ArticleJournal of the National Cancer Institute2015
Integrating Tumor and Stromal Gene Expression Signatures With Clinical Indices for Survival Stratification of Early-Stage Non-Small Cell Lung Cancer.
Article in Journal of the National Cancer Institute, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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38 citing papers in PubMed, 74 citations in OpenAlex.
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- Microenvironmental modulation of the developing tumour: an immune-stromal dialogue.Molecular oncology · 2021Review
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- ECMarker: interpretable machine learning model identifies gene expression biomarkers predicting clinical outcomes and reveals molecular mechanisms of human disease in early stages.Bioinformatics (Oxford, England) · 2021Article
- Digital image analysis in pathologist-selected regions of interest predicts survival more accurately than whole-slide analysis: a direct comparison study in 153 gastric carcinomas.The journal of pathology. Clinical research · 2021Article
- Radiomics Assessment of the Tumor Immune Microenvironment to Predict Outcomes in Breast Cancer.Frontiers in immunology · 2021Article
- Multi-omics Data Analyses Construct TME and Identify the Immune-Related Prognosis Signatures in Human LUAD.Molecular therapy. Nucleic acids · 2020Article
- Denoising Autoencoder, A Deep Learning Algorithm, Aids the Identification of A Novel Molecular Signature of Lung Adenocarcinoma.Genomics, proteomics & bioinformatics · 2020Article
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Authors and funding
13 authors at 1 institution in 1 country.
Funding
Abstract
backgroundAccurate survival stratification in early-stage non-small cell lung cancer (NSCLC) could inform the use of adjuvant therapy. We developed a clinically implementable mortality risk score incorporating distinct tumor microenvironmental gene expression signatures and clinical variables.
methodsGene expression profiles from 1106 nonsquamous NSCLCs were used for generation and internal validation of a nine-gene molecular prognostic index (MPI). A quantitative polymerase chain reaction (qPCR) assay was developed and validated on an independent cohort of formalin-fixed paraffin-embedded (FFPE) tissues (n = 98). A prognostic score using clinical variables was generated using Surveillance, Epidemiology, and End Results data and combined with the MPI. All statistical tests for survival were two-sided.
resultsThe MPI stratified stage I patients into prognostic categories in three microarray and one FFPE qPCR validation cohorts (HR = 2.99, 95% CI = 1.55 to 5.76, P < .001 in stage IA patients of the largest microarray validation cohort; HR = 3.95, 95% CI = 1.24 to 12.64, P = .01 in stage IA of the qPCR cohort). Prognostic genes were expressed in distinct tumor cell subpopulations, and genes implicated in proliferation and stem cells portended poor outcomes, while genes involved in normal lung differentiation and immune infiltration were associated with superior survival. Integrating the MPI with clinical variables conferred greatest prognostic power (HR = 3.43, 95% CI = 2.18 to 5.39, P < .001 in stage I patients of the largest microarray cohort; HR = 3.99, 95% CI = 1.67 to 9.56, P < .001 in stage I patients of the qPCR cohort). Finally, the MPI was prognostic irrespective of somatic alterations in EGFR, KRAS, TP53, and ALK.
conclusionThe MPI incorporates genes expressed in the tumor and its microenvironment and can be implemented clinically using qPCR assays on FFPE tissues. A composite model integrating the MPI with clinical variables provides the most accurate risk stratification.
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