Biology-guided deep learning predicts prognosis and cancer immunotherapy response

Yuming Jiang, Zhicheng Zhang, Wei Wang, Weicai Huang, Chuanli Chen, Sujuan Xi, M. Usman Ahmad, Yulan Ren, Shengtian Sang, Jingjing Xie, Jen-Yeu Wang, Wenjun Xiong, Tuanjie Li, Zhen Han, Qingyu Yuan, Yikai Xu, Lei Xing, George A. Poultsides, Guoxin Li () and Ruijiang Li ()
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Yuming Jiang: Southern Medical University
Zhicheng Zhang: Stanford University School of Medicine
Wei Wang: Sun Yat-sen University Cancer Center
Weicai Huang: Southern Medical University
Chuanli Chen: Southern Medical University
Sujuan Xi: The Seventh Affiliated Hospital of Sun Yat-sen University
M. Usman Ahmad: Stanford University School of Medicine
Yulan Ren: Stanford University School of Medicine
Shengtian Sang: Stanford University School of Medicine
Jingjing Xie: University of California Davis
Jen-Yeu Wang: Stanford University School of Medicine
Wenjun Xiong: Guangzhou University of Chinese Medicine
Tuanjie Li: Southern Medical University
Zhen Han: Southern Medical University
Qingyu Yuan: Southern Medical University
Yikai Xu: Southern Medical University
Lei Xing: Stanford University School of Medicine
George A. Poultsides: Stanford University School of Medicine
Guoxin Li: Southern Medical University
Ruijiang Li: Stanford University School of Medicine

Nature Communications, 2023, vol. 14, issue 1, 1-16

Abstract: Abstract Substantial progress has been made in using deep learning for cancer detection and diagnosis in medical images. Yet, there is limited success on prediction of treatment response and outcomes, which has important implications for personalized treatment strategies. A significant hurdle for clinical translation of current data-driven deep learning models is lack of interpretability, often attributable to a disconnect from the underlying pathobiology. Here, we present a biology-guided deep learning approach that enables simultaneous prediction of the tumor immune and stromal microenvironment status as well as treatment outcomes from medical images. We validate the model for predicting prognosis of gastric cancer and the benefit from adjuvant chemotherapy in a multi-center international study. Further, the model predicts response to immune checkpoint inhibitors and complements clinically approved biomarkers. Importantly, our model identifies a subset of mismatch repair-deficient tumors that are non-responsive to immunotherapy and may inform the selection of patients for combination treatments.

Date: 2023
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DOI: 10.1038/s41467-023-40890-x

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