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A novel network pharmacology approach for leukaemia differentiation therapy using Mogrify®

Oncogene volume 41, pages 5160–5175 (2022)Cite this article

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Abstract

Acute myeloid leukaemia (AML) is a rapidly fatal blood cancer that is characterised by the accumulation of immature myeloid cells in the blood and bone marrow as a result of blocked differentiation. Methods which identify master transcriptional regulators of AML subtype-specific leukaemia cell states and their combinations could be critical for discovering novel differentiation-inducing therapies. In this proof-of-concept study, we demonstrate a novel utility of the Mogrify® algorithm in identifying combinations of transcription factors (TFs) and drugs, which recapitulate granulocytic differentiation of the NB4 acute promyelocytic leukaemia (APL) cell line, using two different approaches. In the first approach, Connectivity Map (CMAP) analysis of these TFs and their target networks outperformed standard approaches, retrieving ATRA as the top hit. We identify dimaprit and mebendazole as a drug combination which induces myeloid differentiation. In the second approach, we show that genetic manipulation of specific Mogrify®-identified TFs (MYC and IRF1) leads to co-operative induction of APL differentiation, as does pharmacological targeting of these TFs using currently available compounds. We also show that loss of IRF1 blunts ATRA-mediated differentiation, and that MYC represses IRF1 expression through recruitment of PML-RARα, the driver fusion oncoprotein in APL, to the IRF1 promoter. Finally, we demonstrate that these drug combinations can also induce differentiation of primary patient-derived APL cells, and highlight the potential of targeting MYC and IRF1 in high-risk APL. Thus, these results suggest that Mogrify® could be used for drug discovery or repositioning in leukaemia differentiation therapy for other subtypes of leukaemia or cancers.

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Fig. 1: Schema of the experimental design and data analysis of ATRA-induced differentiation of NB4 cells.
Fig. 2: Filtering of the TF regulatory networks.
Fig. 3: CMAP-based identification of drugs that induce transcriptional changes recapitulating changes in TF networks under ATRA treatment.
Fig. 4: Experimental validation of the detected drugs.
Fig. 5: MYC and IRF1 influence differentiation in NB4 cells.
Fig. 6: IRF1 transcription is regulated by MYC and ATRA treatment.
Fig. 7: Combined targeting of MYC and IRF1 induces differentiation in NB4 cells.
Fig. 8: Drug combinations induce differentiation in primary APL MNCs.

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Data availability

The raw and processed data were submitted to NCBI GEO. The accession number will be available upon publication.

Code availability

The source code of our pipeline will be available upon publication. Source code for the Mogrify® algorithm is not available.

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Acknowledgements

The authors would like to acknowledge Dr. Sonia P. Chothani for providing the RNA-Seq data analysis pipeline, the ssCMAP developer, Dr. Shu-Dong Zhang, for helpful discussion on the algorithm’s internal calculations, the Duke-NUS Genome Biology Facility (DGBF) for RNA, ChIP- and ATAC-sequencing services, as well as Dr. Gee Chuan Wong/Bryan Y.W. Teo, SGH Department of Haematology for providing primary APL samples. This work was funded by the National Medical Research Council (NMRC) of Singapore (MOH-000059/MOH-CSASI18may-0002) and NMRC/CIRG/1429/2015 to STO). OJLR is supported by NMRC YIRG (NMRC/OFYIRG/0022/2016) and by a Singapore National Research Foundation grant [NRF-CRP20-2017-0002].

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  1. These authors contributed equally: Lin Ming Lee, Eleni G. Christodoulou.

Authors and Affiliations

  1. Programme in Cancer and Stem Cell Biology, Duke–NUS Medical School, Singapore, Singapore

    Lin Ming Lee, Pavithra Shyamsunder, Kian Leong Lee & S. Tiong Ong

  2. Centre for Computational Biology, Duke–NUS Medical School, Singapore, Singapore

    Eleni G. Christodoulou, Bei Jun Chen, Enrico Petretto & Owen J. L. Rackham

  3. Programme in Cardiovascular and Metabolic Disorders, Duke–NUS Medical School, Singapore, Singapore

    Eleni G. Christodoulou, Bei Jun Chen, Enrico Petretto & Owen J. L. Rackham

  4. Comprehensive Cancer Centre, King’s College London, London, UK

    Tsz Kan Fung & Chi Wai Eric So

  5. Department of Haematological Medicine, King’s College Hospital, London, UK

    Tsz Kan Fung & Chi Wai Eric So

  6. Department of Haematology, Singapore General Hospital, Singapore, Singapore

    Gee Chuan Wong & S. Tiong Ong

  7. MRC London Institute of Medical Sciences (LMC), Imperial College London, Faculty of Medicine, London, UK

    Enrico Petretto

  8. Institute for Big Data and Artificial Intelligence in Medicine, School of Science, China Pharmaceutical University (CPU), Nanjing, China

    Enrico Petretto

  9. School of Biological Sciences, University of Southampton, Southampton, SO17 1BJ, UK

    Owen J. L. Rackham

  10. Department of Medical Oncology, National Cancer Centre, Singapore, Singapore

    S. Tiong Ong

  11. Department of Medicine, Duke University Medical Center, Durham, NC, USA

    S. Tiong Ong

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Contributions

Wet lab experiments were performed by LML and PS. RNA-Seq was planned and designed by KLL. RNA-Seq data QC, analysis, Gene Set Functional Enrichments, querying of CMAP, drug ranking, and network analysis were performed by EGC. ChIP data were analysed by BJC. KLL provided guidance on TF GSEA analyses. EGC and LML prepared the manuscript and generated the figures with input from co-authors. KLL, OJLR, EP, and STO designed the study, after conceptualisation by OJLR, EP, and STO. ES and TKF provided the NB4/MR2/LR2 cell lines. GCW provided primary human APL samples from the Singapore General Hospital Haematology Repository.

Corresponding authors

Correspondence to Enrico Petretto, Owen J. L. Rackham or S. Tiong Ong.

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Competing interests

OJLR is a co-inventor of the patent (WO/2017/106932) and is a co-founder, shareholder and director of Mogrify Ltd, a cell therapy company. All other authors declare no competing interest.

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Lee, L.M., Christodoulou, E.G., Shyamsunder, P. et al. A novel network pharmacology approach for leukaemia differentiation therapy using Mogrify®. Oncogene 41, 5160–5175 (2022). https://doi.org/10.1038/s41388-022-02505-5

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  • DOI: https://doi.org/10.1038/s41388-022-02505-5

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