1/ We have designed 2 CyTOF panels including phenotypic, signaling markers, transcription factors but mostly metabolic antibodies associated with the central metabolic pathways. In total, the 2 panels allowed the measurement of 76 unique markers. The entire optimized panels have been validated on AML and B-ALL patient cells.
2/ We used the new CyTOF panels on BM cells isolated from 6 healthy donors. Unexpectedly, when we performed dimension reduction using only the metabolic markers, we were also able to identify the B and myeloid trajectories going from the more immature to the more differentiated subpopulations, highlighting that metabolic markers capture the heterogeneity of the subpopulations belonging to the different lineages.
3/ We then applied these panels to a cohort of 27 adult and 9 pediatric AML patients. Importantly, when we looked at the distribution of the different clusters in each patient, we observed that while there is definitely heterogeneity of cluster abundance between patients, most of the clusters are shared among most of the patients, arguing that these metabolic clustering allows to capture the metabolic heterogeneity among our cohort. We observed few overlaps between adults and children. The only clusters really shared between adult and pediatric samples are the clusters expressing the highest stemness score. Importantly, other stem-like clusters were also very specific to adult or pediatric samples. We observed that in adults these immature clusters highly express anti-apoptotic protein BCL2 (VEN target) and not MCL1 while in clusters specific to children it was exactly the contrary. These differences highlight distinct regulations and metabolic activity of immature subpopulations in adults and pediatrics and therefore potentially response to metabolic inhibitors.
4/ The cells of the whole cohort of 36 AML patients has also been treated ex vivo with VEN, AraC, AZA or combos and analyzed with the 2 metabolic CyTOF panels. In concordance with the literature, all the immature clusters were strongly targeted by VEN. However, we also observed clusters persisting or even being enriched post-VEN. Some of them were highly monocytic but not all of them. VEN being a selective BCL2 inhibitor and targeting cysteine/amino acid uptake and degradation that support mitochondrial metabolism, we wondered how BCL2 expression was affected by VEN treatment. If there was no difference at the bulk level, we observed a significant increase in BCL2 expression in the persistent clusters, suggesting that cells resisting the therapy are able to counteract BCL2 inhibition by VEN.
