Biomarkers for Personalized Medicine and Regenerative Therapy

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1 Biomarkers for Personalized Medicine and Regenerative Therapy Bettina Heidecker, MD University of Miami Division of Cardiology Interdisciplinary Stem Cell Institute

2 Biomarkers in Heart Failure Growing Public Health Problem: US: 550,000 newly diagnosed patients/year* Europe + US: Prevalence: 2%* Felker GM, NEJM 2000: 342: *Heart Disease and Stroke Statistics 2007 Update, AHA

3 Biomarkers in Heart Failure Functional data: EF, PCWP, PAP, LVIDD Laboratory parameters: probnp, CRP, HbA1C Artificial neural networks: Seattle Heart Failure Model Genetic markers: SNPs, Transcriptomics

5 Cytokine Hypothesis of Heart Failure Braunwald E., NEJM, 2008, 358:

6 Biomarkers Reflecting Tissue Damage Inflammation CRP TNF α Fas (APO-1) Interleukins 1, 6 and 18 Oxidative stress Oxidized LDL Myeloperoxidase Urinary biopyrrins Extracellular-matrix remodeling Matrix metalloproteinases Tissue inhibitors of metalloproteinases Collagen propeptides Neurohormones Norepinephrine Renin Angiotensin II Aldosterone Arginine vasopressin Endothelin Myocyte Injury Cardiac specific troponins I and T Myosin light-chain kinase I Heart-type fatty-acid protein Creatine kinase MB fraction Myocyte stress BNP NT-proBNP Midregional fragment of proadrenomedullin ST2 New biomarkers Chromogranin Galectin 3 Osteoprotegerin Adiponectin Growth differentiation factor 15 Braunwald E., NEJM, 2008, 358:

7 Biomarkers Reflecting Tissue Damage and Inflammation Uppsala Longitudinal Study of Adult Men (ULSAM) Initiated in 1970, enrolling 50 yo men, health survey with avg. 10 year follow-up 1135 participants: troponin I, N-terminal N probnp, cystatin C, CRP Subgroup: 661 men without prevalent cardiovascular disease at baseline Zethelius B., NEJM 2008, 358:

9 Biomarkers to Guide Cell Therapy Multiple options of cell types and ways of administration > need for guidance in therapy Select patients with high potential to respond Identify the optimal type of cell for each individual patient

11 Areas of Concern The host Lehrke, S. et al. Circ Res 2006;99:

Placebo (PBS and 1% HSA) (n = 15) Low Dose (20M MSCs) (n=15) High Dose (200M MSCs) (n

12 Prometheus Study Patient Population LV Dysfunction (EF 20-50%) Prior MI (akinetic / dyskinetic segment) Undergoing CABG BM Aspiration & Randomization CABG & IM Injections Placebo (PBS and 1% HSA) (n = 15) Low Dose (20M MSCs) (n=15) High Dose (200M MSCs) (n = 15) WEEK -77 to to MRI MRI MRI MRI CT Echo Echo Echo CT

13 Wanted! The best cell for cardiac regeneration Skeletal myoblast Mesenchymal stem cells Bone marrow derived cells (hematopoietic stem cells, endothelial progenitor cells, mesenchymal stem cells, or side-population cells) Blood derived progenitor cells Embryonic stem cells Cardiospheres

14 Variable Outcome Dependent on Subtype of Cell Serious complications: Teratoma formation and arrhythmia Engraftment and survival Efficacy of regeneration and recovery

15 Transcriptomic Assessment of Stem Cell Characteristics and Function Many genes do not function independently, but interact in clusters and are influenced by their environment Success of MSA Comprehensive approach to screen for genes and pathways that characterize a type of cell Monitor stem cell lineages during cell expansion Cell engineering: Confirm successful manipulation and observe its consequences in a cell

16 Conserved genes among different stem cell lines Human fetal HSC Murine fetal and adult HSC Neural stem cells Embryonic stem cells N. B. Ivanova et al., Science 298, (2002)

17 Shared Pathways Independent of Age and Differentiation N. B. Ivanova et al., Science 298, (2002)

18 Mutation of cell line during expansion T. W. Plaia et al., Stem Cells 2006; 24;

19 Early Success of Transcriptomic Biomarkers in Cardiology Kittleson MM. et al. : Distinguish subtypes of cardiomyopathy Seo D. et al.: Detect severe coronary artery disease requiring cardiac catheterization Deng MC. et al: Detect early rejection of cardiac graft Hall JL. et al; Margulies KB. et al: Molecular signature of recovery in heart failure

20 Prediction of Long Term Clinical Outcome in New Onset Heart Failure New onset heart failure (n=350) Idiopathic dilated cardiomyopathy (n=180) Poor clinical outcome: Event within first 2 years of presentation (LVAD, cardiac tx, death)(n=18) Excellent clinical outcome: Event free survival for at least 5 years after presentation (n=25)

21 PAM analysis Heidecker et al., Circulation in press

22 Transcriptomic Prognostic Biomarker Sensitivity: 74% (95%CI: 69%-79%) Specificity: 90% (95%CI: 87%-93%) Heidecker et al., Circulation in press Log odds ratio: 3.3

23 Good vs Poor Clinical Outcome 46 genes UP FDR=4.6%,FC>1.2 Neuromusc. dev. Protein binding Transcription Ion transport Heidecker et al., Circulation in press

24 Overexpressed genes in patients who will recover from HF Telomerase activity and stem cell survival SMG6 homolog RAD50 homolog Vascular growth HIF3 α EGF receptor PW Recovery during VAD (Margulies, Circ. Res., 2005) SNRP 70kDa Obscurin like 1 RNA binding motif

25 PBMCs: Surrogates for Diseased Tissue in the Future? Tissues Brain Colon Heart Kidney Liver Lung Prostate Spleen Stomach Tissue Blood % 81.9% 82.5% 84.2% 83.2% 83.0% 80.9% 83.9% 85.0% 86.3% Liew C. C. et al, J Lab Clin Med, 2005

26 Clinical Test to Detect Early Rejection of Cardiac Graft from PBMCs

27 Summary Heart Failure: Variable outcome + multiple therapeutic options high demand for biomarkers Current biomarkers reflect tissue damage and inflammation Promising data from clinical trials individual trajectory still causes difficulties Transcriptomics: Comprehensive approach to assess individual s s risk of developing events Selection of the optimal cell or therapy for each individual Characterize entire transcriptome of a cell line at a certain stage of differentiation

28 Conclusions There has not been agreement on reliable standard tests for prediction of individual risk Transcriptomics may add accuracy to current risk assessment, due to its comprehensiveness and ability of detecting gene environment interactions. Transcriptomic biomarkers have been used for individual risk assessment, monitoring and may be used for personalized stem cell therapy.

29 Acknowledgements Joshua Hare, MD Miller School of Medicine,University of Miami Edward Kasper, MD; Ilan Wittstein, MD; Hunter Champion, MD, PhD; Elayne Breton; Stuart Russell, MD; The Johns Hopkins University, Baltimore Kenneth Baughman, MD Brigham and Women s s Hospital, Boston Michelle Kittleson, MD PhD University of California, L.A.

Risk Stratification in Heart Failure: The Role of Emerging Biomarkers

Risk Stratification in Heart Failure: The Role of Emerging Biomarkers David G. Grenache, PhD Associate Professor of Pathology, University of Utah Medical Director, ARUP Laboratories Salt Lake City, UT