Next Generation Myelofibrosis Risk Analysis in the Electronic Health Record

Background: Myelofibrosis (MF) is a devastating myeloproliferative neoplasm that is hallmarked by marrow fibrosis, symptomatic extramedullary hematopoiesis, and risk of leukemic transformation, most commonly driven by janus kinase 2 (JAK2) pathway mutations. MF risk classification systems guide prognosis, decisions regarding allogeneic stem cell transplantation, and disease modifying agents. Key systems include the Dynamic International Prognostic Scoring System (DIPSS) 2009, DIPSS plus 2010, Genetics-Based Prognostic Scoring System (GPSS) 2014, and Mutation-Enhanced International Prognostic Scoring System (MIPSS) 2014. System contributions include dynamic scoring (DIPSS), cytogenetics (DIPSS Plus), and high risk molecular mutations (GPSS and MIPSS). To power the next generation of MF risk prognostication, and ascertain new prognostic factors, large scale electronic health record (EHR) and genomic data will need integration. As a proof of concept, we leveraged our de-identified research EHR (2.9 million records) and linked genomic biobank (288,000 patients) to develop an all-inclusive phenotype-genotype-prognostic system for MF and recapitulate DIPSS, DIPSS Plus, GPSS and MIPSS. Methods: Our previously described methods (Bejan et al. AACR 2018) utilized natural language processing to algorithmically identify 306 MF patients. A subset (N=125) had available DNA for genotyping. We automatically extracted: age greater than 65, leukocyte count (WBC) greater than 25x109/L, hemoglobin (Hgb) less than 10g/dL, platelets (PLT) less than 100 x 109/L, circulating myeloid blasts ≥ 1%, and 10% weight loss compared to baseline as a proxy for constitutional symptoms. Transfusion data was not included. Karyotype data was manually reviewed. Next generation sequencing (NGS) was performed on biobanked peripheral blood DNA with the Trusight Myeloid Panel (Illumina®). Genotyped samples were restricted to dates after MF diagnosis. Multivariate Cox proportional hazard analysis was performed on all clinical and genomic variables. DIPSS plus was calculated without adjustment but lacked transfusion data. DIPSS, GPSS and MIPSS scores were calculated by published methods. Results: Multivariate Cox proportional hazard regression identified Hgb (HR=6.4; P=0.006), myeloid blasts (HR=3.8; P=0.03), and ASXL1 (HR=5.2; P=0.02) as significant in our cohort with regard to overall survival (OS). We noted a strong trend for high risk karyotype (HR=5.6; P=0.07). Our DIPSS model median survival (N