Abstract 5015: Precision profile simulation study for a next generation sequencing bTMB assay

Background: Precision profile simulations (PPS) can be used to assess variability of biomarker profiles and provide valuable insight into assay performance, especially when reliable precision estimates can not be obtained empirically due to scarcity of representative samples or insufficient materials per sample. A PPS was conducted for the GuardantOMNI assay to characterize the expected variability in blood tumor mutational burden (bTMB) score across a representative range of expected bTMB scores in clinical samples. The simulations were aligned to, but not completely prescribed by, the PPS guidance provided in Guidance for Industry and and Food and Drug Administration Staff Class II Special Controls Guidance Document: Ovarian Adnexal Mass Assessment Score Test System. A sample’s bTMB score is a real valued quantity (e.g., bTMB = 21.04 mut/Mb) that is derived by multiplying the number of qualified mutations observed within a targeted panel by a scaling factor. Variability in observed bTMB scores for a given blood sample is governed primarily by sample coverage, tumor shedding level, and the assay somatic variant detection probabilities (a function of underlying variant allele frequencies, VAFs).

Methods: The relationship between site-specific total molecule counts and coverage was modeled utilizing a composite dataset consisting of both clinical and contrived samples. Sample coverage was modeled using variance component estimates from Precision Study data (18 cancer samples each with 6 to 18 replicates).

The reference, single-strand mutant, and double-strand mutant molecule counts for a somatic variant site detected in at least one sample replicate were modeled utilizing a bias corrected Dirichlet Multinomial model.

The variants with the simulated VAF and coverage levels were processed with the GuardantOMNI germline/somatic classifier to account for the uncertainty in germline/somatic classification at lower coverage values.

Results: Precision profiles consisting of simulation derived %CV estimates for 18 clinical samples with a representative set of mean bTMB scores were generated. The PPS bTMB score distributions were consistent with the bTMB scores observed in the Precision Study, supported by visualization and confidence intervals at level 0.05 margins of equivalence for the empirical mean bTMB and standard deviation estimates.

The sample specific %CV estimates were observed, in most instances, to decrease with increasing input levels for matched targeted LoD (Limit of Detection) simulation results.

Precision profile %CV estimates were observed to be inversely related to mean bTMB scores.

Conclusions: The results provide proof of principle that estimation of GuardantOMNI bTMB score precision via an intuitive and interpretable simulation model is viable. The simulation results were consistent with empirical data and general expectations regarding the precision of the bTMB scores.

Citation Format: Kevin Doubleday, Daniel Gaile, Ravi Vijaya-Satya, Xianxian Liu, Kevin D'Auria, Soni Shukla, Han-Yu Chuang, Katie Quinn, Darya Chudova. Precision profile simulation study for a next generation sequencing bTMB assay [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5015.

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

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 > 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 < 0.01), but little to no correlation between TMB and KRAS or PIK3CA driver status, or STK11 loss of function (p > 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.