An integrative prognostic model for transplant outcomes in myelofibrosis

Nico Gagelmann et al
University Medical Center Hamburg-Eppendorf, Hamburg, Germany

Background
  • Allogeneic hematopoietic cell transplantation (allo-HCT) is the only curative option for patients with myelofibrosis, yet transplant outcomes remain heterogeneous, with survival influenced by a complex interplay of disease-related, molecular, and transplant-specific factors.
  • Existing prognostic tools were primarily designed for pre-transplant risk stratification while transplant-specific systems lack molecular granularity and thus may not fully capture variables critical to post-transplant outcomes. Furthermore, most models were based on primarily patients without JAK inhibitor exposure.
  • To address this gap, the authors conducted a large international multicenter study using up-to-date machine learning approaches to develop a novel, comprehensive prognostic model in the modern era of molecular analysis and JAK inhibition.
  • The model aims to more accurately predict overall survival and transplant-related risk in patients with myelofibrosis undergoing allo-HCT, thereby guiding clinical decision-making and improving individualized risk assessment.
Methods
  • The authors analyzed 1,258 patients with myelofibrosis in the era of JAK inhibition undergoing allo-HCT across multiple international centers (60% primary, 40% secondary myelofibrosis). Clinical, molecular, and transplant-related variables were collected.
  • The dataset was randomly split into a 60% training cohort and a 40% independent validation cohort. Recursive partitioning was used to identify potential hierarchies of factors such as age and leukocyte counts.
  • To develop an integrated prognostic model for overall survival (OS) and non-relapse mortality (NRM), we applied both penalized Cox regression and ensemble survival Methods Variable selection and model tuning were performed via cross-validation in the training set.
  • Final model performance was assessed in the validation cohort using concordance indices and calibration metrics. The best-performing model was selected based on predictive accuracy and clinical applicability.
Results
  • Older age (consistent cutoff identified by non-supervised recursive portioning was 60 years, HR 1.39; p<0.001) and poor performance status (HR 1.52; p<0.001) were independently associated with reduced OS and NRM. Anemia, thrombocytopenia, and both severe leukocytosis and leukopenia predicted OS andNRM.
  • Other disease-related risk factors were shorter time to secondary myelofibrosis transformation (continuous HR 1.05; p<0.001). Looking at cytogenetics, regularization models identified complex karyotype as the only predictive factor (HR 1.51; p<0.001).
  • On a molecular level, CALR/MPL mutations were protective, while ASXL1, TP53, U2AF1, and RAS pathway mutations conferred high-risk features. In terms of donor relations, haploidentical (HR 1.49; p<0.001), and mismatched unrelated donors (HR 1.67; p<0.001) consistently showed worse OS and NRM compared with matched related or unrelated transplants.
  • An ensemble survival model was trained on 700 patients and validated in an independent cohort of 558 patients. Model performance was strong (C-index: 0.74 for OS, 0.68 for NRM in training; 0.70 and 0.64, respectively, in validation), with good calibration.
  • The model stratified patients into 4 risk groups based on predicted OS. Five-year OS and NRM rates were 90% and 7% (low risk), 71% and 20% (intermediate), 46% and 34% (high), and 25% and 52% (very high risk) (p<0.001). The 4-tiered model outperformed existing prognostic tools (disease-related: DIPSS, MIPSS70 and MIPSS70v2.0; and transplant-related: MTSS, EBMT scores) by incorporating transplant-specific and molecular risk features.
  • This improved and integrative myelofibrosis transplant scoring system (iMTSS) supports more personalized transplant risk assessment and informed clinical decision-making.
Conclusion
  • The authors developed and validated a robust, integrative myelofibrosis transplant scoring system (iMTSS) undergoing allo-HCT, incorporating clinical, molecular, and transplant-specific variables. The model effectively stratifies patients into four distinct risk groups with significantly different survival outcomes, outperforming existing pre-transplant scoring systems.
  • This tool enables more precise, individualized risk assessment and has the potential to guide transplant decision-making, donor selection, and post-transplant management strategies in clinical practice. A web-based calculator will be developed and presented at the meeting.