title: Prediction of clinical outcome in multiple lung cancer cohorts by integrative genomics: implications for chemotherapy selection.
abstract: 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.
authors.affiliation: JE2492, Faculty of Medicine Paris-Sud, Bicêtre, France. philippe.broet@inserm.fr
authors.forename: Philippe
authors.initials: P
authors.lastname: Broët
authors.forename: Sophie
authors.initials: S
authors.lastname: Camilleri-Broët
authors.forename: Shenli
authors.initials: S
authors.lastname: Zhang
authors.forename: Marco
authors.initials: M
authors.lastname: Alifano
authors.forename: Dhinoth
authors.initials: D
authors.lastname: Bangarusamy
authors.forename: Maxime
authors.initials: M
authors.lastname: Battistella
authors.forename: Yonghui
authors.initials: Y
authors.lastname: Wu
authors.forename: Marianne
authors.initials: M
authors.lastname: Tuefferd
authors.forename: Jean-François
authors.initials: JF
authors.lastname: Régnard
authors.forename: Elaine
authors.initials: E
authors.lastname: Lim
authors.forename: Patrick
authors.initials: P
authors.lastname: Tan
authors.forename: Lance D
authors.initials: LD
authors.lastname: Miller
mesh_terms: D000230:Adenocarcinoma / Q000188:drug therapy / Q000235:genetics / Q000473:pathology; D018287:Carcinoma, Large Cell / Q000188:drug therapy / Q000235:genetics / Q000473:pathology; D002289:Carcinoma, Non-Small-Cell Lung / Q000188:drug therapy* / Q000235:genetics* / Q000473:pathology; D002869:Chromosome Aberrations; D015331:Cohort Studies; D055028:Comparative Genomic Hybridization; D018628:Gene Dosage; D020869:Gene Expression Profiling; D006801:Humans; D008175:Lung Neoplasms / Q000188:drug therapy* / Q000235:genetics* / Q000473:pathology; D009367:Neoplasm Staging; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D016896:Treatment Outcome