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Applying prognostic models for myelofibrosis in practice

Oct 29, 2020
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The overall survival (OS) of patients with primary myelofibrosis (PMF) varies widely, depending on individual clinical and genetic factors. The most common causes of death in PMF are hematologic transformation, disease progression, and infections, along with other causes of morbidity and mortality. Given the heterogeneity of the disease, precise PMF models are essential for individual estimation of disease outcome and management. Consequently, there has been considerable effort to develop discriminatory prognostic models to help clinical decision making in several settings, including hematopoietic stem cell transplantation (HSCT).

The review by How et al.1 covers established and emerging prognostic models for PMF, such as the International Prognostic Scoring System (IPSS); Dynamic IPSS (DIPSS) and its revision DIPSS+; the Mutation-Enhanced IPSS for transplant-age patients (MIPSS70) and MIPSS70+ (containing cytogenetic information); the Genetically-Inspired Prognostic Scoring System (GIPSS); and the Myelofibrosis Secondary to PV and ET-Prognostic Model (MYSEC-PM) for patients with myelofibrosis occurring post-essential thrombocytosis (ET) or polycythemia vera (PV). Moreover, the authors provide guidance on how to use these prognostic models in clinical practice.

Contemporary models1

International Prognostic Scoring System

The IPSS was established based on data from 1,054 patients with PMF to help with prognostication and treatment decisions after diagnosis. The IPSS comprises of five variables: age > 65 years, hemoglobin (Hb) level < 10 g/dL, white blood cell count > 25 GPT/L, circulating blasts ≥ 1%, and presence of constitutional symptoms. Depending on the number of variables present, patients are subdivided into low-, intermediate-1, intermediate-2, and high-risk groups, corresponding with decreasing median OS. Of note, cytogenetic variables are not included.2

Dynamic IPSS and Dynamic IPSS+

In contrast to the IPSS, the DIPSS is a PMF model that can be used at any time of disease assessment. Using a study analyzing 525 patients with PMF, the DIPSS validated the impact of various risk factors on OS: for example, as anemia had the most substantial impact on OS, it was assigned two points within this system. Subsequent research showed that the DIPSS also had predictive value for leukemic transformation. When further prognostic factors were identified, such as red blood cell transfusion, unfavorable karyotype, and thrombocytopenia, they were included in DIPPS+. For assessing leukemia-free survival (LFS), only thrombocytopenia and unfavorable karyotype were identified as significant factors.1-4 The DIPPS and DIPPS+ remain the cornerstones of PMF prognostications due to their easy use and basis on readily available clinical data.

Mutation-Enhanced IPSS

Numerous mutations in myeloproliferative neoplasms (MPN) have been identified in recent years. The inclusion of these high-risk mutations led to the development of multiple genetic-based scoring systems, such as MIPSS70, without cytogenetic information, the MIPSS70+ with cytogenetic information included, and MIPSS70+ version 2.0 (v2.0).

The MIPSS70 includes both clinical and molecular risk factors (Table 1). As the MIPSS70 was specifically designed for the transplant setting, it is therefore age restricted. The MIPSS70 model groups patients into low-, intermediate-, or high-risk categories with a corresponding median OS of 27.7, 7.1, and 2.3 years, respectively. It is also predictive for LFS. The MIPSS70+ incorporates three clinical factors, considers high-risk mutations and karyotype, and places more weight on unfavorable cytogenetic information (Table 1). Recently, MIPSS70+ was updated to MIPSS70 v2.0 with newer genetic findings and differentiation of patients with an unfavorable versus a very high-risk karyotype, adding U2AF1Q157 as a high molecular risk (HMR) mutation (Table 1).

Table 1. Prognostic models for PMF and post-ET/PV MF1

 

Prognostic model

Variables (points awarded)

Risk groups (median OS, years)

Unfavorable karyotype

VHR karyotype

BM, bone marrow; DIPSS, dynamic IPSS; ET, essential thrombocythemia; GIPSS, genetically-inspired prognostic scoring system; Hb, hemoglobin; HLA, human leukocyte antigen; HMR, high molecular risk; int, intermediate; IPSS, International Prognostic Scoring System; KPS, Karnofsky performance status; MF, myelofibrosis; MIPSS70, mutation-enhanced prognostic scoring system for transplant-age patients; MIPSS70+ v2.0, MIPSS70 plus version 2.0; MPN, myeloproliferative neoplasms; MTSS, Myelofibrosis Transplant Scoring System; MYSEC-PM, Myelofibrosis Secondary to PV and ET-Prognostic Model; OS, overall survival; PMF, primary myelofibrosis; PV, polycythemia vera; VHR, very high risk; WBC, white blood cell.

aHMR mutation means the presence of a mutation in any of the following: ASXL1, SRSF2, EZH2, IDH1, IDH2; besides, it means the presence of U2AF1Q157 in the GIPSS and MIPSS701 v2.0.

bSevere anemia: Hb level of 8 g/dL in women and 9 g/dL in men.

cModerate anemia: Hb level of 8 to 9.9 g/dL in women and 9 to 10.9 g/dL in men.

IPSS2

Age > 65 (1)
Hb < 10 g/dL (1)
WBC > 25 Gpt/L (1)
Circulating blasts
1% (1)
Constitutional symptoms (1)

0 = low (11.3)
1 = int-1 (7.9)
2 = int-2 (4.0)
≥ 3 = high (2.3)

Not scored

Not scored

DIPSS3

Age > 65 a (1)
Hb < 10 g/dL (2)
WBC > 25 Gpt/L (1)
Circulating blasts
1% (1)
Constitutional symptoms (1)

0 = low (not reached)
1
2 = int-1 (14.2)
3
4 = int-2 (4.0)
5
6 = high (1.5)

Presence of complex karyotype or sole or 2 abnormalities that included 18, 27/7q2, i(17q), inv(3), 25/5q2, 12p2, or 11q23

Not scored

DIPSS+4

DIPSS low (0)
DIPSS int-1 (1)
DIPSS int-2 (2)
DIPSS high (3)
Unfavorable karyotype (1)
Red cell transfusion (1)
Platelets < 100 Gpt/L (1)

0 = low (15.4)
1 = int-1 (6.5)
2
3 = int-2 (2.9)
4
6 = high (1.3)

Not scored

Not scored

MIPSS705

Hb < 10g/dL (1)
WBC > 25 Gpt/L (2)
Circulating blasts ≥ 2% (1)
Constitutional symptoms (1)
Platelets < 100 Gpt/L (2)
BM fibrosis Grade ≥ 2 (1)
1 HMRa mutation (1)
≥ 2 HMRa mutations (2)
Type 1/like CALR absent (1)

01 = low (27.7)
2
4 = int (7.1)
≥ 5 = high (2.3)

Not scored

Not scored

MIPSS70+5

Hb < 10 g/dL (1)
Circulating blasts ≥ 2% (1)
Constitutional symptoms (1)
1 HMRa mutation (1)
≥ 2 HMRa mutations (2)
Type 1/like CALR absent (2)
Unfavorable karyotype (3)

02 = low (20)
3 = int-1 (6.3)
4
6 = int-2 (3.9)
≥ 7 = high (1.7)

Any karyotype other than normal karyotype or sole abnormalities of 20q-, 13q-, 19, chromosome 1 translocation/duplication, -Y, or sex chromosome abnormality other than -Y

Not scored

GIPSS7

VHR karyotype (2)
Unfavorable karyotype (1)
Type 1/like CALR absent (1)
ASXL1 (1)
SRSF2 (1)
U2AF1Q157 (1)

0 = low (26.4)
1 = int-1 (8.0)
2 = int-2 (4.2)
≥ 3 = high (2.0)

Any karyotype other than very high-risk karyotype, normal karyotype, or sole abnormalities of 20q-, 13q-, 19, chromosome 1 translocation/duplication, -Y, or sex chromosome abnormality other than -Y

Single or multiple abnormalities of -7, i(17q), inv(3)/3q21, 12p-/12p11.2, 11q-/11q23, or other autosomal trisomies not including 18/19

MIPSS70+ v2.06

VHR karyotype (4)
Unfavorable karyotype (3)
≥ 2 HMRa mutations (3)
1 HMRa mutation (2)
Type 1/like CALR absent (2)
Severe anemiab (2)
Moderate anemiac (1)
Circulating blasts ≥ 2% (1)
Constitutional symptoms (2)

0 = very low (26.4)
1 = low (16.4)
3
4 = int (7.7)
5
8 = high (4.1)
≥ 3 = very high (1.8)

Any karyotype other than very high-risk karyotype, normal karyotype, or sole abnormalities of 20q-, 13q-, 19, chromosome 1 translocation/duplication, -Y, or sex chromosome abnormality other than -Y

Single or multiple abnormalities of -7, i(17q), inv(3)/3q21, 12p-/12p11.2, 11q-/11q23, or other autosomal trisomies not including 18/19

MYSEC-PM8

Age (0.15 per year of age)
Hb < 11 g/dL (2)
Platelets < 150 Gpt/L (1)
Circulating blasts ≥ 3% (2)
CALR absent (2)
Constitutional symptoms (1)

< 11 = low (not reached)
11
13 = int-1 (9.3)
14
16 = int-2 (4.4)
> 16 = high (2.0)

Not scored

Not scored

MPN Personalized Risk Calculator9

For variables and scoring, see link

Individualized risk groups

MTSS10

Age ≥ 57 years (1)
WBC count > 25 Gpt/L (1)
Platelets < 150 Gpt/L (1)
ASXL1 (1)
KPS (1)
HLA-matched unrelated donor (2)
Non-CALR/MPL (2)

02 = low (5-year OS, 90%)
3
4 = int (5-year OS, 77%)
5 = high (5-year OS, 50%)
6
9 = very high (5-year OS, 34%)

Not scored

Not scored

 

Genetically-Inspired Prognostic Scoring System

The GIPSS reflects a purely genetic prognostic model and was developed to complement the MIPSS70+ v2.0. The GIPSS categorized patient karyotype as very-high-risk, unfavorable, or favorable (Table 1). The GIPSS was non-inferior to the MIPSS70+ and DIPSS and was validated for OS and LFS in a reference cohort.

Newly emerging models1

Recent work analyzed 69 cancer genes in 2,000 patients with MPN by whole-genome sequencing. Interestingly, p53 mutations were associated with an inferior outcome. The model also integrated clinical and cytogenetic variables to create a more individualized prediction of OS and risk for transformation to acute myeloid leukemia. This MPN Personalized Risk Calculator provides a patient-specific approach based on cancer genomics.

The Myelofibrosis Transplant Scoring System (MTSS) was established to predict the prognosis for MPN patients potentially undergoing HSCT. The MTSS was evaluated in patients with PMF and post-ET/PV MF who underwent HSCT and found unfavorable outcome variables, such as age ≥ 57 years, Karnofsky performance status < 90%, platelet count < 150 Gpt/L, white blood cell count > 25 Gpt/L, HLA-mismatched donor, ASXL1 mutation, and non-CALR/MPL driver mutation. Other variables in a pre-transplant setting (constitutional symptoms, cytogenetic risk, Hb level, ≥ HMR mutations) were not found to be prognostic. The MTSS was more accurate in predicting outcomes in HSCT patients than DIPSS, MIPSS70, and MYSEC-PM.

Application of prognostic models in clinical settings1

Currently, guidelines recommend the use of IPSS, DIPSS, and DIPSS+ for risk stratification in PMF. Although these models are easy to apply in daily clinical practice, risk stratification approaches that incorporate molecular and cytogenetic information such as MIPSS70+ v2.0 provide better risk discrimination, especially for patients with intermediate-2 to high-risk classification according to IPSS, DIPSS, and DIPSS+ who are candidates for HSCT. Also, scoring systems that consider genetic and cytogenetic data seem to have increased prognostic value to estimate the risk of leukemia progression (Figure 1).

Figure 1. Risk stratification approach in primary myelofibrosis1


An unresolved problem is the accurate timing of HSCT. The decision of immediate or delayed HSCT may need to be discussed primarily in younger patients with good clinical status and high genetic risk (as assessed by GIPPS, for example). Here, prognostic models can provide adequate guidance for initiating bone marrow transplantation.

Special situations1

Post-ET/PV myelofibrosis

MPN management guidelines do not differentiate between therapeutic management for PMF patients versus those with post-ET/PV myelofibrosis. Using the MIPSS70 is not applicable in these cases as relevant HMRs for PMF, such as ASXL1, EZH2, and IDH1, are not predictive for post-ET/PV myelofibrosis outcomes. Therefore, the MYSEC-PM was developed explicitly for patients with post-ET/PV myelofibrosis. It identified six variables (age, Hb < 11g/dL, platelets < 150 Gpt/L, peripheral blasts ≥ 3%, constitutional symptoms, lack of CALR mutation) associated with poor outcome (Table 1) and subgroups patients into four risk levels accordingly. In a validation study in a post-ET/PV cohort, MYSEC-PM was superior to IPSS and DIPSS, allowing reclassification of patients from higher to lower risk categories. Therefore, the authors recommend using MYSEC-PM in patients with post-ET/PV myelofibrosis, although routine use needs to be further evaluated. Of note, age is a continuous variable in the MYSEC-PM and has a significant impact. This may unduly reduce the number of patients found eligible for HSCT, and it is therefore recommended to use other models (DIPSS or DIPSS+) for help with therapeutic decision making.

Prefibrotic myelofibrosis

In 2016, PMF was divided into two stages: prefibrotic and overtly fibrotic. Prefibrotic PMF patients have outcomes intermediate between ET and overtly fibrotic patients. The IPSS could discriminate low-, intermediate-, and high-risk patients. However, prefibrotic myelofibrosis patients with intermediate-risk scores had a significantly longer OS (> 10 years) than overtly fibrotic patients. Hence, prefibrotic PMF patients with intermediate risk (based on IPSS) should not undergo HSCT. However, high-risk patients have inferior OS, suggesting HSCT as a therapeutic option in this setting. MIPSS70, MIPSS70+, and MIPSS70+ v2.0 were validated in a cohort of patients with overtly and prefibrotic myelofibrosis. For patients with no available genetic data, the IPSS, DIPSS, and DIPSS+ may be useful alternatives for determining treatment intensity.

Conclusions and future directions

Research in past decades acquired enormous clinical and molecular data in PMF, leading to prognostic models with multiple variables and high data granularity. Earlier models based mostly on clinical data have evolved, and increasingly genetic information is integrated for risk assessment. However, these new models need further validation, especially when applying them to determine eligibility for HSCT.

A significant limitation of existing models is the lack of treatment options for patients with PMF. While prognostic models are critical to identify patients with high-risk features who benefit from HSCT, they have little impact on treatment decisions in transplant-ineligible patients. Due to a lack of drugs for these patients, management is similar between low- and high-risk groups and consists mainly of ruxolitinib, fedratinib, and hydroxyurea. Moreover, treatment initiation is primarily based on symptoms rather than genetic risk type. Treatment strategies are needed that consider those novel genetic markers, to maximize the utility of prognostic models.

  1. How J, Hobbs GS. A practical guide for using myelofibrosis prognostic models in the clinic. J Natl Compr Canc Netw. 2020;18(9):1271-1278. DOI: 10.6004/jnccn.2020.7557
  2. Cervantes F, Dupriez B, Pereira A, et al. New prognostic scoring system for primary myelofibrosis based on a study of the International Working Group for Myelofibrosis Research and Treatment. Blood. 2009;113(13):2895-2901. DOI: 10.1182/blood-2008-07-170449
  3. Passamonti F, Cervantes F, Vannucchi AM, et al. A dynamic prognostic model to predict survival in primary myelofibrosis: a study by the IWG-MRT (International Working Group for Myeloproliferative Neoplasms Research and Treatment). Blood. 2010;115(9):1703-1708. DOI: 10.1182/blood-2009-09-245837
  4. Gangat N, Caramazza D, Vaidya R, et al. DIPSS plus: a refined Dynamic International Prognostic Scoring System for primary myelofibrosis that incorporates prognostic information from karyotype, platelet count, and transfusion status. J Clin Oncol. 2011;29(4):392-397. DOI: 10.1200/JCO.2010.32.2446
  5. Guglielmelli P, Lasho TL, Giada Rotunno, et al. MIPSS70: Mutation-Enhanced International Prognostic Score System for transplantation-age patients with primary myelofibrosis. J Clin Oncol. 2018;36(4):310-318. DOI: 10.1200/JCO.2017.76.4886
  6. Tefferi A, Guglielmelli P, Lasho TL, et al. MIPSS70+ Version 2.0: Mutation and Karyotype-Enhanced International Prognostic Scoring System for primary myelofibrosis. J Clin Oncol. 2018;36(17):1769-1770. DOI: 10.1200/JCO.2018.78.9867
  7. Tefferi A, Finke CM, Lasho TL, et al. U2AF1 mutation types in primary myelofibrosis: phenotypic and prognostic distinctions. Leukemia. 2018;32(10):2263-2303. DOI: 10.1038/s41375-018-0078-0
  8. Passamonti F, Giorgino T, Mora B, et al. A clinical-molecular prognostic model to predict survival in patients with post polycythemia vera and post essential thrombocythemia myelofibrosis. Leukemia. 2017;31(12):2726-2731. DOI: 10.1038/leu.2017.169
  9. Grinfeld J, Nangalia J, Baxter EJ, et al. Classification and personalized prognosis in myeloproliferative Neoplasms. N Engl J Med. 2018;379(15):1416-1430. DOI: 10.1056/NEJMoa1716614
  10. Gagelmann N, Ditschkowski M, Bogdanov R, et al. Comprehensive clinical-molecular transplant scoring system for myelofibrosis undergoing stem cell transplantation. Blood. 2019;133(20):2233-2242. DOI: 10.1182/blood-2018-12-890889

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