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The practical use of myelofibrosis genomics in a clinical setting

Oct 5, 2020

Myelofibrosis (MF), a subtype of the Philadelphia chromosome-negative myeloproliferative neoplasms (MPN), is characterized by Janus kinase (JAK)/signal transducer and activator of transcription (STAT) pathway activation. The most common mutations reported in MF include the JAK2V617F mutation, and mutations in the calreticulin ( CALR) and the thrompopoietin ( TPO) receptor gene (encoded by the myeloproliferative leukemia virus ( MPL) oncogene). The MPN Hub recently published a summary of a recent review of MF, which can be found here.

At the eighth annual meeting of the Society of Hematologic Oncology (SOHO), Raajit Rampal 1gave a presentation on the genomics in MF and their use in clinical practice. Rampal began by presenting the following case study, asking the audience to consider the risk of disease progression and implications for treatment response.

Case Study 1

  • A 69-year-old man seen in primary care for worsening fatigue, mild night sweats, minimal weight loss, and experiencing abdominal discomfort due to splenomegaly.
  • Hemoglobin = 9.0 gm/dL; white blood cell count (WBC) = 12,000 per uL; platelets = 120,000 per uL.
  • Peripheral blood analysis showed teardrop cells, early forms, reticulocytes, and 2% blasts.
  • Bone marrow morphology consistent with primary MF, 2+ fibrosis genomic profiling shows JAK2V617F, ASXL1, DNMT3Amutations.
  • Cytogenetic analysis reveals deletion 20q, trisomy 8.
  • On treatment with ruxolitinib for disease-related symptoms and splenomegaly.

Rampal went on to highlight the International Prognostic Scoring System (IPSS) and the Dynamic International Prognostic Scoring System (DIPSS) as tools currently available to determine risk of disease progression by including variables such as patient age, WBC, hemoglobin levels, blast cells in the peripheral blood, and constitutional symptoms.

Despite the heterogeneity among MPN, the hallmark of these diseases is activation of the JAK-STAT pathway, including

  • JAK2mutation: 95% of polycythemia vera cases, 45–50% of essential thrombocythemia (ET) and MF cases,
  • CALRmutation: 30–40% of ET/MF cases
  • MPLmutation: < 5% of ET/MF cases  

Impact of mutations on prognosis

With 40–50% of patients having more than one mutation of JAK-STAT signaling pathway (for example LNK, CALR, MPL, and JAK2mutations), Rampal stated that this has implications for prognosis.

  • Data from Klampfl et al. 2demonstrated that a CALRexon 9 mutation was associated with a longer overall survival (OS) than either the JAK2V617F or the MPLexon 10 mutations in patients with myelofibrosis.
  • Vannucchi et al. 3showed that ASXL1, EZH2, SRSF2, and IDH1/2 seem to predict disease progression in myelofibrosis.
  • Recent work by Santo and Getta et al. 4found that NRAS and KRAS seem to have a negative impact on survival and promote leukemic transformation.
  • Analysing combinations of these mutations, Tefferi et al. 5found that the best OS was seen in patients with the CALRmutation and no ASXL1mutation ( CALR +ASXL1 ; median 10.4 years), whereas patients with no CALRmutation combined with an ASXL1mutation had the worst OS ( CALR ASXL1 + ; median 2.3 years).

The MIPSS70-plus score assesses prognosis based on mutations

Rampal went on to discuss scores that encompass some of these mutations to enable the stratification of patients into risk groups. The MIPSS70-plus score, for example, includes genetic information as well as clinical variables similar to IPSS and DIPSS.

According to the MIPSS70-plus score, the following variables are associated with a reduced OS 6:

  • Hb < 100 g/L
  • PB blasts ≥ 2%
  • constitutional symptoms
  •  absence of CALRType 1
  •  high molecular risk
  •  ≥ 2 high molecular risk mutations
  • unfavorable karyotype

When new mutations that may have a prognostic value are discovered, these tools are updated. For example, the U2AF1mutation has been found to be associated with anemia and thrombocytopenia; it has been incorporated into MIPSS70-plus v2.0 as well as in the Genetically Inspired Prognostic Scoring System (GIPSS).

Applying this information to the aforementioned case study, the variables suggest that, using the MIPSS70-plus v2.0 tool, the patient would be at very high risk with a 10-year OS of less than 5%, while the MIPSS-70 risk score tool would give a 34% probability of survival for 5 years. Rampal highlights that this is important information can be obtained using a simple online calculator ( http://www.mipss70score.it/).

Rampal then asked whether genetics could inform us how well the patient in the case study would do on ruxolitinib. A study by Patel et al. 7demonstrated that the type and number of mutations could predict how long patients would respond to treatment. The presence of ASXL1, EZH2, and DNMT3mutations specifically suggested a shorter time to treatment failure, which would be of concern with the patient in the case study.

Predicting outcome after transplantation

Another score, the Myelofibrosis Transplant Scoring System (MTSS) 8enables the stratification based on the outcomes of allogeneic hematopoietic stem cell transplantation (allo-HSCT). The following parameters are assessed:

  • leukocyte and platelet count
  • performance status and age
  • HLA mismatched unrelated donor
  • absence of MPLor CALRmutations
  • presence of ASXL1mutations.

 The patient in the case study would be unlikely to benefit from ruxolitinib for a prolonged period, however, using the MTSS tool, the 5-year OS following allo-HSCT is in the range of 77–90%. In contrast, other studies 9, 10have assessed posttransplant outcomes and have found that ASXL1and other mutations have no impact while U2AF1mutations may be associated with worse transplant outcomes. Therefore, tools to predict outcome after transplantation are evolving and more studies are needed to consolidate these findings.

Conclusion

Rampal concluded his presentation by stating that assessing the genomic profile of patients with myelofibrosis is vital for the optimization of treatment and that, as genomic alterations change over time, recurrent profiling should be utilized. He stressed that several other mutations, aside from JAK-STAT activating mutations, can be present and may have prognostic value, such as predicting duration of response to ruxolitinib treatment. MIPSS-70 is a useful tool for risk stratification; however, as genomic alterations are not static but change over time, recurrent profiling should be utilized to inform treatment decisions, such as referral for allo-HSCT.

Expert Opinion

  1. Rampal RK. Genomics in myelofibrosis: Practical guidelines for its use in clinical practice. Oral Presentation. SOHO 2020; Sep 10, 2020; Virtual.
  2. Klampfl T, Gisslinger H, Harutyunyan AS, et al. Somatic mutations of calreticulin in myeloproliferative neoplasms. N Engl J Med. 2013;369(25):2379-2390. DOI: 1056/NEJMoa1311347
  3. Vannucchi AM, Lasho TL, Guglielmelli P, et al. Mutations and prognosis in primary myelofibrosis. Leukemia. 2013;27(9):1861-1869. DOI: 1038/leu.2013.119
  4. Santos FPS, Getta B, Masarova L, et al. Prognostic impact of RAS-pathway mutations in patients with myelofibrosis. Leukemia. 2020;34(3):799-810. DOI: 1038/s41375-019-0603-9
  5. Tefferi A, Guglielmelli P, Lasho TL, et al. CALR and ASXL1 mutations-based molecular prognostication in primary myelofibrosis: an international study of 570 patients. Leukemia. 2014;28(7):1494-500. DOI: 1038/leu.2014.57
  6. Guglielmelli P, Lasho TL, Rotunno G, 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: 1200/JCO.2017.76.4886
  7. Patel KP, Newberry KJ, Luthra R, et al. Correlation of mutation profile and response in patients with myelofibrosis treated with ruxolitinib. Blood. 2015;126(6):790-797. DOI: 1182/blood-2015-03-633404
  8. 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: 1182/blood-2018-12-890889
  9. Al-Ali HK, Gisslinger H, Passamonti F, et al. Baseline mutational status of patients with myelofibrosis and anemia in the realise trial and impact on outcome. Blood. 2019;134(Supplement_1):2952-2952. DOI: 1182/blood-2019-124739
  10. Tamari R, Rapaport F, Zhang N, et al. Impact of high-molecular-risk mutations on transplantation outcomes in patients with myelofibrosis. Biol Blood Marrow Tr. 2019;25(6):1142-1151. DOI: 1016/j.bbmt.2019.01.002