SUMMARY Annotation of prostate cancer genomes provides a foundation for discoveries that can impact disease understanding and treatment. Concordant assessment of DNA copy number, mRNA expression, and focused exon resequencing in 218 prostate cancer tumors identified the nuclear receptor coactivator NCOA2 as an oncogene in ~11 percent of tumors. Additionally, the androgen-driven TMPRSS2-ERG fusion was associated with a previously unrecognized, prostate-specific deletion at chromosome 3p14 that implicates FOXP1, RYBP and SHQ1 as potential cooperative tumor suppressors. DNA copy-number data from primary tumors revealed that copy-number alterations robustly define clusters of low- and high-risk disease beyond that achieved by Gleason score. The genomic and clinical outcome data from these patients is now made available as a public resource.

MicroRNA.org (http://www.microrna.org) is a comprehensive resource of microRNA target predictions and expression profiles. Target predictions are based on a development of the miRanda algorithm which incorporates current biological knowledge on target rules and on the use of an up-to-date compendium of mammalian microRNAs. MicroRNA expression profiles are derived from a comprehensive sequencing project of a large set of mammalian tissues and cell lines of normal and disease origin. Using an improved graphical interface, a user can explore (i) the set of genes that are potentially regulated by a particular microRNA, (ii) the implied cooperativity of multiple microRNAs on a particular mRNA and (iii) microRNA expression profiles in various tissues. To facilitate future updates and development, the microRNA.org database structure and software architecture is flexibly designed to incorporate new expression and target discoveries. The web resource provides users with functional information about the growing number of microRNAs and their interaction with target genes in many species and facilitates novel discoveries in microRNA gene regulation.

Mutations in RAS proteins occur widely in human cancer. Prompted by the confirmation of KRAS mutation as a predictive biomarker of response to epidermal growth factor receptor (EGFR)-targeted therapies, limited clinical testing for RAS pathway mutations has recently been adopted. We performed a multiplatform genomic analysis to characterize, in a nonbiased manner, the biological, biochemical, and prognostic significance of Ras pathway alterations in colorectal tumors and other solid tumor malignancies. Mutations in exon 4 of KRAS were found to occur commonly and to predict for a more favorable clinical outcome in patients with colorectal cancer. Exon 4 KRAS mutations, all of which were identified at amino acid residues K117 and A146, were associated with lower levels of GTP-bound RAS in isogenic models. These same mutations were also often accompanied by conversion to homozygosity and increased gene copy number, in human tumors and tumor cell lines. Models harboring exon 4 KRAS mutations exhibited mitogen-activated protein/extracellular signalregulated kinase kinase dependence and resistance to EGFR-targeted agents. Our findings suggest that RAS mutation is not a binary variable in tumors, and that the diversity in mutant alleles and variability in gene copy number may also contribute to the heterogeneity of clinical outcomes observed in cancer patients. These results also provide a rationale for broader KRAS testing beyond the most common hotspot alleles in exons 2 and 3. Cancer Res; 70(14); 5901-11. ©2010 AACR.

PURPOSE Cancer classification is foundational for patient care and oncology research. Systems such as International Classification of Diseases for Oncology (ICD-O), Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT), and National Cancer Institute Thesaurus (NCIt) provide large sets of cancer classification terminologies but they lack a dynamic modernized cancer classification platform that addresses the fast-evolving needs in clinical reporting of genomic sequencing results and associated oncology research. METHODS To meet these needs, we have developed OncoTree, an open-source cancer classification system. It is maintained by a cross-institutional committee of oncologists, pathologists, scientists, and engineers, accessible via an open-source Web user interface and an application programming interface. RESULTS OncoTree currently includes 868 tumor types across 32 organ sites. OncoTree has been adopted as the tumor classification system for American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE), a large genomic and clinical data-sharing consortium, and for clinical molecular testing efforts at Memorial Sloan Kettering Cancer Center and Dana-Farber Cancer Institute. It is also used by precision oncology tools such as OncoKB and cBioPortal for Cancer Genomics. CONCLUSION OncoTree is a dynamic and flexible community-driven cancer classification platform encompassing rare and common cancers that provides clinically relevant and appropriately granular cancer classification for clinical decision support systems and oncology research.