Association of Omics Features with Histopathology Patterns in Lung Adenocarcinoma.

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 Syst

journal_title

Cell systems

authors

Yu KH,Berry GJ,Rubin DL,Ré C,Altman RB,Snyder M

doi

10.1016/j.cels.2017.10.014

subject

Has Abstract

pub_date

2017-12-27 00:00:00

pages

620-627.e3

issue

6

eissn

2405-4712

issn

2405-4720

pii

S2405-4712(17)30484-2

journal_volume

5

pub_type

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