Zeng Zexian, Gu Shengqing Stan, Wong Cheryl J, Yang Lin, Ouardaoui Nofal, Li Dian, Zhang Wubing, Brown Myles, Liu X Shirley
Department of Data Science, Dana Farber Cancer Institute, Boston, MA 02215, USA.
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA.
Sci Adv. 2022 Oct 14;8(41):eabm8564. doi: 10.1126/sciadv.abm8564.
Most patients with cancer are refractory to immune checkpoint blockade (ICB) therapy, and proper patient stratification remains an open question. Primary patient data suffer from high heterogeneity, low accessibility, and lack of proper controls. In contrast, syngeneic mouse tumor models enable controlled experiments with ICB treatments. Using transcriptomic and experimental variables from >700 ICB-treated/control syngeneic mouse tumors, we developed a machine learning framework to model tumor immunity and identify factors influencing ICB response. Projected on human immunotherapy trial data, we found that the model can predict clinical ICB response. We further applied the model to predicting ICB-responsive/resistant cancer types in The Cancer Genome Atlas, which agreed well with existing clinical reports. Last, feature analysis implicated factors associated with ICB response. In summary, our computational framework based on mouse tumor data reliably stratified patients regarding ICB response, informed resistance mechanisms, and has the potential for wide applications in disease treatment studies.
大多数癌症患者对免疫检查点阻断（ICB）疗法耐药，合适的患者分层仍是一个悬而未决的问题。患者原始数据存在高度异质性、获取难度大以及缺乏适当对照等问题。相比之下，同基因小鼠肿瘤模型能够进行ICB治疗的对照实验。利用来自700多个接受ICB治疗/对照的同基因小鼠肿瘤的转录组学和实验变量，我们开发了一个机器学习框架来模拟肿瘤免疫并识别影响ICB反应的因素。将其应用于人类免疫治疗试验数据时，我们发现该模型可以预测临床ICB反应。我们进一步将该模型应用于预测癌症基因组图谱中对ICB有反应/耐药的癌症类型，这与现有临床报告高度吻合。最后，特征分析揭示了与ICB反应相关的因素。总之，我们基于小鼠肿瘤数据的计算框架能够可靠地根据ICB反应对患者进行分层，阐明耐药机制，并且在疾病治疗研究中具有广泛应用的潜力。
