Prediction of clinical outcome in multiple lung cancer cohorts by integrative genomics: implications for chemotherapy selection.

Published

Journal Article

The role of adjuvant chemotherapy in patients with stage IB non-small-cell lung cancer (NSCLC) is controversial. Identifying patient subgroups with the greatest risk of relapse and, consequently, most likely to benefit from adjuvant treatment thus remains an important clinical challenge. Here, we hypothesized that recurrent patterns of genomic amplifications and deletions in lung tumors could be integrated with gene expression information to establish a robust predictor of clinical outcome in stage IB NSCLC. Using high-resolution microarrays, we generated tandem DNA copy number and gene expression profiles for 85 stage IB lung adenocarcinomas/large cell carcinomas. We identified specific copy number alterations linked to relapse-free survival and selected genes within these regions exhibiting copy number-driven expression to construct a novel integrated signature (IS) capable of predicting clinical outcome in this series (P = 0.02). Importantly, the IS also significantly predicted clinical outcome in two other independent stage I NSCLC cohorts (P = 0.003 and P = 0.025), showing its robustness. In contrast, a more conventional molecular predictor based solely on gene expression, while capable of predicting outcome in the initial series, failed to significantly predict outcome in the two independent data sets. Our results suggest that recurrent copy number alterations, when combined with gene expression information, can be successfully used to create robust predictors of clinical outcome in early-stage NSCLC. The utility of the IS in identifying early-stage NSCLC patients as candidates for adjuvant treatment should be further evaluated in a clinical trial.

Full Text

Cited Authors

  • Broët, P; Camilleri-Broët, S; Zhang, S; Alifano, M; Bangarusamy, D; Battistella, M; Wu, Y; Tuefferd, M; Régnard, J-F; Lim, E; Tan, P; Miller, LD

Published Date

  • February 2009

Published In

Volume / Issue

  • 69 / 3

Start / End Page

  • 1055 - 1062

PubMed ID

  • 19176396

Pubmed Central ID

  • 19176396

Electronic International Standard Serial Number (EISSN)

  • 1538-7445

International Standard Serial Number (ISSN)

  • 0008-5472

Digital Object Identifier (DOI)

  • 10.1158/0008-5472.can-08-1116

Language

  • eng