School of Automation from Northwestern Polytechnical University, China.
Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, China.
Brief Bioinform. 2023 Sep 20;24(5). doi: 10.1093/bib/bbad291.
Identifying personalized cancer driver genes and further revealing their oncogenic mechanisms is critical for understanding the mechanisms of cell transformation and aiding clinical diagnosis. Almost all existing methods primarily focus on identifying driver genes at the cohort or individual level but fail to further uncover their underlying oncogenic mechanisms. To fill this gap, we present an interpretable framework, PhenoDriver, to identify personalized cancer driver genes, elucidate their roles in cancer development and uncover the association between driver genes and clinical phenotypic alterations. By analyzing 988 breast cancer patients, we demonstrate the outstanding performance of PhenoDriver in identifying breast cancer driver genes at the cohort level compared to other state-of-the-art methods. Otherwise, our PhenoDriver can also effectively identify driver genes with both recurrent and rare mutations in individual patients. We further explore and reveal the oncogenic mechanisms of some known and unknown breast cancer driver genes (e.g. TP53, MAP3K1, HTT, etc.) identified by PhenoDriver, and construct their subnetworks for regulating clinical abnormal phenotypes. Notably, most of our findings are consistent with existing biological knowledge. Based on the personalized driver profiles, we discover two existing and one unreported breast cancer subtypes and uncover their molecular mechanisms. These results intensify our understanding for breast cancer mechanisms, guide therapeutic decisions and assist in the development of targeted anticancer therapies.
确定个性化癌症驱动基因，并进一步揭示其致癌机制，对于理解细胞转化的机制和辅助临床诊断至关重要。几乎所有现有的方法主要侧重于在队列或个体水平上识别驱动基因，但未能进一步揭示其潜在的致癌机制。为了填补这一空白，我们提出了一个可解释的框架 PhenoDriver，用于识别个性化癌症驱动基因，阐明它们在癌症发展中的作用，并揭示驱动基因与临床表型改变之间的关联。通过分析 988 名乳腺癌患者，我们证明了 PhenoDriver 在识别队列水平的乳腺癌驱动基因方面的出色表现，优于其他最先进的方法。此外，我们的 PhenoDriver 还可以有效地识别个体患者中具有反复和罕见突变的驱动基因。我们进一步探索和揭示了 PhenoDriver 识别的一些已知和未知乳腺癌驱动基因（例如 TP53、MAP3K1、HTT 等）的致癌机制，并构建了它们调节临床异常表型的子网络。值得注意的是，我们的大多数发现与现有生物学知识一致。基于个性化的驱动基因谱，我们发现了两种现有的和一种未报告的乳腺癌亚型，并揭示了它们的分子机制。这些结果加深了我们对乳腺癌机制的理解，指导治疗决策，并有助于开发靶向抗癌疗法。
