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

Abstract

Background: 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.Methods: 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 formalinfixed, 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. Results:In-silico analyses demonstrated the Oncomine TML panel has sufficient genomic coverage to estimate somatic mutations with a strong correlation (r 2 =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 (r 2 =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.Conclusions: 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.

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

“…This assay counts SNVs including both non-synonymous and synonymous mutations from coding and non-coding regions. The analysis pipeline calls variants with optimized parameters on the tumor sample only, with no requirement for matched normal samples, and applies filters to remove germline variants (27). Libraries were prepared using Oncomine Tumor Mutation Load Assay library preparation kits (Chef-Ready) and barcoded adapters were ligated.…”

Tumor mutational burden quantification from targeted gene panels: major advancements and challenges

“…Comparable results were observed in similar in silico analyses for other gene panels, such as the Trusight170 [39,40] or MSK-IMPACT [26] (Additional file 4: Table S4). Notably, direct measurement of TMB from the Oncomine Tumor Load Assay shows that this panel-based TMB value allows to classify colorectal cancer cases based on their MSI status [39,41]. Since in this cancer type MSI positively correlates with immunotherapy response, this is a further, yet indirect evidence, of the capability to predict immunotherapy response, using a panel-based TMB estimate.…”

Integrated Analysis of Long Non-Coding RNAs and mRNAs Associated With Malignant Transformation of Gastrointestinal Stromal Tumors

“…Oncomine is a widely used database for predicting the correlation between genes and clinical signatures for diverse cancers [34][35][36]. In the Oncomine database, one clinical signature is correlated with hundreds of coding genes and signaling pathways [37].…”