小柯机器人

美国斯坦福大学James Zou团队利用大型真实世界临床基因组学数据对突变-治疗相互作用进行系统的泛癌症分析。相关论文于2022年6月30日在线发表在《自然—医学》杂志上。

研究人员对40903名美国癌症患者进行了大规模的计算分析，这些患者有详细的突变资料、治疗顺序和来自电子健康记录的结果。研究人员系统地识别了458个突变，这些突变预测了8种常见癌症类型中使用特定免疫疗法、化疗药物或靶向疗法的患者的生存率。研究人员进一步描述了影响靶向治疗结果的突变-突变相互作用的特点。这项工作展示了对大型真实世界数据的计算分析是如何产生洞察力、假设和资源从而实现精准肿瘤学的。

据悉，量化不同的癌症疗法对具有特定肿瘤突变的患者的疗效，对于改善患者预后和推进精准医疗至关重要。

附：英文原文

Title: Systematic pan-cancer analysis of mutation–treatment interactions using large real-world clinicogenomics data

Author: Liu, Ruishan, Rizzo, Shemra, Waliany, Sarah, Garmhausen, Marius Rene, Pal, Navdeep, Huang, Zhi, Chaudhary, Nayan, Wang, Lisa, Harbron, Chris, Neal, Joel, Copping, Ryan, Zou, James

Issue&Volume: 2022-06-30

Abstract: Quantifying the effectiveness of different cancer therapies in patients with specific tumor mutations is critical for improving patient outcomes and advancing precision medicine. Here we perform a large-scale computational analysis of 40,903 US patients with cancer who have detailed mutation profiles, treatment sequences and outcomes derived from electronic health records. We systematically identify 458 mutations that predict the survival of patients on specific immunotherapies, chemotherapy agents or targeted therapies across eight common cancer types. We further characterize mutation–mutation interactions that impact the outcomes of targeted therapies. This work demonstrates how computational analysis of large real-world data generates insights, hypotheses and resources to enable precision oncology.

DOI: 10.1038/s41591-022-01873-5

Source: https://www.nature.com/articles/s41591-022-01873-5

Nature Medicine：《自然—医学》，创刊于1995年。隶属于施普林格·自然出版集团，最新IF：87.241
官方网址：https://www.nature.com/nm/
投稿链接：https://mts-nmed.nature.com/cgi-bin/main.plex