Single-Cell Multiomics Data-Driven Measurable Residual Disease (MRD) Assessment Using Multidimensional Flow Cytometry (MFC) in Acute Myeloid Leukemia (AML)

Background: MFC is one of the reference methods for MRD assessment in AML. However, when compared to molecular approaches, MFC lags behind in specificity and sensitivity. Of striking note is the fact that common markers used in MFC were established decades ago at times of limited instrument and reagent performance, as well as of limited understanding of the complexity of normal hematopoiesis. Here we hypothesized that single-cell multiomics could be leveraged for the data-driven development of renewed MFC panels to assess MRD in AML.

Aim: Identify novel leukemic associated aberrant phenotypes (LAIP) and develop more comprehensive panels integrating the identification of different from normal (DfN) patterns with LAIP for improved assessment of MRD in AML using MFC.

Methods: Single-cell RNA and antibody sequencing (scRNA/Ab-seq) was performed in CD34+ hematopoietic progenitor cells isolated by FACS from bone marrow (BM) aspirates of 6 healthy adults and 11 patients with newly diagnosed AML. We selected 596 highly relevant genes for human hematopoiesis, early and lineage-committed progenitors or leukemic stem cells to perform deep-targeted scRNA/Ab-seq using the Rhapsody technology. Differentially expressed genes and antigens were considered if P < .05 and log2FoldChange > |1| were observed. Each candidate marker was tested in BM samples from ≥5 controls and patients prior the development of a new MFC panel. Its performance was tested in 99 AML patients enrolled in the PETHEMA LMA-FLOW protocol developed for cases eligible to intensive chemotherapy, which includes MRD-driven transplant individualization after the first consolidation course. A total of 143 MRD assessments were longitudinally performed after induction, consolidation and transplant.

Results: In total, 42,546 FACSorted HPC with paired transcriptional and proteomic data passed all stringent quality control filtering. As expected, normal HPC clustered together and separately from leukemic cells that showed patient-specific clustering. There were 224 differentially expressed genes between HPC and leukemic cells, of which 19 coded surface antigens. In addition, there were 15 differentially expressed proteins between CD34 progenitors and leukemic cells. Thus, there were in total 31 candidate markers for subsequent testing using MFC, some of which already known (CD3, CD4, CD9, CD10, CD13, CD33, CD34, CD38, CD45 CD117, CD123) while many were of unknown performance (CD25, CD26, CD45RA, CD45RO, CD47, CD58, CD62L, CD74, CD81, CD99, CD112, CD113, CD133, CD161, CD184, CD210, CD241, CD244, CD366, CD371).

Upon comparing expression patterns and mean fluorescence intensity of the unknown candidate markers, CD25, CD45RA, CD62L and CD371 met the technical requirements to accurately distinguish leukemic cells from normal HPC. Accordingly, these were embedded into four comprehensive 10-color antibody combinations (1: CD35, CD64, CD34, CD117, CD11b, CD14, HLADR, CD45, CD300e, CD13; 2: CD15, CD371, CD34, CD117, CD123, CD71, HLADR, CD45, CD33, CD36; 3: nuTdT, CD10, CD34, CD117, CD45RA, CD19, HLADR, CD45, CD22, CD13; 4: CD7, CD56, CD34, CD117, CD25, CD38, HLADR, CD45, CD62L, CD13) aiming at identifying DfN patterns in maturing granulocytic, monocytic, erythroid, basophil and plasmacytoid dendritic cell lineages (combinations 1 and 2) and at identifying conventional and newer LAIP (combinations 3 and 4). With this strategy, we aimed at classifying patients into three subgroups: those with undetectable LAIP/DfN, undetectable LAIP but detectable DfN, and those with positive LAIP/DfN.

Of the 99 AML patients tested for MRD with the new MFC approach, 60 had undetectable LAIP/DfN, 18 were LAIP-/DfN+ and 21 were LAIP/DfN positive. The median relapse-free survival since MRD assessment was not reached, 9 and 3 months, respectively (P <.001). Of note, CD25, CD45RA, CD62L and CD371 accurately distinguished leukemic cells from normal HPC in 63%, 92%, 62% and 42% of patients with detectable LAIP.

Conclusion: Using a single-cell multiomics data-driven approach, we uncovered new LAIP in leukemic cells that improved the detection of MRD. Of note, the identification of DfN patterns in maturing cell lineages showed a prognostic value similar to the more conventional detection of leukemic cells, and should be considered during MRD assessment towards improved identification of AML patients at imminent risk of relapse.

Gui:Becton Dickinson: Current Employment. San-Miguel:Bristol Myers Squibb: Other: Advisory board; Amgen: Consultancy, Other: Advisory Board ; Abbvie: Consultancy, Other: Advisory Board; Celgene: Other: Advisory board; Regeneron: Other: Advisory board; GlaxoSmithKline: Other: Advisory board; Roche: Other: Advisory board; Haemalogix: Other: Advisory board; Janssen-Cilag: Other: Advisory board; Novartis: Other; MSD: Other: Advisory board; Takeda: Other: Advisory board; Karyopharm: Other: Advisory board; Sanofi: Other: Advisory board; SecuraBio: Other: Advisory board. Montesinos:Janssen: Membership on an entity's Board of Directors or advisory committees, Other: research support, Speakers Bureau; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: research support, Research Funding, Speakers Bureau; Astellas: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: research support, Speakers Bureau; Daiichi Sankyo, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: research support, Research Funding, Speakers Bureau; Servier: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: research support, Research Funding, Speakers Bureau; Jazzpharma: Consultancy, Research Funding, Speakers Bureau; Pfizer: Consultancy, Research Funding, Speakers Bureau; Novartis: Consultancy, Research Funding, Speakers Bureau; Kura Oncology: Consultancy; Syndax: Consultancy; Glycomimetics: Consultancy. Paiva:Aztra Zeneca, Bristol Myers Squibb/Celgene, EngMab, Roche, Sanofi, and Takeda: Research Funding; Adaptive, Amgen, Becton Dickinson, Bristol Myers Squibb/Celgene, Janssen, Merck, Novartis, Roche, Sanofi and Takeda: Honoraria; Bristol Myers Squibb/Celgene, Janssen, Sanofi, and Takeda: Consultancy.