Histopathological Images and Multi-Omics Integration Predict Molecular Characteristics and Survival in Lung Adenocarcinoma

Linyan Chen; Hao Zeng; Yu Xiang; Yeqian Huang; Yuling Luo; Xuelei Ma

doi:10.3389/fcell.2021.720110

Histopathological Images and Multi-Omics Integration Predict Molecular Characteristics and Survival in Lung Adenocarcinoma

Front Cell Dev Biol. 2021 Oct 11:9:720110. doi: 10.3389/fcell.2021.720110. eCollection 2021.

Authors

Linyan Chen¹, Hao Zeng¹, Yu Xiang¹, Yeqian Huang², Yuling Luo², Xuelei Ma¹

Affiliations

¹ State Key Laboratory of Biotherapy, Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.
² Department of Pathology, West China Hospital, Sichuan University, Chengdu, China.

Abstract

Histopathological images and omics profiles play important roles in prognosis of cancer patients. Here, we extracted quantitative features from histopathological images to predict molecular characteristics and prognosis, and integrated image features with mutations, transcriptomics, and proteomics data for prognosis prediction in lung adenocarcinoma (LUAD). Patients obtained from The Cancer Genome Atlas (TCGA) were divided into training set (n = 235) and test set (n = 235). We developed machine learning models in training set and estimated their predictive performance in test set. In test set, the machine learning models could predict genetic aberrations: ALK (AUC = 0.879), BRAF (AUC = 0.847), EGFR (AUC = 0.855), ROS1 (AUC = 0.848), and transcriptional subtypes: proximal-inflammatory (AUC = 0.897), proximal-proliferative (AUC = 0.861), and terminal respiratory unit (AUC = 0.894) from histopathological images. Moreover, we obtained tissue microarrays from 316 LUAD patients, including four external validation sets. The prognostic model using image features was predictive of overall survival in test and four validation sets, with 5-year AUCs from 0.717 to 0.825. High-risk and low-risk groups stratified by the model showed different survival in test set (HR = 4.94, p < 0.0001) and three validation sets (HR = 1.64-2.20, p < 0.05). The combination of image features and single omics had greater prognostic power in test set, such as histopathology + transcriptomics model (5-year AUC = 0.840; HR = 7.34, p < 0.0001). Finally, the model integrating image features with multi-omics achieved the best performance (5-year AUC = 0.908; HR = 19.98, p < 0.0001). Our results indicated that the machine learning models based on histopathological image features could predict genetic aberrations, transcriptional subtypes, and survival outcomes of LUAD patients. The integration of histopathological images and multi-omics may provide better survival prediction for LUAD.

Keywords: genomics; histopathology; lung cancer; proteomics; transcriptomics.