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MYELODYSPLASTIC NEOPLASM

Influence of co-mutational patterns in disease phenotype and clinical outcomes of chronic myelomonocytic leukemia

Leukemia (2024)Cite this article

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Chronic myelomonocytic leukemia (CMML) is a clonal disorder of mutant hematopoietic stem/progenitor cells (HSPCs) [1] characterized by high risk of progression to acute myeloid leukemia (AML) [2]. Integration of molecular data with clinical and cytogenetic features has allowed developing multiple prognostic risk models applicable in clinical practice [3, 4]. Despite these advances, our ability to accurately predict disease transformation and our understanding on how distinct genomic events cooperate to determine the disease behavior remains limited. Although previous studies have reported the potential cooperative effects of specific mutations [5, 6], determining if certain recurrent cooperative genomic patterns predict for disease phenotype and clinical outcomes could help define diseases subsets to optimize patient management and direct translational efforts aimed at developing rationally designed novel combinations.

In order to evaluate further if distinct genomic profiles, characterized by recurrent co-occurrence of somatic mutations, define clinicopathologic subgroups of CMML with distinct outcomes, we evaluated a cohort of patients with CMML, defined using the WHO 2016 classification [7], and used computational methods to identify unique clusters defined by recurrent co-mutational patterns and define their clinical features and outcomes. Akaike information criterion (AIC) method was applied to evaluate mixture model components defining clusters solely based on mutational profile. Mutation data was obtained using an amplicon-based next-generation sequencing platform, as detailed in Supplementary Methods, and was available in all 296 patients included in this discovery cohort. Expectation-maximization (EM) algorithm was used for mixture model fitting to multivariate binary data. Unsupervised cluster membership was chosen as a function of the component with the highest weight for any individual, and we examined means of cluster centers. Generalized linear models were used to study the association of overall response (ORR), complete remission (CR), and risk factors. Overall survival (OS) was calculated as the time from diagnosis to death or last follow-up date. Leukemia-free survival (LFS) was calculated from the time of diagnosis until transformation to AML, death or last follow-up date. Patients who were alive at their last follow-up were censored on that date. The Kaplan–Meier product limit method was used to estimate the median OS, EFS, and for each clinical/demographic factor. Univariate and multivariate Cox proportional hazards regression analyses were used to identify any association with each of the variables and survival outcomes. Statistical analyses were performed using R version 4.0.3, Jamovi version 2.0.0, SPSS Statistics version 28 (IBM), (and GraphPad version 9.0.0 (San Diego, CA).

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Fig. 1: Mutational composition and clinical outcomes of genomic clusters in CMML.

Data availability

The datasets generated during and/or analyzed during the current study are not publicly available due to patient privacy concerns but are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported in part by the University of Texas MD Anderson Cancer Center Support Grant CA016672 (all authors) and the University of Texas MD Anderson MDS/AML Moon Shot (GM-B, RK-S, CBB, CC, KT, KAS, CB-R, HK, and GG-M). JJR-S is a recipient of MD Anderson’s Odyssey Fellowship.

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Authors and Affiliations

  1. Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Guillermo Montalban-Bravo, Juan Jose Rodriguez-Sevilla, Danielle Hammond, Kelly Chien, Koji Sasaki, Elias Jabbour, Courtney DiNardo, Koichi Takahashi, Nicholas Short, Ghayas C. Issa, Naveen Pemmaraju, Tapan Kadia, Farhad Ravandi, Naval Daver, Gautam Borthakur, Sherry Pierce, Hagop Kantarjian & Guillermo Garcia-Manero

  2. Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    David Michael Swanson

  3. Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Rashmi Kanagal-Shamanna, Sanam Loghavi & Carlos Bueso-Ramos

Authors
  1. Guillermo Montalban-Bravo
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  22. Guillermo Garcia-Manero
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Contributions

G. Montalban-Bravo, J.J. Rodriguez-Sevilla and G. Garcia-Manero designed the study, analyzed the data and participated in writing the manuscript. K. Sasaki, F. Ravandi, N. Daver, K. Takahashi, D. Hammond, K. Chien, N. J. Short, C. DiNardo, E. Jabbour, G. Borthakur, N. Pemmaraju, T. Kadia, G. C. Issa, H. Kantarjian and G. Garcia-Manero contributed patients and participated in analyzing the data and writing the manuscript. S. Pierce collected and analyzed data. S. Loghavi and R. Kanagal-Shamanna performed histopathological analysis and sequencing analysis. D. M Swanson developed mixture and time-to-event models. R Kanagal-Shamanna, S. Loghavi and C. Bueso-Ramos reviewed bone marrow morphologic and genetic findings, analyzed the data and contributed in writing the manuscript.

Corresponding author

Correspondence to Guillermo Montalban-Bravo.

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Competing interests

KS declares honoraria from Otsuka Pharma, and consultancy fee from Pfizer Japan. MK declares consulting and honoraria from AbbVie, Genentech, F. Hoffman La-Roche, Stemline Therapeutics, Amgen, Forty-Seven, and Kisoji; research funding and/or clinical trial support from AbbVie, Genentech, F. Hoffman La-Roche, Eli Lilly, Cellectis, Calithera, Ablynx, Stemline Therapeutics, Agios, Ascentage, and Astra Zeneca; and stock options/royalties from Reata Pharmaceutical. HK declares research support from AbbVie, Agios, Amgen, Ariad, Astex, BMS, Cyclacel, Daiichi-Sankyo, Immunogen, Jazz Pharma, Novartis, and Pfizer, honoraria from AbbVie, Actinium, Agios, Amgen, Immunogen, Orsinex, Pfizer, and Takeda, and an advisory role with Actinium. GG-M declares support from and an advisory role with Celgene Corporation, Astex, and Amphivena, and grant/research support 15 from Helsinn, Novartis, AbbVie, Onconova, H3 Biomedicine, and Merck. The rest of authors declare no competing financial interests.

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Montalban-Bravo, G., Rodriguez-Sevilla, J.J., Swanson, D.M. et al. Influence of co-mutational patterns in disease phenotype and clinical outcomes of chronic myelomonocytic leukemia. Leukemia (2024). https://doi.org/10.1038/s41375-024-02190-1

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