Novel therapies vs hematopoietic cell transplantation in myelofibrosis: who, when, how?

England, James T.; Gupta, Vikas

doi:10.1182/hematology.2021000279

“…Median survival for PV, ET, and PMF is 14, 20, and 5.7 years respectively [6][7][8]. Allogeneic hematopoietic stem cell transplantation is the only therapy with potential for cure at present, but is limited by significant mortality and morbidity [9]. Thus, JAK inhibition is the cornerstone of treatment for intermediate-and high-risk MF.…”

Section: Introductionmentioning

confidence: 99%

Lysine-Specific Demethylase 1 (LSD1/KDM1A) Inhibition as a Target for Disease Modification in Myelofibrosis

Gill

¹

2022

Cells

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Myelofibrosis (MF) is the most symptomatic form of myeloproliferative neoplasm and carries the worst outcome. Allogeneic hematopoietic stem cell transplantation is the only therapy with potential for cure at present, but is limited by significant mortality and morbidity. JAK inhibition is the mainstay of treatment for intermediate- and high-risk MF. Ruxolitinib is the most widely used JAK1/2 inhibitor and provides durable effects in controlling symptom burden and spleen volumes. Nevertheless, ruxolitinib may not adequately address the underlying disease biology. Its effects on mutant allele burden, bone marrow fibrosis, and the prevention of leukemic transformation are minimal. Multiple small molecules are being tested in multiple phase 2 and 3 studies as either monotherapy or in combination with JAK2 inhibitors. In this review, the role of LSD1/KDM1A inhibition as a potential disease-modification strategy in patients with myelofibrosis is described and discussed.

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“…Consequently, accurate prediction of survival is critical for transplant decision-making. 11 Conventional prognostic models in use are the International Prognostic Scoring System (IPSS), 12 which should be used at diagnosis, and the Dynamic IPSS (DIPSS) and DIPSS plus, 13,14 applicable at any time during the clinical course. These models were derived from patients with PMF, so a scoring system named the Myelofibrosis Secondary to PV and ET-Prognostic Model (MYSEC-PM) was subsequently developed to improve risk stratification in patients with SMF.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, conventional risk models based primarily on clinical variables are still widely used in the clinical setting. 10,11,22–24…”

Section: Introductionmentioning

confidence: 99%

“…However, the main limitation to the general applicability of these models in clinical practice is the need for cytogenetic studies, which in many cases cannot be performed on bone marrow samples due to failure to obtain marrow (dry tap), 12,20,21 and the need for next‐generation sequencing (NGS). Therefore, conventional risk models based primarily on clinical variables are still widely used in the clinical setting 10,11,22–24 …”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Improves Risk Stratification in Myelofibrosis: An Analysis of the Spanish Registry of Myelofibrosis

Mosquera-Orgueira

¹

,

Pérez‐Encinas

²

,

et al. 2022

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Myelofibrosis (MF) is a myeloproliferative neoplasm (MPN) with heterogeneous clinical course. Allogeneic hematopoietic cell transplantation remains the only curative therapy, but its morbidity and mortality require careful candidate selection. Therefore, accurate disease risk prognostication is critical for treatment decision-making. We obtained registry data from patients diagnosed with MF in 60 Spanish institutions (N = 1386). These were randomly divided into a training set (80%) and a test set (20%). A machine learning (ML) technique (random forest) was used to model overall survival (OS) and leukemia-free survival (LFS) in the training set, and the results were validated in the test set. We derived the AIPSS-MF (Artificial Intelligence Prognostic Scoring System for Myelofibrosis) model, which was based on 8 clinical variables at diagnosis and achieved high accuracy in predicting OS (training set c-index, 0.750; test set c-index, 0.744) and LFS (training set c-index, 0.697; test set c-index, 0.703). No improvement was obtained with the inclusion of MPN driver mutations in the model. We were unable to adequately assess the potential benefit of including adverse cytogenetics or high-risk mutations due to the lack of these data in many patients. AIPSS-MF was superior to the IPSS regardless of MF subtype and age range and outperformed the MYSEC-PM in patients with secondary MF. In conclusion, we have developed a prediction model based exclusively on clinical variables that provides individualized prognostic estimates in patients with primary and secondary MF. The use of AIPSS-MF in combination with predictive models that incorporate genetic information may improve disease risk stratification.

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“…Consequently, accurate prediction of survival is critical for transplant decision-making. 11 Conventional prognostic models in use are the International Prognostic Scoring System (IPSS), 12 which should be used at diagnosis, and the Dynamic IPSS (DIPSS) and DIPSS plus, 13,14 applicable at any time during the clinical course. These models were derived from patients with PMF, so a scoring system named the Myelofibrosis Secondary to PV and ET-Prognostic Model (MYSEC-PM) was subsequently developed to improve risk stratification in patients with SMF.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Improves Risk Stratification in Myelofibrosis: An Analysis of the Spanish Registry of Myelofibrosis

Orgueira

¹

,

Encinas

²

,

Sánchez

³

et al. 2022

Blood

1

0

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Myelofibrosis (MF) is a myeloproliferative neoplasm (MPN) with heterogeneous clinical course. Allogeneic hematopoietic cell transplantation remains the only curative therapy, but its morbidity and mortality require careful candidate selection. Therefore, accurate disease risk prognostication is critical for treatment decision-making. We obtained registry data from patients diagnosed with MF in 60 Spanish institutions (N = 1386). These were randomly divided into a training set (80%) and a test set (20%). A machine learning (ML) technique (random forest) was used to model overall survival (OS) and leukemia-free survival (LFS) in the training set, and the results were validated in the test set. We derived the AIPSS-MF (Artificial Intelligence Prognostic Scoring System for Myelofibrosis) model, which was based on 8 clinical variables at diagnosis and achieved high accuracy in predicting OS (training set c-index, 0.750; test set c-index, 0.744) and LFS (training set c-index, 0.697; test set c-index, 0.703). No improvement was obtained with the inclusion of MPN driver mutations in the model. We were unable to adequately assess the potential benefit of including adverse cytogenetics or high-risk mutations due to the lack of these data in many patients. AIPSS-MF was superior to the IPSS regardless of MF subtype and age range and outperformed the MYSEC-PM in patients with secondary MF. In conclusion, we have developed a prediction model based exclusively on clinical variables that provides individualized prognostic estimates in patients with primary and secondary MF. The use of AIPSS-MF in combination with predictive models that incorporate genetic information may improve disease risk stratification.

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Novel therapies vs hematopoietic cell transplantation in myelofibrosis: who, when, how?

Cited by 10 publications

References 61 publications

Lysine-Specific Demethylase 1 (LSD1/KDM1A) Inhibition as a Target for Disease Modification in Myelofibrosis

Lysine-Specific Demethylase 1 (LSD1/KDM1A) Inhibition as a Target for Disease Modification in Myelofibrosis

Machine Learning Improves Risk Stratification in Myelofibrosis: An Analysis of the Spanish Registry of Myelofibrosis

Machine Learning Improves Risk Stratification in Myelofibrosis: An Analysis of the Spanish Registry of Myelofibrosis

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