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As there is growing evidence for the tumor microenvironment's role in tumorigenesis, we investigated the role of fibroblast-expressed kinases in triple-negative breast cancer (TNBC). Using a high-throughput kinome screen combined with 3D invasion assays, we identified fibroblast-expressed PIK3C[delta] (f- PIK3C[delta]) as a key regulator of cancer progression. Although PIK3C[delta] was expressed in primary fibroblasts derived from TNBC patients, it was barely detectable in breast cancer (BC) cell lines. Genetic and pharmacological gain- and loss-of-function experiments verified the contribution of f-PIK3C[delta] in TNBC cell invasion. Integrated secretomics and transcriptomics analyses revealed a paracrine mechanism via which f-PIK3C[delta] confers its protumorigenic effects. Inhibition of f-PIK3C[delta] promoted the secretion of factors, including PLGF and BDNF, that led to upregulation of NR4A1 in TNBC cells, where it acts as a tumor suppressor. Inhibition of PIK3C[delta] in an orthotopic BC mouse model reduced tumor growth only after inoculation with fibroblasts, indicating a role of f-PIK3C[delta] in cancer progression. Similar results were observed in the MMTV-PyMT transgenic BC mouse model, along with a decrease in tumor metastasis, emphasizing the potential immune-independent effects of PIK3C[delta] inhibition. Finally, analysis of BC patient cohorts and TCGA data sets identified f-PIK3C[delta] (protein and mRNA levels) as an independent prognostic factor for overall and disease-free survival, highlighting it as a therapeutic target for TNBC., Introduction Triple-negative breast cancer (TNBC; [ER.sup.-], [PR.sup.-], [HER2.sup.-]) represents a molecularly diverse and highly heterogeneous subtype of breast cancer (BC) (15%-20%) with a poor prognosis and high rates of recurrence [...]

The development of improved cancer therapies is frequently cited as an urgent unmet medical need. Here we describe how genetic interactions are being therapeutically exploited to identify novel targeted treatments for cancer. We discuss the current methodologies that use ‘omics data to identify genetic interactions, in particular focusing on synthetic sickness lethality (SSL) and synthetic dosage lethality (SDL). We describe the experimental and computational approaches undertaken both in humans and model organisms to identify these interactions. Finally we discuss some of the identified targets with licensed drugs, inhibitors in clinical trials or with compounds under development.

Cohesin 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., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

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).