ALBERT

Description: Background:Patients with clonal hematopoiesis (CH) in the absence of WHO-classified myeloid disease are of special interest given their increased prevalence with age, predisposition to morbid cardiovascular complications, and amplified risk of overt hematologic malignancy. Pts are often stratified by normal peripheral blood counts into clonal hematopoiesis of indeterminate potential (CHIP), or those with unexplained cytopenias as clonal cytopenias of undetermined significance (CCUS). However, less is known about pts with elevated counts and clonal hematopoiesis who do not fulfill WHO criteria for any myeloproliferative neoplasia (MPN). We leveraged Vanderbilt University Medical Center's unique biobank, BioVU, to identify the prevalence of JAK2V617Facross 48,000 pts to evaluate the clinical changes in progression from CH to overt myeloid disease. Methods:To develop a reference JAKV617Ftraining set, next generation sequencing via Illumina Trusight Myeloid Panel (NGS) was performed on BioVU samples (N=133) from pts with confirmed myeloproliferative malignancy. Of those pts, 78 harbored JAK2V617Fwith a range of variant allele frequencies (VAF). Matched samples in this training set (N=133) were also analyzed via Infinium® Expanded Multi-Ethnic Genotyping Array (MEGAEX). SNP array JAK2V617Fvariant intensity was extracted (rs77375493; NM_004972.3(JAK2): c.1849G〉T (p.Val617Phe). A regression model was built using NGS VAF as a dependent variable and MEGAEX intensity data as independent variable (r2=0.9931).Based on this model, we imputed JAK2V617FVAF for all 48,000 pts in our cohort. Pts with JAK2V617Fwere subdivided into: clinically confirmed myeloid disease, or JAK2V617Fwithout a diagnosis of MPN. Upon review of the EMR, the latter group was further dived into: 1) probable undiagnosedMPN, 2) CHIP, 3) CCUS, or 4) CH with associated elevated peripheral blood counts (CHAPbc). Only lab values after the date of JAK2V617Fdetection were included. Confirmed malignancy was defined by WHO classification of disease. Pts with evidence of possible WHO classified PV or ET with Hgb 〉18.5g/dl in men, 〉16.5g/dl in women, or PLT count 〉450k/mcl regardless of gender were classified as probable undiagnosedMPN. CHIP was defined as JAK2V617Fwithout abnormal counts across a patient's EMR lifetime, except when confounding events, e.g. trauma surgery or overt iron deficiency anemia, incorrectly skewed values. CCUS was defined as JAK2V617Fin the presence of unexplained cytopenias; hemoglobin (Hgb)

Description: Background: Myelodysplastic syndromes (MDS) are clonal hematologic neoplasms stratified by risk by the international prognostic scoring system (IPSS) and IPSS-revised (IPSS-R) which measure risk by morphologic dysplasia, clinical cytopenias, blast count, and cytogenetic abnormalities. (PMID: 9058730, 22740453) The IPSS/IPSS-R do not consider clinical comorbid conditions, though MDS patients with higher burden of comorbid disease have higher rates of non-leukemic death, particularly those with cardiovascular and pulmonary disease. (19324411) Despite this, there has been limited investigation into how specific comorbid conditions may help define subgroups of patients with MDS. Methods: We identified 2676 cases of MDS as defined by ICD-9 code (238.72 - 238.75) in Vanderbilt's Synthetic Derivative (SD). The SD is a de-identified electronic health record (EHR) of over 2.2 million patients with a companion biorepository of DNA (BioVU) for a subset of these patients, including all of the patients with MDS. The 2676 cases were matched by age, gender, race, burden of comorbidities in EHR, and age at last appointment in EHR with 5287 controls. ICD-9 codes for other myeloid disease (e.g., myeloproliferative neoplasms, acute myeloid leukemia) or history of hematopoietic stem cell transplant were excluded among the controls. Characterization of comorbidities, via phecode analysis, was conducted on all cases and controls. Phecodes are groups of related ICD-9 codes describing a clinical syndrome or medical problem, previously demonstrated to be useful in phenome-wide associated studies in EHRs. (28686612) A case was defined as having a phecode only if a representative ICD-9 code was present on two distinct days in the EHR. Next, a cluster analysis of the study population and their associated comorbidities, via a bipartite stochastic block model, was completed, and the study population was organized into hierarchical structure based upon the similarities in comorbidity patterns among patients. Results: ICD-9 codes from the study population made up 181 phecodes, which were found in hierarchical cluster analysis to further cluster into 54 sub-groups and 16 larger groups. MDS patients clustered throughout all groups, the majority of which contained control patients; yet some MDS cases sub-clustered into groups that included a majority of MDS cases and these were further analyzed. Notably, two groups had equivalent size and MDS status were found to have significant differences in phecode profiles. Group 1 had 795 total patients with 783 MDS cases (98.5%) and Group 2 had 769 total patients with 684 MDS cases (88.9%), as per Fig 1a. There were no significant difference in sex between the two groups. Group 1 patients were significantly younger than Group 2 patients (58.3y vs 62.9y; p = 1.36 x 10-7), yet tended have increased risk of renal, cardiovascular and thromboembolic disease than Group 2, as per Fig 1b. Additionally, a higher proportion of Group 2 patients (695/769 or 90.4%) were alive at time of data extraction than Group 1 patients (451/795 or 56.7%) (OR 4.51, p =

Description: 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=120) for each subgroup; low risk (median survival not met), intermediate-1 (108 months), intermediate-2 (47 months) and high risk (6 months) P=0.0002 (Figure 1a). DIPSS Plus (N=122) integrated karyotype data and PLT count with similar survival with the exception of high risk (4 months) P=0.00003 (Figure 1b). The percentage of patients with driver mutations in JAK2V617F (57%), CALR (3%) and MPLW515 (7.2%); JAK2WT, CALRWT and MPLWT triple negative (34%); high molecular risk ASXL1 (15%), EZH2 (6%), IDH1/2 (7%), SRFS2 (17%); other variants of interest TET2 (9.6%), TP53 (29%) and DNMT3A (16.8%). MIPSS (N=125; 48 months follow up) noted low risk, intermediate-1, and intermediate-2 (median survival not met) and high risk (32 months) P=0.0001 (Figure 1c). GPSS (N=125; 48 months follow up) did not demonstrate statistical separation among groups (Figure 1d). Discussion: This proof of concept transformed raw EHR records into clinical risk scores for MF. The addition of retrospective DNA analysis via NGS opens the possibility of multi-institutional EHR-biobank studies to most accurately create a system to define MF risk. Our sample size limited the significance of age, PLTs, poor risk mutations and other variables previously shown to impact OS. Likewise, we lacked the capacity to track transfusion dependence, previously shown to have prognostic relevance. Still, prognostication via the EHR mimics common scoring systems in MF and supports correct MF case selection, accurate laboratory extraction and reproducible genotyping of biobanked samples. Similar to the original GPSS report, our low risk cohort was small (N=2) and will benefit from expansion of genotyping underway. Finally, this phenotype-genotype-prognostic paradigm represents a technical advance and a unique opportunity to deploy patient specific comorbidities from lifetime EHR records to further refine risk across all myeloid disease. Disclosures Savona: Boehringer Ingelheim: Consultancy; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding.

Description: Background:Treatment decisions in primary myelofibrosis (PMF) are guided by several prognostic systems based on disease-specific risk factors, including complete blood counts and cytogenetics. Patient specific comorbidities, e.g. non-hematopoietic organ dysfunction, are not incorporated into current prognostic models. Likewise, PMF risk stratification has not yet integrated large scale electronic health record (EHR) clinical data to refine these scoring systems. We have identified a PMF cohort within the Synthetic Derivative (SD), a de-identified, research-dedicated mirror of the EHR at Vanderbilt University Medical Center that contains 2.9 million individual records with 148 million ICD codes, and 125 million clinical notes. As a proof of concept, we leveraged the SD to develop a PMF cohort. We then aimed to identify novel patient specific comorbidities that may be associated with reduced overall survival (OS) in PMF via a phenome-wide association (PheWAS) study. Methods:We interrogated the SD for PMF via an algorithm that relied on ICD codes, natural language processing of physician notes, and medication history to identify high probability cases. Confirmation of PMF was based on strict hematologist review using 2016 WHO criteria. To this end, only patients with accessible hematopathology reports and cytogenetics, and more than 1 visit to the institution were enrolled. Patients with transformation to AML at presentation (e.g. within 30 days) were excluded. To evaluate each patient's overall comorbidity burden, we interrogated patient phecodes, which are grouped ICD9 codes shown to better mimic clinical phenotypes (PMID 20335276). Specifically, we extracted all ICD9 codes within 360 days of PMF diagnosis or referral and converted them to phecodes using the map available at https://phewascatalog.org/phecodes. PMF disease-related phecodes or codes that corresponded to DIPSS dependent variables were excluded. We identified 375 phecodes at PMF diagnosis, and conducted a PheWAS study to test the association of each phecode with survival. Survival was calculated as the interval between PMF diagnosis or referral and death or last follow-up (censor); patients who underwent hematopoietic stem cell transplant (HSCT) or transformed to AML were censored at that respective date. Survival from PMF diagnosis was estimated using the Kaplan-Meier method. We utilized the Cox proportional hazards model adjusted for the DIPSS predictors and evaluated the association of each comorbidity with the overall survival and reported those that are statistically significant after multiple testing adjustment (Bonferroni corrected P