Targeted Sequencing Reveals a Relationship Between Mutational Burden and Clinical Phenotype in MPNs
Authors: Getta Bartlomiej , Rapaport Franck , Devlin Sean , Zhao Chen , Knapp Kristina Marie , Patel Minal , Mohanty Abhinita , Van Den Brink Marcel , Levine Ross L. , Arcila Maria , Rampal Raajit K. 
Published: October 2019

The Philadelphia-chromosome negative myeloproliferative neoplasms (MPNs) Essential Thrombocytosis (ET), Polycythemia Vera (PV), and Myelofibrosis (MF) are characterized by mutations, which drive JAK-STAT pathway activation. Several studies have demonstrated the presence of recurrent somatic mutations outside of the JAK-STAT pathway, which accumulate over time, and may impact disease phenotype and outcome. We sought to determine the influence of somatic mutations on clinical phenotype and prognosis.

We sequenced a total of 30 genes recurrently mutated in myeloid malignancies in a cohort of 162 MPN patients (pts) using a next generation sequencing platform. The cohort included 49 pts with ET, 26 PV, 38 Primary Myelofibrosis (MF), 11 Post ET MF, 14 Post PV MF, 12 with leukemic transformations of MPN (LT), 7 with MPN-unclassified (MPN-U) and 5 others. Median age was 59 years and 79 were men. A Total of 288 gene mutations were identified with the most commonly mutated genes being JAK2 (n=121, 74%), TET2 (n=31, 19%), DNMT3A (n=18, 11%), ASXL1 (n=16, 10%), IDH2 (n=10, 6%), RAS (n=12, 7%), TYK2 (n=8, 5%) and TP53 (n=7, 4%). We did not find any mutations in NPM1, CBL, SRSF2 and no FLT3 -ITD. CALR was not assessed in 20 pts and these were excluded from mutation number analysis.

Importantly, we identified a relationship between the absolute number of mutations found per pt, disease phenotype, and age (table 1). Pts with/without prior chemotherapy or radiotherapy exposure did not have a difference in mutation number (1.5 vs. 1.9). Cases of ET or PV with fibrotic transformation had more mutations in ASXL1, RAS, EZH2, PHF6 and MPL than pre fibrotic ET or PV suggesting these may be relevant in disease progression and development of fibrosis. Mutations in TET2, RAS and PHF6 were more frequent in cases with LT compared to those with chronic phase MPN.

Pts over 40 were more likely to have mutations in TET2 (p=0.026) and JAK2 (p=0.019) and ASXL1 mutations were more common in pts with abnormal cytogenetics than in those with normal cytogenetics (p=0.003). Thrombotic events, which are an important cause of morbidity in MPN patients, negatively correlated with mutations in ASXL1 (p=0.044). Prognosis as measured by DIPPS and DIPSS-Plus scores appeared to correlate with the average number of mutations found in MF patients (table 2).

We examined several cases for which serial samples were available, and noted the acquisition of new mutational events despite ongoing therapy. We noted that the most commonly acquired mutations occurred in epigenetic modifying (DNMT3A, TET, IDH, ASXL1) and in growth signaling pathway (RAS, CBL) genes. These occurred despite active therapy and often without an overt change in clinical phenotype. Further details of these serial samples will be presented.

We conclude that the number and spectrum of somatic mutations correlate with disease phenotype of MPN. Younger pts have fewer mutations, as do pts with normal cytogenetics. JAK2 and TET2 mutations were more common in older pts. We show that a subset of pts acquire mutations in epigenetic modifiers and in genes involved in growth signaling pathways during disease course, and that mutations in TET2, RAS and PHF6 were enriched at the time of leukemic transformation. Taken together, these results indicate that mutations outside the JAK-STAT pathway influence disease phenotype, and that the acquisition of mutations over time may predict for disease progression. Serial evaluation of mutational burden over time therefore warrants exploration in the clinical setting.

Table 1. Average number of mutations appeared to correlate with disease phenotype, age and abnormal cytogenetics. Average Number of Mutations N Mean (SD) P-value Age < 40 years 13 1.4 (0.9) 0.026 Age > 40 years 12 2 (1) No Thrombosis 113 2 (1) 0.712 Thrombosis 28 1.9 (1) Normal Cytogenetics 64 1.8 (0.9) 0.016 Abnormal Cytogenetics 40 2.3 (1.2) ET/PV/PMF 99 1.8 (0.8) 0.029 LT 10 3 (1.5) ET/PV 66 1.6 (0.7) 0.01 Post ET/PV MF 22 2.3 (1.1) ET 44 1.6 (0.7) < 0.001 PV 22 1.5 (0.9) PMF 33 2.2 (0.9) Post ET/PV MF 22 2.3 (1.1) LT 10 3 (1.5)

Table 2. Disease prognostic scores in MF appear to correlate with the average number of mutations found per patient. Risk category N Average number mutations DIPSS Low 8 1.5 Intermediate-1 19 2.4 Intermediate-2 9 1.8 High 1 5 DIPSS-Plus Low 6 1.5 Intermediate-1 12 2 Intermediate-2 14 2.2 High 1 5

Figure 1. Comutation map of genomic alterations. Each hash mark on x-axis represents an individual patient. Figure 1. Comutation map of genomic alterations. Each hash mark on x-axis represents an individual patient.

Disclosures

Levine: CTI BioPharma: Membership on an entity's Board of Directors or advisory committees; Loxo Oncology: Membership on an entity's Board of Directors or advisory committees; Foundation Medicine: Consultancy.