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

TL;DR

This paper introduces TMBcalc, a new computational tool that accurately estimates tumor mutational burden using targeted sequencing data across multiple cancer types.

Contribution

The novel TMBcalc pipeline enables reliable TMB estimation from targeted panels, improving clinical applicability.

Findings

TMB estimated via TMBcalc strongly correlates with whole-exome sequencing results.

The pipeline was validated across 17 cancer types and multiple independent datasets.

TMBcalc demonstrates robustness and practicality for clinical use.

Abstract

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.

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.

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

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.

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