Liu Ruishan, Rizzo Shemra, Wang Lisa, Chaudhary Nayan, Maund Sophia, Garmhausen Marius Rene, McGough Sarah, Copping Ryan, Zou James
Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
Department of Computer Science, University of Southern California, Los Angeles, CA, USA.
Nat Commun. 2024 Dec 30;15(1):10884. doi: 10.1038/s41467-024-55251-5.
Evaluating the effectiveness of cancer treatments in relation to specific tumor mutations is essential for improving patient outcomes and advancing the field of precision medicine. Here we represent a comprehensive analysis of 78,287 U.S. cancer patients with detailed somatic mutation profiling integrated with treatment and outcomes data extracted from electronic health records. We systematically identified 776 genomic alterations associated with survival outcomes across 20 distinct cancer types treated with specific immunotherapies, chemotherapies, or targeted therapies. Additionally, we demonstrate how mutations in particular pathways correlate with treatment response. Leveraging the large number of identified predictive mutations, we developed a machine learning model to generate a risk score for response to immunotherapy in patients with advanced non-small cell lung cancer (aNSCLC). Through rigorous computational analysis of large-scale clinico-genomic real-world data, this research provides insights and lays the groundwork for further advancements in precision oncology.
评估癌症治疗相对于特定肿瘤突变的有效性对于改善患者预后和推动精准医学领域的发展至关重要。在此，我们对78287名美国癌症患者进行了全面分析，这些患者具有详细的体细胞突变谱，并与从电子健康记录中提取的治疗和预后数据相结合。我们系统地鉴定了776种与接受特定免疫疗法、化疗或靶向疗法治疗的20种不同癌症类型的生存结果相关的基因组改变。此外，我们还展示了特定通路中的突变如何与治疗反应相关。利用大量已鉴定的预测性突变，我们开发了一种机器学习模型，以生成晚期非小细胞肺癌（aNSCLC）患者对免疫疗法反应的风险评分。通过对大规模临床基因组真实世界数据进行严格的计算分析，本研究提供了见解，并为精准肿瘤学的进一步发展奠定了基础。
