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. [Cancer Res 2009;69(3):1055-62]

Neurotensin Receptor 1 Determines the Outcome of Non–Small Cell Lung Cancer

“…In an attempt to identify a clinical outcome predictor for patients with NSCLC, patterns of genomic alteration and gene expression profiles were analyzed and integrated concerning 74 homogeneous cases of stage Ib pT 2 N 0 lung adenocarcinomas/large cell carcinomas (7). In this study, NTSR1 (probe set: 207360_s_at) was the 48th among the 58 selected probes related with RFS.…”

“…We report the results obtained for NTSR1 from a previous large-scale gene expression analysis carried out on 74 homogeneous cases of stage pT 2 N 0 lung adenocarcinomas/ large cell carcinomas treated by surgery (7). In this study RNA samples were hybridized to the Human U133 Plus 2.0 oligonucleotide arrays (Affymetrix), and 37,771 probes met the quality control criteria and were considered for the analysis (GEO Series accession number GSE10445).…”

“…In this study RNA samples were hybridized to the Human U133 Plus 2.0 oligonucleotide arrays (Affymetrix), and 37,771 probes met the quality control criteria and were considered for the analysis (GEO Series accession number GSE10445). We selected transcripts having a high likelihood of being associated with relapse-free survival (RFS) by considering the false discovery rate error as described in Broet et al (7).…”

“…The current challenge today is to identify factors that would predict tumor relapse despite curative treatment. In a series of stage IB lung adenocarcinomas, we carried out a tandem DNA copy number and gene expression profile using high-resolution microarrays to establish a robust predictor of clinical outcome at the early stages of NSCLC (7). Among the first 50 genes upregulated and associated with disease-free survival was the neurotensin receptor 1 (NTSR1).…”

How close are we to customizing chemotherapy in early non-small cell lung cancer?

“…Most importantly, the authors highlighted the need for focused study planning in order to provide clinically relevant information that might potentially affect current management practice [Subramanian and Simon, 2010]. Indeed, only a handful of gene signature studies have yielded data specifically referring to NSCLC stages IA, IB, or II that would warrant further prospective validation [Kadara et al 2011;Zhu et al 2010;Boutros et al 2009;Broet et al 2009;Roepman et al 2009;Bianchi et al 2007;Lu et al 2006]. Among these prognostic models, two are worthy of particular mention.…”