Delles, Christian

Dominiczak, Anna

Padmanabhan, Sandosh

English

Anna Dominiczak

Christian Delles

Sandosh Padmanabhan

Crossref

Hypertension

Genomics and Precision Medicine for Clinicians and Scientists in Hypertension

Enlighten

    Dominiczak, A. , Delles, C. and Padmanabhan, S. (2017) Genomics and precision medicine for clinicians and scientists in hypertension. Hypertension, 69(4), e10-e13. (doi:10.1161/HYPERTENSIONAHA.116.08252)  This is the author’s final accepted version.  There may be differences between this version and the published version. You are advised to consult the publisher’s version if you wish to cite from it.     http://eprints.gla.ac.uk/136967/            Deposited on: 15 February 2017              Enlighten – Research publications by members of the University of Glasgow http://eprints.gla.ac.uk 1     Genomics and precision medicine for clinicians and scientists in hypertension. 1  Authors: Anna Dominiczak, Christian Delles and Sandosh Padmanabhan  Short: Genomics and Precision Medicine  Word Count: 2500   Address of Address of corresponding author  Professor Anna Dominiczak BHF Glasgow Cardiovascular Research Centre Insitute of Cardiovascular and Medical Sciences University of Glasgow 126 University Pl Glasgow G12 8TA    Tel: +44 141 330 2738 Fax: +44 141 330 5440 email: Anna.Dominiczak@glasgow.ac.uk                                                             1 This is a summary of the lecture AD presented at the AHA Council for Hypertension Annual Meeting in 2016. 2    In 1903 Sir William Osler wrote “the good physician treats the disease; the great physician treats the patient who has the disease”.  These words ring very true in 2016 as we approach the era of precision or stratified medicine.  The precision or stratified medicine (we are going to use these terms interchangeably) is based on identifying subgroups of patients with distinct mechanisms of disease and particular responses to treatments.  This allows us to identify and develop treatments that are effective for particular groups of patients.  Ultimately precision medicine will ensure that the right patient gets the right treatment at the right time.  The above definition broadly follows suggested definition published by the UK Medical Research Council (http://www.mrc.ac.uk/research/initiatives/stratified-medicine/). We can therefore summarise the promise of precision medicine as being able to remove non-responders and toxic responders prior to prescribing medication and to treat with a given drug only responders and patients not predisposed to toxicity.  Such approaches are very well developed in cancer medicine with several examples of successful precision medicine applications in breast, prostate, ovarian, colon and pancreatic cancer 1-6 and this list is by no means complete.  Cardiovascular medicine in general and hypertension in particular both lag behind oncology in this respect.  However, there are examples from rare monogenic syndromes, where a single genetic mutation explains the entire pathophysiology of severe, early onset hypertension as well as dictating perfect pharmacogenetics-led prescription 7,8.  These syndromes include the Glucocorticoid Remediable Aldosteronism, Liddle’s Syndrome and pseudohypoaldosteronism type II to name just a few.  In these 3  syndromes glucocorticoids, amiloride and thiazide diuretics, respectively are the pharmacogenetics-led prescriptions, based on our detailed understanding of the genetic pathophysiology of each syndrome 7,8.  It is much more difficult to apply similar principles to the much more common primary or essential hypertension and yet the current approach of prescribing the same or nearly the same treatment for all is not sustainable long-term.  The methodology, which has brought significant progress in genomics of hypertension is the genome wide association study (GWAS).  In a classic GWAS thousands of patients and controls are included in a three-phase design, where data from the discovery cohort are first reproduced and then validated on separate, large cohorts of cases and controls. The GWAS requires 500,000-2,500,000 markers, known as single nucleotide polymorphisms (SNPs) with a good genome-wide coverage.  The “hits” are presented in a format called a Manhattan plot with p values of 10-8 or below normally considered to achieve a genome-wide significance.  Several GWAS have been published followed by well-designed meta-analyses 9-13.  The early GWAS for hypertension within the Wellcome Trust Case-Control Consortium was negative 9 most likely due to inclusion of hypertensive subjects in the control group, although other causes such as lack of power and relatively low number (500,000) of SNPs should also be considered 9.  The first successful GWAS meta-analysis included some 200,000 subjects and identified 29 genomic regions associated with blood pressure (BP) sub-phenotypes and hypertension as a binary trait 12.  The contribution of each individual SNP was approximately 1 mmHg for systolic and 0.5 mmHg for diastolic BP 12. The weighted genetic risk scores (GRS), 4  which were calculated for all 29 SNPs, showed highly significant associations with coronary heart disease, left ventricular hypertrophy and stroke but not with markers of renal function 12. The quality and the breadth of phenotyping in these very large, multi-centre studies is not perfect, with the limited number of BP and other phenotypes used 14. It is possible that the relative deficiency of more difficult to measure renal biomarkers have led to weaker risk scores related to kidney function.   Further data with potential clinical significance have been generated by even larger meta-analyses published recently 15-17. In one of these studies, Surendran et al,15 genotyped nearly 350,000 individuals to identify 30 new blood pressure or hypertension-associated risk loci. These analyses provide insights into the pathophysiology of hypertension thus highlighting new potential for precision medicine and for druggable targets 15.  The second study by Ehret et al., 16 performed association analyses on 340,000 individuals and identified 66 loci; of which 17 were novel, involved in BP regulation. The 66 index SNPs combined in a risk score showed comparable effects in nearly 65,000 individuals from non-European descent 16.  Similarly to previous studies, 66-SNP GRS was significantly associated with target organ damage in multiple tissues but with minor effects in the kidney 16. The third study by Liu et al. 17 identified 31 new loci, which were enriched for known variants for cardiometabolic traits. In addition, blood pressure associations at 39 previously published loci were confirmed. These large meta-analyses have increased the number of loci associated with BP regulation to 120 with 3.5% of the trait variance now explained 15-17.  5  In parallel experiments, functional data of potential clinical significance have been published using smaller but perfectly selected and phenotyped cohorts.  One good example here is the GWAS of blood pressure extremes, which identified variants in the uromodulin gene (UMOD) associated with hypertension 18. The special feature of this study was the selection of 2000 so called hypercontrols, subjects who had BP below 120/80 mmHg and were free from cardiovascular events during 10 year follow-up 19.  The uromodulin gene and protein are selectively expressed in the thick ascending loop of Henle (Figure 1), the part of the nephron responsible for 25% of sodium reabsorption in the kidney 8,18,20,21. Experiments with Umod knock-out mice showed not only a highly significant BP difference between the Umod +/+ and Umod -/- mice but also revealed that the knock-out mice are unable to increase their BP in response to 2% NaCl 21. Trudu et al 22 performed elegant experiments, which showed that UMOD polymorphisms are associated with a differential BP response to loop diuretics (the specific blocker of the main sodium channel in the thick ascending limb of the loop of Henle, NKCC2) in patients with essential hypertension 22. GWAS studies have been fruitful in discovering novel and robust associations with common traits. However, major challenges exist in identifying causal variants and demonstrating their functional mechanisms together with clinical translation. The novel implication from studies of Padmanabhan et al 18,20,21 and Trudu et al 22 is that based on UMOD rs13333226 genotype, there are two strata of hypertensive patients. The high-UMOD group (AA genotype) has increased UMOD excretion, greater salt sensitivity, hypertension, normal estimated glomerular filtration rate (eGFR) and greater BP response to loop diuretics. The low-UMOD group has decreased UMOD excretion, salt resistance increased eGFR, increased proximal tubular reabsorption of Na+ (possibly related to increased GFR), a poor BP response 6  to loop diuretics and a diminished function of NKCC2. These two contrasting strata present an opportunity for hypertension precision medicine. Volume overload is one of underlying causes for uncontrolled hypertension and yet loop diuretics, which are the most effective diuretics, are rarely used in hypertension without edema, heart failure or chronic kidney disease. A prospective genotype directed trial of a long-acting loop diuretic, torasemide in uncontrolled hypertensive patients will confirm or refute the hypothesis that the AA – high UMOD genotype patients are good responders to loop diuretics. It will simultaneously confirm that the UMOD-NKCC2 interaction is one of the mechanisms of salt-sensitivity. This precision medicine trial has been recently funded by the British Heart Foundation (CS.16/1/31878). Other areas for clinical and preclinical mechanistic studies include hepsin, the newly discovered protease that cleaves UMOD from its  glycophosphatidylinositol (GPI) anchor, other transporters that may influence NKCC2 function – ROMK, Na+/K+ATPase or chloride channels; tubulo-glomerular feedback or other proteins 23,24. This molecular context of UMOD function is depicted in Figure 1.  Whilst this and other similar studies are in progress, it is important to recognise that in addition to generating evidence for stratified or precision therapy, it is essential to show that the stratifier demonstrates increased cost-effectiveness, and improved quality of care.  Dzau et al 25  analysed data from the Health Economics Medical Innovation Simulation, a pre-existing health simulation model, to assess the benefits and cost of personalized precision medicine innovations that would potentially improve identification and screening of presymptomatic, high risk patients. They calculated health generated value expressed as quality-adjusted life years (QALYs). The QALY 7  concept is being widely used in health economic modelling as it measures the effects of disease burden on quantity (life span) and quality of life. These measurements modelled over nearly a 50 year period, showed that even a 10% reduction in one of the six diseases - cancer, diabetes, heart disease, stroke, lung disease and hypertension could potentially save between $33 to $114 billion, in the form of longer and healthier lives (Figure 2). Hypertension and heart disease demonstrate the greatest value, primarily due to their high prevalence. It is clear from this modelling that with 10% incidence reduction for hypertension, the value of health is identical to similar incidence reduction in cancer 25. Even more striking are data that could be generated with 50% incidence reduction, where value of health for hypertension is almost double of the same value modelled for cancer 25. These are very important considerations for policy makers in health systems around the world.  We believe that precision medicine as applied to cardiovascular disease in general and hypertension in particular is here to stay.  It will require major national and international collaborations between clinical academic centres, health systems and industry.  One excellent example of such national effort is the Scottish Ecosystem for Precision Medicine (http://www.imperial.ac.uk/media/imperial-college/institute-of-global-health-innovation/Precision_Medicine_Report.pdf). Using excellent and Scotland-wide electronic health records together with highly collaborative four clinical academic centres and the National Health Service (NHS) free at the point of delivery, we are able to jointly create an excellent system to tackle multiple chronic diseases and cancer with precision medicine tools. Other examples include the UK Catapult for Precision Medicine (http://pm.catapult.org.uk) and President Obama’s Precision Medicine initiative in the United States 26. 8   In summary, the time is right for hypertension researchers to develop precision medicine tools for patients with essential or primary hypertension. These tools will be heavily based on genomics, at least initially, but there is growing evidence that other omics, including transcriptomics, proteomics and metabolomics will be increasingly used in combination with whole genome sequencing to facilitate disease stratification, early diagnostics and the precision prevention of hypertension and its complications.  Funding A. Dominiczak has funding from the Scottish Ecosystem for Precision Medicine. C. Delles is funded by EU-MASCARA" (grant agreement 278249). S.Padmanabhan is funded by the MRC (MR/M016560/1, The AIM-HY Study) and the British Heart Foundation (PG/12/85/29925, CS/16/1/31878, and BHF Centre of Research Excellence Award (RE/13/5/30177, A. Dominiczak and C. Delles). MRC/BBSRC Glasgow Molecular Pathology Node (MR/N0005813/1, A. Dominiczak).  Disclosures Anna Dominiczak: Editor-In-Chief of Hypertension. Christian Delles and Sandosh Padmanabhan are both Hypertension Editorial Board Members.  9  Reference List  1. Harris MH, DuBois SG, Glade Bender JL et al. Multicenter Feasibility Study of Tumor Molecular Profiling to Inform Therapeutic Decisions in Advanced Pediatric Solid Tumors: The Individualized Cancer Therapy (iCat) Study. JAMA Oncol. 2016;2:608-615. 2. Nick AM, Coleman RL, Ramirez PT, Sood AK. A framework for a personalized surgical approach to ovarian cancer. Nat Rev Clin Oncol. 2015;12:239-245. 3. Seufferlein T, Mayerle J. Pancreatic cancer in 2015: Precision medicine in pancreatic cancer--fact or fiction? Nat Rev Gastroenterol Hepatol. 2016;13:74-75. 4. Tasian SK, Loh ML, Hunger SP. Childhood acute lymphoblastic leukemia: Integrating genomics into therapy. Cancer. 2015;121:3577-3590. 5. Beltran H, Eng K, Mosquera JM et al. Whole-Exome Sequencing of Metastatic Cancer and Biomarkers of Treatment Response. JAMA Oncol. 2015;1:466-474. 6. Fillmore CM, Xu C, Desai PT, Berry JM, Rowbotham SP, Lin YJ, Zhang H, Marquez VE, Hammerman PS, Wong KK, Kim CF. EZH2 inhibition sensitizes BRG1 and EGFR mutant lung tumours to TopoII inhibitors. Nature. 2015;520:239-242. 7. Lifton RP, Gharavi AG, Geller DS. Molecular mechanisms of human hypertension. Cell. 2001;104:545-556. 8. Padmanabhan S, Caulfield M, Dominiczak AF. Genetic and molecular aspects of hypertension. Circ Res. 2015;116:937-959. 10  9. WTCCC. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007;447:661-678. 10. Newton-Cheh C, Johnson T, Gateva V et al. Genome-wide association study identifies eight loci associated with blood pressure. Nat Genet. 2009;41:666-676. 11. Levy D, Ehret GB, Rice K et al. Genome-wide association study of blood pressure and hypertension. Nat Genet. 2009;41:677-687. 12. Ehret GB, Munroe PB, Rice KM et al. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature. 2011;478:103-109. 13. Havulinna AS, Kettunen J, Ukkola O, Osmond C, Eriksson JG, Kesaniemi YA, Jula A, Peltonen L, Kontula K, Salomaa V, Newton-Cheh C. A blood pressure genetic risk score is a significant predictor of incident cardiovascular events in 32,669 individuals. Hypertension. 2013;61:987-994. 14. Fox CS, Hall JL, Arnett DK et al. Future translational applications from the contemporary genomics era: a scientific statement from the American Heart Association. Circulation. 2015;131:1715-1736. 15. Surendran P, Drenos F, Young R et al. Trans-ancestry meta-analyses identify rare and common variants associated with blood pressure and hypertension. Nat Genet. 2016;48:1151-1161. 16. Ehret GB, Ferreira T, Chasman DI et al. The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals. Nat Genet. 2016;48:1171-1184. 11  17. Liu C, Kraja AT, Smith JA et al. Meta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci. Nat Genet. 2016;48:1162-1170. 18. Padmanabhan S, Melander O, Johnson T et al. Genome-wide association study of blood pressure extremes identifies variant near UMOD associated with hypertension. PLoS Genet. 2010;6:e1001177. 19. Berglund G, Elmstahl S, Janzon L, Larsson SA. The Malmo Diet and Cancer Study. Design and feasibility. J Intern Med. 1993;233:45-51. 20. Padmanabhan S, Graham L, Ferreri NR, Graham D, McBride M, Dominiczak AF. Uromodulin, an emerging novel pathway for blood pressure regulation and hypertension. Hypertension. 2014;64:918-923. 21. Graham LA, Padmanabhan S, Fraser NJ, Kumar S, Bates JM, Raffi HS, Welsh P, Beattie W, Hao S, Leh S, Hultstrom M, Ferreri NR, Dominiczak AF, Graham D, McBride MW. Validation of uromodulin as a candidate gene for human essential hypertension. Hypertension. 2014;63:551-558. 22. Trudu M, Janas S, Lanzani C et al. Common noncoding UMOD gene variants induce salt-sensitive hypertension and kidney damage by increasing uromodulin expression. Nat Med. 2013;19:1655-1660. 23. Brunati M, Perucca S, Han L et al. The serine protease hepsin mediates urinary secretion and polymerisation of Zona Pellucida domain protein uromodulin. Elife. 2015;4:e08887. 12  24. Renigunta A, Renigunta V, Saritas T, Decher N, Mutig K, Waldegger S. Tamm-Horsfall glycoprotein interacts with renal outer medullary potassium channel ROMK2 and regulates its function. J Biol Chem. 2011;286:2224-2235. 25. Dzau VJ, Ginsburg GS, Van NK, Agus D, Goldman D. Aligning incentives to fulfil the promise of personalised medicine. Lancet. 2015;385:2118-2119. 26. Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med. 2015;372:793-795.    13  Figure Legends  Figure 1. Molecular context of UMOD in the thick ascending limb of loop of Henle cells. UMOD indicates the uromodulin gene, ClC-Ka indicates chloride channel protein class Ka; Clc-Kb, chloride channel protein class Kb; E.R., endoplasmic reticulum; GPI, glycosylphosphatidylinositol; NKCC2, Na+ K+ 2Cl− cotransporter 2; and ROMK, renal outer medullary potassium channel. Reprinted from “Uromodulin, an emerging novel pathway for blood pressure regulation and hypertension” by Padmanabhan et al, Hypertension 2014;64:918. Copyright 2014 by Wolters Kluwer Health. Reprinted with permission  Figure 2. Value of health from hypothetical personalised and precision medicine prevention innovation at two levels of incidence reduction in six diseases in the USA ($ billions). Value of health is measured in the quality–adjusted life-years (QALYs), which estimates the effects of disease burden on the quantity (life span) and quality of life. Reprinted from “Aligning incentives to fulfil the promise of personalised medicine” by Dzau et al, Lancet 2015;385:2118.Copyright 2015 by Elsevier. Health. Reprinted with permission   

Genomics and precision medicine for clinicians and scientists in hypertension

http://eprints.gla.ac.uk/136967/7/136967.pdf

Genomics and precision medicine for clinicians and scientists in hypertension

Abstract

Similar works

Full text

Available Versions

Crossref

Enlighten