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

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.

AI/ML advances in non-small cell lung cancer biomarker discovery

“…However, native ML models cannot handle time-to-event data while accommodating censored observations. Reflecting this, we found that ML studies predicting NSCLC/LAD survival mainly formulated the survival analysis as a classification problem and transformed time-to-event data into dichotomized endpoints ( 90 – 94 , 96 , 100 , 102 , 103 , 106 , 108 – 111 , 113 , 116 , 117 , 134 , 135 ). To this end, utilizing Random Survival Forests (RSF) for continuous time-to-event survival prediction and those aiming to identify optimal time-to-event ML models were emerging ( 98 , 99 , 101 , 105 ), but further applications and research in this area are warranted.…”

“…Under the surrogate biomarker category, we identified 60 ML studies ( Table S1I ). The top predicted biomarkers were EGFR mutation status ( 44 , 58 , 183 – 209 ), PD-L1 expression status ( 190 , 210 – 215 ), ALK mutation status ( 94 , 216 – 219 ), KRAS mutation status ( 44 , 194 , 197 , 220 ), and TMB subtype ( 221 – 223 ) ( Figures 4D, E ).…”

Feature screening for survival trait with application to TCGA high-dimensional genomic data

“…Finally, the analysis results for TCGA HNSCC, TCGA LUAD, and TCGA BRCA data are provided in supplementary materials . Note that we take the published biomarker genes ( GIMAP6, SELL, TIFAB, KCNA3, CCR4 ) related to HNSCC ( Ran et al, 2021 ); ( ALK, BRAF, EGFR, ROS1 ) related to LUAD ( Chen et al, 2021 ); ( TMEM190, TUBA3D, LYVE1, LILBR5, CD209 ) related to BRCA ( Liu et al, 2019 ) as a survival prediction model to make comparisons. We identify nine genes and find the four genes ( PITPNM3 , MXD4 , ABCB1 , BATF ) that are related to HNSCC in the literature ( Aravind et al, 2021 ; Wu et al, 2019b ; da Silva et al, 2021 ; Duz & Karatas, 2021 ; Wang et al, 2020 ; and Wen et al, 2015 ).…”

Multi-scale pathology image texture signature is a prognostic factor for resectable lung adenocarcinoma: a multi-center, retrospective study

“…In recent years, many histopathological biomarkers have been developed for the prognosis of patients with lung cancer. For instance, Yu et al [ 13 ] and Chen et al [ 24 ] employed CellProfiler [ 25 – 27 ] software to quantitatively measure cellular phenotypes in histopathological images, and correlated these features with prognosis. Several studies [ 28 – 30 ] captured cellular-level feature descriptors from segmented nuclei for predicting prognosis in early-stage non-small cell lung cancer.…”