Glioblastoma (GBM) is one of the most aggressive solid tumors for which treatment options and biomarkers are limited. Small extracellular vesicles (sEVs) produced by both GBM and stromal cells are central in the inter-cellular communication that is taking place in the tumor bulk. As tumor sEVs are accessible in biofluids, recent reports have suggested that sEVs contain valuable biomarkers for GBM patient diagnosis and follow-up. The aim of the current study was to describe the protein content of sEVs produced by different GBM cell lines and patient-derived stem cells. Our results reveal that the content of the sEVs mirrors the phenotypic signature of the respective GBM cells, leading to the description of potential informative sEV-associated biomarkers for GBM subtyping, such as CD44. Overall, these data could assist future GBM in vitro studies and provide insights for the development of new diagnostic and therapeutic methods as well as personalized treatment strategies.

SummaryCohesin subunits are frequently mutated in cancer, but how they function as tumor suppressors is unknown. Cohesin mediates sister chromatid cohesion, but this is not always perturbed in cancer cells. Here, we identify a previously unknown role for cohesin. We find that cohesin is required to repress transcription at DNA double-strand breaks (DSBs). Notably, cohesin represses transcription at DSBs throughout interphase, indicating that this is distinct from its known role in mediating DNA repair through sister chromatid cohesion. We identified a cancer-associated SA2 mutation that supports sister chromatid cohesion but is unable to repress transcription at DSBs. We further show that failure to repress transcription at DSBs leads to large-scale genome rearrangements. Cancer samples lacking SA2 display mutational patterns consistent with loss of this pathway. These findings uncover a new function for cohesin that provides insights into its frequent loss in cancer.

Bioinformatics approaches are becoming ever more essential in translational drug discovery both in academia and within the pharmaceutical industry. Computational exploitation of the increasing volumes of data generated during all phases of drug discovery is enabling key challenges of the process to be addressed. Here, we highlight some of the areas in which bioinformatics resources and methods are being developed to support the drug discovery pipeline. These include the creation of large data warehouses, bioinformatics algorithms to analyse ‘big data’ that identify novel drug targets and/or biomarkers, programs to assess the tractability of targets, and prediction of repositioning opportunities that use licensed drugs to treat additional indications.

In response to a need for improved treatments, a number of promising novel targeted cancer therapies are being developed that exploit human synthetic lethal interactions. This is facilitating personalised medicine strategies in cancers where specific tumour suppressors have become inactivated. Mainly due to the constraints of the experimental procedures, relatively few human synthetic lethal interactions have been identified. Here we describe SLant (Synthetic Lethal analysis via Network topology), a computational systems approach to predicting human synthetic lethal interactions that works by identifying and exploiting conserved patterns in protein interaction network topology both within and across species. SLant out-performs previous attempts to classify human SSL interactions and experimental validation of the models predictions suggests it may provide useful guidance for future SSL screenings and ultimately aid targeted cancer therapy development.

Background: The key to interpreting the contribution of a disease-associated mutation in the development and progression of cancer is an understanding of the consequences of that mutation both on the function of the affected protein and on the pathways in which that protein is involved. Protein domains encapsulate function and position-specific domain based analysis of mutations have been shown to help elucidate their phenotypes.Results: In this paper we examine the domain biases in oncogenes and tumour suppressors, and find that their domain compositions substantially differ. Using data from over 30 different cancers from whole-exome sequencing cancer genomic projects we mapped over one million mutations to their respective Pfam domains to identify which domains are enriched in any of three different classes of mutation; missense, indels or truncations. Next, we identified the mutational hotspots within domain families by mapping small mutations to equivalent positions in multiple sequence alignments of protein domainsWe find that gain of function mutations from oncogenes and loss of function mutations from tumour suppressors are normally found in different domain families and when observed in the same domain families, hotspot mutations are located at different positions within the multiple sequence alignment of the domain.Conclusions: By considering hotspots in tumour suppressors and oncogenes independently, we find that there are different specific positions within domain families that are particularly suited to accommodate either a loss or a gain of function mutation. The position is also dependent on the class of mutation.We find rare mutations co-located with well-known functional mutation hotspots, in members of homologous domain superfamilies, and we detect novel mutation hotspots in domain families previously unconnected with cancer. The results of this analysis can be accessed through the MOKCa database (http://strubiol.icr.ac.uk/ extra/MOKCa).