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Mutations in the ATM tumor suppressor gene confer hypersensitivity to DNA-damaging chemotherapeutic agents. To explore genetic resistance mechanisms, we performed genome-wide CRISPR-Cas9 screens in cells treated with the DNA topoisomerase I inhibitor topotecan. Thus, we here establish that inactivating terminal components of the non-homologous end-joining (NHEJ) machinery or of the BRCA1-A complex specifically confer topotecan resistance to ATM-deficient cells. We show that hypersensitivity of ATM-mutant cells to topotecan or the poly-(ADP-ribose) polymerase (PARP) inhibitor olaparib reflects delayed engagement of homologous recombination at DNA-replication-fork associated single-ended double-strand breaks (DSBs), allowing some to be subject to toxic NHEJ. Preventing DSB ligation by NHEJ, or enhancing homologous recombination by BRCA1-A complex disruption, suppresses this toxicity, highlighting a crucial role for ATM in preventing toxic LIG4-mediated chromosome fusions. Notably, suppressor mutations in ATM-mutant backgrounds are different to those in BRCA1-mutant scenarios, suggesting new opportunities for patient stratification and additional therapeutic vulnerabilities for clinical exploitation., Mutations in the ATM tumor suppressor gene confer hypersensitivity to DNA-damaging chemotherapeutic agents. Here, the authors provide evidence that these hypersensitivities reflect a crucial role for ATM at damaged replication forks being to prevent toxic DNA end-joining leading to chromosome fusions and cell death.

Motivation Large pharmacogenomic screenings integrate heterogeneous cancer genomic datasets as well as anti-cancer drug responses on thousand human cancer cell lines. Mining this data to identify new therapies for cancer sub-populations would benefit from common data structures, modular computational biology tools and user-friendly interfaces. Results We have developed GDSCTools: a software aimed at the identification of clinically relevant genomic markers of drug response. The Genomics of Drug Sensitivity in Cancer (GDSC) database (www.cancerRxgene.org) integrates heterogeneous cancer genomic datasets as well as anti-cancer drug responses on a thousand cancer cell lines. Including statistical tools (analysis of variance) and predictive methods (Elastic Net), as well as common data structures, GDSCTools allows users to reproduce published results from GDSC and to implement new analytical methods. In addition, non-GDSC data resources can also be analysed since drug responses and genomic features can be encoded as CSV files. Supplementary information Supplementary data are available at Bioinformatics online.