Genetic Variants Associated With Cancer Therapy-Induced Cardiomyopathy

BACKGROUND: Cancer therapy-induced cardiomyopathy (CCM) is associated with cumulative drug exposures and preexisting cardiovascular disorders. These parameters incompletely account for substantial interindividual susceptibility to CCM. We hypothesized that rare variants in cardiomyopathy genes contribute to CCM. METHODS: We studied 213 patients with CCM from 3 cohorts: retrospectively recruited adults with diverse cancers (n=99), prospectively phenotyped adults with breast cancer (n=73), and prospectively phenotyped children with acute myeloid leukemia (n=41). Cardiomyopathy genes, including 9 prespecified genes, were sequenced. The prevalence of rare variants was compared between CCM cohorts and The Cancer Genome Atlas participants (n=2053), healthy volunteers (n=445), and an ancestry-matched reference population. Clinical characteristics and outcomes were assessed and stratified by genotypes. A prevalent CCM genotype was modeled in anthracycline-treated mice. RESULTS: CCM was diagnosed 0.4 to 9 years after chemotherapy; 90% of these patients received anthracyclines. Adult patients with CCM had cardiovascular risk factors similar to the US population. Among 9 prioritized genes, patients with CCM had more rare protein-altering variants than comparative cohorts ( P≤1.98e-04). Titin-truncating variants (TTNtvs) predominated, occurring in 7.5% of patients with CCM versus 1.1% of The Cancer Genome Atlas participants ( P=7.36e-08), 0.7% of healthy volunteers ( P=3.42e-06), and 0.6% of the reference population ( P=5.87e-14). Adult patients who had CCM with TTNtvs experienced more heart failure and atrial fibrillation ( P=0.003) and impaired myocardial recovery ( P=0.03) than those without. Consistent with human data, anthracycline-treated TTNtv mice and isolated TTNtv cardiomyocytes showed sustained contractile dysfunction unlike wild-type ( P=0.0004 and P<0.002, respectively). CONCLUSIONS: Unrecognized rare variants in cardiomyopathy-associated genes, particularly TTNtvs, increased the risk for CCM in children and adults, and adverse cardiac events in adults. Genotype, along with cumulative chemotherapy dosage and traditional cardiovascular risk factors, improves the identification of patients who have cancer at highest risk for CCM. CLINICAL TRIAL REGISTRATION: URL: https://www.clinicaltrials.gov . Unique identifiers: NCT01173341; AAML1031; NCT01371981.This work was supported in part by grants from the Instituto de Salud Carlos III (ISCIII) (PI15/01551, PI17/01941 and CB16/11/00432 to P.G-P. and L.A-P.), the Spanish Ministry of Economy and Competitiveness (SAF2015-71863-REDT to P.G-P.), the John S. LaDue Memorial Fellowship at Harvard Medical School (Y.K.), Wellcome Trust (107469/Z/15/Z to J.S.W.), Medical Research Council (intramural awards to S.A.C. and J.S.W; MR/M003191/1 to U.T), National Institute for Health Research Biomedical Research Unit at the Royal Brompton and Harefield National Health Service Foundation Trust and Imperial College London (P.J.B., S.A.C., J.S.W.), National Institute for Health Research Biomedical Research Centre at Imperial College London Healthcare National Health Service Trust and Imperial College London (D.O.R., S.A.C., S.P., J.S.W.), Sir Henry Wellcome Postdoctoral Fellowship (C.N.T.), Rosetrees and Stoneygate Imperial College Research Fellowship (N.W.), Fondation Leducq (S.A.C., C.E.S., J.G.S.), Health Innovation Challenge Fund award from the Wellcome Trust and Department of Health (UK; HICF-R6-373; S.A.C., P.J.B., J.S. W.), the British Heart Foundation (NH/17/1/32725 to D.O.R.; SP/10/10/28431 to S.A.C), Alex’s Lemonade Stand Foundation (K.G.), National Institutes of Health (R.A.: U01CA097452, R01CA133881, and U01CA097452; Z.A.: R01 HL126797; B.K.: R01 HL118018 and K23-HL095661; J.G.S. and C.E.S.: 5R01HL080494, R01HL084553), and the Howard Hughes Medical Institute (C.E.S.). The Universitario Puerta de Hierro and Virgen de la Arrixaca Hospitals are members of the European Reference Network on Rare and Complex Diseases of the Heart (Guard-Heart; http://guard-heart.ern-net.eu). This publication includes independent research commissioned by the Health Innovation Challenge Fund (HICF), a parallel funding partnership between the Department of Health and Wellcome Trust. The Centro Nacional de Investigaciones Cardiovasculares (CNIC) is supported by the Ministry of Economy, Industry and Competitiveness and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (SEV-2015-0505). Grants from ISCIII and the Spanish Ministry of Economy and Competitiveness are supported by the Plan Estatal de I+D+I 2013-2016 – European Regional Development Fund (FEDER) “A way of making Europe”.S

Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions

Funder: Science and Technology Development Fund; doi: https://doi.org/10.13039/Funder: Al-Alfi FoundationFunder: Magdi Yacoub Heart FoundationFunder: Rosetrees and Stoneygate Imperial College Research FellowshipFunder: National Health and Medical Research Council (Australia)Abstract: Purpose: Accurate discrimination of benign and pathogenic rare variation remains a priority for clinical genome interpretation. State-of-the-art machine learning variant prioritization tools are imprecise and ignore important parameters defining gene–disease relationships, e.g., distinct consequences of gain-of-function versus loss-of-function variants. We hypothesized that incorporating disease-specific information would improve tool performance. Methods: We developed a disease-specific variant classifier, CardioBoost, that estimates the probability of pathogenicity for rare missense variants in inherited cardiomyopathies and arrhythmias. We assessed CardioBoost’s ability to discriminate known pathogenic from benign variants, prioritize disease-associated variants, and stratify patient outcomes. Results: CardioBoost has high global discrimination accuracy (precision recall area under the curve [AUC] 0.91 for cardiomyopathies; 0.96 for arrhythmias), outperforming existing tools (4–24% improvement). CardioBoost obtains excellent accuracy (cardiomyopathies 90.2%; arrhythmias 91.9%) for variants classified with >90% confidence, and increases the proportion of variants classified with high confidence more than twofold compared with existing tools. Variants classified as disease-causing are associated with both disease status and clinical severity, including a 21% increased risk (95% confidence interval [CI] 11–29%) of severe adverse outcomes by age 60 in patients with hypertrophic cardiomyopathy. Conclusions: A disease-specific variant classifier outperforms state-of-the-art genome-wide tools for rare missense variants in inherited cardiac conditions (https://www.cardiodb.org/cardioboost/), highlighting broad opportunities for improved pathogenicity prediction through disease specificity

Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions

Funder: Science and Technology Development Fund; doi: https://doi.org/10.13039/Funder: Al-Alfi FoundationFunder: Magdi Yacoub Heart FoundationFunder: Rosetrees and Stoneygate Imperial College Research FellowshipFunder: National Health and Medical Research Council (Australia)Abstract: Purpose: Accurate discrimination of benign and pathogenic rare variation remains a priority for clinical genome interpretation. State-of-the-art machine learning variant prioritization tools are imprecise and ignore important parameters defining gene–disease relationships, e.g., distinct consequences of gain-of-function versus loss-of-function variants. We hypothesized that incorporating disease-specific information would improve tool performance. Methods: We developed a disease-specific variant classifier, CardioBoost, that estimates the probability of pathogenicity for rare missense variants in inherited cardiomyopathies and arrhythmias. We assessed CardioBoost’s ability to discriminate known pathogenic from benign variants, prioritize disease-associated variants, and stratify patient outcomes. Results: CardioBoost has high global discrimination accuracy (precision recall area under the curve [AUC] 0.91 for cardiomyopathies; 0.96 for arrhythmias), outperforming existing tools (4–24% improvement). CardioBoost obtains excellent accuracy (cardiomyopathies 90.2%; arrhythmias 91.9%) for variants classified with >90% confidence, and increases the proportion of variants classified with high confidence more than twofold compared with existing tools. Variants classified as disease-causing are associated with both disease status and clinical severity, including a 21% increased risk (95% confidence interval [CI] 11–29%) of severe adverse outcomes by age 60 in patients with hypertrophic cardiomyopathy. Conclusions: A disease-specific variant classifier outperforms state-of-the-art genome-wide tools for rare missense variants in inherited cardiac conditions (https://www.cardiodb.org/cardioboost/), highlighting broad opportunities for improved pathogenicity prediction through disease specificity

A Nationwide Study on Hypertrophic Cardiomyopathy in Iceland: Evidence of a MYBPC3 Founder Mutation.

To access publisher's full text version of this article click on the hyperlink at the bottom of the page-The geographic isolation and homogeneous population of Iceland is ideally suited to ascertain clinical and genetic characteristics of hypertrophic cardiomyopathy (HCM) at the population level.-Medical records and cardiac imaging studies obtained between 1997 and 2010 were reviewed to identify Icelandic patients with HCM. Surviving patients were recruited for clinical and genetic studies. A previously identified Icelandic mutation, MYBPC3 c.927-2A>G, was genotyped, and mutation-negative samples were sequenced for HCM genes and other hypertrophic genes. Record review identified 180 HCM patients. Genetic analyses of 151 patients defined pathogenic mutations in 101 (67%), including MYBPC3 c.927-2A>G (88 patients; 58%), 4 other MYBPC3 or MYH7 mutations (5 patients; 3.3%), and 2 GLA mutations (8 patients; 5.3%). Haplotype and genetic genealogical data defined MYBPC3 c.927-2A>G as a founder mutation, introduced into the Icelandic population in the 15(th) century, with a current population prevalence of 0.36%. MYBPC3 c.927-2A>G mutation carriers exhibited phenotypic diversity but were younger at diagnosis (42 vs. 49 years, p=0.001) and sustained more adverse events (15% vs. 2%, p=0.02) than mutation-negative patients. All-cause mortality for HCM patients was similar to an age-matched Icelandic population (Hazard Ratio 0.98, p=0.9). HCM-related mortality (0.78%/year) occurred at a mean age of 68 compared to 81 years for non-HCM related mortality (p=0.02).-A founder MYBPC3 mutation that arose over 550 years ago is the predominant cause of HCM in Iceland. The MYBPC3 c.927-2A>G mutation is associated with low adverse event rates but earlier cardiovascular mortality, illustrating the impact of genotype on outcomes in HCM.Akureyri Hospital Research Fund (G.G.), Landspitali – The National University Hospital of Iceland (Research Fund project A-2013-011; B.A.), the National Institutes of Health (C.E.S. and J.G.S; HL084553), the Fondation Leducq (C.E.S. and J.G.S.; 11CVD01), the Howard Hughes Medical Institute (C.E.S.) and the LaDue Foundation (M.B.

Increased frequency of de novo copy number variants in congenital heart disease by integrative analysis of single nucleotide polymorphism array and exome sequence data

RATIONALE: Congenital heart disease (CHD) is among the most common birth defects. Most cases are of unknown pathogenesis. OBJECTIVE: To determine the contribution of de novo copy number variants (CNVs) in the pathogenesis of sporadic CHD. METHODS AND RESULTS: We studied 538 CHD trios using genome-wide dense single nucleotide polymorphism arrays and whole exome sequencing. Results were experimentally validated using digital droplet polymerase chain reaction. We compared validated CNVs in CHD cases with CNVs in 1301 healthy control trios. The 2 complementary high-resolution technologies identified 63 validated de novo CNVs in 51 CHD cases. A significant increase in CNV burden was observed when comparing CHD trios with healthy trios, using either single nucleotide polymorphism array (P=7x10(-5); odds ratio, 4.6) or whole exome sequencing data (P=6x10(-4); odds ratio, 3.5) and remained after removing 16% of de novo CNV loci previously reported as pathogenic (P=0.02; odds ratio, 2.7). We observed recurrent de novo CNVs on 15q11.2 encompassing CYFIP1, NIPA1, and NIPA2 and single de novo CNVs encompassing DUSP1, JUN, JUP, MED15, MED9, PTPRE SREBF1, TOP2A, and ZEB2, genes that interact with established CHD proteins NKX2-5 and GATA4. Integrating de novo variants in whole exome sequencing and CNV data suggests that ETS1 is the pathogenic gene altered by 11q24.2-q25 deletions in Jacobsen syndrome and that CTBP2 is the pathogenic gene in 10q subtelomeric deletions. CONCLUSIONS: We demonstrate a significantly increased frequency of rare de novo CNVs in CHD patients compared with healthy controls and suggest several novel genetic loci for CHD