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

Abstract

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.

Mutational signatures and increased retrotransposon insertions in Xeroderma Pigmentosum variant skin tumors

“…Most of the XP-V skin tumors have, as expected, a high frequency of mutations. A high TMB is, per se , an emerging biomarker of sensitivity to immune checkpoint inhibitors, with accumulating evidence of more significant benefit of immunotherapy in tumors with high TMB 14,56 . Therefore, based on recent findings’ assumption that TMB is an important biomarker for a good outcome in cancer patients 57 , we strongly argue that immune checkpoint inhibitors might benefit to XP patients.…”

Increased diagnostic yield from negative whole genome‐slice panels using automated reanalysis

“…Panels are powerful clinical diagnostic tools due to cost-effectiveness, straightforward pretest counseling, and relative ease of analysis. 1 However, they provide limited assessment leading to missed diagnostic opportunities.…”

“…Alternative testing strategies involve gene panels where analysis focuses on predefined lists of genes with established clinical validity. Panels are powerful clinical diagnostic tools due to cost‐effectiveness, straightforward pretest counseling, and relative ease of analysis 1 . However, they provide limited assessment leading to missed diagnostic opportunities.…”

Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis

“…ML is a subgroup of artificial intelligence (AI) that focuses on making predictions by identifying patterns in data using mathematical algorithms [12] . It has served as an assisting tool in cancer phenotyping and therapy for decades [13] , [14] , [15] , [16] , [17] , [18] , [19] , and has been widely implemented in advanced approaches for early detection, cancer type classification, signature extraction, tumor microenvironment (TME) deconvolution, prognosis prediction, and drug response evaluation [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] . Herein, we present an overview of the main ML algorithms that have been used to integrate complex biomedical data ( e.g.…”