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How does genomic profiling impact myeloproliferative neoplasms?

Sep 28, 2021
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The activation of the Janus kinase and signal transducer and activator of transcription (JAK-STAT) pathway is the hallmark of myeloproliferative neoplasms (MPN) pathogenesis. The most well-known driver mutations occur in the JAK2, calreticulin (CALR), and thrombopoietin receptor gene (MPL) genes; however, the mutation profile of chronic-phase MPN includes many other additional mutations.

At the Texas Virtual MPN Workshop (TMW) 2021: Second Annual Workshop and Meeting, Raajit K. Rampal of Memorial Sloan Kettering Cancer Center talked about the disease spectrum of MPN through the prism of genomics.1 The MPN Hub is happy to provide a summary of the key messages.

The impact of additional mutations

Despite consistency in the central pathway activating MPN, an explanation for the disease variability observed lies in the presence of additional mutations, such as TET2 and ASXL1. In the case of chronic-phase MPN, mutations that occur in very low frequency, such as TP53, appear to make a difference in disease phenotype.

TET2 mutations

The gene expression signature of patients with TET2-mutant MPN is different to patients with non-TET2 mutant MPN. Also, JAK2V167F mutant murine models comparing mice with JAK2 only and mice with both JAK2 and TET2 mutations showed that the latter resulted in larger spleen sizes and increased numbers of white blood cells.

DNMT3A mutations

In JAK2 mutant murine models comparing mice with and without DNMT3A mutations, bone marrow fibrosis and splenomegaly was observed in the mice harbouring DNMT3A mutations, a phenotype consistent with myelofibrosis (MF).

TP53 mutations

Studies have demonstrated that the loss of TP53 in the context of JAK2 results in a fully penetrative leukemic phenotype.

Overall, these results demonstrate the impact of additional mutations on disease biology.

The order of mutations influences phenotype

Not only the presence, but also the order of mutations can have an impact on disease phenotype and the risk of a thrombotic event in patients with MPN. In fact, patients with TET2 mutations occurring first have a lower proportion of thrombosis compared to patients that had a JAK2 mutation first (p = 0.02). In patients with both JAK2 and TET2 mutations, the likelihood of being diagnosed with polycythemia vera is shown to be higher in patients with JAK2 mutations occurring before TET2 mutations compared with patients who had a TET2 mutation first. In the case of a TET2 mutation occurring first, patients had a higher chance of being diagnosed with essential thrombocythemia.

A recent study by Egeren, et al. has demonstrated that the relative prevalence of JAK2 mutant cells within hematopoietic stem cells populations may also differ.2 More specifically, the JAK2V617F mutation frequency was found to be higher in megakaryocyte/erythroid progenitors and lower in lymphoid progenitors and granulocyte-macrophage progenitors (combined p <10-10 for erythroid vs lymphoid and erythroid progenitors vs granulocyte-macrophage progenitors for all individuals with V617F mutation). In contrast, the JAK2V617L mutation showed no significant megakaryocyte-erythroid lineage bias.

The genomic impact on the risk of thrombosis

Moving beyond the disease phenotype, genetics may also influence clinical outcomes. For example, patients with essential thrombocythemia have variations in the incidence of thrombosis depending on which JAK-STAT driver mutations they have, type 1 CARL, JAK2 etc. JAK2 mutations seem to carry the lowest rate of thrombosis-free survival followed by MPL, and CARL respectively.

Table 1 below, summarizes two different scoring systems used to assess the risk of thrombosis and inform treatment decisions for cytoreductive therapy. As shown in the table, the revised-IPSET-thrombosis risk scoring system allows for a more robust risk classification with the addition of mutations.

Table 1. Thrombosis risk revised scoring system*

Risk

ELN criteria

Revised-IPSET-Thrombosis

Very low

NA

  • No prior thrombosis history
  • Age <61 years
  • Negativity for the JAK2V617F mutation

Low

  • No prior thrombosis or bleeding history
  • Age <60 years
  • Platelet count <1500 × 109/L
  • No prior thrombosis history
  • Age <61 years
  • Positivity for the JAK2 V617F mutation

Intermediate

NA

  • No prior thrombosis history
  • Age >60 years
  • Negativity for the JAK2V617F mutation

High

  • Prior thrombosis or bleeding history
  • Age ≥60 years
  • Platelet count >1500 × 109/L
  • Prior thrombosis history
  • Age >60 years
  • Positivity for the JAK2V617F mutation

ELN, European LeukemiaNet; IPSET, international prognostic score for thrombosis in essential thrombocythemia; NA, not applicable.
*Adapted from Rampal R.1

The implications on disease progression

Disease progression risk remains of great importance to patients and physicians; Rampal also discussed recent data on genomic risk factors in terms of disease progression. In the context of polycythemia vera, SRSF2 mutations seem to be associated with a greater degree of progression. Other mutations that have a significantly negative effect on disease progression to MF include EZH2, SRSF2, IDH1/2, NRAS, and KRAS. NRAS and KRAS mutations have been associated with an adverse prognostic impact in terms of overall survival and leukemic transformation in patients with MF. In the future, this list will continue to expand as more patients are sequenced and more gene profiling assays are performed.

Different risk scoring systems are used for assessment in the setting of MF; however, not all of them incorporate genomic profile characteristics. The MIPSS70-plus scoring system (Table 2) can be used for prognostication, to inform decision making and treatment choice, as it considers both clinical and genomic variables. By taking into account the impact of driver mutations such as CARL type 1, high–molecular risk mutations, and unfavorable karyotype it manages to calculate a well-rounded risk score for patients with MF.

Table 2. MIPSS70-plus risk score*

Variables

HR (95% CI)

p value

Weighted value

Hb <100g/L

1.5 (1.1–2.0)

0.005

1

PB blasts ≥2%

1.6 (1.2–2.3)

0.002

1

Constitutional symptoms

1.9 (1.4–2.5)

<0.001

1

Absence CARL type 1

2.4 (1.7–3.5)

<0.001

2

HMR

1.8 (1.3–2.5)

<0.001

1

≥2 HMR mutations

2.4 (1.4–4.0)

<0.001

2

Unfavorable karyotype

3.1 (2.3–4.3)

<0.001

3

CI, confidence interval; Hb, hemoglobin; HR, hazard ratio; HMR, high-molecular risk; PB, peripheral blood.
*Adapted from Rampal R.1
Any mutation in: ASXL1, EZH2, SRSF2, and IDH1/2.
Any abnormal karyotype other than normal karyotype or sole abnormalities of 20q-, 13q-, +9, chromosome 1 translocation/duplication, -Y, or sex chromosome abnormality other than -Y.

The impact of mutation profile on treatment response

Genomics can inform on the odds of remaining on therapy and other long-term treatment and transplant outcomes. In fact, the presence of ASXL1, EZH2, DNMT3 was shown to be associated with a quicker time to treatment failure with ruxolitinib. The number of mutations appear to have an impact on the cumulative survival of patients treated with ruxolitinib. Patients who had acquired three different mutations had considerably shorter life spans compared to patients with one or two mutations. Although, it is not yet clear how this works biologically, treating physicians may want to take this finding into consideration when treating patients with this agent.

Conclusion

To conclude, the phenotypic spectrum of MPN is linked to the mutational profile as well as to the order of the mutational acquisition, while the impact of specific individual mutations remains to be determined, preclinical models have shown that they also appear to impact disease phenotype. Additionally, the risk of thrombosis is in-part influenced by JAK-STAT driver mutations and the risk of disease progression across MPN strongly associates with the presence of certain non-JAK-STAT pathway mutations. However, it is important to note that the clinical context also matters, as the presence of JAK-STAT mutations alone does not equate to an MPN diagnosis. Other criteria that assist in deducing an MPN diagnosis include advanced age, which is a major contributor to the risk clonal hematopoiesis, and the trajectory of mutant stem cells over time.

  1. Rampal R. MPNs as a disease spectrum: are genomics the key to one’s destiny? Texas Virtual MPN Workshop 2021: Second Annual Workshop and Meeting; Aug 19–20, 2021; Virtual.
  2. Egeren VD, Escabi J, Nguyen M, et al. Reconstructing the lineage histories and differentiation trajectories of individual cancer cells in myeloproliferative neoplasms. Cell Stem Cell. 2021;28(3):514-523. DOI: 1016/j.stem.2021.02.001

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