Identification and validation of a gene expression signature that predicts outcome in adult men with germ cell tumors

James Korkola, Jane Houldsworth, Darren R. Feldman, Adam B. Olshen, Li Xuan Qin, Sujata Patil, Victor E. Reuter, George J. Bosl, R. S K Chaganti

Research output: Contribution to journalArticle

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Abstract

Purpose: Germ cell tumor (GCT) is the most common malignancy in young adult men. Currently, patients are risk-stratified on the basis of clinical presentation and serum tumor markers. The introduction of molecular markers could improve outcome prediction. Patients and Methods: Expression profiling was performed on 74 nonseminomatous GCTs (NSGCTs) from cisplatintreated patients (ie, training set) and on 34 similarly treated patients with NSGCTs (ie, validation set). A gene classifier was developed by using prediction analysis for microarrays (PAM) for the binary end point of 5-year overall survival (OS). A predictive score was developed for OS by using the univariate Cox model. Results: In the training set, PAM identified 140 genes that predicted 5-year OS (cross-validated classification rate, 60%). The PAM model correctly classified 90% of patients in the validation set. Patients predicted to have good outcome had significantly longer survival than those with poor predicted outcome (P <.001). For the OS end point, a 10-gene model had a predictive accuracy (ie, concordance index) of 0.66 in the training set and a concordance index of 0.83 in the validation set. Dichotomization of the samples on the basis of the median score resulted in significant differences in survival (P = .002). For both end points, the gene-based predictor was an independent prognostic factor in a multivariate model that included clinical risk stratification (P <.01 for both). Conclusion: We have identified gene expression signatures that accurately predict outcome in patients with GCTs. These predictive genes should be useful for the prediction of patient outcome and could provide novel targets for therapeutic intervention.

Original languageEnglish (US)
Pages (from-to)5240-5247
Number of pages8
JournalJournal of Clinical Oncology
Volume27
Issue number31
DOIs
StatePublished - Nov 1 2009
Externally publishedYes

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Germ Cell and Embryonal Neoplasms
Transcriptome
Survival
Microarray Analysis
Genes
Tumor Biomarkers
Proportional Hazards Models
Young Adult
Biomarkers

ASJC Scopus subject areas

  • Cancer Research
  • Oncology
  • Medicine(all)

Cite this

Identification and validation of a gene expression signature that predicts outcome in adult men with germ cell tumors. / Korkola, James; Houldsworth, Jane; Feldman, Darren R.; Olshen, Adam B.; Qin, Li Xuan; Patil, Sujata; Reuter, Victor E.; Bosl, George J.; Chaganti, R. S K.

In: Journal of Clinical Oncology, Vol. 27, No. 31, 01.11.2009, p. 5240-5247.

Research output: Contribution to journalArticle

Korkola, J, Houldsworth, J, Feldman, DR, Olshen, AB, Qin, LX, Patil, S, Reuter, VE, Bosl, GJ & Chaganti, RSK 2009, 'Identification and validation of a gene expression signature that predicts outcome in adult men with germ cell tumors', Journal of Clinical Oncology, vol. 27, no. 31, pp. 5240-5247. https://doi.org/10.1200/JCO.2008.20.0386
Korkola, James ; Houldsworth, Jane ; Feldman, Darren R. ; Olshen, Adam B. ; Qin, Li Xuan ; Patil, Sujata ; Reuter, Victor E. ; Bosl, George J. ; Chaganti, R. S K. / Identification and validation of a gene expression signature that predicts outcome in adult men with germ cell tumors. In: Journal of Clinical Oncology. 2009 ; Vol. 27, No. 31. pp. 5240-5247.
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abstract = "Purpose: Germ cell tumor (GCT) is the most common malignancy in young adult men. Currently, patients are risk-stratified on the basis of clinical presentation and serum tumor markers. The introduction of molecular markers could improve outcome prediction. Patients and Methods: Expression profiling was performed on 74 nonseminomatous GCTs (NSGCTs) from cisplatintreated patients (ie, training set) and on 34 similarly treated patients with NSGCTs (ie, validation set). A gene classifier was developed by using prediction analysis for microarrays (PAM) for the binary end point of 5-year overall survival (OS). A predictive score was developed for OS by using the univariate Cox model. Results: In the training set, PAM identified 140 genes that predicted 5-year OS (cross-validated classification rate, 60{\%}). The PAM model correctly classified 90{\%} of patients in the validation set. Patients predicted to have good outcome had significantly longer survival than those with poor predicted outcome (P <.001). For the OS end point, a 10-gene model had a predictive accuracy (ie, concordance index) of 0.66 in the training set and a concordance index of 0.83 in the validation set. Dichotomization of the samples on the basis of the median score resulted in significant differences in survival (P = .002). For both end points, the gene-based predictor was an independent prognostic factor in a multivariate model that included clinical risk stratification (P <.01 for both). Conclusion: We have identified gene expression signatures that accurately predict outcome in patients with GCTs. These predictive genes should be useful for the prediction of patient outcome and could provide novel targets for therapeutic intervention.",
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AU - Qin, Li Xuan

AU - Patil, Sujata

AU - Reuter, Victor E.

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N2 - Purpose: Germ cell tumor (GCT) is the most common malignancy in young adult men. Currently, patients are risk-stratified on the basis of clinical presentation and serum tumor markers. The introduction of molecular markers could improve outcome prediction. Patients and Methods: Expression profiling was performed on 74 nonseminomatous GCTs (NSGCTs) from cisplatintreated patients (ie, training set) and on 34 similarly treated patients with NSGCTs (ie, validation set). A gene classifier was developed by using prediction analysis for microarrays (PAM) for the binary end point of 5-year overall survival (OS). A predictive score was developed for OS by using the univariate Cox model. Results: In the training set, PAM identified 140 genes that predicted 5-year OS (cross-validated classification rate, 60%). The PAM model correctly classified 90% of patients in the validation set. Patients predicted to have good outcome had significantly longer survival than those with poor predicted outcome (P <.001). For the OS end point, a 10-gene model had a predictive accuracy (ie, concordance index) of 0.66 in the training set and a concordance index of 0.83 in the validation set. Dichotomization of the samples on the basis of the median score resulted in significant differences in survival (P = .002). For both end points, the gene-based predictor was an independent prognostic factor in a multivariate model that included clinical risk stratification (P <.01 for both). Conclusion: We have identified gene expression signatures that accurately predict outcome in patients with GCTs. These predictive genes should be useful for the prediction of patient outcome and could provide novel targets for therapeutic intervention.

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