Background: Alterations in methylation patterns, miRNA expression, and stem cell protein expression occur in germ cell tumors (GCTs). Our goal is to integrate molecular data across platforms to identify molecular signatures in the three main histologic subtypes of Type I and Type II GCTs (yolk sac tumor (YST), germinoma, and teratoma).
Methods: We included 39 GCTs and 7 paired adjacent tissue samples in the current analysis. Molecular data available for analysis include DNA methylation data (Illumina GoldenGate Cancer Methylation Panel I), miRNA expression (NanoString nCounter miRNA platform), and stem cell factor expression (SABiosciences Human Embryonic Stem Cell Array). We evaluated the cross platform correlations of the data features using the Maximum Information Coefficient (MIC).
Results: In analyses of individual datasets, differences were observed by tumor histology. Germinomas had higher expression of transcription factors maintaining stemness, while YSTs had higher expression of cytokines, endoderm and endothelial markers. We also observed differences in miRNA expression, with miR-371-5p, miR-122, miR-302a, miR-302d, and miR-373 showing elevated expression in one or more histologic subtypes. Using the MIC, we identified correlations across the data features, including six major hubs with higher expression in YST (LEFTY1, LEFTY2, miR302b, miR302a, miR 126, and miR 122) compared with other GCT.
Conclusions: While prognosis for GCTs is overall favorable, many patients experience resistance to chemotherapy, relapse and/or long term adverse health effects following treatment. Targeted therapies, based on integrated analyses of molecular tumor data such as that presented here, may provide a way to secure high cure rates while reducing unintended health consequences.