癌症驱动基因在驱动肿瘤细胞生长方面具有至关重要的作用，准确地识别这些基因对于推动我们对癌症发病机制的理解和开发靶向抗癌药物具有重要意义。尽管目前发现癌症驱动基因的方法主要依赖于整合多组学数据，但许多现有模型过于复杂，难以准确解释结果。本研究旨在通过引入InDEP，一种基于级联森林的可解释机器学习框架，来解决这个问题。InDEP采用易于解释的特征、基于决策树的级联森林以及KernelSHAP模块，能够实现细粒度的事后解释。结合多组学数据，InDEP能够在基因和癌症类型两个层面上识别驱动基因的关键特征。该框架能够准确地识别驱动基因，发现使基因成为驱动基因的新模式，并完善癌症驱动基因目录。与现有最先进的方法相比，InDEP在测试集上证明了更高的准确性，并成功识别出可靠的候选驱动基因。在InDEP识别驱动基因中，突变特征是主要的驱动因素，其他组学特征也有所贡献。在基因层面上，该框架得出结论认为，替代型突变是大多数基因被识别为驱动基因的主要原因。InDEP识别可靠的候选驱动基因的能力为精准肿瘤学和发现新的生物医学知识开辟了新的途径。该框架可以通过提供一种可解释的方法来识别癌症驱动基因及其对癌症发病机制的贡献，推动癌症研究的进展，并促进靶向抗癌药物的开发。© 作者（们）2023. 由牛津大学出版社发表。版权所有。欲获取许可，请发送电子邮件至：journals.permissions@oup.com。

Cancer driver genes are critical in driving tumor cell growth, and precisely identifying these genes is crucial in advancing our understanding of cancer pathogenesis and developing targeted cancer drugs. Despite the current methods for discovering cancer driver genes that mainly rely on integrating multi-omics data, many existing models are overly complex, and it is difficult to interpret the results accurately. This study aims to address this issue by introducing InDEP, an interpretable machine learning framework based on cascade forests. InDEP is designed with easy-to-interpret features, cascade forests based on decision trees and a KernelSHAP module that enables fine-grained post-hoc interpretation. Integrating multi-omics data, InDEP can identify essential features of classified driver genes at both the gene and cancer-type levels. The framework accurately identifies driver genes, discovers new patterns that make genes as driver genes and refines the cancer driver gene catalog. In comparison with state-of-the-art methods, InDEP proved to be more accurate on the test set and identified reliable candidate driver genes. Mutational features were the primary drivers for InDEP's identifying driver genes, with other omics features also contributing. At the gene level, the framework concluded that substitution-type mutations were the main reason most genes were identified as driver genes. InDEP's ability to identify reliable candidate driver genes opens up new avenues for precision oncology and discovering new biomedical knowledge. This framework can help advance cancer research by providing an interpretable method for identifying cancer driver genes and their contribution to cancer pathogenesis, facilitating the development of targeted cancer drugs.© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.