RBFOX2 deregulation promotes pancreatic cancer progression and metastasis through alternative splicing.

RNA splicing is an important biological process associated with cancer initiation and progression. However, the contribution of alternative splicing to pancreatic cancer (PDAC) development is not well understood. Here, we identify an enrichment of RNA binding proteins (RBPs) involved in splicing regulation linked to PDAC progression from a forward genetic screen using Sleeping Beauty insertional mutagenesis in a mouse model of pancreatic cancer.

Source, co-occurrence, and prognostic value of PTEN mutations or loss in colorectal cancer.

Somatic PTEN mutations are common and have driver function in some cancer types. However, in colorectal cancers (CRCs), somatic PTEN-inactivating mutations occur at a low frequency (~8-9%), and whether these mutations are actively selected and promote tumor aggressiveness has been controversial. Analysis of genomic data from ~53,000 CRCs indicates that hotspot mutation patterns in PTEN partially reflect DNA-dependent selection pressures, but also suggests a strong selection pressure based on protein function.

Tumor sequencing of African ancestry reveals differences in clinically relevant alterations across common cancers.

Cancer genomes from patients with African (AFR) ancestry have been poorly studied in clinical research. We leverage two large genomic cohorts to investigate the relationship between genomic alterations and AFR ancestry in six common cancers. Cross-cancer type associations, such as an enrichment of MYC amplification with AFR ancestry in lung, breast, and prostate cancers, and depletion of BRAF alterations are observed in colorectal and pancreatic cancers.

Pan-Cancer Analysis of Copy-Number Features Identifies Recurrent Signatures and a Homologous Recombination Deficiency Biomarker to Predict Poly (ADP-Ribose) Polymerase Inhibitor Response.

Purpose: Copy-number (CN) features reveal the molecular state of cancers and may have predictive and prognostic value in the treatment of cancer. We sought to apply published CN analysis methods to a large pan-cancer data set and characterize ubiquitous CN signatures across tumor types, including potential utility for treatment selection.

Methods: We analyzed the landscape of CN features in 260,333 pan-cancer samples.