The creatine kinase pathway is a metabolic vulnerability in EVI1-positive acute myeloid leukemia

Expression of the MECOM (also known as EVI1) proto-oncogene is deregulated by chromosomal translocations in some cases of acute myeloid leukemia (AML) and is associated with poor clinical outcome. Here, through transcriptomic and metabolomic profiling of hematopoietic cells, we reveal that EVI1 overexpression alters cellular metabolism. A screen using pooled short hairpin RNAs (shRNAs) identified the ATP-buffering, mitochondrial creatine kinase CKMT1 as necessary for survival of EVI1-expressing cells in subjects with EVI1-positive AML. EVI1 promotes CKMT1 expression by repressing the myeloid differentiation regulator RUNX1. Suppression of arginine-creatine metabolism by CKMT1-directed shRNAs or by the small molecule cyclocreatine selectively decreased the viability, promoted the cell cycle arrest and apoptosis of human EVI1-positive cell lines, and prolonged survival in both orthotopic xenograft models and mouse models of primary AML. CKMT1 inhibition altered mitochondrial respiration and ATP production, an effect that was abrogated by phosphocreatine-mediated reactivation of the arginine-creatine pathway. Targeting CKMT1 is thus a promising therapeutic strategy for this EVI1-driven AML subtype that is highly resistant to current treatment regimens. Keywords: AML; RUNX1; CKMT1; cyclocreatine; arginine metabolismNational Cancer Institute (U.S.) (NIH 1R35 CA210030-01)Stand Up To CancerBridge ProjectNational Cancer Institute (U.S.) (David H. Koch Institute for Integrative Cancer Research at MIT. Grant P30-CA14051

A Novel Model to Generate Heterogeneous and Realistic Time-Series Data for Post-Stroke Rehabilitation Assessment

The application of machine learning-based tele-rehabilitation faces the challenge of limited availability of data. To overcome this challenge, data augmentation techniques are commonly employed to generate synthetic data that reflect the configurations of real data. One such promising data augmentation technique is the Generative Adversarial Network (GAN). However, GANs have been found to suffer from mode collapse, a common issue where the generated data fails to capture all the relevant information from the original dataset. In this paper, we aim to address the problem of mode collapse in GAN-based data augmentation techniques for post-stroke assessment. We applied the GAN to generate synthetic data for two post-stroke rehabilitation datasets and observed that the original GAN suffered from mode collapse, as expected. To address this issue, we propose a Time Series Siamese GAN (TS-SGAN) that incorporates a Siamese network and an additional discriminator. Our analysis, using the longest common sub-sequence (LCSS), demonstrates that TS-SGAN generates data uniformly for all elements of two testing datasets, in contrast to the original GAN. To further evaluate the effectiveness of TS-SGAN, we encode the generated dataset into images using Gramian Angular Field and classify them using ResNet-18. Our results show that TS-SGAN achieves a significant accuracy increase of classification accuracy (35.2%-42.07%) for both selected datasets. This represents a substantial improvement over the original GAN