Multicenter Study

. 2020 Nov;2(11):e594-e606.

doi: 10.1016/s2589-7500(20)30225-9. Epub 2020 Oct 19.

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

Cheng Lu¹, Kaustav Bera¹, Xiangxue Wang¹, Prateek Prasanna², Jun Xu³, Andrew Janowczyk⁴, Niha Beig¹, Michael Yang⁵, Pingfu Fu⁶, James Lewis⁷, Humberto Choi⁸, Ralph A Schmid⁹, Sabina Berezowska¹⁰, Kurt Schalper¹¹, David Rimm¹¹, Vamsidhar Velcheti¹², Anant Madabhushi¹³

Affiliations

¹ Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
² Department of Biomedical Informatics, Stony Brook University, New York.
³ Jiangsu Key Laboratory of Big Data Analysis Technique, Nanjing University of Information Science and Technology, Nanjing, China.
⁴ Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA; Precision Oncology Center, Lausanne University Hospital, Lausanne, Switzerland.
⁵ University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
⁶ Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA.
⁷ Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA.
⁸ Department of Pulmonary Medicine, Cleveland Clinic, Cleveland, OH, USA.
⁹ Division of General Thoracic Surgery, Inselspital University Hospital Bern, Bern, Switzerland.
¹⁰ The Institute of Pathology, University of Bern, Bern, Switzerland.
¹¹ Department of Pathology, Yale University School of Medicine, New Haven, CT, USA.
¹² Perlmutter Cancer Center, New York University, NY, USA.
¹³ Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA; Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, OH, USA.

PMID: 33163952
PMCID: PMC7646741
DOI: 10.1016/s2589-7500(20)30225-9

Multicenter Study

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

Cheng Lu et al. Lancet Digit Health. 2020 Nov.

. 2020 Nov;2(11):e594-e606.

doi: 10.1016/s2589-7500(20)30225-9. Epub 2020 Oct 19.

Authors

Affiliations

¹ Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
² Department of Biomedical Informatics, Stony Brook University, New York.
³ Jiangsu Key Laboratory of Big Data Analysis Technique, Nanjing University of Information Science and Technology, Nanjing, China.
⁴ Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA; Precision Oncology Center, Lausanne University Hospital, Lausanne, Switzerland.
⁵ University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
⁶ Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA.
⁷ Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA.
⁸ Department of Pulmonary Medicine, Cleveland Clinic, Cleveland, OH, USA.
⁹ Division of General Thoracic Surgery, Inselspital University Hospital Bern, Bern, Switzerland.
¹⁰ The Institute of Pathology, University of Bern, Bern, Switzerland.
¹¹ Department of Pathology, Yale University School of Medicine, New Haven, CT, USA.
¹² Perlmutter Cancer Center, New York University, NY, USA.
¹³ Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA; Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, OH, USA.

PMID: 33163952
PMCID: PMC7646741
DOI: 10.1016/s2589-7500(20)30225-9

Abstract

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.

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Conflict of interest statement

Declaration of interests CL, KB, and AM have a pending patent (Predicting Cancer Recurrence Using Local Co-occurrence of Cell Morphology). AM is an equity holder in Elucid Bioimaging and in Inspirata, to whom his technology has been licensed. He is currently a scientific advisory board member at Aiforia. He is also involved in a National Institutes of Health U24 grant with PathCore and is involved in four grants with Inspirata. His work has received sponsored research funding from Bristol Myers Squibb, AstraZeneca, and Philips, outside of the submitted work. AJ has patents 10528848 and 9111179 issued, and patents 20190266726, 20190251687, 20180129911, and 20160307305 pending (US patents, registered in Case Western Reserve University). VV reports grants and personal fees from Merck, Bristol Myers Squibb, Genentech, AstraZeneca, Celgene, Novartis, Amgen, Fulgent Genetics, Reddy Labs, Alkermes, Nektar Therapeutics, Novocure, and Foundation Medicine, outside of the submitted work; and advisory or consulting fees from Genentech, Merck, Bristol Myers Squibb, AstraZeneca, Foundation Medicine, Nektar Therapeutics, Alkermes, Reddy Labs, and Millennium Pharma, outside of the submitted work. All other authors declare no competing interests.

Figures

**Figure 1:. Kaplan-Meier 5-year overall survival according to risk category**
HR=hazard ratio. LUAD=lung adenocarcinoma. LUSC=lung squamous cell carcinoma. NA=not applicable. TCGA=The Cancer Genome Atlas.

**Figure 2:. Cellular diversity feature maps in LUSC risk model (A), LUAD risk model (B), and mutational status classification (C)**
(A) Representative cases of LUSC and CellDiv feature map illustration. (B) Representative cases of LUAD and CellDiv feature map illustration. In (A) and (B), the first column shows haematoxylin and eosin-stained images with low-risk and high-risk patients as identified by the CellDiv model. The segmented nuclei contour and connecting edges are shown in the second column. The third column shows CellDiv features that capture the CellDiv in terms of nuclear shape (ie, area in panel A and eccentricity in panel B). Each colour patch represents individual LNGs in the image, where the blue and yellow colours represent the low and high normalised feature values. (C) Representative cases of *KRAS* mutation positive versus *KRAS* mutation negative, and the corresponding CellDiv feature map. LNG=local nuclear graph. LUAD=lung adenocarcinoma. LUSC=lung squamous cell carcinoma.

**Figure 3:. Association between biological processes and the CellDiv features used to construct the prognostic models for LUAD**
The strength of association of biological processes, shown in rows, with the CellDiv features, shown in columns, by ssGSEA analysis. Wilcoxon rank sum test p values are shown, where p<0·05 shows an association between histomorphometric features used in the CellDiv models and certain pathways (while p<0·05). LUAD=lung adenocarcinoma. ssGSEA=single-sample gene set enrichment analysis.

**Figure 4:. Association between biological processes and the CellDiv features used to construct the prognostic models for LUSC**
The strength of association of biological processes, shown in rows, with the CellDiv features, shown in columns, by ssGSEA analysis. Wilcoxon rank sum test p values are shown, where p<0·05 shows an association between histomorphometric features used in the CellDiv models and certain pathways (while p<0·05). LUSC=lung squamous cell carcinoma. BMP=bone morphogenetic protein. TGF=transforming growth factor. ssGSEA=single-sample gene set enrichment analysis.

See this image and copyright information in PMC

References

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

Affiliations

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

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

Publication types

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources