Abstract
Background: Intra-tumoral heterogeneity emerging from aberrant differentiation and the accumulation of blasts at different maturation stages underscores AML biology. Importantly, the heterogeneity of AML cells contributes to therapy resistance and poor outcomes. Revealing the underlying biological programs that govern AML differentiation and are shared among patients can translate into effective therapeutic strategies.
Methods: We leveraged single cell gene expression profiling (scRNA) of bone marrow mononulear cells from 20 newly diagnosed adult AML patients (median age 73 years; range 52-87 years). After doublet removal and quality assessment, a total of 111,130 single cells passed the filtering criteria. Of those, 56,168 (50.5%) cells (patient range: 293- 9781 cells) were identified as AML cells by integrating flow cytometry, immunohistochemistry, conventional cytogenetic and FISH at time of diagnosis with the scRNA expression profiles.
Results: To determine the shared cellular states across different AML patients, we first applied an unsupervised Bayesian hierarchical clustering and identified four AML cell clusters. The four clusters had distinct expression of the lineage and maturation markers: CD34 (primitive), CD33 (myeloid), and CD14 and CD68 (monocytic), suggesting different AML maturation states in these clusters. To further explore the degree of differentiation in each cluster, we applied diffusion mapping, Slingshot pseudotime analysis and CytoTRACE profiling, all of which supported cluster-specific differentiation states. Specifically, Cluster 2 (CD34 high, CD14 negative) was early in the pseudotemporal trajectory, while Cluster 1 (CD14 high, CD34 negative) was towards the end of the trajectory. Clusters 3 and 4 were intermediary. Remarkably, each of the four clusters had contributions from at least 16 patients. These results support our approach to unmask intra-tumoral heterogeneity patterns associated with maturation and shared across different patients, independent of patient characteristics, molecular profiles and cytogenetics.
To further explore the biologic pathways correlating with the four clusters that are surrogates of maturation states, we calculated single cell enrichment scores across a comprehensive gene signature collections from curated Molecular Signatures Database and other previously published studies. Cluster 1 was mainly enriched in immune-related pathways including IL6, JAK-STAT3 signaling, and interferon gamma response suggestive of an inflammatory component, and associated with the CD14+ CD68+ CD34- monocytic pattern of differentiation. Cluster 2 had the highest LSC104 stemness score and CD34 expression. Hedgehog signaling pathway was also highly enriched in this cluster and correlated with LSC104 score (r=0.28, p<0.01). This is of interest as hedgehog plays a fundamental role in the leukemic stem cell quiescence. Cluster 3 and 4, harboring lower CD34 expression compared to Cluster 2, but are CD14- CD68- were mainly enriched in metabolic processes including fatty acid metabolism, heme metabolism and erythroid lineage and oxidative phosphorylation, in addition to DNA repair and interferon alpha response.
To understand how these defined populations and hierarchies relate to clinical outcomes, we deconvolved bulk RNA signatures in 3 AML cohorts: TCGA, BEAT AML and Abbas et al 2021, using the four clusters as reference. We then assigned each AML patient a cluster membership based on a deconvolution cut-off for cluster abundance. This translated into four distinct clinical groups with significantly different outcomes based on our cluster annotation. Remarkably, AML patients in Cluster 1 (monocytic enriched) had the worst outcomes (Panel B demonstrating TCGA cohort results, p=0.0084).
Conclusions: We conducted unsupervised clustering of AML patients and characterized gene and pathway profiles of each cluster revealing a distinct spectrum of differentiation states from primitive to monocytic. Interestingly, AML patients with enrichment to monocytic phenotype characterized by higher inflammatory states demonstrated the worst outcome. Non-monocytic clusters are less variable in marker genes and stemness scores. However, the cluster with the highest CD34 expression are enriched in hedgehog signaling pathway, suggesting a potentially biological programs distinguishing non-monocytic AML cells.
Disclosures
Konopleva:Stemline Therapeutics, F. Hoffman La-Roche; Janssen: Membership on an entity's Board of Directors or advisory committees; AbbVie, Genentech, F. Hoffman La-Roche, Eli Lilly, Cellectis, Calithera, Ablynx, Stemline Therapeutics, Agios, Ascentage, Astra Zeneca; Rafael Pharmaceutical; Sanofi, Forty-Seven: Research Funding; Reata Pharmaceuticals, Novartis and Eli Lilly: Patents & Royalties; Stocks, Reata Pharmaceuticals: Current equity holder in publicly-traded company; AbbVie, Genentech, F. Hoffman La-Roche, Stemline Therapeutics, Amgen, Forty-Seven, Kisoji; Janssen: Consultancy; Forty-Seven; F. Hoffman LaRoche: Honoraria. Green:Sanofi: Research Funding; Abbvie: Research Funding; Daiichi Sankyo: Consultancy, Honoraria; Monte Rosa Therapeutics: Honoraria; Tessa Therapeutics: Honoraria; KDAc Therapeutics: Current holder of stock options in a privately-held company; Allogene: Research Funding; Kite/Gilead: Research Funding. Daver:Agios, Celgene, SOBI and STAR Therapeutics: Consultancy, Membership on an entity's Board of Directors or advisory committees; Kartos and Jazz Pharmaceuticals: Other: Data monitoring committee member; Karyopham Therapeutics and Newave Pharmaceutical: Research Funding; Astellas, AbbVie, Genentech, Daiichi-Sankyo, Novartis, Jazz, Amgen, Servier, Karyopharm, Trovagene, Trillium, Syndax, Gilead, Pfizer, Bristol Myers Squibb, Kite, Actinium, Arog, Immunogen, Arcellx, and Shattuck: Consultancy, Other: Advisory Role; Astellas, AbbVie, Genentech, Daiichi-Sankyo, Gilead, Immunogen, Pfizer, Bristol Myers Squibb, Trovagene, Servier, Novimmune, Incyte, Hanmi, Fate, Amgen, Kite, Novartis, Astex, KAHR, Shattuck, Sobi, Glycomimetics, Trillium: Research Funding.
