Chen, Chuan

Myelodysplastic syndromes (MDS), which are hematopoietic stem cell malignancies, are often accompanied by signs of chronic inflammation, that can in turn drive the pathogenetic process. The precise mechanisms dysregulating inflammatory and innate immune signaling in MDS hematopoietic stem and progenitor cells (HSPCs) remain under investigation. We report that mice lacking Tet2 developed a hyper-inflammatory state, which can be further fueled by LPS-induced inflammation. This chronic inflammation temporarily promotes the self-renewal and myeloid differentiation of Tet2-deficient HSPCs; however, it ultimately accelerates HSC exhaustion and triggers MDS development in chimeric mouse models. Driving this phenotype was the activation of the NLRC4 inflammasome pathway. Notably, Nlrc4 depletion in Tet2-deficient mice impaired MDS development and improved the survival of these mice. These findings uncover a novel mechanistic basis for the MDS-associated inflammation and provide new targeting opportunities in its management.

Abstract Measurable residual disease (MRD) assessment by flow cytometry (FC) plays an essential role in prognosis and therapy escalation of B-cell acute lymphoblastic leukemia (B-ALL). However, the high degree of expertise and manual analysis time required limits the availability of this assay. To overcome this limitation, we developed a data-enhancing artificial intelligence (AI) pipeline that accelerates and simplifies MRD analysis. Unaltered FC files from 171 B-ALL MRD-positive and 89 MRD-negative cases were processed through an AI pipeline trained with 31 expert-gated negative controls. Cluster-informed downsampling reduced FC files from 1.2 million to 155 884 cells per case, on average, (87% cellularity reduction), whereas preserving small MRD populations (median, 100% retention for MRD of <1%) and allowing for true percentage MRD estimates using a correction factor. A deep neural network cell classifier automatically identified normal hematopoietic subsets (macro-averaged F1 score of 0.86); and an AI measure of anomaly discriminated B-ALL from benign mononuclear (area under the curve [AUC] of 0.98) or B-lymphoid cells (AUC of 0.94). Manual analysis of AI-enhanced files was completed in only 1.01 minutes per case, on average (standard deviation of ±0.57); with 100% positive agreement with conventional analysis (for MRD of ≥0.01%), 100% negative agreement, and excellent quantitative correlation (R2 = 0.92). Our cloud-based AI-enhancement solution accelerates B-ALL MRD identification without compromising test performance and has the potential of facilitating B-ALL MRD analysis by more clinical laboratories.