Gene expression profiling reveals reproducible human lung adenocarcinoma subtypes in multiple independent patient cohorts

D. Neil Hayes, Stefano Monti, Giovanni Parmigiani, C. Blake Gilks, Katsuhiko Naoki, Arindam Bhattacharjee, Mark A. Socinski, Charles Perou, Matthew Meyerson

Research output: Contribution to journalArticle

Abstract

Purpose: Published reports suggest that DNA microarrays identify clinically meaningful subtypes of lung adenocarcinomas not recognizable by other routine tests. This report is an investigation of the reproducibility of the reported tumor subtypes. Methods: Three independent cohorts of patients with lung cancer were evaluated using a variety of DNA microarray assays. Using the integrative correlations method, a subset of genes was selected, the reliability of which was acceptable across the different DNA microarray platforms. Tumor subtypes were selected using consensus clustering and genes distinguishing subtypes were identified using the weighted difference statistic. Gene lists were compared across cohorts using centroids and gene set enrichment analysis. Results: Cohorts of 31, 72, and 128 adenocarcinomas were generated for a total of 231 microarrays, each with 2,553 reliable genes. Three adenocarcinoma subtypes were identified in each cohort. These were named bronchioid, squamoid, and magnoid according to their respective correlations with gene expression patterns from histologically defined bronchioalveolar carcinoma, squamous cell carcinoma, and large-cell carcinoma. Tumor subtypes were distinguishable by many hundreds of genes, and lists generated in one cohort were predictive of tumor subtypes in the two other cohorts. Tumor subtypes correlated with clinically relevant covariates, including stage-specific survival and metastatic pattern. Most notably, bronchioid tumors were correlated with improved survival in early-stage disease, whereas squamoid tumors were associated with better survival in advanced disease. Conclusion: DNA microarray analysis of lung adenocarcinomas identified reproducible tumor subtypes which differ significantly in clinically important behaviors such as stage-specific survival.

Original languageEnglish (US)
Pages (from-to)5079-5090
Number of pages12
JournalJournal of Clinical Oncology
Volume24
Issue number31
DOIs
StatePublished - Nov 1 2006
Externally publishedYes

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Gene Expression Profiling
Oligonucleotide Array Sequence Analysis
Neoplasms
Genes
Survival
Adenocarcinoma
Large Cell Carcinoma
Adenocarcinoma of lung
Microarray Analysis
Cluster Analysis
Squamous Cell Carcinoma
Lung Neoplasms
Carcinoma
Gene Expression

ASJC Scopus subject areas

  • Cancer Research
  • Oncology

Cite this

Hayes, D. N., Monti, S., Parmigiani, G., Gilks, C. B., Naoki, K., Bhattacharjee, A., ... Meyerson, M. (2006). Gene expression profiling reveals reproducible human lung adenocarcinoma subtypes in multiple independent patient cohorts. Journal of Clinical Oncology, 24(31), 5079-5090. https://doi.org/10.1200/JCO.2005.05.1748

Gene expression profiling reveals reproducible human lung adenocarcinoma subtypes in multiple independent patient cohorts. / Hayes, D. Neil; Monti, Stefano; Parmigiani, Giovanni; Gilks, C. Blake; Naoki, Katsuhiko; Bhattacharjee, Arindam; Socinski, Mark A.; Perou, Charles; Meyerson, Matthew.

In: Journal of Clinical Oncology, Vol. 24, No. 31, 01.11.2006, p. 5079-5090.

Research output: Contribution to journalArticle

Hayes, DN, Monti, S, Parmigiani, G, Gilks, CB, Naoki, K, Bhattacharjee, A, Socinski, MA, Perou, C & Meyerson, M 2006, 'Gene expression profiling reveals reproducible human lung adenocarcinoma subtypes in multiple independent patient cohorts', Journal of Clinical Oncology, vol. 24, no. 31, pp. 5079-5090. https://doi.org/10.1200/JCO.2005.05.1748
Hayes, D. Neil ; Monti, Stefano ; Parmigiani, Giovanni ; Gilks, C. Blake ; Naoki, Katsuhiko ; Bhattacharjee, Arindam ; Socinski, Mark A. ; Perou, Charles ; Meyerson, Matthew. / Gene expression profiling reveals reproducible human lung adenocarcinoma subtypes in multiple independent patient cohorts. In: Journal of Clinical Oncology. 2006 ; Vol. 24, No. 31. pp. 5079-5090.
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