Spectrum of Genomic Changes in Myeloproliferative Neoplasms

Figure 1. Figure 1. Genomic Landscape of Myeloproliferative Neoplasms. Panel A shows the frequency of recurrently mutated genes and chromosomal abnormalities in the cohort of 2035 patients. Mutations were stratified according to type (missense, nonsense, affecting a splice site, or other [e.g., stop, gain, or loss]). Insertions and deletions (del) were categorized according to whether they resulted in a shift in the codon reading frame (by either 1 or 2 base pairs [bp]) or were in frame. Chromosomes are indicated by Chr plus a numeral (e.g., Chr9 denotes chromosome 9). Chromosomal gains include whole-chromosome gains (trisomy) and subchromosomal amplifications. Chromosomal losses include whole-chromosome deletions (monosomy) and subchromosomal deletions. Loss of heterozygosity (LOH) was predominantly copy-number neutral, but in some cases, chromosome losses could not be ruled out. Panel B shows the site within the gene and protein consequence of PPM1D mutations. Colored shapes represent the characteristics of the patient who had the specific mutation (shapes indicate the subtype of myeloproliferative neoplasm, and colors the phenotypic driver). A triple-negative finding indicates nonmutated JAK2, CALR, and MPL. The term aa denotes amino acid, ET essential thrombocythemia, fs frameshift, MF myelofibrosis, PP2C protein phosphatase 2C domain, and PV polycythemia vera. Panel C shows clonal structures of two patients with PPM1D mutations determined by genotyping of hematopoietic colonies derived from peripheral-blood mononuclear cells. Each circle represents a group of hematopoietic colonies that share the same genotype: wild type (white), other driver mutations (black), and PPM1D mutated (yellow). Wild-type colonies are represented at the top of each diagram, with subsequent mutant subclones shown below. Somatic mutations acquired in each subclone are indicated beside respective nodes and represent those that were acquired in addition to mutations present in earlier subclones. The term hom denotes homozygous. Panel D shows the site within the gene and protein consequence of noncanonical mutations of JAK2 and MPL. The V617F and exon 12 mutations in JAK2 and W515 mutations in MPL are not shown. Mutations highlighted in red are likely to be relevant to disease pathogenesis, with previous studies having shown somatic acquisition, familial inheritance, or functional consequences for the specific variants (see box of abbreviations). FERM denotes the 4.1–ezrin–radixin–moesin domain, SH2 Src homology 2, Sig signal, and TM transmembrane.

Targeted sequencing for the full coding sequence of 69 genes and genomewide copy-number information was undertaken in 1887 patients, and 148 patients underwent whole-exome sequencing, as reported previously.8 The cohort of 2035 patients included 1321 patients with essential thrombocythemia, 356 with polycythemia vera, 309 with myelofibrosis, and 49 with other diagnoses of myeloproliferative neoplasms (Table S3 in the Supplementary Appendix). A total of 33 genes had driver mutations in at least 5 patients (Figure 1A, and Tables S4 and S5 in the Supplementary Appendix). Mutations in JAK2, MPL, and CALR accounted for 1831 driver mutations and were the sole abnormality in 45% of the patients. A total of 1075 driver mutations were identified across other genes. Loss of heterozygosity was frequent for JAK2 V617F, especially in patients with polycythemia vera, but was infrequent for CALR and MPL (Fig. S1 in the Supplementary Appendix).

We identified 45 truncating mutations in the terminal exon of PPM1D in 38 patients within the cohort (1.9%) (Figure 1B); thus, PPM1D was the eighth most commonly mutated gene in myeloproliferative neoplasms. These mutations have also been detected in solid tumors, blood samples obtained from healthy persons, and patients with breast or ovarian tumors, often after chemotherapy.21,22 In our cohort, 10 patients had PPM1D mutations that were detectable only in a later sample obtained during treatment with hydroxyurea. However, PPM1D mutations were also detected at or within 1 month after diagnosis in 20 patients. Analysis of single-cell–derived hematopoietic colonies identified mutated PPM1D in a patient with triple-negative essential thrombocythemia (i.e., nonmutated JAK2, CALR, or MPL) but also identified mutated PPM1D that was subclonal to JAK2 V617F in a patient with polycythemia vera (Figure 1C). These data confirm that PPM1D mutations can occur within the myeloproliferative neoplasm clone and be present at diagnosis; thus, their presence does not always indicate age-related clonal hematopoiesis or therapy-related disease evolution.

Mutations in MLL3 were detected in 20 patients (1.0%) and were predominantly nonsense or frameshift, as has been reported in patients with acute myeloid leukemia (Figure 1A, and Table S4 in the Supplementary Appendix).23 Among these 20 patients, 7 patients had triple-negative myeloproliferative neoplasms, which suggests that MLL3 could be an important tumor-suppressor gene in these patients.

Whether mutations in JAK2 and MPL outside the known hot spots could be relevant to patients with myeloproliferative neoplasms has been unclear.24,25 We identified noncanonical variants in JAK2 and MPL in 16 patients with triple-negative essential thrombocythemia and in 1 patient with triple-negative myelofibrosis (Figure 1D). Of these, three groups of variants were likely to be relevant to disease pathogenesis: JAK2 R683G and JAK2 E627A in 2 patients with essential thrombocythemia (reported in acute lymphoblastic leukemia in which they activate JAK226-28); JAK2 R867 in 2 patients with essential thrombocythemia (associated with familial thrombocythemia29); and MPL S505N and MPL S204P in 4 and 5 patients, respectively, with essential thrombocythemia.24 MPL S204P co-occurred with loss of heterozygosity (LOH) at chromosome 1p, which suggests a clonal advantage to acquired homozygosity for this variant.

Factors Influencing Classification into Disease Subtypes

Figure 2. Figure 2. Factors Affecting Disease Classification at Presentation and Timing of Somatic Mutations. Histograms show the frequency of driver mutations or chromosomal changes (gains, losses, or LOH) that were identified in different molecular subtypes of myeloproliferative neoplasm (MPN) (excluding 24 patients with >1 detectable phenotypic driver mutation) (Panel A) and according to the age of the patient at diagnosis (Panel B). Forest plots showing the associations between genetic or demographic features and presentation with essential thrombocythemia (ET) as compared with polycythemia vera (PV) in patients with JAK2 V617F mutations (Panel C) and the presentation in chronic-phase (CP) disease as compared with MF across patients with JAK2, CALR, or MPL mutations (Panel D). Significant associations from univariate analyses after correction for multiple hypothesis testing are shown. P values were derived from logistic-regression modeling, identifying independent associations. Causes of reductions in the hemoglobin (Hb) level, mean corpuscular hemoglobin (MCH) level, mean corpuscular volume (MCV), and platelet (Plt) count are indicated. Of 671 patients who had more than one somatic mutation, the order of mutation acquisition of at least one pair of mutations was determined in 271 patients (40%) (Panel E). These ordered pairings were used to determine the relative probabilities of a gene occurring first or second for a given pairing with the use of Bradley–Terry modeling, which provided an estimate of the overall timing of mutation acquisition. The horizontal axis shows the log odds of a gene occurring second in a gene pair. For example, as compared with JAK2, PPM1D mutations have a log odds of 1.45 and therefore are e1.45, or 4.3, times more likely to occur second in the pair. Any pair of genes can be assessed in this manner by calculating the exponential of the difference in log odds for gene A and gene B. The error bars indicate 95% confidence intervals.

Currently, patients with myeloproliferative neoplasms are classified as having essential thrombocythemia, polycythemia vera, or myelofibrosis on the basis of clinical and laboratory criteria,2-5 but the biologic factors underlying these distinctions are incompletely understood. The number of driver mutations per patient was higher in those with myelofibrosis than in those with polycythemia vera or essential thrombocythemia (Figure 2A), as previously reported,8 and increased according to the age of the patient (Figure 2B).

The distinction between JAK2 V617F–mutated essential thrombocythemia and polycythemia vera rests on whether the red-cell mass or hematocrit is elevated. We found that acquired driver mutations correlated with hematologic variables (Fig. S2 in the Supplementary Appendix) and were the strongest determinants of a patient with JAK2 V617F–mutated chronic-phase disease receiving a diagnosis of essential thrombocythemia as compared with polycythemia vera, although germline genetic background and demographic factors also contributed (Figure 2C, and Fig. S2 in the Supplementary Appendix). LOH at chromosome 9p (9pLOH), causing JAK2 V617F homozygosity, or a high JAK2 V617F allele burden correlated with polycythemia vera, as did mutated NFE2, a transcription factor critical to erythroid differentiation.

Germline polymorphisms that have been associated with red-cell variables in the general population were distributed unevenly, with alleles associated with lower hemoglobin level and higher platelet counts being enriched in patients with essential thrombocythemia (Figure 2C). Furthermore, the JAK2 46/1 haplotype, which is known to increase the predisposition to myeloproliferative neoplasms,18 correlated with polycythemia vera (odds ratio, 2.3; 95% confidence interval [CI], 1.7 to 3.3; P=0.004), possibly through increasing odds of JAK2 V617F homozygosity by 9pLOH (odds ratio, 2.7; 95% CI, 2.0 to 3.9; P<0.001). Older age and male sex also increased the odds of polycythemia vera. These data show that the location of any chronic-phase disease on the hemoglobin and red-cell mass continuum is influenced by many factors and that any arbitrary threshold to label patients’ disease as being one subtype or the other will not distinguish among patients with different underlying biologic factors.

Mutations in spliceosome components, epigenetic regulators, and the RAS pathway were strongly associated with accelerated phase (myelofibrosis), as compared with chronic-phase disease (essential thrombocythemia or polycythemia vera), as were male sex, older age, and germline loci associated with platelet count and red-cell variables (Figure 2D).

The order in which mutations are acquired in myeloproliferative neoplasms has previously been shown to influence disease phenotype.1314 CALR and MPL mutations occurred more commonly early in disease, whereas mutations in NRAS, TP53, PPM1D, and NFE2 were acquired significantly later in disease (Figure 2E, and Fig. S3 in the Supplementary Appendix). Some of the earlier-occurring mutations in genes such as SF3B1 and DNMT3A are also associated with age-related clonal hematopoiesis,30,31 which suggests that some myeloproliferative neoplasms could arise from an antecedent asymptomatic clone. In patients with multiple mutations, JAK2 V617F was more commonly a secondary event in patients with essential thrombocythemia and an earlier event in those with polycythemia vera or myelofibrosis (Figs. S4 and S5 in the Supplementary Appendix), a finding that confirms and generalizes observations that had previously been shown for JAK2 relative to TET2 or DNMT3A.13,14

Genomic Subgroups in Myeloproliferative Neoplasms

Hematologic cancers may be subclassified according to driver mutations that distinguish subgroups of patients,32,3334 with the use of patterns of mutually exclusive or co-mutated genes. In our cohort, driver mutations showed complex patterns of assortment (Fig. S6 in the Supplementary Appendix). We used Bayesian modeling to identify genomic subgroups of myeloproliferative neoplasms with maximum within-group similarity and maximum between-group discrimination.

Figure 3. Figure 3. Genomic Subgroups in MPN and Phenotypic Characteristics. According to a Bayesian clustering algorithm (Dirichlet process), patients could be classified into six distinct subgroups on the basis of the presence or absence of mutations and chromosomal abnormalities. The remaining patients either had no detectable genomic changes or had clonal markers that were not defining for one of the six groups. The flowchart shows the logic that allows patients to be classified into the total of eight groups. Proportions of patients with essential thrombocythemia (ET), polycythemia vera (PV), myelofibrosis (MF, either primary or after chronic-phase disease), or other MPN diagnoses are shown, as are rates of overall survival and myelofibrotic or leukemic transformation among patients in the individual subgroups. The 18 genetic aberrations involved EZH2, IDH1, IDH2, ASXL1, PHF6, CUX1, ZRSR2, SRSF2, U2AF1, KRAS, NRAS, GNAS, CBL, Chr7/7qLOH, Chr4qLOH, RUNX1, STAG2, and BCOR. Patients who had more than one mutation across JAK2, CALR, and MPL and deletion at chromosome 20q could belong to more than one classification. In patients who had myeloproliferation with other driver mutations, other diagnoses should be considered, depending on the nature of the genetic aberration. Chromosome 9pLOH was judged to be present if detectable at a 10% clonal fraction. The number of asterisks indicates the P value (*P<0.05, **P<0.01, and ***P<0.001) for the comparison with patients with MPN with heterozygous JAK2 mutation.

We identified eight genomic subgroups in myeloproliferative neoplasms, defined according to simple rules (Figure 3, and Fig. S7 in the Supplementary Appendix). TP53 mutations, often co-occurring with aberrations at chromosome 17p, and deletions at chromosome 5q identified the first subgroup. TP53 mutations often occur later in disease (Figure 2E) but dominate the genomic and clinical features of these patients regardless of the initial driver of the myeloproliferative neoplasm. As in patients with other blood cancers with TP53 mutations,32,35 these patients have a dismal prognosis with a high risk of transformation to acute myeloid leukemia (hazard ratio vs. the JAK2-heterozygous subgroup, 15.5; 95% CI, 7.5 to 31.4; P<0.001) and early death (hazard ratio, 2.4; 95% CI, 1.6 to 3.6; P<0.001).

The second subgroup was defined by the presence of one or more mutations in 16 myeloid cancer genes, especially chromatin and spliceosome regulators, LOH at chromosome 4q, and aberrations in chromosomes 7 and 7q. This subgroup was enriched for patients with myelofibrosis (odds ratio, 6.5; 95% CI, 4.9 to 8.7; P<0.001) and myelodysplastic–myeloproliferative neoplasms (including all seven patients with chronic myelomonocytic leukemia or atypical chronic myeloid leukemia) but also included 8.4% of patients with essential thrombocythemia and 11.5% of those with polycythemia vera. Patients were at increased risk for transformation to myelofibrosis (hazard ratio vs. the JAK2-heterozygous subgroup, 5.4; 95% CI, 2.7 to 11.0; P<0.001) and shorter event-free survival, regardless of myeloproliferative neoplasm subtype or phenotypic driver mutation (hazard ratio for disease progression or death, 2.6; 95% CI, 2.1 to 3.2; P<0.001).

Patients who were not identified in the above two subgroups were classified according to their dominant myeloproliferative neoplasm phenotypic driver mutation. Patients with CALR mutations, which co-occurred with LOH at chromosome 19p and with deletion at chromosome 20q, or those with MPL mutations all presented with essential thrombocythemia or myelofibrosis. Patients with MPL-mutated myelofibrosis had an elevated rate of acute myeloid leukemia transformation (hazard ratio vs. the JAK2-heterozygous subgroup, 8.6; 95% CI, 1.4 to 49.1; P=0.02), but otherwise the two subgroups had a clinical course that was similar to that in the JAK2 subgroups. Patients with JAK2 V617F heterozygosity constituted most of the patients with JAK2-mutated essential thrombocythemia but also some of the patients with polycythemia vera or myelofibrosis; these patients had generally favorable outcomes. The subgroup of patients with JAK2 homozygosity was enriched for patients with NFE2 mutations and for patients with polycythemia vera. Myelofibrosis transformations occurred more frequently in this subgroup (hazard ratio vs. the JAK2-heterozygous subgroup, 3.0; 95% CI, 1.3 to 6.6; P=0.007).

A seventh subgroup (36 patients [1.8%]) had identifiable driver mutations but none of the class-defining drivers identified above. This included patients with mutations in genes such as TET2 and DNMT3A that are not disease-specific or with mutations in genes that have been associated with other myeloid cancers (such as KIT in systemic mastocytosis). The eighth subgroup (192 patients [9.4%]) had no detectable driver mutations and may have included patients with either reactive thrombocythemia or myeloproliferative neoplasms with unidentified drivers. Patients were typically young and female and had received a diagnosis of essential thrombocythemia. This subgroup had particularly benign outcomes; only 1 patient (0.5%) had myelofibrosis transformation and 2 (1%) had acute myeloid leukemia transformation during a median follow-up of 8.0 years (hazard ratio for disease progression or death vs. the JAK2-heterozygous subgroup, 0.56; 95% CI, 0.38 to 0.78; P=0.005).

We applied our proposed classification scheme to an external cohort of 270 patients with myeloproliferative neoplasms (137 patients with essential thrombocythemia, 14 with polycythemia vera, and 119 with myelofibrosis) that had sufficient genomic characterization so that our flowchart could be applied. The subgroup proportions were similar in the two cohorts (Fig. S7 in the Supplementary Appendix).

Factors Influencing Disease Progression

A key determinant of the treatment of patients with myeloproliferative neoplasms is the predicted prognosis. For example, patients who are expected to have a benign future clinical course would probably benefit from treatments that are aimed at minimizing thrombotic risk, and those who are expected to have progression to leukemia or myelofibrotic bone marrow failure could be candidates for intensive therapy or clinical trials of new agents. We developed multivariate statistical models, incorporating 63 clinical and genomic variables, that estimated a patient’s probability of transition between stages of disease — namely, chronic-phase disease (essential thrombocythemia or polycythemia vera), advanced-phase disease (myelofibrosis), acute myeloid leukemia, and death.

Figure 4. Figure 4. Modeling Outcome in Patients. Panel A shows the transition states during a patient’s disease and the factors contributing to the risk of each transition. Patients may have presented with either chronic-phase disease (polycythemia vera, essential thrombocythemia, or unclassifiable MPN) or myelofibrosis (MF), as represented by the two central, rounded rectangles. The patient may have subsequently remained alive in these disease states or, alternatively, could have transitioned to one of four states: death in chronic-phase disease, death in MF, MF transformation of chronic-phase disease, and acute myeloid leukemia (AML) transformation of either chronic-phase disease or MF. Individual models were created for each of these four disease-state transitions and combined into a single multistate model allowing for the prediction of probability of being in each disease state occurring at any time point in the future (up to 25 years after diagnosis), as calculated on an individual patient basis. Pie charts show the variables that contributed most to the predicted risk for each of the four transitions. These show the effect on disease transitions of both rare variables with a strong effect and common variables with a milder effect. Variables with a hazard ratio of more than 2.0 are shown in blue type. The numbers of patients with chronic-phase disease or MF are shown alongside the numbers of patients who transitioned to other states. Patients may have transitioned more than once during their clinical course (e.g., from chronic-phase disease to MF and then to AML). The risk of AML transformation was highest among patients with MF. WCC denotes white-cell count; the arrows by the clinical variables indicate whether the value increased (up arrow) or decreased (down arrow). Panel B shows the model predictions, as compared with the actual event-free survival (EFS), among patients. Comparisons of the actual EFS with the predicted EFS derived from multistate random-effects Cox proportional-hazards modeling for patients with chronic-phase disease and MF, for both the training cross-validation cohort and the external validation cohort, are shown. Each cohort was split into equally sized subgroups of patients, and each subgroup is represented by a data point plotted according to the observed and predicted EFS. Overall, the models show good correlation between predicted and actual outcomes for both the training and external validation cohorts at several time points (brown indicates the EFS at 5 years, blue at 10 years, and red at 20 years). The dashed line indicates points at which predicted outcomes perfectly match observed outcomes.

We determined the fraction of explained variation for each outcome that was attributable to different prognostic factors (Figure 4A). Death in the chronic phase was influenced predominantly by age, with genomic features having little predictive power — a finding that suggests that once cytoreduction has achieved adequate control of blood counts, causes of death are dominated by those that would also occur in the general population.36 These would, therefore, not be well predicted by the specific genomic features of the myeloproliferative neoplasm.

By contrast, genomic features played a substantial role in predicting progression from chronic-phase disease to myelofibrosis and to acute leukemia transformation (Figure 4A). CALR mutations were independently associated with an increased risk of myelofibrotic transformation, as previously reported.37 Mutations in epigenetic regulators, splicing factors, and RAS signaling were all associated with myelofibrotic and leukemic transformation — some of these associations have been identified previously.10-12 Whether mutations were clonal or subclonal had little effect on prognosis (see the Supplementary Appendix). Clinical features of the disease, such as anemia, splenomegaly, or thrombocytosis, still retained independent predictive power for transformation events, which suggests that these variables reflect important features of the disease state that are not captured in the genomic landscape. Outcomes in patients with myelofibrosis did not significantly differ on the basis of whether the myelofibrosis was primary or occurred after essential thrombocythemia or polycythemia vera.

Personally Tailored Prognosis

Current prognostic models for myeloproliferative neoplasms, which are focused on myelofibrosis, use simple scoring systems and group patients into broad prognostic categories. Many factors influence clinical outcomes, with a wide range of effect sizes, which means that current schemes discard information that is relevant to prognosis. We explored whether our multivariate, multistate prognostic models could generate accurate predictions for individual patients.

Figure 5. Figure 5. Personalized Predictions of Outcomes in Patients. Panels A and B show example tiles that represent personalized predicted outcomes in individual patients. Panel A shows the predicted outcomes of a 79-year-old woman who presented with essential thrombocythemia (ET) with a hemoglobin level of 104 g per liter, a white-cell count of 8400 per cubic millimeter, and a platelet count of 2,300,000 per cubic millimeter, and mutated CALR, SRSF2, and IDH2 along with LOH in chromosome 18q. For such a patient presenting with chronic-phase (CP) disease (PV or ET), the model incorporates all clinical, demographic, laboratory, and genomic variables to predict the overall probabilities over time of being alive in CP, dying in CP, being alive in myelofibrosis (MF) after CP, dying in MF after CP, transitioning to AML from CP, and transitioning to AML from MF after CP. The varying probabilities of each of these transitions can be judged from the vertical axis and their respective Kaplan–Meier curves over a 25-year period shown along the horizontal axis. The black curve shows the predicted Kaplan–Meier curve of overall survival. This patient transitioned to MF and died within 5 years; this outcome is shown along the bottom of the plot, where the length of the horizontal black line shows the duration of follow-up and the cause of death is indicated by the shading of the circle. For a patient who presented with MF, as shown in Panel B, the same model predicts the probabilities of being alive in MF, dying in MF, and transitioning to AML over a period of 25 years. Panel B shows the predicted and actual outcomes of a 57-year-old man with MF who had a hemoglobin level of 125 g per liter, a white-cell count of 27,000 per cubic millimeter, and a platelet count of 119,000 per cubic millimeter, and mutated TET2, ASXL1, CBL, and BCOR along with deletion in chromosomes 7q and 11q. This patient died in MF within 2 years. All patients with chronic-phase disease or MF who had either a disease event (death or disease progression) or had more than 10 years of follow-up (>5 years for patients with MF) were ranked according to their overall predicted EFS. The predicted and actual outcomes for 40 individual patients with CP (Panel C) and MF (Panel D) showing how patients in the cohort may be distinguished in terms of EFS and cause of death.

The usefulness of personally tailored predictions can be assessed in two ways: do the predictions usefully distinguish among patients according to prognosis, and are the predictions more informative than conventional schemas? Regarding the first question, not only is our model able to generate a wide range of specific risk predictions (regarding long-term survival, death in chronic-phase disease, and myelofibrotic and leukemic transformation) but they correlate well with observed outcomes (Figure 4B and Figure 5, and Fig. S8 and Tables S6 and S7 in the Supplementary Appendix), both in cross-validation of an internal cohort and in an external validation cohort of 515 patients with myeloproliferative neoplasms (137 patients with essential thrombocythemia, 188 with polycythemia vera, and 190 with myelofibrosis).

Internal cross-validation showed concordances of 76 to 86% for overall survival, event-free survival, and transformation to acute leukemia as well as good performance on absolute predictive accuracy (Figure 4B, and Tables S6 and S7 in the Supplementary Appendix). Concordances were similar in the external cohort, despite the fact that patients in the external cohort received diagnoses at another center, were evaluated by different pathologists who used different diagnostic criteria, and underwent sequencing at a different facility with the use of a different gene panel from the training cohort (Figure 4B). Thus, the model provides considerable discriminatory power that accurately generalizes to other real-world cohorts. Owing to the existence of different diagnostic criteria, the model does not rely heavily on the exact classification label of the patient’s disease. Indeed, removing the distinction between polycythemia vera and essential thrombocythemia, but simply retaining the distinction between myelofibrosis and chronic-phase disease, did not reduce the predictive accuracy of the model (Fig. S9 in the Supplementary Appendix).

Our model showed superior performance to current major prognostic schemas in clinical use, such as the International Prognostic Scoring System (IPSS),38 the Dynamic IPSS (DIPSS),39 the high molecular risk category for myelofibrosis,10 and the International Prognostic Score for Essential Thrombocythemia score40 (Fig. S9 and Tables S6 and S7 in the Supplementary Appendix). Furthermore, we identified substantial heterogeneity in disease outcomes within individual prognostic categories of current prognostic schemas (shown for DIPSS in Fig. S10 in the Supplementary Appendix); this was especially prominent for intermediate-risk patients and allowed for more informative predictions in a group with otherwise uncertain outcomes. This means that not so many patients need be screened before some emerge as having an increased risk of poor outcomes; the numbers needed to test across different scenarios are shown in Table S8 in the Supplementary Appendix. The inclusion of mutations and chromosomal changes beyond JAK2, CALR, and MPL improved the predictive power of prognostic models (Tables S6 and S7 in the Supplementary Appendix).

We have implemented a free, user-friendly online calculator of individualized patient outcomes (https://cancer.sanger.ac.uk/mpn-multistage/) that enables the exploration of data from patients in our cohort, and the generation of new patient predictions according to available clinical, laboratory and genomic features. Further validation of our model with the use of additional cohorts of patients with myeloproliferative neoplasms will be important, given the bias toward including patients with essential thrombocythemia in this study.