Integrative Analysis for Lung Adenocarcinoma Predicts Morphological Features Associated With Genetic Variations

Abstract

Lung cancer is one of the most deadly cancers and lung adenocarcinoma (LUAD) is the most common histological type of lung cancer. However, LUAD is highly heterogeneous due to genetic difference as well as phenotypic differences such as cellular and tissue morphology. In this paper, we systematically examine the relationships between histological features and gene transcription. Specifically, we calculated 283 morphological features from histology images for 201 LUAD patients from TCGA project and identified the morphological feature with strong correlation with patient outcome. We then modeled the morphology feature using multiple co-expressed gene clusters using Lasso-regression. Many of the gene clusters are highly associated with genetic variations, specifically DNA copy number variations, implying that genetic variations play important roles in the development cancer morphology. As far as we know, our finding is the first to directly link the genetic variations and functional genomics to LUAD histology. These observations will lead to new insight on lung cancer development and potential new integrative biomarkers for prediction patient prognosis and response to treatments.

AI in Medical Imaging Informatics: Current Challenges and Future Directions

“…An important computational issue is how to effectively integrate the omics data with digitized pathology images for biomedical research. Multiple statistical and machine learning methods have been applied for this purpose including consensus clustering [ 250 ], linear classifier [ 251 ], LASSO regression modeling [ 252 ], and deep learning [ 253 ]. These methods have been applied to studies on cancers, including breast [ 250 ], lung [ 252 ], and colorectal [ 253 ].…”

“…Multiple statistical and machine learning methods have been applied for this purpose including consensus clustering [ 250 ], linear classifier [ 251 ], LASSO regression modeling [ 252 ], and deep learning [ 253 ]. These methods have been applied to studies on cancers, including breast [ 250 ], lung [ 252 ], and colorectal [ 253 ]. The studies not only demonstrated that integration of morphological features extracted from digitized pathology images and -omics data can improve the accuracy of prognosis but also provided insights on the molecular basis of cancer cell and tissue organizations.…”

Plakophilin-2 Promotes Lung Adenocarcinoma Development via Enhancing Focal Adhesion and Epithelial–Mesenchymal Transition

“… 1 , 2 The development of LUAD is a complicated, multiple-stage process, and various factors including genetic mutations, cigarette smoking, and environment toxins contribute to the tumorigenesis. 3–5 The advances in diagnosis and therapy greatly improve the outcome of LUAD patients. 6 However, the prognosis of LUAD patients remains poor due to the tumor metastasis and recurrence, while the 5-year survival rate of LUAD is only around 15%.…”

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

“…Since cancer can be characterized by tissue and cellular morphological features from histopathological images and by molecular features from molecular omics data, an interesting scientific question arises — will tumor genetic changes be reflected at the tissue morphological level? Integrative analysis of multimodal data has been previously carried out in cancers such as glioblastoma ( 32 ), kidney cancer ( 3 , 6 ), lung cancer ( 33 ), breast cancer ( 34 , 35 ), and ovarian cancer ( 36 ). Using glioblastomas as an example, Cooper et al.…”