Refining Triage to Polycythaemia Vera or Primary Myelofibrosis Based on Targeted Molecular Profiling By Clinical Next Generation Sequencing
Authors: Jinming Song , Hussaini Mohammad Omar , Shao Haipeng , Padron Eric , Lancet Jeffrey E , Komrokji Rami S. , Zhang Ling , Moscinski Lynn C. 
Published: October 2019

Background: Primary myelofibrosis (PMF) and polycythemia vera (PV) are myeloproliferative neoplasms (MPN) that can both share a similar bone marrow morphology with panmyelosis and fibrosis, posing a diagnostic challenge, particularly when the differential is between cellular phase of PMF and PV, or fibrotic PMF and post-PV myelofibrosis. Despite advances in genomic analysis, limited information is known regarding their differences in genetic profile/signature. It has been well known that constitutive tyrosine kinase activation due to JAK2 V617F mutation is seen in both PV and PMF. MPL and CALR mutations do segregate with PMF but may not be found in all cases. Accordingly, we analyzed next generation sequencing (NGS) data to look for potential biomarkers that may further aid in distinguishing these two entities.

Design: The IRB approved study intended to recruit patients with diagnosis of PMF and PV who have myeloid gene mutation profiles available. Clinical information and molecular data from both a CLIA certified reference laboratory and our institution from May 2011 to June 2015 were retrieved. Cases with other myeloid neoplasms were excluded. The gene mutation profiles by Next Generation sequencing (NGS) and conventional karyotyping were acquired and compared. Clinicopathologic features including disease progression, degree of fibrosis in bone marrow, percentage of blasts, bone marrow cellularity, and circulating blood count (CBC) are correlated. Student t-test was used for numerical variables and Chi square (x2) test was used for categorical variables.

Results: Of the 62 patients qualified in the study, 36 patients were diagnosed with PMF (Age 68.5 ± 12.2, M:F ratio of 1:1) and 26 patients with PV (Age 66.5 ± 11.9, M:F ratio of 1.6). The majority of patients (34/36 PMF and 26/26 PV) showed persistent disease with only two PMF patients progressing to acute myeloid leukemia (AML). In accordance with prior reports, JAK2 V617F mutation was more prevalent in PV (23/26, 88%) than in PMF (17/36, 47%)(p<0.05), while MPL mutation was found in PMF (5/36, 14%) but not in PV (0/26) (p<0.001). Overall, PMF patients tended to have more non JAK2 mutations (mean = 1.6 ± 1) than PV patients (mean= 0.54 ± 0.65) (p = 0.005), even though the PV patients tended to have a longer history of disease. Interestingly, ASXL1 mutations (mainly frame-shift, reportedly pathologic) appear to be more prevalent in PMF (28%) than in PV (8%) patients (p = 0.058). SRSF2 mutations were found in 14% of PMF patients but absent in all 26 PV patients (p=0.068). Mutations in a subset of other analyzed genes (TET2, EZH2, IDH2, and CUX1) were also more frequent in PMF than in PV patients (25% vs 15%, 8% vs 0%, 8% vs 0%, and 6% vs 0%, respectively), but not statistically significant due to limited number of cases. The highest number of mutations (n=4) was in a case of PMF that progressed to AML, suggesting a 'dosage' effect of driver mutations on outcomes similar to that described in MDS. The other patient that progressed from PMF to AML harbored JAK2, ASXL1, SRSF2 mutations along with del(20q). ASXL1 mutation was associated with del(20q) in 4/62 cases, all of which were PMF patients including the case that has progressed to AML. JAK2 mutation was associated with del(20q) in 7 out of the 62 cases, 6 (86%) of which were PMF patients. No gene mutations were uniquely associated with degree of fibrosis, blast count, cellularity, white blood cell counts, hemoglobin, or platelet counts.

Conclusion: Our results indicate that PMF patients tend to have more non JAK2 mutations (e.g., ASXL1, SRSF2) than PV. Furthermore, the mutations, including JAK2 mutations, are more likely to be associated with del(20q) in PMF patients. Our findings provide insight into the genetic landscape of PMF and PV and offer potential biomarkers that may be helpful to distinguish between these entities, thus benefiting patient stratification for clinical practice.

Disclosures

Lancet: Seattle Genetics: Consultancy; Pfizer: Research Funding; Boehringer-Ingelheim: Consultancy; Kalo-Bios: Consultancy; Amgen: Consultancy; Celgene: Consultancy, Research Funding. Komrokji:Celgene: Consultancy, Research Funding; Incite: Consultancy; Novartis: Speakers Bureau; GSK: Research Funding.