The cBio Cancer Genomics Portal (http://cbioportal.org) is an open-access resource for interactive exploration of multidimensional cancer genomics data sets, currently providing access to data from more than 5,000 tumor samples from 20 cancer studies. The cBio Cancer Genomics Portal significantly lowers the barriers between complex genomic data and cancer researchers who want rapid, intuitive, and high-quality access to molecular profiles and clinical attributes from large-scale cancer genomics projects and empowers researchers to translate these rich data sets into biologic insights and clinical applications.
As large-scale re-sequencing of genomes reveals many protein mutations, especially in human cancer tissues, prediction of their likely functional impact becomes important practical goal. Here, we introduce a new functional impact score (FIS) for amino acid residue changes using evolutionary conservation patterns. The information in these patterns is derived from aligned families and sub-families of sequence homologs within and between species using combinatorial entropy formalism. The score performs well on a large set of human protein mutations in separating disease-associated variants (∼19 200), assumed to be strongly functional, from common polymorphisms (∼35 600), assumed to be weakly functional (area under the receiver operating characteristic curve of ∼0.86). In cancer, using recurrence, multiplicity and annotation for ∼10 000 mutations in the COSMIC database, the method does well in assigning higher scores to more likely functional mutations ('drivers'). To guide experimental prioritization, we report a list of about 1000 top human cancer genes frequently mutated in one or more cancer types ranked by likely functional impact; and, an additional 1000 candidate cancer genes with rare but likely functional mutations. In addition, we estimate that at least 5% of cancer-relevant mutations involve switch of function, rather than simply loss or gain of function.
We use a new algorithm (combinatorial entropy optimization [CEO]) to identify specificity residues and functional subfamilies in sets of proteins related by evolution. Specificity residues are conserved within a subfamily but differ between subfamilies, and they typically encode functional diversity. We obtain good agreement between predicted specificity residues and experimentally known functional residues in protein interfaces. Such predicted functional determinants are useful for interpreting the functional consequences of mutations in natural evolution and disease.
Parathyroid carcinoma (PC) is an extremely rare malignancy lacking effective therapeutic intervention. We generated and analyzed whole-exome sequencing data from 17 patients to identify somatic and germline genetic alterations. A panel of selected genes was sequenced in a 7-tumor expansion cohort. We show that 47% (8 of 17) of the tumors harbor somatic mutations in the CDC73 tumor suppressor, with germline inactivating variants in 4 of the 8 patients. The PI3K/AKT/mTOR pathway was altered in 21% of the 24 cases, revealing a major oncogenic pathway in PC. We observed CCND1 amplification in 29% of the 17 patients, and a previously unreported recurrent mutation in putative kinase ADCK1 . We identified the first sporadic PCs with somatic mutations in the Wnt canonical pathway, complementing previously described epigenetic mechanisms mediating Wnt activation. This is the largest genomic sequencing study of PC, and represents major progress toward a full molecular characterization of this rare malignancy to inform improved and individualized treatments.