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.

中文翻译：

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TMBcalc：用于识别泛癌肿瘤突变负担基因特征的计算管道

背景：在精准医学时代，确定预测因素以选择最有可能从免疫药物治疗中受益的患者是肿瘤学中的一个关键且开放的挑战。方法：本文提出了肿瘤突变负荷（TMB）的泛癌分析。我们开发了一种新颖的计算管道 TMBcalc 来计算 TMB。我们的方法可以识别小而可靠的基因特征，以从定制的靶向测序面板中估计 TMB。为此，我们的流程基于从 TCGA 获得的 17 种癌症类型数据进行了训练。结果：我们的结果表明，通过识别的签名计算出的 TMB 与从全外显子组测序 (WES) 获得的 TMB 密切相关。结论：我们在几个独立数据集的基础上严格分析了我们方法的有效性。我们特别对以下方面进行了全面测试： (i) 来自 TCGA 数据库的 126 个样本；很少有与结肠癌、乳腺癌和肝癌相关​​的独立全外显子组测序 (WES) 数据集，全部从 EGA 和 ICGC 数据门户获得。这项严格的评估清楚地强调了我们方法的稳健性和实用性，将其定位为推动临床实践领域取得实质性进展的有前途的途径。