Cell Systems
Volume 5, Issue 6, 27 December 2017, Pages 620-627.e3
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Association of Omics Features with Histopathology Patterns in Lung Adenocarcinoma


Gene and protein expression levels predicted lung adenocarcinoma grade

Quantitative histopathology features correlated with omics classifications

An integrative omics-pathology model better predicted stage I patients' prognoses

The improved survival prediction results were replicated in an independent cohort


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.


machine learning
cancer genomics
cancer transcriptomics
cancer proteomics
cancer imaging
quantitative pathology
predictive medicine
precision medicine
lung adenocarcinoma

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