Druggable proteins influencing cardiac structure and function: Implications for heart failure therapies and cancer cardiotoxicity

Dysfunction of either the right or left ventricle can lead to heart failure (HF) and subsequent morbidity and mortality. We performed a genome-wide association study (GWAS) of 16 cardiac magnetic resonance (CMR) imaging measurements of biventricular function and structure. Cis-Mendelian randomization (MR) was used to identify plasma proteins associating with CMR traits as well as with any of the following cardiac outcomes: HF, non-ischemic cardiomyopathy, dilated cardiomyopathy (DCM), atrial fibrillation, or coronary heart disease. In total, 33 plasma proteins were prioritized, including repurposing candidates for DCM and/or HF: IL18R (providing indirect evidence for IL18), I17RA, GPC5, LAMC2, PA2GA, CD33, and SLAF7. In addition, 13 of the 25 druggable proteins (52%; 95% confidence interval, 0.31 to 0.72) could be mapped to compounds with known oncological indications or side effects. These findings provide leads to facilitate drug development for cardiac disease and suggest that cardiotoxicities of several cancer treatments might represent mechanism-based adverse effects.

[1]  A. Hingorani,et al.  Joint Genetic Inhibition of PCSK9 and CETP and the Association With Coronary Artery Disease: A Factorial Mendelian Randomization Study. , 2022, JAMA cardiology.

[2]  P. Munroe,et al.  Genome-wide association analysis reveals insights into the genetic architecture of right ventricular structure and function , 2022, Nature Genetics.

[3]  A. Philippakis,et al.  Genetic analysis of right heart structure and function in 40,000 people , 2021, Nature Genetics.

[4]  A. Hingorani,et al.  Human Genomics and Drug Development. , 2021, Cold Spring Harbor perspectives in medicine.

[5]  A. Hingorani,et al.  Cholesteryl ester transfer protein (CETP) as a drug target for cardiovascular disease , 2021, Nature Communications.

[6]  Tom R. Gaunt,et al.  Validation of lipid-related therapeutic targets for coronary heart disease prevention using human genetics , 2020, Nature Communications.

[7]  A. Feldman,et al.  Cardiomyocyte contractile impairment in heart failure results from reduced BAG3-mediated sarcomeric protein turnover , 2020, Nature Communications.

[8]  J. Danesh,et al.  Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals , 2020, Nature Metabolism.

[9]  A. Philippakis,et al.  Analysis of cardiac magnetic resonance imaging in 36,000 individuals yields genetic insights into dilated cardiomyopathy , 2020, Nature Communications.

[10]  Spiros C. Denaxas,et al.  Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure , 2020, Nature Communications.

[11]  Daniel F. Freitag,et al.  Genetic drug target validation using Mendelian randomisation , 2019, Nature Communications.

[12]  M. Petrucci,et al.  The Anti-CD38 Antibody Therapy in Multiple Myeloma , 2019, Cells.

[13]  R. Vasan,et al.  Risk factor-based subphenotyping of heart failure in the community , 2019, PloS one.

[14]  Brandon K. Fornwalt,et al.  Routinely reported ejection fraction and mortality in clinical practice: where does the nadir of risk lie? , 2019, European heart journal.

[15]  Wenjia Bai,et al.  Fully Automated, Quality-Controlled Cardiac Analysis From CMR: Validation and Large-Scale Application to Characterize Cardiac Function , 2019, JACC. Cardiovascular imaging.

[16]  Karsten B. Sieber,et al.  A catalog of genetic loci associated with kidney function from analyses of a million individuals , 2019, Nature Genetics.

[17]  Mark R. Hurle,et al.  Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases , 2019, bioRxiv.

[18]  Helen E. Parkinson,et al.  The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019 , 2018, Nucleic Acids Res..

[19]  H. Kampinga,et al.  Myopathy associated BAG3 mutations lead to protein aggregation by stalling Hsp70 networks , 2018, Nature Communications.

[20]  P. Donnelly,et al.  The UK Biobank resource with deep phenotyping and genomic data , 2018, Nature.

[21]  Andrew D. Johnson,et al.  Genome‐wide mapping of plasma protein QTLs identifies putatively causal genes and pathways for cardiovascular disease , 2018, Nature Communications.

[22]  Tanya M. Teslovich,et al.  Biobank-driven genomic discovery yields new insight into atrial fibrillation biology , 2018, Nature Genetics.

[23]  Jack Bowden,et al.  Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression , 2018, International journal of epidemiology.

[24]  Stephen Burgess,et al.  Genomic atlas of the human plasma proteome , 2018, Nature.

[25]  Spiros C. Denaxas,et al.  Phenome-wide association analysis of LDL-cholesterol lowering genetic variants in PCSK9 , 2018, bioRxiv.

[26]  Samuel E. Jones,et al.  Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry , 2018, bioRxiv.

[27]  Andrew D. Johnson,et al.  Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes , 2018, Nature Genetics.

[28]  Henning Hermjakob,et al.  The Reactome pathway knowledgebase , 2013, Nucleic Acids Res..

[29]  G. Hasenfuss,et al.  T helper cells with specificity for an antigen in cardiomyocytes promote pressure overload-induced progression from hypertrophy to heart failure , 2017, Scientific Reports.

[30]  Xinqiang Han,et al.  Precision cardio-oncology: understanding the cardiotoxicity of cancer therapy , 2017, npj Precision Oncology.

[31]  Stephen Burgess,et al.  Mendelian randomization with fine‐mapped genetic data: Choosing from large numbers of correlated instrumental variables , 2017, Genetic epidemiology.

[32]  Giovanni Malerba,et al.  Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes , 2017, Nature Genetics.

[33]  N. Sheehan,et al.  A framework for the investigation of pleiotropy in two‐sample summary data Mendelian randomization , 2017, Statistics in medicine.

[34]  George Papadatos,et al.  The ChEMBL database in 2017 , 2016, Nucleic Acids Res..

[35]  John P. Overington,et al.  The druggable genome and support for target identification and validation in drug development , 2016, Science Translational Medicine.

[36]  Volkmar Falk,et al.  2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure , 2016, Revista espanola de cardiologia.

[37]  Sanjiv J. Shah,et al.  Effect of Vericiguat, a Soluble Guanylate Cyclase Stimulator, on Natriuretic Peptide Levels in Patients With Worsening Chronic Heart Failure and Reduced Ejection Fraction: The SOCRATES-REDUCED Randomized Trial. , 2015, JAMA.

[38]  Marc Robinson-Rechavi,et al.  A benchmark of gene expression tissue-specificity metrics , 2015, bioRxiv.

[39]  Latarsha J. Carithers,et al.  The Genotype-Tissue Expression (GTEx) Project. , 2015, Biopreservation and biobanking.

[40]  J. Danesh,et al.  A comprehensive 1000 Genomes-based genome-wide association meta-analysis of coronary artery disease , 2016 .

[41]  D. DeMets,et al.  Cardiovascular drug development: is it dead or just hibernating? , 2015, Journal of the American College of Cardiology.

[42]  G. Davey Smith,et al.  Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression , 2015, International journal of epidemiology.

[43]  B. Berger,et al.  Efficient Bayesian mixed model analysis increases association power in large cohorts , 2014, Nature Genetics.

[44]  M. Daly,et al.  LD Score regression distinguishes confounding from polygenicity in genome-wide association studies , 2014, Nature Genetics.

[45]  M. Brown,et al.  Genetic associations and functional characterization of M1 aminopeptidases and immune-mediated diseases , 2014, Genes and Immunity.

[46]  T. Meehan,et al.  An atlas of active enhancers across human cell types and tissues , 2014, Nature.

[47]  J. Kastelein,et al.  Varespladib and cardiovascular events in patients with an acute coronary syndrome: the VISTA-16 randomized clinical trial. , 2014, JAMA.

[48]  J. McMurray,et al.  Inflammatory cytokines in chronic heart failure: interleukin‐8 is associated with adverse outcome. Results from CORONA , 2014, European journal of heart failure.

[49]  Rafael C. Jimenez,et al.  The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases , 2013, Nucleic Acids Res..

[50]  Tom R. Gaunt,et al.  Secretory Phospholipase A2-IIA and Cardiovascular Disease , 2013, Journal of the American College of Cardiology.

[51]  Tom R. Gaunt,et al.  Population Genomics of Cardiometabolic Traits: Design of the University College London-London School of Hygiene and Tropical Medicine-Edinburgh-Bristol (UCLEB) Consortium , 2013, PLoS ONE.

[52]  G. Lip,et al.  Inflammation in atrial fibrillation. , 2012, Journal of the American College of Cardiology.

[53]  S. Thompson,et al.  Avoiding bias from weak instruments in Mendelian randomization studies. , 2011, International journal of epidemiology.

[54]  G. Rücker,et al.  Treatment-effect estimates adjusted for small-study effects via a limit meta-analysis. , 2011, Biostatistics.

[55]  E. Lundberg,et al.  Towards a knowledge-based Human Protein Atlas , 2010, Nature Biotechnology.

[56]  S. De Flora,et al.  Cardiotoxicity of Anticancer Drugs: The Need for Cardio-Oncology and Cardio-Oncological Prevention , 2010, Journal of the National Cancer Institute.

[57]  L. Schomburg,et al.  Concerted peptide trimming by human ERAP1 and ERAP2 aminopeptidase complexes in the endoplasmic reticulum , 2005, Nature Immunology.

[58]  John D. Storey A direct approach to false discovery rates , 2002 .