Citations

Abstract Pancreatic Ductal Adenocarcinoma (PDAC) is the third leading cause of cancer-related deaths in United States with a 5-year survival rate of only 8%. Inherited predisposition plays an important role in PDAC risk. Rare, moderately to highly penetrant mutations in hereditary cancer and pancreatitis genes, identified in families with a high incidence of disease, account for a small fraction of cases. At the other end of the spectrum, common risk variants of low penetrance have been discovered through genome-wide association studies (GWAS). Currently, over 20 common PDAC risk signals have been identified through GWAS in populations of European ancestry. However, these loci explain only a small fraction of genetic heritability for PDAC, and the causal genes for the large majority of these are unknown. Most susceptibility alleles discovered through GWAS are located in noncoding regions of the genome and are likely to function through allele specific regulation of gene expression. Transcriptome-wide association studies integrate expression quantitative trait loci (eQTL) with GWAS data to discover genes whose cis-regulated expression is associated with complex traits. To identify novel susceptibility loci and likely causal genes, we applied this approach to PDAC. Firstly, we used two reference datasets generated from histologically normal pancreatic tissue samples from individuals of European ancestry, the DCEG/LTG (n=95) and Genotype-Tissue Expression (GTEx v7, n=174), to build robust predictors for gene expression (n=2,872-5,906; r2>0.01), using 5 different models (Top1, Lasso, Enet, Blup and Bslmm) in FUSION and PrediXcan. Gene expression predictors (n=2,043-21,422) were also generated by Enet in 48 different tissues (n=74-421 samples) from 608 individuals of European ancestry (GTEx). We then applied these predictor models to PDAC GWAS summary statistics from 9,040 PC cases and 12,496 controls. We discovered 24 genes whose genetically predicted expression is significantly associated with PDAC risk (FDR < 0.05), including 13 candidate genes at 10 novel loci (CELA3B, SMC2, SMC2-AS1, RP11-80H5.9, SMUG1, BTBD6, RCCD1, PNMT, CDK12, PGAP3, SUPT4H1, RP11-888D10.3 and PGPEP1) and 11 at 5 known risk loci (TERT, CLPTM1L, ZDHHC11B, INHBA, ABO, PDX1, KLF5, WDR59, CFDP1, BCAR1 and TMEM170A). Pathway enrichment analysis for genes differentially expressed in the top vs. bottom quartile of CELA3B expression highlighted higher expression of inflammatory and immune response genes in both the DCEG/LTG and GTEx pancreatic transcriptome datasets, indicating that low expression of this gene may be associated with an inflammatory state in the pancreas. Our findings showcase the utility of integrating multiple datasets to generate hypotheses and provide novel clues for the genetic mechanisms underlying pancreatic cancer risk. Citation Format: Jun Zhong, Ashley Jermusyk, Lang Wu, Jason W. Hoskins, Irene Collins, Mingfeng Zhang, Song Lei, Charles C. Chung, Tongwu Zhang, Wenming Xiao, Rachael Stolzenberg-Solomon, Alison P. Klein, Brian M. Wolpin, Xiao-Ou Shu, Stephen J. Chanock, Sara Olson, Nilanjan Chatterjee, Jill Smith, Jianxin Shi, Peter Kraft, Gloria M. Petersen, Wei Zheng, Laufey T. Amundadottir. Large-scale transcriptome-wide association study (TWAS) identifies novel candidate susceptibility genes for pancreatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1591.

Objective The hypertrophic cardiomyopathy (HCM) risk-sudden cardiac death (SCD) calculator endorsed by the 2014 European Society of Cardiology has not been independently validated in the Asians. We aimed to investigate whether the HCM Risk-SCD calculator effectively predicts SCD in Korean HCM population. Methods An observational, longitudinal cohort study was performed in 730 patients with HCM from 2007 to 2017. The primary endpoint was a composite of SCD and appropriate implantable cardioverter-defibrillator (ICD) therapy. Results During a follow-up period of 4288 person-years, 16 (2.2%) patients reached the primary endpoint. This validation study revealed a calibration slope of 0.892 and C-statistics of 0.718. The primary endpoint occurred in 1.1% (7/615), 4.6% (3/65) and 12.0% (6/50) of low-risk, intermediate-risk and high-risk groups, respectively. Although most patients (85.2%) without the primary endpoint were classified into the low-risk group, 7 of 11 SCD (63.6%) occurred in the low-risk group. In univariable and multivariable analysis, sex (woman) was significantly associated with the primary endpoint and emerged as independent predictor. The addition of sex to the HCM Risk-SCD calculator significantly improved the predictive value of the primary endpoint (net reclassification improvement 0.557, p=0.015). Conclusions In the Korean HCM population, the HCM Risk-SCD calculator had a high negative predictive value and accuracy for predicting SCD or appropriate ICD therapy, but misclassified a few patients experiencing the primary endpoint as low-risk or intermediate-risk groups.

Abstract In clinical cancer treatment, genomic alterations would often affect the response of patients to anticancer drugs. Studies have shown that molecular features of tumors could be biomarkers predictive of sensitivity or resistance to anticancer agents, but the identification of actionable mutations are often constrained by the incomplete understanding of cancer genomes. Recent progresses of next-generation sequencing technology greatly facilitate the extensive molecular characterization of tumors and promote precision medicine in cancers. More and more clinical studies, cancer cell lines studies, CRISPR screening studies as well as patient-derived model studies were performed to identify potential actionable mutations predictive of drug response, which provide rich resources of molecularly and pharmacologically profiled cancer samples at different levels. Such abundance of data also enables the development of various computational models and algorithms to solve the problem of drug sensitivity prediction, biomarker identification and in silico drug prioritization by the integration of multiomics data. Here, we review the recent development of methods and resources that identifies mutation-dependent effects for cancer treatment in clinical studies, functional genomics studies and computational studies and discuss the remaining gaps and future directions in this area.

Background and Purpose— Hypertrophic cardiomyopathy patients with atrial fibrillation are at increased risk of stroke, and anticoagulation is strongly recommended. However, limited data are available regarding the safety and effectiveness of non–vitamin K antagonist oral anticoagulants (NOACs) for primary prevention of stroke. Methods— Using the Korean Health Insurance Review and Assessment Service database, we identified 2397 patients with hypertrophic cardiomyopathy and nonvalvular atrial fibrillation on oral anticoagulation from 2013 to 2016 without history of ischemic stroke, intracranial hemorrhage (ICH), or gastrointestinal bleeding (992 on warfarin and 1405 on NOACs). Inverse probability of treatment weighting with propensity scores was used to balance covariates between treatment groups. Risk for ischemic stroke, ICH, gastrointestinal bleeding, death, and their composite outcome associated with NOAC use was assessed with warfarin use as the reference. Results— During a mean follow-up of 1.6 years, the incidence rates of ischemic stroke, ICH, gastrointestinal bleeding, death, and composite outcome were all significantly lower in the NOAC than in the warfarin group (stroke, 2.8 versus 5.0; ICH, 0.5 versus 1.3; gastrointestinal bleeding, 2.3 versus 3.0; death, 3.0 versus 5.1; composite, 7.5 versus 12.5 events per 100 person-years). NOACs were associated with significantly lower risks of ischemic stroke (hazard ratio [HR], 0.47; 95% CI, 0.32–0.68), ICH (HR, 0.31; 95% CI, 0.14–0.69), gastrointestinal bleeding (HR, 0.62; 95% CI, 0.40–0.96), death (HR, 0.45; 95% CI, 0.31–0.65), and the composite outcome (HR, 0.48; 95% CI, 0.38–0.61) than warfarin. The same trend was observed regardless of the NOAC dose and across various high-risk subgroups. In analysis of individual NOACs, all NOACs were associated with lower risks of ischemic stroke and composite outcome. Conclusions— NOACs showed superior effectiveness and safety versus warfarin in the primary prevention of stroke versus warfarin in real-world Asian hypertrophic cardiomyopathy with atrial fibrillation.

Abstract Aims Native T1 times from T1 mapping cardiac magnetic resonance (CMR) are associated with myocardial fibrosis in aortic stenosis (AS). We investigated whether changing patterns in native T1 predict clinical outcomes after aortic valve replacement (AVR) in severe AS patients. Methods and results Forty-three patients with severe AS (65.9 ± 8.1 years; 24 men) who underwent T1 mapping CMR at baseline and 1 year after AVR were prospectively enrolled. Upper limit of native T1 from healthy volunteers was used to define normal myocardium and diffuse fibrosis (native T1 < 1208.4 and ≥1208.4 ms, respectively). Participants were categorized into Group 1 (pre- and post-AVR normal myocardium; n = 11), Group 2 (pre-AVR diffuse fibrosis and post-AVR normal myocardium; n = 18), and Group 3 (post-AVR diffuse fibrosis; n = 14). Native T1 significantly decreased 1 year after AVR (pre-AVR, 1233.8 ± 49.7 ms; post-AVR, 1189.1 ± 58.4 ms; P < 0.001), which was associated with left ventricular (LV) mass regression (△native T1 vs. △LV mass index, r = 0.454, P = 0.010) and systolic function improvement (△native T1 vs. △LV ejection fraction, r = −0.379, P = 0.012). Group 2 showed greater functional improvements, whereas these benefits were blunted in Group 3. Group 3 had significantly worse outcomes than Group 1 [hazard ratio (HR), 9.479, 95% confidence interval (CI) 1.176–76.409; P = 0.035] and Group 2 (HR 3.551, 95% CI 1.178–10.704; P = 0.024). Conclusion AVR-induced changes in native T1 values are associated with LV systolic functional changes as well as prognosis in severe AS. Post-AVR T1 mapping CMR can be used as an imaging biomarker.

Abstract Background and Objectives Pancreatic cancer is strongly associated with thrombosis. We investigated early postoperative venous thromboembolism (PVTE) mortality among patients with pancreatic surgery and compared outcomes in adenocarcinoma pancreatic cancer (ACPC) to non‐adenocarcinoma pancreatic neoplasm (NACPN). Methods We analyzed a prospectively collected database of patients who underwent pancreatic cancer or neoplasm‐related surgery. As NACPN is underrepresented in other studies, we selected NACPN patients and a random sample of ACPC patients. PVTE was defined as VTE occurring within 3 months of surgical intervention. Statistical analysis was performed using Cox proportional hazards regression. Results A total of 441 pancreatic surgery patients were included, with 331 ACPC and 110 NACPN. Median follow‐up was 449 days during which 90 (20.4%) patients developed VTE. PVTE occurred in 53 (12.0%) patients, including 41 (12.4%) ACPC patients and 12 (10.9%) NACPN patients. Those with PVTE had 60% higher mortality rate. A multivariable analysis found that PVTE is an independent predictor of increased mortality (HR Adj, 1.6; 95% CI, 1.1‐2.2; P < .01). The mortality impact was not consistent between ACPC (HR, 3.2; 95% CI, 1.3‐7.9) and NACPN groups (HR, 1.3; 95% CI, 0.9‐1.8). Conclusions Postoperative venous thromboembolism is an independent predictor of increased mortality in pancreatic surgery, specifically in adenocarcinoma pancreatic cancer surgery.

Abstract Event-free survival (EFS) is controversial as an end point for speeding approvals in newly diagnosed acute myeloid leukemia (AML). We aimed to examine the robustness of EFS, specifically timing of complete remission (CR) in defining induction failure and impact of hematopoietic cell transplantation (HCT). The study included 1884 untreated AML patients enrolled across 5 trials conducted through Alliance for Clinical Trials in Oncology using anthracycline and cytarabine induction chemotherapy. EFS was defined as time from randomization/registration to induction failure, relapse, or death. Three definitions of induction failure were evaluated: failure to achieve CR by 60 days after randomization/registration, failure to achieve CR by the end of all protocol-defined induction courses, and failure to achieve CR by the end of all protocol-defined treatment. We considered either censoring or no censoring at time of non–protocol-mandated HCT. Although relapse and death are firm end points, the determination of induction failure was not consistent across studies. There was minimal impact of censoring at HCT on EFS estimates; however, median EFS estimates differed considerably based on the timing of CR in defining induction failure, with the magnitude of difference being large enough in most cases to lead to incorrect conclusions about efficacy in a single-arm trial, if the trial definition was not consistent with the definition used for the historical control. Timing of CR should be carefully examined in the historical control data used to guide the design of single-arm trials using EFS as the primary end point. Trials were registered at www.clinicaltrials.gov as #NCT00085124, #NCT00416598, # NCT00651261, #NCT01238211, and #NCT01253070.

PURPOSEDrug development is becoming increasingly expensive and time consuming. Drug repurposing is one potential solution to accelerate drug discovery. However, limited research exists on the use of electronic health record (EHR) data for drug repurposing, and most published studies have been conducted in a hypothesis-driven manner that requires a predefined hypothesis about drugs and new indications. Whether EHRs can be used to detect drug repurposing signals is not clear. We want to demonstrate the feasibility of mining large, longitudinal EHRs for drug repurposing by detecting candidate noncancer drugs that can potentially be used for the treatment of cancer.PATIENTS AND METHODSBy linking cancer registry data to EHRs, we identified 43,310 patients with cancer treated at Vanderbilt University Medical Center (VUMC) and 98,366 treated at the Mayo Clinic. We assessed the effect of 146 noncancer drugs on cancer survival using VUMC EHR data and sought to replicate significant associations (false discovery rate < .1) using the identical approach with Mayo Clinic EHR data. To evaluate replicated signals further, we reviewed the biomedical literature and clinical trials on cancers for corroborating evidence.RESULTSWe identified 22 drugs from six drug classes (statins, proton pump inhibitors, angiotensin-converting enzyme inhibitors, β-blockers, nonsteroidal anti-inflammatory drugs, and α-1 blockers) associated with improved overall cancer survival (false discovery rate < .1) from VUMC; nine of the 22 drug associations were replicated at the Mayo Clinic. Literature and cancer clinical trial evaluations also showed very strong evidence to support the repurposing signals from EHRs.CONCLUSIONMining of EHRs for drug exposure–mediated survival signals is feasible and identifies potential candidates for antineoplastic repurposing. This study sets up a new model of mining EHRs for drug repurposing signals.