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.

Cancer is a heterogeneous disease with different combinations of genetic alterations driving its development in different individuals. We introduce CoMEt, an algorithm to identify combinations of alterations that exhibit a pattern of mutual exclusivity across individuals, often observed for alterations in the same pathway. CoMEt includes an exact statistical test for mutual exclusivity and techniques to perform simultaneous analysis of multiple sets of mutually exclusive and subtype-specific alterations. We demonstrate that CoMEt outperforms existing approaches on simulated and real data. We apply CoMEt to five different cancer types, identifying both known cancer genes and pathways, and novel putative cancer genes.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-015-0700-7) contains supplementary material, which is available to authorized users.

The behavior and genetics of serous epithelial ovarian cancer (EOC) metastasis, the form of the disease lethal to patients, is poorly understood. The unique properties of metastases are critical to understand to improve treatments of the disease that remains in patients after debulking surgery. We sought to identify the genetic and phenotypic landscape of metastatic progression of EOC to understand how metastases compare to primary tumors. DNA copy number and mRNA expression differences between matched primary human tumors and omental metastases, collected at the same time during debulking surgery before chemotherapy, were measured using microarrays. qPCR and immunohistochemistry validated findings. Pathway analysis of mRNA expression revealed metastatic cancer cells are more proliferative and less apoptotic than primary tumors, perhaps explaining the aggressive nature of these lesions. Most cases had copy number aberrations (CNAs) that differed between primary and metastatic tumors, but we did not detect CNAs that are recurrent across cases. A six gene expression signature distinguishes primary from metastatic tumors and predicts overall survival in independent datasets. The genetic differences between primary and metastatic tumors, yet common expression changes, suggest that the major clone in metastases is not the same as in primary tumors, but the cancer cells adapt to the omentum similarly. Together, these data highlight how ovarian tumors develop into a distinct, more aggressive metastatic state that should be considered for therapy development.

Cancer is a heterogeneous disease with different combinations of genetic alterations driving its development in different individuals. We introduce CoMEt, an algorithm to identify combinations of alterations that exhibit a pattern of mutual exclusivity across individuals, often observed for alterations in the same pathway. CoMEt includes an exact statistical test for mutual exclusivity and techniques to perform simultaneous analysis of multiple sets of mutually exclusive and subtype-specific alterations. We demonstrate that CoMEt outperforms existing approaches on simulated and real data. We apply CoMEt to five different cancer types, identifying both known cancer genes and pathways, and novel putative cancer genes.

Supplementary data are available at Bioinformatics online.

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Congenital self-healing reticulohistiocytosis (CSHR) is a rare disorder characterized by benign skin lesions with a tendency to self-heal. Multiple skin lesions are usually present in CSHR. It is very difficult to distinguish between CSHR and an invasive Langerhans cell histiocytosis. We present a case of a 5-month-old infant girl who had hypopigmented skin lesions distributed over her neck, thorax and torso. The skin lesions regressed spontaneously 2 months after the diagnosis of CSHR and the child has remained in complete remission without any sign of recurrence over a 2year follow-up. BRAF V600E mutation was detected in lesional cells along with a low Ki-67 proliferative activity of about 6%. BRAF oncogene-induced senescence might contribute to a mechanism of self-regression in CSHR; however, the exact role of the somatic BRAF V600E mutation in CSHR remains to be determined.

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