An agenda to advance research in myelodysplastic syndromes: a TOP 10 priority list from the first international workshop in MDS

Stahl, Maximilian; Abdel‐Wahab, Omar; Wei, Andrew H.; Savona, Michael R.; Xu, Mina L; Xie, Zhuoer; Taylor, Justin; Starczynowski, Daniel T.; Sanz, Guillermo; Sallman, David A.; Roboz, Gail J.; Patnaik, Mrinal M.; Padron, Eric; Odenike, Olatoyosi; Nazha, Aziz; Nimer, Stephen D.; Majeti, Ravindra; Little, Richard F.; Gore, Steven D.; List, Alan F.; Kuchroo, Vijay; Komrokji, Rami; Kim, Tae Kon; Kim, Nina; Hourigan, Christopher S.; Halene, Stephanie; Griffiths, Elizabeth A.; Greenberg, Peter L.; Figueroa, María E.; Fenaux, Pierre; Efficace, Fabio; DeZern, Amy E.; Porta, Matteo Giovanni Della; Daver, Naval; Churpek, Jane E.; Carraway, Hetty E.; Brunner, Andrew M.; Borate, Uma; Bennett, John M.; Bejar, Rafael; Boultwood, Jacqueline; Loghavi, Sanam; Bewersdorf, Jan Philipp; Platzbecker, Uwe; Steensma, David P.; Sekeres, Mikkael A.; Buckstein, Rena; Zeidan, Amer M.

doi:10.1182/bloodadvances.2022008747

Cited by 9 publications

(4 citation statements)

References 31 publications

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“…The association between MPN diagnosis and growth rate suggests a possible avenue for early detection by predicting which patients are more likely to remain asymptomatic and which are more likely to undergo malignant transformation. Understanding the role of evolutionary dynamics to predict risk of progression in clonal hematopoiesis and provide prognostic information in hematological malignancies has been noted as a top clinical priority ( Savona et al 2015 , Stahl et al 2023 ).…”

Section: Discussionmentioning

confidence: 99%

cloneRate: fast estimation of single-cell clonal dynamics using coalescent theory

Johnson,

Shuai,

Schweinsberg

et al. 2023

Bioinformatics

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Motivation While evolutionary approaches to medicine show promise, measuring evolution itself is difficult due to experimental constraints and the dynamic nature of body systems. In cancer evolution, continuous observation of clonal architecture is impossible, and longitudinal samples from multiple timepoints are rare. Increasingly available DNA sequencing datasets at single-cell resolution enable the reconstruction of past evolution using mutational history, allowing for a better understanding of dynamics prior to detectable disease. There is an unmet need for an accurate, fast, and easy-to-use method to quantify clone growth dynamics from these datasets. Results We derived methods based on coalescent theory for estimating the net growth rate of clones using either reconstructed phylogenies or the number of shared mutations. We applied and validated our analytical methods for estimating the net growth rate of clones, eliminating the need for complex simulations used in previous methods. When applied to hematopoietic data, we show that our estimates may have broad applications to improve mechanistic understanding and prognostic ability. Compared to clones with a single or unknown driver mutation, clones with multiple drivers have significantly increased growth rates (median 0.94 versus 0.25 per year; P = 1.6×10−6). Further, stratifying patients with a myeloproliferative neoplasm (MPN) by the growth rate of their fittest clone shows that higher growth rates are associated with shorter time to MPN diagnosis (median 13.9 versus 26.4 months; P = 0.0026). Availability and implementation We developed a publicly available R package, cloneRate, to implement our methods (Package website: https://bdj34.github.io/cloneRate/). Source code: https://github.com/bdj34/cloneRate/.

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Section: Discussionmentioning

confidence: 99%

cloneRate: fast estimation of single-cell clonal dynamics using coalescent theory

Johnson,

Shuai,

Schweinsberg

et al. 2023

Bioinformatics

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show abstract

Section: Discussionmentioning

confidence: 99%

Estimating single cell clonal dynamics in human blood using coalescent theory

Johnson

Shuai

Schweinsberg

et al. 2023

Preprint

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While evolutionary approaches to medicine show promise, measuring evolution itself is difficult due to experimental constraints and the dynamic nature of body systems. In cancer evolution, continuous observation of clonal architecture is impossible, and longitudinal samples from multiple timepoints are rare. Increasingly available DNA sequencing datasets at single cell resolution enable the reconstruction of past evolution using mutational history, allowing for a better understanding of dynamics prior to detectable disease. We derive methods based on coalescent theory for estimating the net growth rate of clones from either reconstructed phylogenies or the number of shared mutations. Using single-cell datasets from blood, we apply and validate our analytical methods for estimating the net growth rate of hematopoietic clones, eliminating the need for complex simulations. We show that our estimates may have broad applications to improve mechanistic understanding and prognostic ability. Compared to clones with a single or unknown driver mutation, clones with multiple drivers have significantly increased growth rates (median 0.94 vs. 0.25 per year; p = 1.6 x 10-6). Further, stratifying patients with a myeloproliferative neoplasm (MPN) by the growth rate of their fittest clone shows that higher growth rates are associated with shorter time from clone initiation to MPN diagnosis (median 13.9 vs. 26.4 months; p = 0.0026)

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“…Falini and Martelli, in this issue of the American Journal of Hematology, 3 illustrate the similarities and differences in MDS and AML between the International Consensus Classification (ICC) 4 and what is currently proposed for the fifth edition WHO classification, 5 and compare them to the current revised fourth edition WHO classification 6 . While there are many similarities between the two new classifications (which is not unexpected when similar groups review similar literature), there are indeed differences which have also been highlighted elsewhere 7,8 . Some are seemingly minor changes in terminology, while others represent new or divergent disease definitions.…”

mentioning

confidence: 96%

“…6 While there are many similarities between the two new classifications (which is not unexpected when similar groups review similar literature), there are indeed differences which have also been highlighted elsewhere. 7,8 Some are seemingly minor changes in terminology, while others represent new or divergent disease definitions. These likely in part reflect the different approaches used by the two groups: while both included expert hematologists, geneticists and pathologists, the ICC used the consensus CAC approach followed by the prior WHO classifications, while the fifth edition drafted an initial classification scheme and then received additional clinical input later during the writing process rather than developing the draft classification "upfront" in a large group CAC discussion setting.…”

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confidence: 99%

Moving toward more molecular classifications of myelodysplastic syndrome and acute myeloid leukemia

Arber

Orazi

2022

American J Hematol

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The increase in our understanding of the genetic complexity of myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML), as well at the rapid introduction of more targeted therapies for these diseases has made the diagnosis and classification of AML and MDS more challenging and nuanced. The third, fourth, and revised fourth edition World Health Organization (WHO) classifications, published by the International Agency on Cancer (IARC), have succeeded in incorporating new genetic criteria in the various disease classification schemes by carefully handling the overwhelming amount of data and complexity of these disorders. Updating an existing classification system requires not only integrating the most recent published literature, but also developing a consensus by overcoming differences of opinion on how to incorporate the new data. The updating process was developed by the Society for Hematopathology and the European Association for Hematopathology through a Clinical Advisory Committee (CAC). The CAC met prior to each classification being developed, to come to a consensus on what to change and how to incorporate those changes into comprehensive diagnostic criteria. IARC also provided input and published the classification within their Blue Book series.

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An agenda to advance research in myelodysplastic syndromes: a TOP 10 priority list from the first international workshop in MDS

Cited by 9 publications

References 31 publications

cloneRate: fast estimation of single-cell clonal dynamics using coalescent theory

cloneRate: fast estimation of single-cell clonal dynamics using coalescent theory

Estimating single cell clonal dynamics in human blood using coalescent theory

Moving toward more molecular classifications of myelodysplastic syndrome and acute myeloid leukemia

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