Format

Send to

Choose Destination
PLoS One. 2015 Dec 1;10(12):e0142846. doi: 10.1371/journal.pone.0142846. eCollection 2015.

Development and Validation of a Gene-Based Model for Outcome Prediction in Germ Cell Tumors Using a Combined Genomic and Expression Profiling Approach.

Author information

1
Cell Biology Program, Sloan-Kettering Institute for Cancer Research, New York, New York, United States of America.
2
Departments of Medicine and Pathology, Weill Cornell Medical College, New York, New York, United States of America.
3
Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America.
4
Department of Medicine, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America.
5
Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America.

Abstract

Germ Cell Tumors (GCT) have a high cure rate, but we currently lack the ability to accurately identify the small subset of patients who will die from their disease. We used a combined genomic and expression profiling approach to identify genomic regions and underlying genes that are predictive of outcome in GCT patients. We performed array-based comparative genomic hybridization (CGH) on 53 non-seminomatous GCTs (NSGCTs) treated with cisplatin based chemotherapy and defined altered genomic regions using Circular Binary Segmentation. We identified 14 regions associated with two year disease-free survival (2yDFS) and 16 regions associated with five year disease-specific survival (5yDSS). From corresponding expression data, we identified 101 probe sets that showed significant changes in expression. We built several models based on these differentially expressed genes, then tested them in an independent validation set of 54 NSGCTs. These predictive models correctly classified outcome in 64-79.6% of patients in the validation set, depending on the endpoint utilized. Survival analysis demonstrated a significant separation of patients with good versus poor predicted outcome when using a combined gene set model. Multivariate analysis using clinical risk classification with the combined gene model indicated that they were independent prognostic markers. This novel set of predictive genes from altered genomic regions is almost entirely independent of our previously identified set of predictive genes for patients with NSGCTs. These genes may aid in the identification of the small subset of patients who are at high risk of poor outcome.

PMID:
26624623
PMCID:
PMC4666461
DOI:
10.1371/journal.pone.0142846
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Public Library of Science Icon for PubMed Central
Loading ...
Support Center