Dataset Information

Cancer-Specific Thresholds Adjust for Whole Exome Sequencing-based Tumor Mutational Burden Distribution.

ABSTRACT: Purpose:To understand the clinical context of tumor mutational burden (TMB) when comparing a pan-cancer threshold and a cancer-specific threshold. Materials and Methods:Using whole exome sequencing (WES) data from primary tumors in The Cancer Genome Atlas (TCGA) (n=3,534) and advanced/metastatic tumors from Weill Cornell Medicine (WCM Advanced) (n=696), TMB status was determined using a pan-cancer and cancer-specific threshold. Survival curves, number of samples classified as TMB high, and predicted neoantigens were used to evaluate the differences between thresholds. Results:The distribution of TMB varied dramatically between cancer types. A cancer-specific threshold was able to adjust for the different TMB distributions, while the pan-cancer threshold was often too stringent. The dynamic nature of the cancer-specific threshold resulted in more tumors being classified as TMB high compared to the static pan-cancer threshold. Additionally, no significant difference in survival outcomes was found with the cancer-specific threshold compared to the pan-cancer one. Further, the cancer-specific threshold maintains higher predicted neoantigen load for the TMB high samples compared to the TMB low samples, even when the threshold is lower than the pan-cancer threshold. Conclusion:TMB is relative to the context of cancer type, metastatic state, and disease stage. Compared to a pan-cancer threshold, a cancer-specific threshold classifies more patients as TMB high while maintaining clinical outcomes that were not significantly different. Furthermore, the cancer-specific threshold identifies patients with a high number of predicted neoantigens. Due to the potential impact in cancer patient care, TMB status should be determined in a cancer-specific manner.

SUBMITTER: Fernandez EM

PROVIDER: S-EPMC6716608 | biostudies-other | 2019

REPOSITORIES: biostudies-other

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Cancer-Specific Thresholds Adjust for Whole Exome Sequencing-based Tumor Mutational Burden Distribution.

Fernandez Evan M EM Eng Kenneth K Beg Shaham S Beltran Himisha H Faltas Bishoy M BM Mosquera Juan Miguel JM Nanus David M DM Pisapia David J DJ Rao Rema A RA Robinson Brian D BD Rubin Mark A MA Elemento Olivier O Sboner Andrea A Shah Manish A MA Song Wei W

JCO precision oncology 20190731

<h4>Purpose</h4>To understand the clinical context of tumor mutational burden (TMB) when comparing a pan-cancer threshold and a cancer-specific threshold.<h4>Materials and methods</h4>Using whole exome sequencing (WES) data from primary tumors in The Cancer Genome Atlas (TCGA) (n=3,534) and advanced/metastatic tumors from Weill Cornell Medicine (WCM Advanced) (n=696), TMB status was determined using a pan-cancer and cancer-specific threshold. Survival curves, number of samples classified as TMB ...[more]

PMID: 31475242

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Project description:BackgroundTumor mutational burden (TMB) is a promising biomarker for stratifying patient subpopulation who would benefit from immune checkpoint blockade (ICB) therapies. Although great efforts have been made for standardizing TMB measurement, mutation calling and TMB quantification can be challenging in samples with low tumor content including liquid biopsies. The effect of varying tumor content on TMB estimation by different assay methods has never been systematically investigated.MethodWe established a series of reference standard DNA samples derived from 11 pairs of tumor-normal matched human cell lines across different cancer types. Each tumor cell line was mixed with its matched normal at 0% (control), 1%, 2%, 5%, and 10% mass-to-mass ratio to mimic the clinical samples with low tumor content. TMB of these reference standards was evaluated by both ∼1000× whole-exome sequencing (wesTMB) and targeted panel sequencing (psTMB) at four different vendors. Both regression and classification analyses of TMB were performed for theoretical investigation and clinical practice purposes.ResultsLinear regression model was established that demonstrated in silico psTMB determined by regions of interest (ROI) as a great representative of wesTMB based on TCGA dataset. It was also true in our reference standard samples as the predicted psTMB interval based on the observed wesTMB captured the intended 90% of the in silico psTMB values. Although ∼1000× deep WES was applied, reference standard samples with less than 5% of tumor proportions are below the assay limit of detection (LoD) of wesTMB quantification. However, predicted wesTMB based on observed psTMB accurately classify (>0.97 AUC) for TMB high and low patient stratification even in samples with 2% of tumor content, which is more clinically relevant, as TMB determination should be a qualitative assay for TMB high and low patient classification. One targeted panel sequencing vendor using an optimized blood psTMB pipeline can further classify TMB status accurately (>0.82 AUC) in samples with only 1% of tumor content.ConclusionsWe developed a linear model to establish the quantitative correlation between wesTMB and psTMB. A set of DNA reference standards was produced in aid to standardize TMB measurements in samples with low tumor content across different targeted sequencing panels. This study is a significant contribution aiming to harmonize TMB estimation and extend its future application in clinical samples with low tumor content including liquid biopsy.

Project description:BackgroundIdentifying actionable driver mutations via tissue-based comprehensive genomic profiling (CGP) is paramount in treatment decisions for metastatic non-squamous, non-small-cell lung cancer (NSCLC). However, the role of CGP remains elusive in resectable NSCLC. Here, we elucidate the feasibility of CGP in early-stage NSCLC Korean patients and compare the tumor mutational burden (TMB) and mutation landscape using three different platforms.MethodsAll surgically resected NSCLC samples (N = 96) were analyzed to assess the concordance in TMB calculation and targetable mutations using whole-exome sequencing (WES) and TruSight Oncology 500 (TSO500). In all, 26 samples were analyzed with Foundation One CDx Assay (F1CDx). Programmed death-ligand 1 (PD-L1) expression was evaluated using Vectra Polaris.ResultsStage distribution post-surgery was 80% I (N = 77) and 20% II (N = 19). Ninety-nine percent (N = 95) were adenocarcinoma. The median TMB with WES and TSO500 was 1.6 and 4.7 mut/Mb, respectively (p < 0.05). Using all three platforms, the median TMB was 1.9, 5.5, and 4 mut/Mb for WES, TSO500, and F1CDx, respectively (p = 0.0048). Linear regression analysis of TMB values calculated between WES and TSO500 resulted in a concordance correlation coefficient of 0.83. For the PD-L1 tumor proportion score of <1% (negative, N = 18), 1-49% (low, N = 68), and ⩾50% (high, N = 10), the R2 values were 0.075, 0.79, and 0.95, respectively. The R2 values for TMB concordance were variable between the three platforms. Mutation landscape revealed EGFR mutation (51%, N = 49) as the most common actionable driver mutation, comprising L858R (N = 22), E19del (N = 20), and other non-common EGFR mutations (N = 7).ConclusionTSO500 and F1CDx showed robust analytical performance for TMB assessment with TSO500 showing stronger concordance of TMB with high PD-L1 expression. As the paradigm for the management of early-resected NSCLC continues to evolve, understanding TMB and the mutation landscape may help advance clinical outcomes for this subset of patients.

Dataset Information

Cancer-Specific Thresholds Adjust for Whole Exome Sequencing-based Tumor Mutational Burden Distribution.

Publications

Cancer-Specific Thresholds Adjust for Whole Exome Sequencing-based Tumor Mutational Burden Distribution.

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