Machine Learning Identifies Clinical and Genetic Factors Associated With Anthracycline Cardiotoxicity in Pediatric Cancer Survivors

BACKGROUND Despite known clinical risk factors, predicting anthracycline cardiotoxicity remains challenging. OBJECTIVES This study sought to develop a clinical and genetic risk prediction model for anthracycline cardiotoxicity in childhood cancer survivors. METHODS We performed exome sequencing in 289 childhood cancer survivors at least 3 years from anthracycline exposure. In a nested case-control design, 183 case patients with reduced left ventricular ejection fraction despite low-dose doxorubicin (\u3c= 250 mg/m(2)), and 106 control patients with preserved left ventricular ejection fraction despite doxorubicin \u3e250 mg/m(2) were selected as extreme phenotypes. Rare/low-frequency variants were collapsed to identify genes differentially enriched for variants between case patients and control patients. The expression levels of 5 top-ranked genes were evaluated in human induced pluripotent stem cell-derived cardiomyocytes, and variant enrichment was confirmed in a replication cohort. Using random forest, a risk prediction model that included genetic and clinical predictors was developed. RESULTS Thirty-one genes were differentially enriched for variants between case patients and control patients (p \u3c 0.001). Only 42.6% case patients harbored a variant in these genes compared to 89.6% control patients (odds ratio: 0.09; 95% confidence interval: 0.04 to 0.17; p = 3.98 x 10(-15)). A risk prediction model for cardiotoxicity that included clinical and genetic factors had a higher prediction accuracy and lower misclassification rate compared to the clinical-only model. In vitro inhibition of gene-associated pathways (PI3KR2, ZNF827) provided protection from cardiotoxicity in cardiomyocytes. CONCLUSIONS Our study identified variants in cardiac injury pathway genes that protect against cardiotoxicity and informed the development of a prediction model for delayed anthracycline cardiotoxicity, and it also provided new targets in autophagy genes for the development of cardio-protective drugs

Fifth European Dirofilaria and Angiostrongylus Days (FiEDAD) 2016

Peer reviewe

Machine Learning Identifies Clinical and Genetic Factors Associated With Anthracycline Cardiotoxicity in Pediatric Cancer Survivors

Background: Despite known clinical risk factors, predicting anthracycline cardiotoxicity remains challenging. Objectives: This study sought to develop a clinical and genetic risk prediction model for anthracycline cardiotoxicity in childhood cancer survivors. Methods: We performed exome sequencing in 289 childhood cancer survivors at least 3 years from anthracycline exposure. In a nested case-control design, 183 case patients with reduced left ventricular ejection fraction despite low-dose doxorubicin (≤250 mg/m2), and 106 control patients with preserved left ventricular ejection fraction despite doxorubicin \u3e250 mg/m2 were selected as extreme phenotypes. Rare/low-frequency variants were collapsed to identify genes differentially enriched for variants between case patients and control patients. The expression levels of 5 top-ranked genes were evaluated in human induced pluripotent stem cell–derived cardiomyocytes, and variant enrichment was confirmed in a replication cohort. Using random forest, a risk prediction model that included genetic and clinical predictors was developed. Results: Thirty-one genes were differentially enriched for variants between case patients and control patients (p \u3c 0.001). Only 42.6% case patients harbored a variant in these genes compared to 89.6% control patients (odds ratio: 0.09; 95% confidence interval: 0.04 to 0.17; p = 3.98 × 10–15). A risk prediction model for cardiotoxicity that included clinical and genetic factors had a higher prediction accuracy and lower misclassification rate compared to the clinical-only model. In vitro inhibition of gene-associated pathways (PI3KR2, ZNF827) provided protection from cardiotoxicity in cardiomyocytes. Conclusions: Our study identified variants in cardiac injury pathway genes that protect against cardiotoxicity and informed the development of a prediction model for delayed anthracycline cardiotoxicity, and it also provided new targets in autophagy genes for the development of cardio-protective drugs. (Preventing Cardiac Sequelae in Pediatric Cancer Survivors [PCS2]; NCT01805778

A Validated Model for Sudden Cardiac Death Risk Prediction in Pediatric Hypertrophic Cardiomyopathy

Background: Hypertrophic cardiomyopathy is the leading cause of sudden cardiac death (SCD) in children and young adults. Our objective was to develop and validate a SCD risk prediction model in pediatric hypertrophic cardiomyopathy to guide SCD prevention strategies. Methods: In an international multicenter observational cohort study, phenotype-positive patients with isolated hypertrophic cardiomyopathy 70% prediction accuracy and incorporates risk factors that are unique to pediatric hypertrophic cardiomyopathy. An individualized risk prediction model has the potential to improve the application of clinical practice guidelines and shared decision making for implantable cardioverter defibrillator insertion. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT0403679

Heterozygous STAT1 gain-of-function mutations underlie an unexpectedly broad clinical phenotype

Since their discovery in patients with autosomal dominant (AD) chronic mucocutaneous candidiasis (CMC) in 2011, heterozygous STAT1 gain-of-function (GOF) mutations have increasingly been identified worldwide. The clinical spectrum associated with them needed to be delineated. We enrolled 274 patients from 167 kindreds originating from 40 countries from 5 continents. Demographic data, clinical features, immunological parameters, treatment, and outcome were recorded. The median age of the 274 patients was 22 years (range, 1-71 years); 98% of them had CMC, with a median age at onset of 1 year (range, 0-24 years). Patients often displayed bacterial (74%) infections, mostly because of Staphylococcus aureus (36%), including the respiratory tract and the skin in 47% and 28% of patients, respectively, and viral (38%) infections, mostly because of Herpesviridae (83%) and affecting the skin in 32% of patients. Invasive fungal infections (10%), mostly caused by Candida spp. (29%), and mycobacterial disease (6%) caused by Mycobacterium tuberculosis, environmental mycobacteria, or Bacille Calmette-Guérin vaccines were less common. Many patients had autoimmune manifestations (37%), including hypothyroidism (22%), type 1 diabetes (4%), blood cytopenia (4%), and systemic lupus erythematosus (2%). Invasive infections (25%), cerebral aneurysms (6%), and cancers (6%) were the strongest predictors of poor outcome. CMC persisted in 39% of the 202 patients receiving prolonged antifungal treatment. Circulating interleukin-17A-producing T-cell count was low for most (82%) but not all of the patients tested. STAT1 GOF mutations underlie AD CMC, as well as an unexpectedly wide range of other clinical features, including not only a variety of infectious and autoimmune diseases, but also cerebral aneurysms and carcinomas that confer a poor prognosis