Classification and Personalized Prognosis in Myeloproliferative Neoplasms

Abstract

Background: 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. (Funded by the Wellcome Trust and others.).

Figures

Figure 1. Genomic landscape of myeloproliferative neoplasms.

(A) Frequency of recurrently mutated genes and chromosomal abnormalities in the cohort. Mutations are stratified according to type, namely missense, nonsense, affecting a splice site or other (eg stop-gain/loss etc). Insertions and deletions are categorised by whether they resulted in a shift in the codon reading frame, by either 1 or 2 base pairs, or were in-frame. Chromosomal gains include whole-chromosome gains (trisomy) and sub-chromosomal amplifications. Chromosomal losses include whole-chromosome deletions (monosomy) and sub-chromosomal deletions. *Loss of heterozygosity (LOH) was predominantly copy number neutral, but in some cases, chromosome losses could not be excluded. (B) Site within the gene and protein consequence of PPM1D mutations are illustrated. PP2C, Protein phosphatase 2C domain (C) Clonal structures of two PPM1D-mutated patients determined by genotyping of hematopoietic colonies (BFU-E) derived from peripheral blood mononuclear cells. Each circle represents a clone; non-PPM1D mutated (black); PPM1D-mutated (yellow). The earliest detectable clone is represented at the top of each diagram, with subsequent subclones shown below. Somatic mutations acquired in each sub-clone are indicated beside respective nodes, and represent those that are acquired in addition to mutations present in earlier subclones. ET, Essential thrombocythemia; PV, Polycythemia vera (D) Site within the gene and protein consequence of non-canonical mutations of JAK2 and MPL are illustrated. V617F and exon 12 mutations in JAK2, and W515 mutations in MPL are not shown. Colored shapes represent the characteristics of the patient carrying the specific mutation (shape, MPN subtype; color, phenotypic driver). Mutations highlighted in red are likely to be relevant to disease pathology and where previous studies have demonstrated somatic acquisition, familial inheritance or functional consequences for the specific variants.

Figure 2. Factors affecting disease classification at presentation and timing of somatic mutations.

Histogram showing the frequency of driver mutations and/or chromosomal changes (gains, losses, or LOH) identified in (A) the different molecular subgroups of MPN (excluding 24 patients with >1 detectable phenotypic driver mutation), and (B) according to patient age at diagnosis. ET, Essential thrombocythemia; PV, Polycythemia vera; MF, Myelofibrosis. (C-D) Forest plots showing the associations between genetic or demographic features and presentation with ET versus PV in JAK2V617F-mutated patients (C), and presentation in chronic phase versus MF across JAK2-, CALR-, or MPL-mutated patients (D). Significant associations from univariate analyses after correction for multiple hypothesis testing are shown. p-values are derived from logistic regression modelling, identifying independent associations. (E) Of 671 patients that harbored more than one somatic mutation, the order of mutation acquisition of at least one pair of mutations was determined in 271 patients (40%). These ordered pairings were used to determine the relative probabilities of occurring first or second for a given pairing using Bradley-Terry modelling, providing 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, compared to JAK2, PPM1D mutations have a log odds of 1.45, and therefore are e1.25=4.3 times more likely to occur secondary to JAK2. 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.

Figure 3. Genomic sub-groups in MPN and phenotypic characteristics.

Using a Bayesian clustering algorithm (Dirichlet process), patients could be classified into 6 distinct subgroups based on the presence or absence of mutations and chromosomal abnormalities. Remaining patients either had no detectable genomic changes or had clonal markers that were not defining for one of the 6 groups. The flowchart shows the logic allowing patients to be classified into the total of 8 groups. Proportions of patients with essential thrombocytosis (ET), polycythemia vera (PV), myelofibrosis (primary or secondary MF) or other MPN diagnoses are shown, as well as rates of overall survival and myelofibrotic or leukemic transformation for patients within the individual sub-groups. ^^Patients that have more than one mutation across JAK2, CALR or 20q-, and MPL can belong to more than one classification. $ at least a 10% clone, consider other diagnoses in such patients depending on the nature of the genetic aberration.

Figure 4. Modelling outcome in patients.

(A) Model predictions versus actual event free survival in patients. Comparisons of the actual event-free survival (EFS) versus the predicted EFS derived from multistate random-effects Cox proportional hazards modelling for patients in chronic phase (CP) and myelofibrosis (MF) patients, for both the training and external validation cohorts, are shown. Each cohort was split into equally sized subgroups of patients, and each of these subgroups is represented by a data point plotted according to the observed and predicted EFS, overall showing good correlation between predicted and actual outcomes for both training and external validation cohorts at several different timepoints (brown, 5 year EFS; blue 10 year EFS; red, 20 year EFS). (B) Transition states during a patient’s disease and the factors contributing to the risk of each transition. Patients may present in either chronic phase (CP, comprising patients with PV, ET or MPNu) or myelofibrosis (MF), as represented by the two central red rounded rectangles. The patient may subsequently remain alive in these disease states, alternatively, the patient can transition to one of four states: (i) Death in CP, (ii) Death in MF, (iii) MF transformation of CP, and (iv) Acute myeloid leukemia (AML) transformation of either CP or MF. Individual models were created for each of these 4 disease-state transitions and combined into a single multistate model allowing for the prediction of probability of being each disease state occurring at any time-point in the future (up to 25yrs post diagnosis) being calculated on an individual patient basis. Pie charts show those variables that contribute the most to the predicted risk for each of the 4 transitions. These demonstrate the impact on disease transitions of both rare variables with a strong effect and common variables with a milder effect. Variables with a hazard ratio of >2.0 are written in blue letters, and those variables with hazard ratio <0.5 are written in orange letters. The number of patients presenting in CP and MF are shown in brackets alongside the numbers that transitioned to other states. Of note, patients may transition more than once during their clinical course, for example, from CP to MF, and then to AML. *Risk of AML transformation was highest for patients with MF.

Figure 5. Personalised predictions of patient outcome.

Each of the tiles represents the personalised predicted outcome of an individual patient. Two tiles (A) and (B) have been enlarged for illustrative purposes. The top left panel (A) depicts the predicted outcomes of a 79 year old female patient who presented with ET with hemoglobin (Hb) 104g/l, white cell count (WCC) 8.4x109/l and platelet count (Plt) of 2300x109/l, mutations in CALR, SRSF2, IDH2 and 18q loss of heterozygosity (LOH). For such a patient presenting in chronic phase (CP, comprising PV or ET), the model incorporates all clinical, demographic, laboratory and genomic parameters to predict the overall probabilities over time of (i) being alive in CP (grey), (ii) suffering death in CP (light blue), (iii) being alive in post-CP MF (brown), (iv) suffering death in post-CP MF (turquoise), (v) transforming to AML from CP (pink), or (vi) transforming to AML from post-CP MF (magenta). The varying probabilities of each of these transitions can be judged from the vertical axis and their respective Kaplan-Meiers over a 25 year time period shown along the horizontal axis. The labelled black curve shows the predicted Kaplan-Meier curve of overall survival. The patient in (A) transformed to myelofibrosis (MF) and died within 5yrs and this actual outcome is shown along the bottom of the plot where the length of the horizontal black line depicts the duration of follow-up, and the cause of death (if occurred during follow up) by the shading of the circle at the end of the black line. For a patient presenting in MF, as shown in panel (B), the same model predicts the probabilities of (i) being alive in MF (brown), (ii) suffering death in MF (turquoise) or transforming to AML (magenta) over 25yrs. This tile shows the predicted and actual outcome of a 57 year old male patient diagnosed with MF with Hb 125g/l, WCC 27x109/l and Plt 119x109/l, mutated TET2, ASXL1, CBL and BCOR along with chr7q- and 11q-. This patient died in MF within 2yrs as shown along the bottom of the plot. All patients diagnosed in chronic phase (CP, namely ET or PV) or MF, with either a disease event (death or disease progression) or >10 year follow-up (>5yrs for MF patients), were ranked by their overall predicted event free survival (EFS). The predicted and actual outcomes for 36 individual patients in CP and MF evenly spaced across this ranking are shown in panels (C) and (D) demonstrating discrimination between patients in terms of event free survival and cause of death across the cohort.