Machine Learning on Syngeneic Mouse Tumor Profiles To Model Clinical Immunotherapy Response

Authors

Zexian Zeng
Shengqing Stan Gu
Cheryl J Wong
Lin Yang
Nofal Ouardaoui
Dian Li
Wubing Zhang
Myles Brown
X Shirley Liu

Publication Date

10-14-2022

Journal

Science Advances

Abstract

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.

Comments

Supplementary Material

PMID: 36240281

DOI

10.1126/sciadv.abm8564

PMID

36240281

PMCID

PMC9565795

PubMedCentral® Posted Date

October 2022

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes