Molecular Analysis of Thymoma

• Type of Material: Article
• Authors: Sunil Badve, Emory University; Chirayu Goswami, Indiana University; Yesim Goekmen-Polar, Indiana University; Robert P. Nelson, Jr, Indiana University; John Henley, Columbus Regional Hospital; Nick Miller, Indiana University; Narjis A. Zaheer, Indiana University; George W. Sledge, Jr, Indiana University; Lang Li, Indiana University; Kenneth A. Kesler, Indiana University; Patrick J. Loehrer, Sr, Indiana University
• Language: English
• Date: 2012-08-13
• Publisher: PUBLIC LIBRARY SCIENCE
• Publication Version: Final Published Version
• Copyright Statement: © 2012 Badve et al.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1371/journal.pone.0042669
• Title of Journal or Parent Work: PLOS ONE
• Volume: 7
• Issue: 8
• Start Page: e42669
• End Page: e42669
• Grant/Funding Information: This work was supported in part by the Barbara P. Hochberg Foundation and William P. Loehrer Family Research Fund. The funders had no role in study design data collection and analysis, decision to publish, or preparation of the manuscript.
• Keywords: TUMORS; B10; MALIGNANCIES; Multidisciplinary Sciences; Science & Technology; EXPRESSION ANALYSIS; STAGE; CANCER; CARCINOMAS; GENETIC ABERRATIONS; RISK; Science & Technology - Other Topics
• Research Categories: Biology, Genetics; Health Sciences, Oncology

Abstract

Histologic classification of thymomas has significant limitations with respect to both subtype definitions and consistency. In order to better understand the biology of the disease processes, we performed whole genome gene expression analysis. RNA was extracted from fresh frozen tumors from 34 patients with thymomas and followup data was available. Using the Illumina BeadStudio® platform and Human Ref-8 Beadchip, gene expression data was analyzed with Partek Genomics Suite®, and Ingenuity Pathways Analysis (IPA). Unsupervised clustering of gene expression data, representing one of the largest series in literature, resulted in identification of four molecular clusters of tumors (C1-C4), which correlated with histology (P = 0.002). However, neither histology nor clusters correlated with clinical outcomes. Correlation of gene expression data with clinical data showed that a number of genes were associated with either advanced stage at diagnosis or development of recurrence or metastases. The top pathways associated with metastases were amino acid metabolisms, biosynthesis of steroids and glycosphingolipids, cell cycle checkpoint proteins and Notch signaling. The differential expression of some of the top genes related to both metastases and stage was confirmed by RT-PCR in all cases of metastases and matched nonmetastatic cases. A number of potential candidates for therapeutics were also identified. © 2012 Badve et al.