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Metabolites that are elevated in tumours inhibit the lysine demethylase KDM4B, resulting in aberrant hypermethylation of histone 3 lysine 9 and decreased homology-dependent DNA repair.

Deregulation of metabolism and disruption of genome integrity are hallmarks of cancer(1). Increased levels of the metabolites 2-hydroxyglutarate, succinate and fumarate occur in human malignancies owing to somatic mutations in the isocitrate dehydrogenase-1 or -2 (IDH1 or IDH2) genes, or germline mutations in the fumarate hydratase (FH) and succinate dehydrogenase genes (SDHA, SDHB, SDHC and SDHD), respectively(2-4). Recent work has made an unexpected connection between these metabolites and DNA repair by showing that they suppress the pathway of homology-dependent repair (HDR)(5,6) and confer an exquisite sensitivity to inhibitors of poly (ADP-ribose) polymerase (PARP) that are being tested in clinical trials. However, the mechanism by which these oncometabolites inhibit HDR remains poorly understood. Here we determine the pathway by which these metabolites disrupt DNA repair. We show that oncometabolite-induced inhibition of the lysine demethylase KDM4B results in aberrant hypermethylation of histone 3 lysine 9 (H3K9) at loci surrounding DNA breaks, masking a local H3K9 trimethylation signal that is essential for the proper execution of HDR. Consequently, recruitment of TIP60 and ATM, two key proximal HDR factors, is substantially impaired at DNA breaks, with reduced end resection and diminished recruitment of downstream repair factors. These findings provide a mechanistic basis for oncometabolite-induced HDR suppression and may guide effective strategies to exploit these defects for therapeutic gain.

Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses(1). The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset(2-5). Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.

The results obtained by seventy different teams analysing the same functional magnetic resonance imaging dataset show substantial variation, highlighting the influence of analytical choices and the importance of sharing workflows publicly and performing multiple analyses.