Computational Image Analysis Identifies Histopathological Image Features Associated With Somatic Mutations and Patient Survival in Gastric Adenocarcinoma

Jun Cheng; Yuting Liu; Wei Huang; Wenhui Hong; Lingling Wang; Xiaohui Zhan; Zhi Han; Dong Ni; Kun Huang; Jie Zhang

doi:10.3389/fonc.2021.623382

Computational Image Analysis Identifies Histopathological Image Features Associated With Somatic Mutations and Patient Survival in Gastric Adenocarcinoma

Front Oncol. 2021 Mar 31:11:623382. doi: 10.3389/fonc.2021.623382. eCollection 2021.

Authors

Jun Cheng^{1

2

3}, Yuting Liu^{1

2}, Wei Huang⁴, Wenhui Hong^{1

2}, Lingling Wang^{1

2}, Xiaohui Zhan⁵, Zhi Han⁶, Dong Ni^{1

2

3}, Kun Huang⁶, Jie Zhang⁷

Affiliations

¹ National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Shenzhen University, Shenzhen, China.
² Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
³ Marshall Laboratory of Biomedical Engineering Shenzhen University, Shenzhen, China.
⁴ Department of Radiation Oncology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, School of Medicine, South China University of Technology, Guangzhou, China.
⁵ School of Basic Medicine, Chongqing Medical University, Chongqin, China.
⁶ Department of Medicine, Indiana University, School of Medicine, Indianapolis, IN, United States.
⁷ Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States.

Abstract

Computational analysis of histopathological images can identify sub-visual objective image features that may not be visually distinguishable by human eyes, and hence provides better modeling of disease phenotypes. This study aims to investigate whether specific image features are associated with somatic mutations and patient survival in gastric adenocarcinoma (sample size = 310). An automated image analysis pipeline was developed to extract quantitative morphological features from H&E stained whole-slide images. We found that four frequently somatically mutated genes (TP53, ARID1A, OBSCN, and PIK3CA) were significantly associated with tumor morphological changes. A prognostic model built on the image features significantly stratified patients into low-risk and high-risk groups (log-rank test p-value = 2.6e-4). Multivariable Cox regression showed the model predicted risk index was an additional prognostic factor besides tumor grade and stage. Gene ontology enrichment analysis showed that the genes whose expressions mostly correlated with the contributing features in the prognostic model were enriched on biological processes such as cell cycle and muscle contraction. These results demonstrate that histopathological image features can reflect underlying somatic mutations and identify high-risk patients that may benefit from more precise treatment regimens. Both the image features and pipeline are highly interpretable to enable translational applications.

Keywords: computational pathology; gastric adenocarcinoma; gastric cancer; genotype-phenotype association; prognosis; whole-slide image.