Germ cell tumors (GCTs) are the most common solid malignancy in young adult men, but the genes and genomic regions involved in their etiology are not fully defined. We report here an investigation of DNA copy number changes in GCTs using 1 Mb BAC arrays. As expected, 12p gain was the defining genomic alteration, occurring in 72/74 GCTs. Parallel expression profiling of these tumors identified potential oncogenes from gained regions (LYN and RAB25) and potential tumor suppressor genes in regions of loss (SYNPO2, TTC12, IGSF4, and EPB41L3). Notably, we observed specific genomic alterations associated with histology, including gain of 17p11.2-q21.32 and loss of 2p25.3 in embryonal carcinoma, gain of 8p23.3-12 and loss of 5p15. 33-35.3, 11q23.1-25, and 13q12.11-34 in seminoma, and gain of 1q31.3-42.3, 3p, 14q11.2-32.33, and 20q and loss of 8q11.1-23.1 in yolk sac tumors (YST). Many significant genes that mapped to these regions had previously been associated with specific histologies, such as EOMES (chr3) and BMP2 (chr20) in YST and SPRY2 (chr13) and SOX17 (chr8) in seminomas. Additionally, our results suggest a model in which histologic differentiation of GCTs may drive genomic evolution. V V C 2007 Wiley-Liss, Inc.

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.