INTEGRATIVE ANALYSIS FOR LUNG ADENOCARCINOMA PREDICTS MORPHOLOGICAL FEATURES ASSOCIATED WITH GENETIC VARIATIONS

Pac Symp Biocomput. 2017:22:82-93. doi: 10.1142/9789813207813_0009.

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.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, N.I.H., Extramural

MeSH terms

  • Adenocarcinoma / diagnostic imaging
  • Adenocarcinoma / genetics*
  • Adenocarcinoma / pathology*
  • Adenocarcinoma of Lung
  • Algorithms
  • Biomarkers, Tumor / genetics
  • Computational Biology
  • Gene Expression Profiling
  • Gene Regulatory Networks
  • Genetic Variation*
  • Humans
  • Lung Neoplasms / diagnostic imaging
  • Lung Neoplasms / genetics*
  • Lung Neoplasms / pathology*
  • Multigene Family
  • Prognosis
  • Regression Analysis

Substances

  • Biomarkers, Tumor