Exploring synthetic lethal network for the precision treatment of clear cell renal cell carcinoma

Von Hippel-Lindau protein signalling in clear cell renal cell carcinoma

The distinct pathological and molecular features of kidney cancer in adaptation to oxygen homeostasis render this malignancy an attractive model for investigating hypoxia signalling and potentially developing potent targeted therapies. Hypoxia signalling has a pivotal role in kidney cancer, particularly within the most prevalent subtype, known as renal cell carcinoma (RCC). Hypoxia promotes various crucial pathological processes, such as hypoxia-inducible factor (HIF) activation, angiogenesis, proliferation, metabolic reprogramming and drug resistance, all of which contribute to kidney cancer development, growth or metastasis formation. A substantial portion of kidney cancers, in particular clear cell RCC (ccRCC), are characterized by a loss of function of Von Hippel-Lindau tumour suppressor (VHL), leading to the accumulation of HIF proteins, especially HIF2α, a crucial driver of ccRCC. Thus, therapeutic strategies targeting pVHL-HIF signalling have been explored in ccRCC, culminating in the successful development of HIF2α-specific antagonists such as belzutifan (PT2977), an FDA-approved drug to treat VHL-associated diseases including advanced-stage ccRCC. An increased understanding of hypoxia signalling in kidney cancer came from the discovery of novel VHL protein (pVHL) targets, and mechanisms of synthetic lethality with VHL mutations. These breakthroughs can pave the way for the development of innovative and potent combination therapies in kidney cancer.

The BAP1 nuclear deubiquitinase is involved in the nonhomologous end-joining pathway of double-strand DNA repair through interaction with DNA-PK

BRCA1-associated protein 1 (BAP1) has emerged as a major tumor suppressor gene in diverse cancer types, notably in malignant pleural mesothelioma (DPM), and has also been identified as a germline cancer predisposition gene for DPM and other select cancers. However, its role in the response to DNA damage has remained unclear. Here, we show that BAP1 inactivation is associated with increased DNA damage both in Met-5A human mesothelial cells and human DPM cell lines. Through proteomic analyses, we identified PRKDC as an interaction partner of BAP1 protein complexes in DPM cells and 293 T human embryonic kidney cells. PRKDC encodes the catalytic subunit of DNA protein kinase (DNA-PKcs) which functions in the nonhomologous end-joining (NHEJ) pathway of DNA repair. Double-stranded DNA damage resulted in prominent nuclear expression of BAP1 in DPM cells and phosphorylation of BAP1 at serine 395. A plasmid-based NHEJ assay confirmed a significant effect of BAP1 knockdown on cellular NHEJ activity. Combination treatment with X-ray irradiation and gemcitabine (as a radiosensitizer) strongly suppressed the growth of BAP1-deficient cells. Our results suggest reciprocal positive interactions between BAP1 and DNA-PKcs, based on phosphorylation of BAP1 by the latter and deubiquitination of DNA-PKcs by BAP1. Thus, functional interaction of BAP1 with DNA-PKcs supports a role for BAP1 in NHEJ DNA repair and may provide the basis for new therapeutic strategies and new insights into its role as a tumor suppressor.

SL-Cloud: A Cloud-based resource to support synthetic lethal interaction discovery

Synthetic lethal interactions (SLIs), genetic interactions in which the simultaneous inactivation of two genes leads to a lethal phenotype, are promising targets for therapeutic intervention in cancer, as exemplified by the recent success of PARP inhibitors in treating BRCA1/2-deficient tumors. We present SL-Cloud, a new component of the Institute for Systems Biology Cancer Gateway in the Cloud (ISB-CGC), that provides an integrated framework of cloud-hosted data resources and curated workflows to enable facile prediction of SLIs. This resource addresses two main challenges related to SLI inference: the need to wrangle and preprocess large multi-omic datasets and the availability of multiple comparable prediction approaches. SL-Cloud enables customizable computational inference of SLIs and testing of prediction approaches across multiple datasets. We anticipate that cancer researchers will find utility in this tool for discovery of SLIs to support further investigation into potential drug targets for anticancer therapies.

SL-Cloud: A Cloud-based resource to support synthetic lethal interaction discovery

Synthetic lethal interactions (SLIs), genetic interactions in which the simultaneous inactivation of two genes leads to a lethal phenotype, are promising targets for therapeutic intervention in cancer, as exemplified by the recent success of PARP inhibitors in treating BRCA1/2-deficient tumors. We present SL-Cloud, a new component of the Institute for Systems Biology Cancer Gateway in the Cloud (ISB-CGC), that provides an integrated framework of cloud-hosted data resources and curated workflows to enable facile prediction of SLIs. This resource addresses two main challenges related to SLI inference: the need to wrangle and preprocess large multi-omic datasets and the availability of multiple comparable prediction approaches. SL-Cloud enables customizable computational inference of SLIs and testing of prediction approaches across multiple datasets. We anticipate that cancer researchers will find utility in this tool for discovery of SLIs to support further investigation into potential drug targets for anticancer therapies.