ACS – Recent and Evolving Aspects Definition and Diagnostics,

Acquired and Genetic Factors, Ischemic and Reperfusion Injury

Jason Kovacic MD, PhD

ACS – Recent and Evolving Aspects Definition and Diagnostics,

Acquired and Genetic Factors, Ischemic and Reperfusion Injury

Spectrum of symptomatic CAD and Acute

Coronary Syndrome (ACS) Definition

Kovacic and Fuster, Circ Res, 2014

STEMI NSTEMI

Ischemic

Sudden

Death

Unstable

Angina

Stable

Angina

True “Textbook” Definition of ACS

Evolving/practical use

of the term ACS

Positive troponin

Publications using “ACS”, “Troponin” and “CK-MB”

Kovacic and Fuster, Circ Res, 2014

Spectrum of symptomatic CAD and Acute Coronary

Syndrome (ACS) Definition

Kovacic and Fuster, Circ Res, 2014 / Fog Bentzon et al. Circ Res 2014

STEMI NSTEMI

Ischemic

Sudden

Death

Unstable

Angina

Stable

Angina

True “Textbook” Definition of ACS

Evolving/practical use

of the term ACS

Positive troponin

ACS - Diagnostics

• History – Risk factors; chest pain

• Exam

• EKG

• Labs – Troponin, other

• Imaging – CTA/stress test

• Invasive angiography

• Other

ACS – Recent and Evolving Aspects Definition and Diagnostics,

Acquired and Genetic Factors, Ischemic and Reperfusion Injury

Contemporary interpretations suggest heritable factors account for 30–60% of the inter-individual variation in the risk of CAD

Schunkert et al. Nature Genetics; 2011

These factors account for only ~50% of the risk of developing CAD:

• Smoking • Hypertension • Diabetes • Dyslipidemia

• LDL, HDL, triglycerides, Lp(a)

• Obesity • Sedentary Lifestyle • Diet

Genetic factors thought to account

for the remainder of risk

These factors account for only ~50% of the risk of developing CAD:

• Smoking • Hypertension • Diabetes • Dyslipidemia

• LDL, HDL, triglycerides, Lp(a)

• Obesity • Sedentary Lifestyle • Diet

Genetic factors thought to account

for the remainder of risk

DNA, RNA, Protein?

DNA

RNA

Protein

Biologic Effect

GWAS and DNA-based approach to CAD

DNA

RNA

Protein

Atherosclerosis and CAD

63,746 CAD cases 130,681 controls without clinically manifest CAD

Identified and validated 46 risk loci for CAD

Identified a further 104 CAD-related variants that are yet to be validated

Nature Genetics 2013; 45:25-33

Nature Genetics 2013; 45:25-33

IL-6R

ApoB

Lipoprotein lipase

The nature of CAD heritability identified to date

• 46 validated loci, and potentially > 100-150 variants identified that contribute to CAD risk

• Each has minimal-modest effect

– Odds Ratio of 1.04 – 1.2 (4 – 20% increased risk)

• 50% of these variants occur in over half the population

• At least 25% of these variants occur in 75% of the population

Bjorkegren, Kovacic et al. JACC 2015

Manolio et al. Nature; 2009

63,746 CAD cases + 130,681 controls = 194,427 subjects 46 validated risk loci for CAD A further 104 CAD-related loci identified that are yet to be validated

Collectively, all these variants explain 10-11% of the heritability of CAD

Nature Genetics 2013; 45:25-33

Risk of developing CAD

Kovacic Lab

Mount Sinai

Fog Bentzon et al. Circ Res 2014

Atherosclerosis Lesion Types and their Development

Development of Atherosclerotic Plaque

Triggering Level

Clinical Manifestation Level

Inflammation in Atherosclerosis Atherosclerosis-prone ApoE-/- mouse after 18 weeks of high-fat diet

50 µm

CD31 – endothelial cells

Auto-fluorescence

CD45 – leukocytes & inflammatory cells

DAPI

Breakdown of elastic laminae via inflammatory release of MMPs, collagenases and elastases

Kovacic Lab

Mount Sinai

Rocha, Tomey, Libby, Fuster, Kovacic. 2015

Kovacic Lab

Mount Sinai

Rocha, Tomey, Libby, Fuster, Kovacic. 2015

Known genetic risk variants and CAD

Nearby gene (allele)

Chromosome location

SNP Odd’s Ratio

Associated with LDL cholesterol (n = 7) LPA 6q25.3 rs3798220 1.92 (1.48–2.49) APOB 2p24.1 rs515135 1.03 SORT1 1p13.3 rs599839 1.29 (1.18–1.40) LDLR 19p13.2 rs1122608 1.14 (1.09–1.19) APOE 19q13.32 rs2075650 1.14 (1.09–1.19) ABCG5-ABCG8 2p21 rs6544713 1.07 (1.04–1.11) PCSK9 1p32.3 rs11206510 1.15 (1.10–1.21)

Associated with HDL cholesterol (n = 1) ANKS1A 6p21.31 rs12205331 1.04

Associated with Triglycerides (n = 2) TRIB1 8q24.13 rs10808546 1.08 (1.04–1.12) ZNF259, APOA5-A4-C3-A1

11q23.3 rs964184 1.13 (1.10–1.16)

Known genetic risk variants and CAD

Nearby gene (allele)

Chromosome location

SNP Odd’s Ratio

Associated with Hypertension (n = 4) SH2B3 12q24.12 rs3184504 1.13 (1.08–1.18)

CYP127A1, CNNM2, NT5C2

10q24.32 rs12413409 1.12 (1.08–1.16)

GUCYA3 4q31.1 rs7692387 1.13

FURIN-FES 15q26.1 rs17514846 1.04

Associated with Myocardial Infarction (n = 1) ABO 9q34.2 rs579459 1.10 (1.07–1.13)

Known genetic risk variants and CAD Nearby gene (allele)

Chromosome location

SNP Odd’s Ratio

Mechanism of Risk Unknown (n = 32) PHACTR1 6p24.1 rs12526453 1.13 (1.09–1.17)

MRPS6 21q22.11 rs9982601 1.19 (1.13–1.27)

MRAS 3q22.3 rs2306374 1.15 (1.11–1.19)

WDR12 2q33.1 rs6725887 1.16 (1.10–1.22)

CDKN2A, CDKN2B 9p21.3 rs4977574 1.25 (1.18–1.31) to 1.37 (1.26–1.48)

MIA3 1q41 rs17465637 1.20 (1.12–1.30)

KIAA1462 10p11.23 rs2505083 1.07 (1.04–1.09)

PPAP2B 1p32.2 rs17114036 1.17 (1.13–1.22)

TCF21 6q23.2 rs12190287 1.08 (1.06–1.10)

BCAP29 7q22.3 rs10953541 1.08 (1.05–1.11)

ZC3HC1 7q32.2 rs11556924 1.09 (1.07–1.12)

LIPA 10q23.31 rs1412444 1.09 (1.07–1.12)

PDGF 11q22.3 rs974819 1.07 (1.04–1.09)

COL4A1, COL4A2 13q34 rs4773144 1.07 (1.05–1.09)

HHIPL1 14q32.2 rs2895811 1.07 (1.05–1.10)

ADAMTS7 15q25.1 rs3825807 1.08 (1.06–1.10)

SMG6, SRR 17p13.3 rs216172 1.07 (1.05–1.09)

RASD1, SMCR3, PEMT 17p11.2 rs12936587 1.07 (1.05–1.09)

UBE2Z, GIP, ATP5G1, SNF8 17q21.32 rs46522 1.06 (1.04–1.08)

IRX1, ADAMTS16 5p13.3 rs11748327 1.25 (1.18–1.33)

BTN2A1 6p22.1 rs6929846 1.51 (1.28–1.77)

C6orf105 6p24.1 rs6903956 1.65 (1.44–1.90)

HCG27 and HLA-C 6p21.3 rs3869109 1.15

EDNRA Chr4 rs1878406 1.09

HDAC9 7p21.1 rs2023938 1.13

VAMP5-VAMP8 2p11.2 rs1561198 1.07

ZEB2-AC074093.1 Chr2 rs2252641 1

SLC22A4-SLC22A5 Chr5 rs273909 1.11

KCNK5 6p21 rs10947789 1.01

PLG 6q26 rs4252120 1.07

LPL 8p22 rs264 1.06

FLT1 13q12 rs9319428 1.1

Known genetic risk variants and CAD

Nearby gene (allele)

Chromosome location

SNP Odd’s Ratio

Presumed associated with Inflammation (n = 3) CXCL12 10q11.21 rs1746048 1.33 (1.20–1.48)

IL5 5q31.1 rs2706399 1.02 (1.01–1.03)

IL6R 1q21 rs4845625 1.09

→→The known CAD risk variants are overwhelmingly associated with events important in the early

development of atherosclerosis (dyslipidemia and hypertension), rather than late events with clinical

consequences like diabetes and inflammation

Bjorkegren, Kovacic et al. JACC 2015

GWAS favors the identification of alleles that exert their effect during the development of atherosclerosis and which are independent of environmental influences (smoking, diet, sedentary lifestyle etc.)

GWAS and DNA-based approach to CAD

DNA

RNA

Protein

Atherosclerosis and CAD

DNA

RNA

Protein

Atherosclerosis

and CAD

Genomic activity measures (e.g., RNA,

proteins, metabolites) to define disease

driving molecular of CAD

Key drivers of CAD

Aim: to identify disease-driving molecular processes in CAD

- DNA, - RNA, - Protein, - Metabolite

Systems Biology and Systems Genetics

Systems genetics can be summarized as using genomic activity measures (e.g., RNA, proteins, metabolites) to define disease driving molecular processes,

thereby permitting their contribution to complex disease heritability to be

understood.

Johnston-Cox, Bjorkegren, Kovacic. 2015

HEART

VASCULATURE

KIDNEY

IMMUNE SYSTEM

transcriptional network

protein network

metabolite network

Non-coding RNA network

GI TRACT

BRAIN

ENVIRONMENT

EN

VIR

ON

ME

NT

ENVIRONMENT

EN

VIR

ON

ME

NT

Systems Genetics Captures Environmental Influences (smoking, diet, pollution, exercise, obesity, stress etc.)

Tissues of Interest Related to Atherosclerosis

Atherosclerotic Arterial Wall

Atherosclerosis- Free Artery Wall

(LIMA)

Liver

Abdominal Fat

Skeletal Muscle

Subcutaneous Fat

Whole blood

Bjorkegren, Kovacic et al. JACC 2015

Systems Genetics CAD cohorts

• The STAGE (the STockholm Atherosclerosis Gene Expression) cohort: – 156 CAD patients with full clinical characterization,

– DNA and 8 RNA tissue samples:

• atherosclerotic and non-atherosclerotic arterial wall, blood monocytes (macrophages), blood, liver, abdominal and s.c. fat and skeletal muscle) collected during CABG

Shang et al ATVB 2014 Hagg et al PLoS genetics 2014 Foroughi et al Circ Genetics 2015

Systems Genetics and CAD findings from STAGE

• With 156 subjects, STAGE independently identified 22 of 46 accepted CAD risk loci, which originally required ~200,000 subjects to identify and validate using DNA-based GWAS

• Loci acting across multiple tissues, rather than 1-2 tissues, increase the risk of having CAD

• Identified a causal inflammatory CAD network that is enriched with genes in the trans-endothelial migration of leukocytes (TEML) pathway and with Lim-domain binding 2 (LDB2) as a key driver - now validated as a novel CAD network target for TEML

Shang et al ATVB 2014 Hagg et al PLoS genetics 2014 Foroughi et al Circ Genetics 2015

Talukdar, Franzen, Giannarelli, Kovacic, et al, Bjorkegren. In press, Cell Systems

Systems Genetics CAD cohorts

• The STAGE (the STockholm Atherosclerosis Gene Expression) cohort: – 156 CAD patients with full clinical characterization,

– DNA and 8 RNA tissue samples:

• atherosclerotic and non-atherosclerotic arterial wall, blood monocytes (macrophages), blood, liver, abdominal and s.c. fat and skeletal muscle) collected during CABG

• The STARNET (the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Team) cohort: – 1000 CAD cases

– 100 non-CAD controls having non-CABG open-thorax surgery

– DNA and 8 RNA tissues sampled as for STAGE.

*RNAseq (25 million read depth, 50 pb, single strand) and 700 DNA samples (OmniExpressExome Chip; 700k genome-wide+250k exome) with IlluminaHighSeq2000 platform (2013)

STARNET has defined tissue of action of know CAD loci

Franzen, Giannarelli, Kovacic, et al, Schadt, Bjorkegren. In press

A gene-regulatory network of 21/46 risk loci identified by meta-analysis in the CARDIOGRAM GWAS of CAD

Franzen, Giannarelli, Kovacic, et al, Schadt, Bjorkegren. In press

Applying The Power of Systems Genetics

Bjorkegren, Kovacic et al. JACC 2015

Summary

→ inflammation → cross-tissue disease regulation → other late events in athero development → interaction among proteins, genes and loci → environmental effects (smoking etc) → therapeutic targets

ACS – Recent and Evolving Aspects Definition and Diagnostics,

Acquired and Genetic Factors, Ischemic and Reperfusion Injury

Murphy and Steenbergen. Physiol Rev, 2008

Lancet, 2015

Systems Genetics Is Expected To Make MAJOR Inroads On Understanding The Basis of CAD

→ Especially ACS and Late Events With Clinical Consequences

Using systems biology and the digital universe of

data to better diagnose and treat patients

Acknowledgements

Kovacic Lab:

• Solene Evrard

• Aya Kitabayashi

• Valentina d‘Escamard

• Katherine Michelis

• Dongkwon Yang

• Meera Purushothaman

• Maria Paola Santini

• Jonathan Lee

• Laura Lecce

Dept. of Genetics:

• Johan Bjorkegren

• Ke Hao

• Shamus Peng

• Oscar Franzen

• Eric Schadt

• Andrew Kasarskis

CVRC:

• Roger Hajjar

• Valentin Fuster