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Editorials
transplant outcomes using targeted mutational analysis with a next-generation sequencing panel. In this cohort, TP53 and RAS pathway mutations were strongly associ- ated with poorer overall survival and earlier relapse inde- pendently of transplant conditioning intensity. There was also an indication that patients with JAK2 mutations had increased NRM after ablative conditioning transplants.7 In this context, there has been an attempt to include genom- ic data into the GITMO index.8 In a cohort of 401 patients, using massively parallel sequencing for muta- tional analysis, TP53 mutations were again identified as adverse prognostic markers. In addition, spliceosome mutations signifying a secondary-type acute myeloid leukemia phenotype, ASXL1 and RUNX1 were also asso- ciated with poorer outcomes. With MDS mutational analysis becoming routine practice in the clinic, it is important that future iterations of transplant risk models incorporate MDS genomic data.
The impact of disease persistence as measurable resid- ual disease − variably measured by multi-parameter flow cytometry, cytogenetics/fluorescence in situ hybridiza- tion, and increasingly by next-generation sequencing − has been of great interest as an independent dynamic pre- dictor of relapse. In MDS, the presence of measurable residual disease in the early post-transplant period (assessed by multi-parameter flow cytometry or cytoge- netics/fluorescence in situ hybridization) is associated with significantly poorer outcomes.9 Further studies are needed to better define the impact of pre- and post-trans- plant measurable residual disease, but we expect this to be an important and dynamic predictor for individualized MDS transplant risk prediction in the future.
With regards to individualized risk prediction, the authors of the current study define a user-friendly nomogram for scoring the various elements of the index in finer detail and with greater prognostic power (c-statistic 0.609) (Figure 1). In Figure 1 we have in addition highlighted three missing variables that would likely add to the discriminatory power of this index (i.e. pre-transplant mutational/genomic analy- sis, HCT-CI and minimal residual disease status).
We also note that for poor-risk cohorts, failure after transplantation includes both NRM and relapse at equiv- alent frequency (~40%). This offers opportunities for progress, especially for reducing NRM failures. For instance, cytomegalovirus serostatus of the recipient and its impact on post-transplant survival and immune recon- stitution has been an area of increasing research.10 In this study there was a moderately high risk of cytomegalovirus reactivation (39%) with significant impairment of overall survival.6 As the authors point out, optimizing the use of antiviral agents active against cytomegalovirus, such as letermovir, which have been shown to be useful in high-risk settings,11 may improve NRM in a lower-risk HLA-matched cohort. Similarly, avoiding ablative conditioning in TP53- and JAK2-mutant
MDS, in which it offers no benefit and may even be dele- terious, may further reduce NRM after transplantation, while ablative conditioning may improve outcomes of RAS pathway-mutant MDS.8 In the future, studies of pre- emptive immunomodulation strategies (e.g. tumor vac- cines, donor lymphocyte infusions) based on individual dynamic risk scoring before and after transplantation may be considered.
In summary, Gagelmann et al. present a new composite risk index to predict MDS transplant survival outcomes which incorporates both disease- and patient-related fac- tors. They document a moderate improvement of predic- tive power compared to existing indices. A useful nomo- gram is provided as a step towards individualized out- come prediction. External validation in an independent dataset, and the future incorporation of the HCT CI, MDS genomic data and minimal residual disease status will be important next steps toward the goal of individu- alized, dynamic MDS transplant outcome prediction and treatment decision-making.
References
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6. Gagelmann N. Optimized EBMT transplant-specific risk score in myelodysplastic syndromes after allogeneic stem-cell transplanta- tion. Haematologica.2019;104(5):929-936.
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8. Lindsley RC, Saber W, Mar BG, et al. Prognostic mutations in myelodysplastic syndrome after stem-cell transplantation. N Engl J Med. 2017;376(6):536-547.
9. DellaPortaMG,GallìA,BacigalupoA,etal.Clinicaleffectsofdriver somatic mutations on the outcomes of patients with myelodysplas- tic syndromes treated with allogeneic hematopoietic stem-cell trans- plantation. J Clin Oncol. 2016;34(30): 3627-3637.
10. FestucciaM,BakerK,GooleyTA,etal.Post-hematopoieticstemcell transplantation minimal residual disease and early relapses in MDS and AML evolving from MDS. Blood. 2015;126(23):2019-2019.
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12. Marty FM, Ljungman P, Chemaly RF, et al. Letermovir prophylaxis for cytomegalovirus in hematopoietic-cell transplantation. N Engl J Med. 2017;377(25):2433-2444.
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