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The role of deubiquitylase ubiquitin-specific protease 7 (USP7) in the regulation of the p53-dependent DNA damage response (DDR) pathway is well established. Whereas previous studies have mostly focused on the mechanisms underlying how USP7 directly controls p53 stability, we recently showed that USP7 modulates the stability of the DNA damage responsive E3 ubiquitin ligase RAD18. This suggests that targeting USP7 may have therapeutic potential even in tumors with defective p53 or ibrutinib resistance. To test this hypothesis, we studied the effect of USP7 inhibition in chronic lymphocytic leukemia (CLL) where the ataxia telangiectasia mutated (ATM)-p53 pathway is inactivated with relatively high frequency, leading to treatment resistance and poor clinical outcome. We demonstrate that USP7 is upregulated in CLL cells, and its loss or inhibition disrupts homologous recombination repair (HRR). Consequently, USP7 inhibition induces significant tumor-cell killing independently of ATM and p53 through the accumulation of genotoxic levels of DNA damage. Moreover, USP7 inhibition sensitized p53-defective, chemotherapy-resistant CLL cells to clinically achievable doses of HRR-inducing chemotherapeutic agents in vitro and in vivo in a murine xenograft model. Together, these results identify USP7 as a promising therapeutic target for the treatment of hematological malignancies with DDR defects, where ATM/p53-dependent apoptosis is compromised., (© 2017 by The American Society of Hematology.)

Background: Optimal ranking of literature importance is vital in overcoming article overload. Existing ranking methods are typically based on raw citation counts, giving a sum of 'inbound' links with no consideration of citation importance. PageRank, an algorithm originally developed for ranking webpages at the search engine, Google, could potentially be adapted to bibliometrics to quantify the relative importance weightings of a citation network. This article seeks to validate such an approach on the freely available, PubMed Central open access subset (PMC-OAS) of biomedical literature., Results: On-demand cloud computing infrastructure was used to extract a citation network from over 600,000 full-text PMC-OAS articles. PageRanks and citation counts were calculated for each node in this network. PageRank is highly correlated with citation count (R = 0.905, P < 0.01) and we thus validate the former as a surrogate of literature importance. Furthermore, the algorithm can be run in trivial time on cheap, commodity cluster hardware, lowering the barrier of entry for resource-limited open access organisations., Conclusions: PageRank can be trivially computed on commodity cluster hardware and is linearly correlated with citation count. Given its putative benefits in quantifying relative importance, we suggest it may enrich the citation network, thereby overcoming the existing inadequacy of citation counts alone. We thus suggest PageRank as a feasible supplement to, or replacement of, existing bibliometric ranking methods.