Cancers exhibit extensive mutational heterogeneity and the resulting long tail phenomenon complicates the discovery of the genes and pathways that are significantly mutated in cancer. We perform a Pan-Cancer analysis of mutated networks in 3281 samples from 12 cancer types from The Cancer Genome Atlas (TCGA) using HotNet2, a novel algorithm to find mutated subnetworks that overcomes limitations of existing single gene and pathway/network approaches.. We identify 14 significantly mutated subnetworks that include well-known cancer signaling pathways as well as subnetworks with less characterized roles in cancer including cohesin, condensin, and others. Many of these subnetworks exhibit co-occurring mutations across samples. These subnetworks contain dozens of genes with rare somatic mutations across multiple cancers; many of these genes have additional evidence supporting a role in cancer. By illuminating these rare combinations of mutations, Pan-Cancer network analyses provide a roadmap to investigate new diagnostic and therapeutic opportunities across cancer types.

Identifying functional regions in the human genome is a major goal in human genetics. Great efforts have been made to functionally annotate the human genome either through computational predictions, such as genomic conservation, or high-throughput experiments, such as the ENCODE project. These efforts have resulted in a rich collection of functional annotation data of diverse types that need to be jointly analyzed for integrated interpretation and annotation. Here we present GenoCanyon, a whole-genome annotation method that performs unsupervised statistical learning using 22 computational and experimental annotations thereby inferring the functional potential of each position in the human genome. With GenoCanyon, we are able to predict many of the known functional regions. The ability of predicting functional regions as well as its generalizable statistical framework makes GenoCanyon a unique and powerful tool for whole-genome annotation. The GenoCanyon web server is available at http://genocanyon.med.yale.edu

Purpose Aberrant promoter methylation and genomic instability occur frequently during colorectal cancer (CRC) development. CpG island methylator phenotype (CIMP) has been shown to associate with microsatellite instability, BRAF mutation and often found in the right-side colon. Nevertheless, the relative importance of CIMP and chromosomal instability (CIN) for tumorigenesis has yet to be thoroughly investigated in sporadic CRCs. Experimental Design We determined CIMP in 161 primary CRCs and 66 matched normal mucosae using a quantitative bisulfite/PCR/LDR/Universal Array assay. The validity of CIMP was confirmed in a subset of 60 primary tumors using MethyLight assay and five independent markers. In parallel, chromosomal instability was analyzed in the same study cohort using Affymetrix 50K Human Mapping arrays. Results The identified CIMP-positive cancers correlate with microsatellite instability (p=0.075) and the BRAF mutation V600E (p=0.00005). The array-based high-resolution analysis of chromosomal aberrations indicated that the degree of aneuploidy is spread over a wide spectrum among analyzed CRCs. Whether CIN was defined by copy number variations in selected microsatellite loci (criterion 1) or considered as a continuous variable (criterion 2), CIMP-positive samples showed a strong correlation with low-degree chromosomal aberrations (p=0.075 and 0.012, respectively). Similar correlations were observed when CIMP was determined using MethyLight assay (p=0.001 and 0.013, respectively). Conclusion CIMP-positive tumors generally possess lower chromosomal aberrations, which may only be revealed using a genome-wide approach. The significant difference in the degree of chromosomal aberrations between CIMP-positive and the remainder samples suggests that epigenetic (CIMP) and genetic (CIN) abnormalities may arise from independent molecular mechanisms of tumor progression.

Background. Incorporation of next-generation sequencing (NGS) technology into clinical utility in targeted and immunotherapies requires stringent validation, including the assessment of tumor mutational burden (TMB) and microsatellite instability (MSI) status by NGS as important biomarkers for response to immune checkpoint inhibitors. Materials and Methods. We designed an NGS assay, Cancer Sequencing YS panel (CSYS), and applied algorithms to detect five classes of genomic alterations and two genomic features of TMB and MSI. Results. By stringent validation, CSYS exhibited high sensitivity and predictive positive value of 99.7% and 99.9%, respectively, for single nucleotide variation; 100% and 99.9%, respectively, for short insertion and deletion (indel); and 95.5% and 100%, respectively, for copy number alteration (CNA). Moreover, CSYS achieved 100% specificity for both long indel (50-3,000 bp insertion and deletion) and gene rearrangement. Overall, we used 33 cell lines and

BackgroundHuman cancer is caused by the accumulation of somatic mutations in tumor suppressors and oncogenes within the genome. In the case of oncogenes, recent theory suggests that there are only a few key “driver” mutations responsible for tumorigenesis. As there have been significant pharmacological successes in developing drugs that treat cancers that carry these driver mutations, several methods that rely on mutational clustering have been developed to identify them. However, these methods consider proteins as a single strand without taking their spatial structures into account. We propose an extension to current methodology that incorporates protein tertiary structure in order to increase our power when identifying mutation clustering.ResultsWe have developed iPAC (identification of Protein Amino acid Clustering), an algorithm that identifies non-random somatic mutations in proteins while taking into account the three dimensional protein structure. By using the tertiary information, we are able to detect both novel clusters in proteins that are known to exhibit mutation clustering as well as identify clusters in proteins without evidence of clustering based on existing methods. For example, by combining the data in the Protein Data Bank (PDB) and the Catalogue of Somatic Mutations in Cancer, our algorithm identifies new mutational clusters in well known cancer proteins such as KRAS and PI3KC α. Further, by utilizing the tertiary structure, our algorithm also identifies clusters in EGFR, EIF2AK2, and other proteins that are not identified by current methodology. The R package is available at: http://www.bioconductor.org/packages/2.12/bioc/html/iPAC.html.ConclusionOur algorithm extends the current methodology to identify oncogenic activating driver mutations by utilizing tertiary protein structure when identifying nonrandom somatic residue mutation clusters.