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Multiregion gene expression profiling reveals heterogeneity in molecular subtypes and immunotherapy response signatures in lung cancer.

Won-Chul LeeLixia DiaoJing WangJianhua ZhangEmily B RoartySusan VargheseChi-Wan ChowJunya FujimotoCarmen BehrensTina CasconeWeiyi PengNeda KalhorCesar A MoranAnnikka WeissferdtFaye M JohnsonWilliam N WilliamStephen G SwisherJiun-Kae Jack LeeWaun Ki HongJohn V HeymachIgnacio I WistubaP Andrew FutrealJianjun Zhang
Published in: Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc (2018)
Intra-tumor heterogeneity may be present at all molecular levels. Genomic intra-tumor heterogeneity at the exome level has been reported in many cancer types, but comprehensive gene expression intra-tumor heterogeneity has not been well studied. Here, we delineated the gene expression intra-tumor heterogeneity by exploring gene expression profiles of 35 tumor regions from 10 non-small cell lung cancer tumors (three or four regions/tumor), including adenocarcinoma, squamous cell carcinoma, large-cell carcinoma, and pleomorphic carcinoma of the lung. Using Affymetrix Gene 1.0 ST arrays, we generated the gene expression data for every sample. Inter-tumor heterogeneity was generally higher than intra-tumor heterogeneity, but some tumors showed a substantial level of intra-tumor heterogeneity. The analysis of various clinically relevant gene expression signatures including molecular subtype, epithelial-to-mesenchymal transition, and anti-PD-1 resistance signatures also revealed heterogeneity between different regions of the same tumor. The gene expression intra-tumor heterogeneity we observed was associated with heterogeneous tumor microenvironments represented by stromal and immune cells infiltrated. Our data suggest that RNA-based prognostic or predictive molecular tests should be carefully conducted in consideration of the gene expression intra-tumor heterogeneity.
Keyphrases
  • gene expression
  • single cell
  • dna methylation
  • radiation therapy
  • bone marrow
  • deep learning
  • artificial intelligence
  • papillary thyroid
  • data analysis