that the distribution of WES-calibrated TMB scores across this cohort of samples is consistent with TCGA, with median 10 mutations/Mb and upper-tertile of 14 mutations/Mb across tumor types. The number of non-synonymous coding SNVs per sample correlated highly with synonymous coding SNV and intronic SNV counts (Pearson’s r &amp;gt; 0.7 for each). Including this additional signal in TMB calculation improves clinical sensitivity by up to 5%. In MSS samples, indels were highly correlated with SNVs, indicating that both likely arise from a similar underlying mechanism. We found no clear correlation between high TMB and chromosomal instability, with high TMB samples exemplifying a range of tumor ploidies. TMB association with oncogenic drivers is consistent with existing literature, with lower median TMB in EGFR-driven lung tumors (p &amp;lt; 0.01), but little to no correlation between TMB and KRAS or PIK3CA driver status, or STK11 loss of function (p &amp;gt; 0.05), suggesting these latter events could be independent clinical biomarkers to TMB. Conclusions: Panel-based TMB scores can leverage synonymous and non-coding mutations to strengthen the signal of exome-wide mutation load. As more patient outcome data becomes available, TMB algorithms and orthogonal biomarkers of tumor genome immunogenicity will evolve further for improved guidance of patient response to immunotherapy. Sequencing panels with high sensitivity for TMB, via large panel space, and the ability to detect copy-number variations and MSI-status, will be important for biomarker development and clinical applications. Citation Format: Katie Quinn, Elena Helman, Tracy Nance, Jennifer Yen, John Latham, Kristin Gleitsman, Ravi Vijaya-Satya, Carlo Artieri, Alex Artyomenko, Marcin Sikora, Darya Chudova, Richard B. Lanman, AmirAli Talasaz. Landscape and genomic correlates of ctDNA-based tumor mutational burden across six solid tumor 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 3404.

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.