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Hans-Peter Lenhof

Machine learning methods trained on cancer cell line panels are intensively studied for the prediction of optimal anti-cancer therapies. While classification approaches distinguish effective from ineffective drugs, regression approaches aim to quantify the degree of drug effectiveness. However, the high specificity of most anti-cancer drugs induces a skewed distribution of drug response values in favor of the more drug-resistant cell lines, negatively affecting the classification performance (class imbalance) and regression performance (regression imbalance) for the sensitive cell lines.
Wilms tumor (WT) is the most common renal tumor in childhood. We and others have previously identified oncogenic driver mutations affecting the microprocessor genes DROSHA and DGCR8 that lead to altered miRNA expression patterns. In the case of DGCR8, a single recurrent hotspot mutation (E518K) was found in the RNA binding domain. To functionally assess this mutation in vitro, we generated mouse Dgcr8-KO embryonic stem cell (mESC) lines with an inducible expression of wild-type or mutant DGCR8, mirroring the hemizygous mutant expression seen in WT.
SUMOylation is a post-translational modification of proteins that regulates these proteins’ localization, turnover or function. Aberrant SUMOylation is frequently found in cancers but its origin remains elusive. Using a genome-wide transposon mutagenesis screen in a MYC-driven B-cell lymphoma model, we here identify the SUMO isopeptidase (or deconjugase) SENP6 as a tumor suppressor that links unrestricted SUMOylation to tumor development and progression. Notably, SENP6 is recurrently deleted in human lymphomas and SENP6 deficiency results in unrestricted SUMOylation.