MAGIC-DR: An interpretable machine-learning guided approach for acute myeloid leukemia measurable residual disease analysis.

Multiparameter flow cytometry is widely used for acute myeloid leukemia minimal residual disease testing (AML MRD) but is time consuming and demands substantial expertise. Machine learning offers potential advancements in accuracy and efficiency, but has yet to be widely adopted for this application. To explore this, we trained single cell XGBoost classifiers from 98 diagnostic AML cell populations and 30 MRD negative samples. Performance was assessed by cross-validation. Predictions were integrated with UMAP as a heatmap parameter for an augmented/interactive AML MRD analysis framework, which was benchmarked against traditional MRD analysis for 25 test cases. The results showed that XGBoost achieved a median AUC of 0.97, effectively distinguishing diverse AML cell populations from normal cells. When integrated with UMAP, the classifiers highlighted MRD populations against the background of normal events. Our pipeline, MAGIC-DR, incorporated classifier predictions and UMAP into flow cytometry standard (FCS) files. This enabled a human-in-the-loop machine learning guided MRD workflow. Validation against conventional analysis for 25 MRD samples showed 100% concordance in myeloid blast detection, with MAGIC-DR also identifying several immature monocytic populations not readily found by conventional analysis. In conclusion, Integrating a supervised classifier with unsupervised dimension reduction offers a robust method for AML MRD analysis that can be seamlessly integrated into conventional workflows. Our approach can support and augment human analysis by highlighting abnormal populations that can be gated on for quantification and further assessment. This has the potential to speed up MRD analysis, and potentially improve detection sensitivity for certain AML immunophenotypes.

A specific dispiropiperazine derivative that arrests cell cycle, induces apoptosis, necrosis and DNA damage.

Dispiropiperazine compounds are a class of molecules known to confer biological activity, but those that have been studied as cell cycle regulators are few in number. Here, we report the characterization and synthesis of two dispiropiperazine derivatives: the previously synthesized spiro[2',3]-bis(acenaphthene-1'-one)perhydrodipyrrolo-[1,2-a:1,2-d]-pyrazine (SPOPP-3, 1), and its previously undescribed isomer, spiro[2',5']-bis(acenaphthene-1'-one)perhydrodipyrrolo-[1,2-a:1,2-d]-pyrazine (SPOPP-5, 2). SPOPP-3 (1), but not SPOPP-5 (2), was shown to have anti-proliferative activity against a panel of 18 human cancer cell lines with IC50 values ranging from 0.63 to 13 µM. Flow cytometry analysis revealed that SPOPP-3 (1) was able to arrest cell cycle at the G2/M phase in SW480 human cancer cells. Western blot analysis further confirmed the cell cycle arrest is in the M phase. In addition, SPOPP-3 (1) was shown to induce apoptosis, necrosis, and DNA damage as well as disrupt mitotic spindle positioning in SW480 cells. These results warrant further investigation of SPOPP-3 (1) as a novel anti-cancer agent, particularly for its potential ability to sensitize cancer cells for radiation-induced cell death, enhance cancer immunotherapy, overcome apoptosis-related drug resistance and for possible use in synthetic lethality cancer treatments.