Donal McLornan, MBBCh, MRCP, PhD, FRCPath, is a consultant, Hematology and Stem Cell Transplantation, University College London Hospitals NHS Foundation Trust.

Donal McLornan, MBBCh, MRCP, PhD, FRCPath, explains the challenges of choosing optimal timing for allogeneic hematopoietic cell transplantation in myelofibrosis.

Dr McLornan on the Challenges of Identifying Optimal Timing for Transplantation in Myelofibrosis

“Where the future lies are the integration of those [JAK inhibitors and new agents] into conventional conditioning but also thinking about maintenance strategies because one of the biggest challenges is yes, we can identify the right patient for transplant and we can bring them through transplant, but unfortunately, 1 in 5 patients will relapse.”

Donal McLornan, MBBCh, MRCP, PhD, FRCPath, a consultant in hematology and stem cell transplantation at the University College London Hospitals NHS Foundation Trust, explained the ongoing challenge of identifying the optimal timing for patients with myelofibrosis to undergo allogeneic hematopoietic cell transplantation.

Historically, discussions about transplantation for patients with myelofibrosis have been centered on appropriate timing, McLornan began. In recent years, more available therapies, such as JAK inhibitors, have been integrated into the myelofibrosis treatment paradigm, he noted. Since 2011, the myelofibrosis landscape has seen 4 FDA approvals for JAK inhibitors, including 

The management of myelofibrosis also depends on patients’ symptoms and spleen response, which include disease that is low-risk (asymptomatic), intermediate 1 (symptomatic and a bulky spleen), intermediate 2 (transplant eligible), and high- to very high–risk (not transplant eligible). Of note, patients with intermediate 1 and intermediate 2 disease could benefit most from receiving JAK inhibitors to maximize spleen and symptom response. Those with high- to very high–risk disease may also benefit from first-line JAK inhibitors to maximize spleen and symptom response and could continue to second-line JAK inhibitors if they have no response or lose response to first-line therapy.

However, with newer agents that have emerged, there is still a lot to learn about them, including the way these agents can modify the disease and how to best utilize them before, during, and after transplantation, he emphasized.

Considering the integration of these treatments into conventional conditioning, along with maintenance strategies, is where the future lies, McLornan explained. He added that identifying and selecting patients who would benefit from transplantation has been established. Nevertheless, 1 in 5 patients will relapse following transplantation, which is a significant challenge to navigate, he said. The future, with the prediction of outcome, will be the best integration of drugs, especially as a maintenance strategy post-transplantation, McLornan concluded.

Videos

Dr McLornan on the Future of an EBMT Machine Learning Model for Identifying and Stratifying Transplant Risk in Myelofibrosis

“I’ve worked in transplantation in [patients with] myelofibrosis for almost 2 decades and, despite that experience, choosing the right time and the right patient to bring forward for transplant is still challenging.”

Donal McLornan, MBBCh, MRCP, PhD, FRCPath, consultant, Hematology and Stem Cell Transplantation, University College London Hospitals NHS Foundation Trust, discusses future research directions for an EBMT machine learning model for identifying and stratifying transplant risk in myelofibrosis.

On March 27, 2025, a team from the European Society for Blood and Marrow Transplantation (EBMT) announced that a machine learning model they had developed for identifying and stratifying transplant risk for patients with myelofibrosis outperformed standard statistical models. The machine learning approach was trained using records of 3887 patients with myelofibrosis who received their allogeneic-hematopoietic cell transplantation between 2005 and 2020. Data from 1296 patients were used to assess and validate the model. 

The open-access tool outperformed standard models in terms of accuracy, identifying a subset of patients with high-risk disease who had a 40% chance of dying within 1 year of transplant and a non-relapse mortality rate of approximately 35%. Moreover, the model identified 25% of patients as being a part of this group at a high-risk for a poor outcome after transplantation, compared with 10.1% via a Cox-derived score and 8.2% using the Center for International Blood and Marrow Transplant Research model.

Despite his nearly 20 years of experience, selecting patients with myelofibrosis who are good candidates for transplantation remains a challenge, partially due to patient interest in clinical trials and new agents, McLornan said. Additionally, the integration of these new approaches into the transplantation algorithm is not fully understood, he continued. The machine learning model, along with several other factors, will aid clinicians in understanding which patients should be candidates for transplant, he noted.

McLornan encourages investigators to use the tool in order to gain familiarity with how it functions. In the future, the EBMT team hopes to integrate the mutational profile of the patient, as well as the prior agents they have received with sequencing information into the machine learning tool, he continued. This information would further refine the model, increasing its usefulness, he concluded.

Donal McLornan, MBBCh, MRCP, PhD, FRCPath, discusses future research directions for an EBMT machine learning model for identifying and stratifying transplant risk in myelofibrosis.

Donal McLornan, MBBCh, MRCP, PhD, FRCPath

Articles

Dr McLornan on the Challenges of Identifying Optimal Timing for Transplantation in Myelofibrosis

Dr McLornan on the Future of an EBMT Machine Learning Model for Identifying and Stratifying Transplant Risk in Myelofibrosis