TMBcalc: a computational pipeline for identifying pan-cancer Tumor Mutational Burden gene signatures

Abstract

Background: In the precision medicine era, identifying predictive factors to select patients most likely to benefit from treatment with immunological agents is a crucial and open challenge in oncology.

Methods: This paper presents a pan-cancer analysis of Tumor Mutational Burden (TMB). We developed a novel computational pipeline, TMBcalc, to calculate the TMB. Our methodology can identify small and reliable gene signatures to estimate TMB from custom targeted-sequencing panels. For this purpose, our pipeline has been trained on top of 17 cancer types data obtained from TCGA.

Results: Our results show that TMB, computed through the identified signature, strongly correlates with TMB obtained from whole-exome sequencing (WES).

Conclusion: We have rigorously analyzed the effectiveness of our methodology on top of several independent datasets. In particular we conducted a comprehensive testing on: (i) 126 samples sourced from the TCGA database; few independent whole-exome sequencing (WES) datasets linked to colon, breast, and liver cancers, all acquired from the EGA and the ICGC Data Portal. This rigorous evaluation clearly highlights the robustness and practicality of our approach, positioning it as a promising avenue for driving substantial progress within the realm of clinical practice.

Keywords: DNA-seq; Tumor Mutational Burden; analysis pipeline; pan-cancer; personalized medicine.