Abstract
Abstract High dimensional assays, such as DNA microarrays for gene expression, have helped delineate the complexity and diversity of most major cancer types. The Cancer Genome Atlas (TCGA), with its goal to comprehensively genomically characterize over twenty different types of tumors, gives us a wealth of data to start looking at similarities across tumor types. Much of the current data is now generated using Massively Parallel Sequencing (MPS) approaches, which yields tens of millions of “reads” per sample. Illumina Hiseq-based mRNA-sequencing data for twelve tumor types representing over 3,500 samples were analyzed for patterns unique to each tumors, and patterns present across multiple tumor types. Tumor types tested included rectal adenocarcinoma, colon adenocarcinoma, lung squamous cell carcinoma and adenocarcinoma, uterine corpus endometrial carcinoma, ovarian serous cystadenocarcinoma, kidney clear cell, breast ductal and lobular carcinoma, glioblastoma multiforme, bladder carcinomas, acute myeloid leukemia, and head and neck squamous cell carcinoma. Tissue-specific expression was the dominant signature; however, there were similarities across sets of tumors including a common “squamous” pattern that was populated by tumors from three distinct anatomic sites. Pathway or Module-based analysis also provided methods to comparisons across tissues. Cross tissue analysis of mRNA-seq data provides interesting observations of common tumor biology with potential insights into treatment. Citation Format: Katherine A. Hoadley, Cheng Fan, Matthew D. Wilkerson, Lisle E. Mose, Stuart R. Jefferys, J. Todd Auman, Joel S. Parker, D. Neil Hayes, Charles M. Perou. Multi-tumor analysis of TCGA data identifies expression commonalities across tumor types. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr SY12-03. doi:10.1158/1538-7445.AM2013-SY12-03
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