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2897 Mapping the Architecture of Adverse Risk AML Using Single-Cell RNA Sequencing

Program: Oral and Poster Abstracts
Session: 617. Acute Myeloid Leukemia: Biology, Cytogenetics, and Molecular Markers in Diagnosis and Prognosis: Poster III
Hematology Disease Topics & Pathways:
Biological Processes, microenvironment
Monday, December 7, 2020, 7:00 AM-3:30 PM

Ghayas C. Issa, MD1, Vakul Mohanty, PhD2*, Yuanxin Wang3*, Zoe Alaniz, BS4*, Jabra Zarka, MD5*, Jake Leighton6*, Nicholas Navin, PhD6*, Marina Konopleva, MD, PhD7, Ken Chen, PhD2* and Michael Andreeff, MD, PhD4

1Department of Leukemia, Universit Y of Texas At Houston, Houston, TX
2Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX
3Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
4Section of Molecular Hematology and Therapy, Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX
5Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX
6Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX
7Department of Leukemia, University of Texas, MD Anderson Cancer Center, Houston, TX

Introduction: Adverse risk acute myeloid leukemia (AML) is difficult to treat with high relapse and mortality rates. Despite increasing knowledge on the role of the microenvironment in the pathogenesis of AML, there has been no detailed mapping of the ecosystem surrounding leukemia cells especially in AML with a mutated TP53 gene. We performed single-cell RNA sequencing (scRNA-seq) to characterize the bone marrow before and after treatment in patients with adverse risk AML.

Methods: We collected 51 samples from 24 patients who met criteria for adverse risk AML according to the ELN 2017 risk stratification. We applied Drop-Seq on bone marrow biopsies taken before and after treatment which allowed us to profile approximately 170,000 cells. scRNA-seq data were annotated using SingleR (Aran et al. Nat. Immunol. 2019). Subsequently SEURAT was used for nearest-neighborhood clustering (Stuart et al. Cell 2019). For samples with a complex karyotype (19/24 patients), inferCNV was used to distinguish leukemia cells from their ecosystem by inferring large-scale copy number variations from scRNA-seq data (Tirosh et al. Science 2016). We next hypothesized that leukemia cells with a complex karyotype have some similarities in gene expression compared to leukemia cells with a normal karyotype. Therefore, complex karyotype samples were used as a training set, which allowed identification of a gene signature that successfully delineated malignant and non-malignant cells from samples with normal karyotype AML. We subsequently focused on analyzing gene expression differences in immune cells according to the response status.

Results: Baseline characteristics of patients included in this analysis are outlined in Figure 1A. The majority of patients had mutations in TP53 (79%) and a complex karyotype (79%). There were 5 patients (21%) with FLT3-ITD and TP53 wild-type. Using copy number alterations to delineate malignant cells, there was a strong correlation between the percentage of inferred leukemia cells and the blast % by morphology in all samples with r = 0.94 (P = 0.002). Figure 1B is an example of the concordance between the karyotype by conventional cytogenetics and inferred copy number alterations in single-cell populations. In this example, the pre-treatment sample for patient 30 had multiple leukemia clusters where chromosomal abnormalities were detected including deletions of chromosomes 5 and 7. These contrasted with the copy number neutral profile of T cells, NK cells and B cells (heat map not shown). Similarly, when using gene expression to delineate malignant cells in samples with a normal karyotype, there was a high correlation between the percentage of inferred leukemia cells and the blast % by morphology in those samples with r = 0.90 (P=0.02). This allowed us to compare various compartments pre-and post-treatment at high resolution. In patients who responded to induction chemotherapy, there was a statistically significant increase in the percentage of CD8 T cells and NK cells in post-treatment samples (CD8 T cells, P = 0.0049; NK cells, P = 0.012). This expansion in CD8 T cells and NK cells was not seen in patients who did not respond to treatment, indicating perhaps a role of these cells in response to chemotherapy. In order to understand determinants of response and resistance in the immune compartments, we performed differential gene expression followed by pathway analysis comparing responders and non-responders pre and post-treatment. We found that CD8 T cells and NK cells from non-responders have significant upregulation of the oxidative phosphorylation and MYC pathways following treatment compared to those with a clinical response (Figure 1D).

Conclusions: In conclusion, we show that leukemia cells can be identified reliably in their microenvironment using copy number alterations and characteristic gene expression that can be applied to samples with a normal karyotype. Response in AML is associated with an increase in CD8 T cells and NK cells, highlighting the role of the immune system in targeting leukemia following chemotherapy. Resistance to therapy could be related to upregulation of oxidative phosphorylation and MYC in CD8 T cells and NK cells following treatment. Further analysis to characterize the ecosystem of AML is ongoing.

Disclosures: Issa: Syndax: Research Funding; Celegene: Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees. Konopleva: Reata Pharmaceutical Inc.;: Patents & Royalties: patents and royalties with patent US 7,795,305 B2 on CDDO-compounds and combination therapies, licensed to Reata Pharmaceutical; Sanofi: Research Funding; AbbVie: Consultancy, Research Funding; Ascentage: Research Funding; Agios: Research Funding; Ablynx: Research Funding; Eli Lilly: Research Funding; Calithera: Research Funding; Kisoji: Consultancy; F. Hoffmann La-Roche: Consultancy, Research Funding; Forty-Seven: Consultancy, Research Funding; AstraZeneca: Research Funding; Rafael Pharmaceutical: Research Funding; Stemline Therapeutics: Consultancy, Research Funding; Cellectis: Research Funding; Amgen: Consultancy; Genentech: Consultancy, Research Funding. Andreeff: Daiichi-Sankyo; Jazz Pharmaceuticals; Celgene; Amgen; AstraZeneca; 6 Dimensions Capital: Consultancy; Centre for Drug Research & Development; Cancer UK; NCI-CTEP; German Research Council; Leukemia Lymphoma Foundation (LLS); NCI-RDCRN (Rare Disease Clin Network); CLL Founcdation; BioLineRx; SentiBio; Aptose Biosciences, Inc: Membership on an entity's Board of Directors or advisory committees; Amgen: Research Funding; Daiichi-Sankyo; Breast Cancer Research Foundation; CPRIT; NIH/NCI; Amgen; AstraZeneca: Research Funding.

*signifies non-member of ASH