Targeted Next-Generation Sequencing in Polycythemia Vera and Essential Thrombocythemia

Tefferi, Ayalew; Lasho, Terra L.; Finke, Christy; Elala, Yoseph; Barraco, Daniela; Hanson, Curtis A.; Ketterling, Rhett P.; Pardanani, Animesh; Gangat, Naseema

doi:10.1182/blood.V126.23.354.354

Abstract

Background: Polycythemia Vera (PV) is associated with JAK2 mutations. In essential thrombocythemia (ET), ̴ 85% of patients harbor one of 3 "driver" mutations, with frequencies of ̴ 58%, 23% and 4%, for JAK2, CALR and MPL, respectively; ̴ 15% are wild type for all three mutations and are referred to as "triple negative". We applied next-generation sequencing (NGS) with a 27-gene panel of myeloid malignancy-relevant genes, in order to describe the prevalence of "non-driver" mutations and their prognostic relevance in PV and ET.

Methods: Targeted capture assays were carried out on bone marrow or whole blood DNA for the following genes: TET2, DNMT3A, IDH1, IDH2, ASXL1, EZH2, SUZ12, SRSF2, SF3B1, ZRSR2, U2AF1, PTPN11, Tp53, SH2B3, RUNX1, CBL, NRAS, JAK2, CSF3R, FLT3, KIT, CALR, MPL, NPM1, CEBPA, IKZF, and SETBP1.Paired-end indexed libraries were prepared using the NEBNext Ultra Library prep protocol (NEB, Ipswich, MA /Agilent Technologies Inc, Santa Clara, CA). Capture libraries were assembled according to Nimblegen standard library protocol (Roche Nimblegen, Inc, Basel, Switzerland). Base-calling was performed using Illumina's RTA version 1.17.21.3. Genesifter® software (PerkinElmer, Danvers, Massachusetts) was utilized to analyze targeted sequence data. Nucleotide variants were called using the Genome Analysis Toolkit (GATK -Broad Institute, Cambridge, MA). Specific variants were deemed as mutations if they are associated with a hematologic malignancy (COSMIC database), or if they have not been associated with a dbSNP.

Results: 314 patients with PV (n =133; median age 64 years, 53% females) or ET (n =181; median age 58 years, 59% females) were evaluated. Median follow-up was 9.8 years for PV and 9.2 years for ET. During this time 59 (44%) deaths, 15 (11%) fibrotic and 5 (4%) leukemic transformations were documented for PV and the corresponding percentages for ET were approximately 33%, 8% and 2%. Driver mutation distribution was 98% JAK2 for PV and 52% JAK2, 24% CALR, 20% triple-negative and 4% MPL for ET.

Polycythemia Vera

Mutations other than JAK2, CALR or MPL were seen in 58 (44%) patients; 29% harbored one, 14% two and one 3 mutations. None of 3 JAK2 -unmutated cases expressed non-driver mutations. Mutational frequencies were 18% TET2, 11% ASXL1, 5% SH2B3, 3% SF3B1 and ≤2% SETBP1, IDH2, DNMT3A, CEBPA, CSF3R and SUZ12, SRSF2, ZRSR2, TP53, CBL, NRAS, RUNX1, KIT, PTPN11 and FLT3. "Number of mutations" significantly affected overall (OS; Figure 1) and myelofibrosis-free (MFS) survival; respective HR (95% CI) were 2.6 (1.3-5.2) and 1.7 (0.97-3.1) and 13.7 (2.9-63.4) and 5.1 (1.5-17.6) for ≥2 mutations and one mutation, respectively.

OS was also adversely affected by SRSF2 (p=0.006) and RUNX1 (p=0.04) and borderline affected by TET2 (p=0.06), IDH2 (p=0.08), ASXL1 (p=0.19) and SF3B1 (p=0.19) mutations. In multivariable analysis, SRSF2 and RUNX1 retained significance whereas the others remained borderline significant. In both univariate and multivariable analyses, leukemia-free survival (LFS) was adversely affected by IDH2 and RUNX1 mutations. In univariate analysis, ASXL1, IDH2, RUNX1 and KIT mutations predicted fibrotic progression, whereas SETBP1 was of borderline significance (p=0.07); all, including SETBP1, were significant during multivariable analysis.

Essential thrombocythemia

Mutations other than JAK2, CALR or MPL were seen in 83 (46%) patients; 35% harbored one, 7% two and 4% three mutations; prevalence in JAK2, CALR, MPL mutated and triple-negative cases was 52%, 43%, 43% and 35%, respectively (p=0.52). Mutational frequencies were: 13% TET2, 11% ASXL1, 6% DNMT3A, 5% SF3B1, 4% CEBPA, 2% TP53, SH2B3, EZH2 and CSF3R and <2% for SETBP1, IDH2, SRSF2, ZRSR2, CBL, NRAS, RUNX1, U2AF1, KIT, PTPN11 and FLT3. "Number of mutations" significantly affected OS (Figure 2) but not MFS or LFS; HR (95% CI) for OS were 6.6 (2.5-17.8) for 3 mutations and 2.2 (1.3-3.9) for one or two mutations.

In univariate analysis, survival was adversely affected by CBL, EZH2, SF3B1, SRSF2 and IDH2 mutations. In multivariable analysis, EZH2 and SF3B1 remained significant. Univariate analysis identified SETBP1 and SF3B1 mutations as risk factors for fibrotic progression and EZH2, TP53 and CSF3R mutations for leukemic transformation.

Conclusions: "Non-driver" mutations occur in more than 40% of patients with PV or ET; the number of such mutations and presence of certain specific ones likely predict OS, MFS or LFS.

Figure 1.