Session: 634. Myeloproliferative Syndromes: Clinical: Interferon Therapy and Mutational Analysis in the MPNs
Hematology Disease Topics & Pathways:
Diseases, MPN, Polycythemia vera, thrombocythemia, Clinically relevant, Myeloid Malignancies
Survival prediction in essential thrombocythemia (ET) and polycythemia vera (PV) is currently based on clinically-derived risk variables, including age, thrombosis history and leukocyte count (Blood. 2012;120:1197; Leukemia. 2013;27:1874). We have previously reported on the prognostic contribution of mutations, in both ET and PV (Blood Adv. 2016;1:21). The current study examines the possibility of integrating genetic and clinical information for predicting overall (OS), leukemia-free (LFS) and myelofibrosis-free (MFFS) survival in ET and PV.
Study patients were recruited from the Mayo Clinic, Rochester, MN, USA and the University of Florence, Florence, Italy, based on availability of next-generation sequencing (NGS)-derived mutation information. Diagnoses were according to the 2016 World Health Organization criteria (Blood. 2016;127:2391). NGS-detected coding region variants were filtered through the Exome Aggregation Consortium (ExAC) database and annotated by the Catalogue of Somatic Mutations in Cancer (COSMIC) database as mutants or variants of uncertain significance (VUS). Conventional statistics was employed for outcome analysis and assessment of model performance.
906 molecularly-annotated patients (416 from Mayo and 490 from Florence), including 502 ET and 404 PV cases, were included in the current study. The Mayo/Florence cohorts included 270/232 ET (median age 57/54 years, 60%/59% females) and 146/258 PV (median age 63/58 years, 52%/44% females) patients; median follow-up was 9.9/12.9 years for ET and 10.7/12.4 years for PV; during this time 39%/38% deaths and 4.4%/6.5% leukemic and 16%/33% fibrotic progressions were documented for ET, with the corresponding figures for PV being 50%/26%, 4.8%/3% and 13%/36%. Cytogenetic information was available in 84%/63% ET and 80%/49% PV cases in the Mayo/Florence cohorts.
In both Mayo and Florence cohorts, multivariable analysis of mutations identified primarily spliceosome mutations to adversely affect OS (SF3B1 and SRSF2 in ET and SRSF2 in PV) and MFFS (U2AF1 and SF3B1 in ET); in addition, TP53 mutations was associated with leukemic transformation in ET, in both patient cohorts. Other mutations with independently significant contribution, validated in one but not both cohorts, included EZH2 (OS and LFS in ET), IDH2 (OS and LFS in PV) and RUNX1 (LFS in PV and ET).
For the purposes of the current study, mutations considered “adverse” for subsequent analysis and development of prognostic models required validation in both Mayo and Florence cohorts. Age-adjusted, all-inclusive multivariable analysis of genetic and clinical variables identified the following as independent risk factors for OS, in both Mayo and Florence cohorts: ET ─ SRSF2/SF3B1 mutations, age >60 years and male sex; PV ─ SRSF2 mutations, age >67 years and leukocyte count ≥11 x 109/l. In addition, multivariable analysis flagged leukocytosis in ET (Florence cohort only) and abnormal karyotype in PV (Mayo cohort only) as additional factors.
Development of mutation-enhanced international prognostic systems for ET and PV
In order to optimize the number of informative cases, the two Mayo and Florence databases were subsequently combined and subjected to multivariable analysis that confirmed the independent survival effect, in ET, of SRSF2/SF3B1 mutations (HR 2.8, 95% CI 1.8-4.3), age >60 years (HR 6.7, 95% CI 4.8-9.4) and male sex (HR 1.8, 95% CI 1.4-2.4) and, in PV, of SRSF2 mutations (HR 7.0, 95% CI 2.3-17.4), age >60 years (HR 5.7, 95% CI 3.3-10.1), and leukocyte count ≥11 x 109/l (HR 2.4, 95% CI 1.5-3.9); the combined analysis also flagged independent prognostic contribution from abnormal karyotype in PV (HR 2.1, 95% CI 1.1-3.6). HR-weighted risk point allocation resulted in new, mutation-enhanced 4-tiered risk models for ET (MIPSS-ET based on mutations, age and sex; figure 1a) and PV (MIPSS-PV based on mutations, karyotype, leukocyte count and age; figure 1b). Both models displayed high predictive accuracy and were internally validated by bootstrapping. The combined analysis also confirmed the impact of TP53 mutations on LFS (figure 1c) and U2AF1/SF3B1 mutations on MFFS (figure 1d), in ET.
Disclosures: No relevant conflicts of interest to declare.
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