A prognostic model for overall survival of patients with early-stage non-small cell lung cancer: a multicentre, retrospective study.

Lu, Cheng; Bera, Kaustav; Wang, Xiangxue; Prasanna, Prateek; Xu, Jun; Janowczyk, Andrew; Beig, Niha; Yang, Michael; Fu, Pingfu; Lewis, James; Choi, Humberto; Schmid, Ralph; Berezowska, Sabina; Schalper, Kurt; Rimm, David; Velcheti, Vamsidhar; Madabhushi, Anant (2020). A prognostic model for overall survival of patients with early-stage non-small cell lung cancer: a multicentre, retrospective study. The Lancet. Digital health, 2(11), e594-e606. Elsevier 10.1016/s2589-7500(20)30225-9

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Background

Intratumoural heterogeneity has been previously shown to be related to clonal evolution and genetic instability and associated with tumour progression. Phenotypically, it is reflected in the diversity of appearance and morphology within cell populations. Computer-extracted features relating to tumour cellular diversity on routine tissue images might correlate with outcome. This study investigated the prognostic ability of computer-extracted features of tumour cellular diversity (CellDiv) from haematoxylin and eosin (H&E)-stained histology images of non-small cell lung carcinomas (NSCLCs).

Methods

In this multicentre, retrospective study, we included 1057 patients with early-stage NSCLC with corresponding diagnostic histology slides and overall survival information from four different centres. CellDiv features quantifying local cellular morphological diversity from H&E-stained histology images were extracted from the tumour epithelium region. A Cox proportional hazards model based on CellDiv was used to construct risk scores for lung adenocarcinoma (LUAD; 270 patients) and lung squamous cell carcinoma (LUSC; 216 patients) separately using data from two of the cohorts, and was validated in the two remaining independent cohorts (comprising 236 patients with LUAD and 335 patients with LUSC). We used multivariable Cox regression analysis to examine the predictive ability of CellDiv features for 5-year overall survival, controlling for the effects of clinical and pathological parameters. We did a gene set enrichment and Gene Ontology analysis on 405 patients to identify associations with differentially expressed biological pathways implicated in lung cancer pathogenesis.

Findings

For prognosis of patients with early-stage LUSC, the CellDiv LUSC model included 11 discriminative CellDiv features, whereas for patients with early-stage LUAD, the model included 23 features. In the independent validation cohorts, patients predicted to be at a higher risk by the univariable CellDiv model had significantly worse 5-year overall survival (hazard ratio 1·48 [95% CI 1·06-2·08]; p=0·022 for The Cancer Genome Atlas [TCGA] LUSC group, 2·24 [1·04-4·80]; p=0·039 for the University of Bern LUSC group, and 1·62 [1·15-2·30]; p=0·0058 for the TCGA LUAD group). The identified CellDiv features were also found to be strongly associated with apoptotic signalling and cell differentiation pathways.

Interpretation

CellDiv features were strongly prognostic of 5-year overall survival in patients with early-stage NSCLC and also associated with apoptotic signalling and cell differentiation pathways. The CellDiv-based risk stratification model could potentially help to determine which patients with early-stage NSCLC might receive added benefit from adjuvant therapy.

Funding

National Institue of Health and US Department of Defense.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Gastro-intestinal, Liver and Lung Disorders (DMLL) > Clinic of Thoracic Surgery
04 Faculty of Medicine > Service Sector > Institute of Pathology
04 Faculty of Medicine > Pre-clinic Human Medicine > BioMedical Research (DBMR) > Forschungsbereich Mu50 > Forschungsgruppe Thoraxchirurgie

UniBE Contributor:

Schmid, Ralph and Berezowska, Sabina Anna

Subjects:

600 Technology > 610 Medicine & health
500 Science > 570 Life sciences; biology

ISSN:

2589-7500

Publisher:

Elsevier

Language:

English

Submitter:

Thomas Michael Marti

Date Deposited:

05 Jan 2021 14:52

Last Modified:

10 May 2021 08:32

Publisher DOI:

10.1016/s2589-7500(20)30225-9

PubMed ID:

33163952

BORIS DOI:

10.48350/150027

URI:

https://boris.unibe.ch/id/eprint/150027

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