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Genomic Classification of Myeloproliferative Neoplasms and Integration in Predictive Models

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In a study reported in The New England Journal of Medicine, Grinfeld et al identified distinct genomic subgroups among patients with myeloproliferative neoplasms that, when combined with clinical variables, offered the potential for individualized predictions of clinical outcomes.

Study Details

In the study, coding exons from 69 myeloid cancer genes were sequenced in 2,035 patients with myeloproliferative neoplasms, including 1,321 with essential thrombocythemia, 356 with polycythemia vera, 309 with myelofibrosis, and 49 with other diagnoses. Genomic classifications were developed and integrated with clinical variables to provide prognostic models.

Key Findings

  • 33 genes had driver mutations in at least 5 patients; mutations in JAK2, CALR, or MPL were the sole abnormality in 45% of patients.
  • Numbers of driver mutations increased with age and advanced disease.
  • Driver mutations, germline polymorphisms, and demographic variables predicted whether patients received a diagnosis of essential thrombocythemia vs polycythemia vera or a diagnosis of chronic-phase disease vs myelofibrosis.
  • Eight genomic subgroups were defined that were associated with distinct clinical phenotypes, including blood counts, risk of leukemic transformation, and event-free survival.
  • Integration of 63 clinical and genomic variables allowed construction of prognostic models that permitted individualized predictions of clinical outcomes in patients with chronic-phase myeloproliferative neoplasms and myelofibrosis.
  • Use of the models showed good correlation between predicted and observed outcomes; internal cross-validation in a training cohort showed concordances of 76% to 86% for overall survival, event-free survival, and transformation to acute leukemia. Concordances were similar in an external cohort.
  • The models were found to improve predictive accuracy within individual categories of established prognostic schemas.

The investigators concluded, “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.”

They stated, “We have implemented a free, user-friendly online calculator of individualized patient outcomes 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.”

The study was funded by the Wellcome Trust and others.

The content in this post has not been reviewed by the American Society of Clinical Oncology, Inc. (ASCO®) and does not necessarily reflect the ideas and opinions of ASCO®.


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