A single-cell atlas identifies pretreatment features of primary imatinib resistance in chronic myeloid leukemia.


Journal

Blood
ISSN: 1528-0020
Titre abrégé: Blood
Pays: United States
ID NLM: 7603509

Informations de publication

Date de publication:
01 06 2023
Historique:
accepted: 15 02 2023
received: 07 06 2022
medline: 5 6 2023
pubmed: 2 3 2023
entrez: 1 3 2023
Statut: ppublish

Résumé

Primary resistance to tyrosine kinase inhibitors (TKIs) is a significant barrier to optimal outcomes in chronic myeloid leukemia (CML), but factors contributing to response heterogeneity remain unclear. Using single-cell RNA (scRNA) sequencing, we identified 8 statistically significant features in pretreatment bone marrow, which correlated with either sensitivity (major molecular response or MMR) or extreme resistance to imatinib (eventual blast crisis [BC] transformation). Employing machine-learning, we identified leukemic stem cell (LSC) and natural killer (NK) cell gene expression profiles predicting imatinib response with >80% accuracy, including no false positives for predicting BC. A canonical erythroid-specifying (TAL1/KLF1/GATA1) regulon was a hallmark of LSCs from patients with MMR and was associated with erythroid progenitor [ERP] expansion in vivo (P < .05), and a 2- to 10-fold (6.3-fold in group A vs 1.09-fold in group C) erythroid over myeloid bias in vitro. Notably, ERPs demonstrated exquisite TKI sensitivity compared with myeloid progenitors (P < .001). These LSC features were lost with progressive resistance, and MYC- and IRF1-driven inflammatory regulons were evident in patients who progressed to transformation. Patients with MMR also exhibited a 56-fold expansion (P < .01) of a normally rare subset of hyperfunctional adaptive-like NK cells, which diminished with progressive resistance, whereas patients destined for BC accumulated inhibitory NKG2A+ NK cells favoring NK cell tolerance. Finally, we developed antibody panels to validate our scRNA-seq findings. These panels may be useful for prospective studies of primary resistance, and in assessing the contribution of predetermined vs acquired factors in TKI response heterogeneity.

Identifiants

pubmed: 36857629
pii: 494749
doi: 10.1182/blood.2022017295
doi:

Substances chimiques

Imatinib Mesylate 8A1O1M485B
Protein Kinase Inhibitors 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

2738-2755

Commentaires et corrections

Type : CommentIn

Informations de copyright

© 2023 by The American Society of Hematology.

Auteurs

Vaidehi Krishnan (V)

Cancer & Stem Cell Biology Signature Research Programme, Duke-NUS Medical School, Singapore.

Florian Schmidt (F)

Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, Singapore.

Zahid Nawaz (Z)

Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, Singapore.

Prasanna Nori Venkatesh (PN)

Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, Singapore.

Kian Leong Lee (KL)

Cancer & Stem Cell Biology Signature Research Programme, Duke-NUS Medical School, Singapore.

Xi Ren (X)

Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, Singapore.

Zhu En Chan (ZE)

Cancer & Stem Cell Biology Signature Research Programme, Duke-NUS Medical School, Singapore.

Mengge Yu (M)

Cancer & Stem Cell Biology Signature Research Programme, Duke-NUS Medical School, Singapore.

Meera Makheja (M)

Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, Singapore.

Nirmala Arul Rayan (NA)

Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, Singapore.

Michelle Gek Liang Lim (MGL)

Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, Singapore.

Alice Man Sze Cheung (AMS)

Department of Haematology, Singapore General Hospital, Singapore.

Sudipto Bari (S)

Division of Medical Sciences, National Cancer Centre, Singapore.
Advanced Cell Therapy and Research Institute, Singapore.
Oncology Academic Clinical Program, Duke-NUS Medical School, Singapore.

Wee Joo Chng (WJ)

Cancer Science Institute of Singapore, National University of Singapore, Singapore.
National University Cancer Institute, National University Hospital System, Singapore.
NUS Center for Cancer research and Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.

Hein Than (H)

Department of Haematology, Singapore General Hospital, Singapore.

John Ouyang (J)

Cardiovascular & Metabolic Disorders Signature Research Programme, Duke-NUS Medical School, Singapore.

Owen Rackham (O)

Cardiovascular & Metabolic Disorders Signature Research Programme, Duke-NUS Medical School, Singapore.
School of Biological Sciences, University of Southampton, Southampton, United Kingdom.

Tuan Zea Tan (TZ)

Genomic and Data Analytics Core, Cancer Science Institute of Singapore, National University of Singapore, Singapore.

William Ying Khee Hwang (WYK)

Department of Haematology, Singapore General Hospital, Singapore.
Division of Medical Sciences, National Cancer Centre, Singapore.
Advanced Cell Therapy and Research Institute, Singapore.

Charles Chuah (C)

Cancer & Stem Cell Biology Signature Research Programme, Duke-NUS Medical School, Singapore.
Department of Haematology, Singapore General Hospital, Singapore.

Shyam Prabhakar (S)

Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, Singapore.

S Tiong Ong (ST)

Cancer & Stem Cell Biology Signature Research Programme, Duke-NUS Medical School, Singapore.
Department of Haematology, Singapore General Hospital, Singapore.
Division of Medical Oncology, National Cancer Centre, Singapore.
Department of Medicine, Duke University Medical Center, Durham, NC.

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