Abstract:
:Adenocarcinoma accounts for more than 40% of lung malignancy, and microscopic pathology evaluation is indispensable for its diagnosis. However, how histopathology findings relate to molecular abnormalities remains largely unknown. Here, we obtained H&E-stained whole-slide histopathology images, pathology reports, RNA sequencing, and proteomics data of 538 lung adenocarcinoma patients from The Cancer Genome Atlas and used these to identify molecular pathways associated with histopathology patterns. We report cell-cycle regulation and nucleotide binding pathways underpinning tumor cell dedifferentiation, and we predicted histology grade using transcriptomics and proteomics signatures (area under curve >0.80). We built an integrative histopathology-transcriptomics model to generate better prognostic predictions for stage I patients (p = 0.0182 ± 0.0021) compared with gene expression or histopathology studies alone, and the results were replicated in an independent cohort (p = 0.0220 ± 0.0070). These results motivate the integration of histopathology and omics data to investigate molecular mechanisms of pathology findings and enhance clinical prognostic prediction.
journal_name
Cell Systjournal_title
Cell systemsauthors
Yu KH,Berry GJ,Rubin DL,Ré C,Altman RB,Snyder Mdoi
10.1016/j.cels.2017.10.014subject
Has Abstractpub_date
2017-12-27 00:00:00pages
620-627.e3issue
6eissn
2405-4712issn
2405-4720pii
S2405-4712(17)30484-2journal_volume
5pub_type
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