Vulnerabilities in mIDH2 AML confer sensitivity to APL-like targeted combination therapy.

Although targeted therapies have proven effective and even curative in human leukaemia, resistance often ensues. IDH enzymes are mutated in ~20% of human AML, with targeted therapies under clinical evaluation. We here characterize leukaemia evolution from mutant IDH2 (mIDH2)-dependence to independence identifying key targetable vulnerabilities of mIDH2 leukaemia that are retained during evolution and progression from early to late stages. Mechanistically, we find that mIDH2 leukaemia are metastable and vulnerable at two distinct levels. On the one hand, they are characterized by oxidative and genotoxic stress, in spite of increased 1-carbon metabolism and glutathione levels. On the other hand, mIDH2 leukaemia display inhibition of LSD1 and a resulting transcriptional signature of all-trans retinoic acid (ATRA) sensitization, in spite of a state of suppressed ATRA signalling due to increased levels of PIN1. We further identify GSH/ROS and PIN1/LSD1 as critical nodes for leukaemia maintenance and the combination of ATRA and arsenic trioxide (ATO) as a key therapeutic modality to target these vulnerabilities. Strikingly, we demonstrate that the combination of ATRA and ATO proves to be a powerfully synergistic and effective therapy in a number of mouse and human mIDH1/2 leukemic models. Thus, our findings pave the way towards the treatment of a sizable fraction of human AMLs through targeted APL-like combinatorial therapies.

Genome-Scale Signatures of Gene Interaction from Compound Screens Predict Clinical Efficacy of Targeted Cancer Therapies.

Identifying reliable drug response biomarkers is a significant challenge in cancer research. We present computational analysis of resistance (CARE), a computational method focused on targeted therapies, to infer genome-wide transcriptomic signatures of drug efficacy from cell line compound screens. CARE outputs genome-scale scores to measure how the drug target gene interacts with other genes to affect the inhibitor efficacy in the compound screens. Such statistical interactions between drug targets and other genes were not considered in previous studies but are critical in identifying predictive biomarkers. When evaluated using transcriptome data from clinical studies, CARE can predict the therapy outcome better than signatures from other computational methods and genomics experiments. Moreover, the CARE signatures for the PLX4720 BRAF inhibitor are associated with an anti-programmed death 1 clinical response, suggesting a common efficacy signature between a targeted therapy and immunotherapy. When searching for genes related to lapatinib resistance, CARE identified PRKD3 as the top candidate. PRKD3 inhibition, by both small interfering RNA and compounds, significantly sensitized breast cancer cells to lapatinib. Thus, CARE should enable large-scale inference of response biomarkers and drug combinations for targeted therapies using compound screen data.