Comprehensive evaluation of published gene expression prognostic signatures for biomarker-based lung cancer clinical studies

H. Tang, S. Wang, G. Xiao, J. Schiller, V. Papadimitrakopoulou, J. Minna, I. I. Wistuba, Y. Xie

Research output: Contribution to journalReview article

17 Citations (Scopus)

Abstract

Background: A more accurate prognosis for non-small-cell lung cancer (NSCLC) patients could aid in the identification of patients at high risk for recurrence. Many NSCLC mRNA expression signatures claiming to be prognostic have been reported in the literature. The goal of this study was to identify the most promising mRNA prognostic signatures in NSCLC for further prospective clinical validation. Experimental design: We carried out a systematic review and meta-analysis of published mRNA prognostic signatures for resected NSCLC. The prognostic performance of each signature was evaluated via a meta-analysis of 1927 early stage NSCLC patients collected from 15 studies using three evaluation metrics (hazard ratios, concordance scores, and time-dependent receiver-operating characteristic curves). The performance of each signature was then evaluated against 100 random signatures. The prognostic power independent of clinical risk factors was assessed by multivariate Cox models. Results: Through a literature search, we identified 42 lung cancer prognostic signatures derived from genome-wide expression profiling analysis. Based on meta-analysis, 25 signatures were prognostic for survival after adjusting for clinical risk factors and 18 signatures carried out significantly better than random signatures. When analyzing histology types separately, 17 signatures and 8 signatures are prognostic for adenocarcinoma and squamous cell lung cancer, respectively. Despite little overlap among published gene signatures, the top-performing signatures are highly concordant in predicted patient outcomes. Conclusions: Based on this large-scale meta-analysis, we identified a set of mRNA expression prognostic signatures appropriate for further validation in prospective clinical studies.

Original languageEnglish (US)
Article numbermdw683
Pages (from-to)733-740
Number of pages8
JournalAnnals of Oncology
Volume28
Issue number4
DOIs
StatePublished - Apr 1 2017

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Transcriptome
Non-Small Cell Lung Carcinoma
Lung Neoplasms
Biomarkers
Meta-Analysis
Messenger RNA
Squamous Cell Neoplasms
Proportional Hazards Models
ROC Curve
Histology
Adenocarcinoma
Research Design
Clinical Studies
Genome
Prospective Studies
Recurrence
Survival
Genes

Keywords

  • Meta-analysis
  • Non-small-cell lung cancer
  • Prognostic gene signatures

ASJC Scopus subject areas

  • Hematology
  • Oncology

Cite this

Comprehensive evaluation of published gene expression prognostic signatures for biomarker-based lung cancer clinical studies. / Tang, H.; Wang, S.; Xiao, G.; Schiller, J.; Papadimitrakopoulou, V.; Minna, J.; Wistuba, I. I.; Xie, Y.

In: Annals of Oncology, Vol. 28, No. 4, mdw683, 01.04.2017, p. 733-740.

Research output: Contribution to journalReview article

Tang, H. ; Wang, S. ; Xiao, G. ; Schiller, J. ; Papadimitrakopoulou, V. ; Minna, J. ; Wistuba, I. I. ; Xie, Y. / Comprehensive evaluation of published gene expression prognostic signatures for biomarker-based lung cancer clinical studies. In: Annals of Oncology. 2017 ; Vol. 28, No. 4. pp. 733-740.
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N2 - Background: A more accurate prognosis for non-small-cell lung cancer (NSCLC) patients could aid in the identification of patients at high risk for recurrence. Many NSCLC mRNA expression signatures claiming to be prognostic have been reported in the literature. The goal of this study was to identify the most promising mRNA prognostic signatures in NSCLC for further prospective clinical validation. Experimental design: We carried out a systematic review and meta-analysis of published mRNA prognostic signatures for resected NSCLC. The prognostic performance of each signature was evaluated via a meta-analysis of 1927 early stage NSCLC patients collected from 15 studies using three evaluation metrics (hazard ratios, concordance scores, and time-dependent receiver-operating characteristic curves). The performance of each signature was then evaluated against 100 random signatures. The prognostic power independent of clinical risk factors was assessed by multivariate Cox models. Results: Through a literature search, we identified 42 lung cancer prognostic signatures derived from genome-wide expression profiling analysis. Based on meta-analysis, 25 signatures were prognostic for survival after adjusting for clinical risk factors and 18 signatures carried out significantly better than random signatures. When analyzing histology types separately, 17 signatures and 8 signatures are prognostic for adenocarcinoma and squamous cell lung cancer, respectively. Despite little overlap among published gene signatures, the top-performing signatures are highly concordant in predicted patient outcomes. Conclusions: Based on this large-scale meta-analysis, we identified a set of mRNA expression prognostic signatures appropriate for further validation in prospective clinical studies.

AB - Background: A more accurate prognosis for non-small-cell lung cancer (NSCLC) patients could aid in the identification of patients at high risk for recurrence. Many NSCLC mRNA expression signatures claiming to be prognostic have been reported in the literature. The goal of this study was to identify the most promising mRNA prognostic signatures in NSCLC for further prospective clinical validation. Experimental design: We carried out a systematic review and meta-analysis of published mRNA prognostic signatures for resected NSCLC. The prognostic performance of each signature was evaluated via a meta-analysis of 1927 early stage NSCLC patients collected from 15 studies using three evaluation metrics (hazard ratios, concordance scores, and time-dependent receiver-operating characteristic curves). The performance of each signature was then evaluated against 100 random signatures. The prognostic power independent of clinical risk factors was assessed by multivariate Cox models. Results: Through a literature search, we identified 42 lung cancer prognostic signatures derived from genome-wide expression profiling analysis. Based on meta-analysis, 25 signatures were prognostic for survival after adjusting for clinical risk factors and 18 signatures carried out significantly better than random signatures. When analyzing histology types separately, 17 signatures and 8 signatures are prognostic for adenocarcinoma and squamous cell lung cancer, respectively. Despite little overlap among published gene signatures, the top-performing signatures are highly concordant in predicted patient outcomes. Conclusions: Based on this large-scale meta-analysis, we identified a set of mRNA expression prognostic signatures appropriate for further validation in prospective clinical studies.

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