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ACUTE MYELOID LEUKEMIA

Single cell RNA sequencing of AML initiating cells reveals RNA-based evolution during disease progression

Leukemia volume 35, pages 2799–2812 (2021)Cite this article

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Abstract

The prognosis of most patients with AML is poor due to frequent disease relapse. The cause of relapse is thought to be from the persistence of leukemia initiating cells (LIC’s) following treatment. Here we assessed RNA based changes in LICs from matched patient diagnosis and relapse samples using single-cell RNA sequencing. Previous studies on AML progression have focused on genetic changes at the DNA mutation level mostly in bulk AML cells and demonstrated the existence of DNA clonal evolution. Here we identified in LICs that the phenomenon of RNA clonal evolution occurs during AML progression. Despite the presence of vast transcriptional heterogeneity at the single cell level, pathway analysis identified common signaling networks involving metabolism, apoptosis and chemokine signaling that evolved during AML progression and become a signature of relapse samples. A subset of this gene signature was validated at the protein level in LICs by flow cytometry from an independent AML cohort and functional studies were performed to demonstrate co-targeting BCL2 and CXCR4 signaling may help overcome therapeutic challenges with AML heterogeneity. It is hoped this work will facilitate a greater understanding of AML relapse leading to improved prognostic biomarkers and therapeutic strategies to target LIC’s.

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Fig. 1: RNA clonal evolution.
Fig. 2: RNA cluster group biomarkers.
Fig. 3: Metabolic dysregulation.
Fig. 4: Apoptotic dysregulation.
Fig. 5: Cytokine signaling dysregulation.
Fig. 6: Flow cytometry validation study.
Fig. 7: Co-targeting BCL2 and CXCR4 leads to enhanced efficacy against AML cells in vitro and in vivo.

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Acknowledgements

The authors are grateful to the patients who contributed to this study and thank the Children’s Oncology Group for providing some of the bone marrow samples used in this study. This study was supported by pilot funding from the Case Comprehensive Cancer Center (NCI P30CA0437093) and the Computational Genomic Epidemiology of Cancer Fellowship (NCI 5T32CA094186-17, L.S.). This research was also supported by the following Shared Resources of the Case Comprehensive Cancer Center (NCI P30CA0437093): Hematopoietic Cell Biorepository and Cellular Therapy, Integrated Genomics, and Cytometry & Imaging Microscopy. This work made use of the High Performance Computing Resources in the Core Facility for Advanced Research Computing at Case Western Reserve University. The authors thank Jose Cancelas, Folashade Otegbeye, Sheela Karunaithi, and Grace Lee for their scientific feedback and Caroline Perry and Kristin Waite for their critical reading and editing of the paper.

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Author notes

  1. These authors contributed equally: L. C. Stetson, Dheepa Balasubramanian.

Authors and Affiliations

  1. Department of Pathology, Case Western Reserve University, Cleveland, OH, USA

    L. C. Stetson, Dheepa Balasubramanian, Susan Pereira Ribeiro, Tammy Stefan, Kalpana Gupta, Xuan Xu, Slim Fourati, Anne Roe, Zachary Jackson, Robert Schauner, Ashish Sharma, Banumathi Tamilselvan, Rafick-Pierre Sekaly & David N. Wald

  2. Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH, USA

    L. C. Stetson, Marcos de Lima, Yogen Saunthararajah, Jaroslaw Maciejewski, Thomas LaFramboise, Jill S. Barnholtz-Sloan, Rafick-Pierre Sekaly & David N. Wald

  3. Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, OH, USA

    Samuel Li & Thomas LaFramboise

  4. Department of Medicine, University Hospitals Cleveland Medical Center, Cleveland, OH, USA

    Marcos de Lima

  5. Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA

    Tae Hyun Hwang

  6. BD Biosciences, San Jose, CA, USA

    Robert Balderas

  7. Department of Translational Hematology and Oncology Research, Cleveland Clinic, Cleveland, OH, USA

    Yogen Saunthararajah & Jaroslaw Maciejewski

  8. Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA

    Jill S. Barnholtz-Sloan

  9. Department of Pathology, University Hospitals Cleveland Medical Center and Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA

    David N. Wald

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  1. L. C. Stetson
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Contributions

DW conceived of and supervised the project. DB, TS, and KG performed single cell RNA sequencing experiments. LS designed and performed the computational analysis. SF, AS, JB-S, TH, ZJ, RS, and SL contributed to the computational analysis. DW, RS, SPR, RB, AR, XX, and BT designed, optimized, implemented, analyzed and interpreted the flow cytometry data. JM, YS, and ML provided AML samples and assisted in data analysis and interpretation. DW and LS wrote the paper with contributions from RS, ZJ, TL, JB-S, YS, JM, AR, SPR, and DB.

Corresponding author

Correspondence to David N. Wald.

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Stetson, L.C., Balasubramanian, D., Ribeiro, S.P. et al. Single cell RNA sequencing of AML initiating cells reveals RNA-based evolution during disease progression. Leukemia 35, 2799–2812 (2021). https://doi.org/10.1038/s41375-021-01338-7

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  • DOI: https://doi.org/10.1038/s41375-021-01338-7

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