We have developed GoMiner, a program package that organizes lists of 'interesting' genes (for example, under-and overexpressed genes from a microarray experiment) for biological interpretation in the context of the Gene Ontology. GoMiner provides quantitative and statistical output files and two useful visualizations. The first is a tree-like structure analogous to that in the AmiGO browser and the second is a compact, dynamically interactive 'directed acyclic graph'. Genes displayed in GoMiner are linked to major public bioinformatics resources. RationaleGene-expression profiling and other forms of high-throughput genomic and proteomic studies are revolutionizing biology. That much is universally agreed. But the new technologies pose new challenges. The first is the experiment itself, the second is statistical analysis of results, the third is biological interpretation. That third challenge is often the most vexing and time-consuming. In gene-expression microarray studies, for example, one generally obtains a list of dozens or hundreds of genes that differ in expression between samples and then asks: 'What does all of this mean biologically?' The work of the Gene Ontology (GO) Consortium [1] provides a way to address that question. GO organizes genes into hierarchical categories based on biological process, molecular function and subcellular localization. In the past, this GO information was queried one gene at a time. Recently, batch processing has been introduced [2], but with a flat-format output that does not communicate the richness of GO's hierarchical structure.We have developed, and present here, the program package GoMiner as a freely available computer resource that fully incorporates the hierarchical structure of the Gene Ontology to automate the functional categorization of gene lists of any length. GoMiner is downloadable free of charge from [3] or [4]. GoMiner was developed particularly for biological interpretation of microarray data; one can input a list of underand overexpressed genes and a list of all genes on the array, and then calculate enrichment or depletion of categories with genes that have changed expression. GoMiner thus facilitates analysis and organization of the results for rapid interpretation of 'omic' [5,6] data. For concreteness, the descriptions in

Adrenocortical carcinoma (ACC) is a rare neoplasm with a heterogeneous outcome and limited treatment options. To understand its molecular and genomic landscape as a part of The Cancer Genome Atlas (TCGA) project, we performed the genomic, transcriptomic, epigenomic and proteomic profiling of 91 ACCs. We identified potential driving alterations including amplifications (TERT, TERF2 and CDK4), deletions (ZNRF3, CDKN2A and RB1) and point mutations in genes unknown to participate in adrenal disease (RPL22) and genes known to initiate familial syndromes that occasionally include adrenocortical neoplasms (TP53, CTNNB1, PRKAR1A, MEN1). The finding of PRKAR1A expands the catalogue of pathogenic pathways underlying ACC, suggesting of the protein kinase alpha signaling pathway as a potential target for molecular interventions. Novel transcript fusions potentially leading to overactive kinases included EXOSC10-MTOR and PPP1CB-BRE. DNA copy number analysis unveiled prevalent DNA losses leading to hypodiploidy as well as whole genome doubling (WGD) in 51% of ACC. The similar penetrance of loss of heterozygosity before and after WGD suggests a sequential development from hypodiploidy to polyploidy along the doubling in a subset of ACCs, which was endorsed by the worse outcome for WGD samples relative to nonWGD ACCs. An association between TERT expression and WGD was observed, suggesting a role for telomere regulation. These findings present ACC as a model disease for studies of WGD which is a frequent event in many tumor types. Unsupervised clustering of DNA methylation, copy number, gene expression, miRNA expression and protein abundance converged into three classes with specific biological characteristics and a respective median event free survival of 8, 38 and >100 months (p-value 1.7e-13). Comparison of the subtypes suggested additional drivers such as protein kinase C (PKC) phosphorylation and upregulation of a miRNA cluster at chromosome Xq27.3, which complemented the genomic alterations identified in these subtypes. To gain more insights into this rare cancer type, we placed ACC in a broader context of cancer genomic profiles including an array of other cancer types. These analyses revealed interesting shared features, including beta-catenin activation with a subset of endometroid cancer, DNA mismatch repair gene mutational signature with gastrointestinal cancers and a smoking signature with lung cancer. These findings highlight the commonalities between ACC and other lineages of cancer. Taken together, we found Wnt signaling pathway and p53/Rb signaling pathway were the most frequently altered pathways in ACC. Meanwhile, new players surfaced from our analyses including the PKA and PKC pathways. Our results present a comprehensive genomic landscape and refined molecular classification of ACC improve our understanding of its pathogenesis, and will ultimately improve the care of patients. Citation Format: Siyuan Zheng, Andrew D. Cherniack, Ninad Dewal, Richard A. Moffitt, Ludmila Danilova, Bradley A. Murray, Antonio M. Lerario, Tobias Else, Theo A. Knijnenburg, Giovanni Ciriello, Seungchan Kim, Guillaume Assie, Olena Morozova, Rehan Akbani, Juliann Shih, Katherine A. Hoadley, Toni K. Choueiri, Jens Waldmann, Ozgur Mete, Gordon A. Robertson, Matthew Meyerson, Michael J. Demeure, Felix Beuschlein, Anthony Gill, Ana C. Latronico, Maria C. Fragosa, Leslie Cope, Electron Kebebew, Mouhammed A. Habra, Timothy G. Whitsett, Kimberly J. Bussey, William E. Rainey, Sylvia Asa, Jérôme Bertherat, Martin Fassnacht, David A. Wheeler, The Cancer Genome Atlas Research Network, Gary D. Hammer, Thomas J. Giordano, Roel Verhaak. Comprehensive Pan-Genomic characterization of adrenocortical carcinoma. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2976. doi:10.1158/1538-7445.AM2015-2976

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