PBAF loss leads to DNA damage-induced inflammatory signaling through defective G2/M checkpoint maintenance.

The PBRM1 subunit of the PBAF (SWI/SNF) chromatin remodeling complex is mutated in ∼40% of clear cell renal cancers. PBRM1 loss has been implicated in responses to immunotherapy in renal cancer, but the mechanism is unclear. DNA damage-induced inflammatory signaling is an important factor determining immunotherapy response. This response is kept in check by the G2/M checkpoint, which prevents progression through mitosis with unrepaired damage. We found that in the absence of PBRM1, p53-dependent p21 up-regulation is delayed after DNA damage, leading to defective transcriptional repression by the DREAM complex and premature entry into mitosis. Consequently, DNA damage-induced inflammatory signaling pathways are activated by cytosolic DNA. Notably, p53 is infrequently mutated in renal cancer, so PBRM1 mutational status is critical to G2/M checkpoint maintenance. Moreover, we found that the ability of PBRM1 deficiency to predict response to immunotherapy correlates with expression of the cytosolic DNA-sensing pathway in clinical samples. These findings have implications for therapeutic responses in renal cancer.

The role of the SWI/SNF chromatin remodelling complex in the response to DNA double strand breaks.

Mammalian cells possess multiple closely related SWI/SNF chromatin remodelling complexes. These complexes have been implicated in the cellular response to DNA double strand breaks (DSBs). Evidence suggests that SWI/SNF complexes contribute to successful repair via both the homologous recombination and non-homologous end joining pathways. In addition, repressing transcription near DSBs is dependent on SWI/SNF activity. Understanding these roles is important because SWI/SNF complexes are frequently dysregulated in cancer, and DNA DSB repair defects have the potential to be therapeutically exploited. In this graphical review, we summarise what is known about SWI/SNF contribution to DNA DSB responses in mammalian cells and provide an overview of the SWI/SNF-encoding gene alteration spectrum in human cancers.

Predicting synthetic lethal interactions using conserved patterns in protein interaction networks.

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.