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Classification and Personalized Prognosis in Myeloproliferative Neoplasms
TAKE-HOME MESSAGE
- This study characterized the genomes of 2035 patients with myeloproliferative neoplasms to identify distinct genetic subgroups which could be helpful in the individualized diagnosis, risk stratification, and treatment. The investigators sequenced coding exons from 69 myeloid cancer genes and found that 33 genes in at least 5 patients had driver mutations. Mutations in JAK2, CALR, or MPL were a sole abnormality in 45% patients. There were eight genomic subgroups identified which showed distinct clinical phenotypes.
- Integration of genomic data with clinical variables enabled the individualized prediction of patient outcomes, which could help in the management of myeloproliferative neoplasms.
abstract
This abstract is available on the publisher's site.
Access this abstract nowBACKGROUND
Myeloproliferative neoplasms, such as polycythemia vera, essential thrombocythemia, and myelofibrosis, are chronic hematologic cancers with varied progression rates. The genomic characterization of patients with myeloproliferative neoplasms offers the potential for personalized diagnosis, risk stratification, and treatment.
METHODS
We sequenced coding exons from 69 myeloid cancer genes in patients with myeloproliferative neoplasms, comprehensively annotating driver mutations and copy-number changes. We developed a genomic classification for myeloproliferative neoplasms and multistage prognostic models for predicting outcomes in individual patients. Classification and prognostic models were validated in an external cohort.
RESULTS
A total of 2035 patients were included in the analysis. A total of 33 genes had driver mutations in at least 5 patients, with mutations in JAK2, CALR, or MPL being the sole abnormality in 45% of the patients. The numbers of driver mutations increased with age and advanced disease. Driver mutations, germline polymorphisms, and demographic variables independently predicted whether patients received a diagnosis of essential thrombocythemia as compared with polycythemia vera or a diagnosis of chronic-phase disease as compared with myelofibrosis. We defined eight genomic subgroups that showed distinct clinical phenotypes, including blood counts, risk of leukemic transformation, and event-free survival. Integrating 63 clinical and genomic variables, we created prognostic models capable of generating personally tailored predictions of clinical outcomes in patients with chronic-phase myeloproliferative neoplasms and myelofibrosis. The predicted and observed outcomes correlated well in internal cross-validation of a training cohort and in an independent external cohort. Even within individual categories of existing prognostic schemas, our models substantially improved predictive accuracy.
CONCLUSIONS
Comprehensive genomic characterization identified distinct genetic subgroups and provided a classification of myeloproliferative neoplasms on the basis of causal biologic mechanisms. Integration of genomic data with clinical variables enabled the personalized predictions of patients' outcomes and may support the treatment of patients with myeloproliferative neoplasms.
Additional Info
- 2018 Top Stories in Benign Hematology: The Era of Personalized Treatment of Myeloid Neoplasms: Bringing Genomics to the Bedside
- Sequential Mutational Evaluation of CALR -Mutated Myeloproliferative Neoplasms With Thrombocytosis Reveals an Association Between CALR Allele Burden Evolution and Disease Progression
- Mutation-Enhanced International Prognostic Systems for Essential Thrombocythemia and Polycythemia Vera
Disclosure statements are available on the authors' profiles:
Classification and Personalized Prognosis in Myeloproliferative Neoplasms
N. Engl. J. Med 2018 Oct 11;379(15)1416-1430, J Grinfeld, J Nangalia, EJ Baxter, DC Wedge, N Angelopoulos, R Cantrill, AL Godfrey, E Papaemmanuil, G Gundem, C MacLean, J Cook, L O'Neil, S O'Meara, JW Teague, AP Butler, CE Massie, N Williams, FL Nice, CL Andersen, HC Hasselbalch, P Guglielmelli, MF McMullin, AM Vannucchi, CN Harrison, M Gerstung, AR Green, PJ CampbellFrom MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.
Towards A Genomic Classification of Philadelphia Chromosome–Negative Myeloproliferative Neoplasms
In the October 11, 2018, issue of NEJM, investigators from a number of European countries reported on the results of a large multigene-sequencing effort in 2035 patients with myeloproliferative neoplasms (MPN),1 over half with essential thrombocythemia (ET), the most benign of these chronic conditions. The coding exons of 69 myeloid malignancy–associated genes were interrogated, and matched germline samples were not sequenced in most patients. A total of 148 patients underwent whole-exome sequencing. No mutation other than in the well-established phenotypic driver genes JAK2, CALR, and MPL was found in 45% of the patients. Overall, 33 genes carried driver mutations in at least 5 patients. A total of 1075 driver mutations were identified across all the genes sequenced; however, it does not appear that functional studies in animal models were conducted. Expectedly, mutations in JAK2 and CALR were the most frequent, followed by the “CHIP” mutations TET2, ASXL1, and DNMT3A, in that order. MPL was next, followed by CBL, which encodes a physiologic negative regulator of JAK2. Surprisingly, PPM1D, a gene best known for being mutated in some patients who go on to develop therapy-related myeloid neoplasms after chemotherapy for other malignancies,2,3 turned out to be the eighth most commonly mutated gene in this cohort. Mutations in other genes well-recognized in myeloid malignancies including MPN, such as the spliceosome genes, TP53, the epigenetic regulators EZH2, IDH1, and IDH2, other signaling genes—for example, those in the RAS pathway and KIT, and NFE2, which encodes an erythroid transcription factor—comprised the majority of the remainder. Some patients (with ET) had “non-canonical” mutations in JAK2 or MPL, some of which have previously been described in patients with T-cell lymphoblastic lymphoma,4 or in individuals with hereditary thrombocytosis.5
Several known themes emerged from (ie, were corroborated by) this analysis. ET and polycythemia vera (PV) may be considered to represent “chronic-phase” MPN, whereas primary myelofibrosis (PMF) is genomically considerably more complex and represents an advanced phase of the disease. Indeed, it has been suggested that PMF be considered a myelodysplastic/myeloproliferative neoplasm (MDS/MPN),6 and, in this analysis, a subgroup of patients with mutations in spliceosome complex genes and epigenetic regulators, loss of heterozygosity (LOH) at 4q and chromosome 7/7q aberrations were highly enriched for individuals with myelofibrosis and MDS/MPN. Approximately 20% of patients in this subgroup had PV or ET, but these patients were characterized by a higher risk of myelofibrotic progression and worse outcomes regardless of morphologic disease classification or phenotypic driver mutation. Indeed, other targeted multigene profiling efforts in PV and ET have uncovered similar findings.7
Not surprisingly, LOH at 9p (causing JAK2 V617F homozygosity), high JAK2 V617F allele burden, the 46/1 (GGCC) JAK2 haplotype, and mutated NFE2 all correlated with PV, as did early acquisition of JAK2 V617F, as has previously been published.8 Mutations in MPL and CALR were generally acquired early, whereas mutations in TP53, NFE2, PPM1D, and NRAS tended to be late genetic events. Outcomes in the TP53-mutated subgroup were particularly dismal—this has been a uniform theme across myeloid malignancies. Although anecdotal reports of CALR-mutated PV exist,9 all patients with CALR or MPL mutations in this analysis had ET or myelofibrosis, as would be expected. Homozygosity for JAK2 V617F was associated with a diagnosis of PV and mutated NFE2, and these patients tended to have a greater predilection for progression to MF, an established clinical correlate of higher JAK2 V617F allele burden.10 No driver mutations were identified in 9.4% of patients, who were typically young, female, and carried a diagnosis of ET; this subgroup had excellent outcomes, as has previously been described in patients with “triple-negative” ET,11 raising the question whether or not these patients, in fact, have a clonal disease. At variance with what is widely accepted12, however, patients with PMF and those with post-PV or post-ET myelofibrosis had similar outcomes in this study. Similarly, patients with MPL-mutated myelofibrosis experienced higher rates of leukemic transformation and ET patients with mutated CALR had a higher propensity to develop post-ET myelofibrosis, findings that are not consistent across studies.13-16
Somewhat akin to the Cancer Genome Atlas Network effort with acute myeloid leukemia,17 the authors proposed eight genomically defined subgroups of MPN patients, validated in an external cohort: TP53-mutated, those with mutations in 1 or more of 16 myeloid cancer genes, especially spliceosome and epigenetic regulators, CALR-mutated, MPL-mutated, JAK2 V617F homozygotes, JAK2 V617F heterozygotes, those with other driver mutations, and those with no identifiable mutations by targeted multigene sequencing. Genomic features were powerful predictors of myelofibrotic progression and leukemic transformation, although clinical disease features such as anemia, splenomegaly, and thrombocytosis still retained independent predictive power for transformation events. Finally, the authors developed and validated, both internally and against an external cohort, a “personally tailored prognosis” model (and implemented a free online tool), which they found had superior predictive and discriminatory power to the traditional International Prognostic Scoring System (IPSS)18 and Dynamic IPSS (DIPSS)19 for PMF, the “high–molecular risk” categorization in PMF,20 and the International Prognostic Score for ET (IPSET) model.21 However, the incremental benefit of this model in the age of integrated clinical-molecular risk stratification systems such as the MIPSS70 and variants of it,22,23 at least in PMF, is unclear.
In summary, the authors are to be applauded for such a large-scale sequencing effort, and the study overall confirms and extends our understanding of the underlying genetic complexity of MPN, but falls short on clinically applicable information that would help manage our patients with MPN today.
References