Personalized Medicine Strategies for the future
Transcript
Personalized Medicine Strategies for the future
Personalized Medicine Strategies for the future Loreto Gesualdo UOC Nefrologia, Dialisi e Trapianto Azienda Ospedaliero-Universitaria Policlinico Università degli Studi di Bari Eric C. Faulkner Eric C. Faulkner Eric C. Faulkner PMC, march 2011 What Is Personalized Medicine? “Personalized Medicine” refers to the tailoring of medical treatment to the individuals into subpopulations that differ in their susceptibility to a particular disease or their response to a specific treatment. Preventative or therapeutic interventions can then be concentrated on those who will benefit, sparing expense and side effects for those who will not. PMC 2010 >2300 2010 > 6800 papers The “Omics” Era Transcriptomics Genomics Proteomics Metabolomics The new lexicon of “…omics” Gene DNA Structural and Comparative Genomics Transcription RNA Translation Protein Environment Functional Genomics Transcriptomics (Gene Expression) Proteomics PTMs Biochemical Circuitry Phenotypes Monarch Monarch butterfly butterfly Metabolomics SYSTEMS BIOLOGY NETWORK IN PUGLIA REGION DEB Systems Biology Proteomics DETO CNR CARSO Transcriptomics Genomics and Metabolomics BI BA O NC A GENOMICA RETE REGIONALE DI OMICA E BIOLOGIA DEI SISTEMI A PROTEOMIC A MIC O L O AB MET I S M TE L BIO O GIA D I S I E “State-of-the-art” Core Facilities Renal Center How can we improve our understanding and treatment of renal diseases? Renal Disease (Clinical Phase) Renal Disease (Pre-clinical Phase) Tools Needed for Prediction and Personalized Care Decision Support Tools: Assess Risk Refine Assessment Monitor Progression Predict/Diagnose Predict Events Initiating Events Earliest Molecular Detection Earliest Clinical Detection Typical Current Intervention Cost Disease Burden Baseline Risk 1/reversibility Inform Therapeutics Time Baseline Risk Sources of New Biomarkers: Stable Genomics: Single Nucleotide Polymorphisms (SNPs) Haplotype Mapping Gene Sequencing Preclinical Progression Dynamic Genomics: Gene Expression Proteomics Metabolomics Molecular Imaging Disease Initiation and Progression Therapeutic Decision Support ©2005 RALPH SNYDERMAN Prospective Health Care Participating Population Low Risk High Risk Early Chronic 1/reversibility Late Chronic Cost Disease Burden Risk Assessment and Decision Support Tools Time Personalized Health Plan Personal Lifestyle Plan Risk Modification Disease Management ©2005 RALPH SNYDERMAN The Empirical strategy for Drug Therapy Patients with same diagnosis treated with the same medications Toxicity (ADR) Non-Responders Adverse Drug Reactions (ADRs) are the fourth leading cause of hospitalization, fifth leading cause of mortality in the USA Relationship between Drug Dose and Response High Sensitive (toxicity) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ Toxicity Response _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ Efficacy Resistant (poor resp.) Low Low Drug Dose High Many factors influence drug delivery & response but inheritance can have a predominant effect ? PharmacoPharmacogenetics genetics E. Vessell, 1980s Microarray technology and pharmacogenomics Esempi di DNA analysis ü AmpliChip CYP450 Test (Roche Diagnostics) ü Test per la TPMT (Tiopurina metiltransferasi) [St. Jude Children’s Research Hospital] AmpliChip CYP450 Test ü Permette l’analisi contemporanea di 33 polimorfismi, duplicazioni e delezioni nei due geni del citocromo P450 (2D6 e 2C19) principalmente coinvolti nel metabolismo ossidativo di diverse classi di farmaci (25%). ü Alcune varianti alleliche di tali geni nel 7-10% dei bianchi e nell’1-2% degli asiatici, mostrano infatti alterata o assente attività enzimatica. CYP2C19: S-mephenytoin hydroxylase CYP2D6: Debrisoquine hydroxylase Varianti alleliche e fenotipi ØIl gene CYP2D6 ha circa 70 varianti alleliche che danno 4 fenotipi diversi predittivi della capacità di risposta: ØIl gene CYP2C19 ha 2 principali varianti alleliche che danno attività enzimatica normale o assente Dal genotipo alla predizione del fenotipo CYP2D6 CYP2C19 Eric C. Faulkner Eric C. Faulkner Pharmacogenomics will select the medications each person, thereby toxicity…….. make it increasingly possible to and doses that are optimal for improving efficacy and reducing …… and will help us to define the mechanisms underlying some of the beneficial/detrimental effects of immunosuppressive drugs. Microarray technology and pharmacogenomics MYCOPHENOLIC ACID INDUCES THE EXPRESSION OF NEUTRAL ENDOPEPTIDASE: A NOVEL POTENTIAL ANTI-FIBROTIC MECHANISM Sezione di Nefrologia, Dialisi e Trapianto, DETO “Policlinico”, Università degli Studi di Bari Zaza et al., J Am Soc Nephrol. 2010 Dec;21(12):2157-68. TREATMENT SCHEMA (training group) Time 0 Time 0 (PBMC) (PBMC) Time 1 (3 months) (PBMC) Time 1 (3 months) Time (PBMC) 2 (6 months) (PBMC) Continuing with CyA+AZA (50 mg/day) (n=15) CyA+AZA (n=35) (50 mg/day) Switching to CyA+MPA (720 mg/day) (n=20) Microarray (n=5) Microarray (n=5) Microarray (n=5) • All patients at the baseline were treated with: CS, CyA, AZA • All patients had ClCr more than 60 ml/min • No patients had episodes of AR before enrollment All patients suffering from cardiovascular diseases, infectious diseases, diabetes, chronic lung diseases, neoplasm, or inflammatory diseases and patients receiving antibiotics or non-steroidal anti-inflammatory agents were excluded. MICROARRAY ANALYSIS TIME 1 (CyA+MPA) TIME 0 (CyA+AZA) PT 1 PT 2 PT 1 PT 3 PT 3 PT 4 PT 5 PT 2 PT 4 3 MONTHS PT 5 AZA Myfortic RNA from peripheral blood mononuclear cells (PBMC) GeneChip® HG U133 A Array (more then 20.000 genes) STATISTICAL ANALYSIS PCA AND HIERARCHICAL CLUSTERING SHOW A CLEAR DISCRIMINATION BETWEEN PATIENTS AT TIME 0 VERSUS TIME 1 PT 4 PT 1 PT 5 PT 2 PT 3 PT 1 PT 4 TIME 1 (MPA) PT 5 PT 2 PT 3 TIME 0 (CyA+AZA) TIME 1 (CyA+MPA) TIME 0 (AZA) TOP-RANKED GENES DIFFERENTIALLY REGULATED AFTER 3 MONTHS OF TREATMENT WITH MPA (selected by microarray analysis) Probe set ID Gene Symbol Description T0 (AZA) T1(EC-MPA) (Log2 mean±SD) (Log2 mean±SD) Up-regulated genes 203434_s_at 215321_at 207286_at 205765_at 219990_at 220302_at 210666_at 211088_s_at NEP RPIB9 CEP135 CYP3A5 E2F8 MAK IDS PLK4 Neutral endopeptidase Rap2-binding protein 9 Centrosomal protein 135kDa Cytochrome P450, family 3, subfamily A, polypeptide 5 E2F transcription factor 8 Male germ cell-associated kinase Iduronate 2-sulfatase Polo-like kinase 4 3.94 ± 1.86 2.12 ± 0.41 2.21 ± 0.53 1.94 ± 0.52 2.32 ± 1.76 4.50 ± 1.55 6.13 ± 1.53 2.26 ± 1.58 7.08 ± 0.02 4.75 ± 1.00 4.87 ± 0.52 4.32 ± 1.43 4.55 ± 1.23 6.78 ± 0.59 8.01 ± 0.52 4.36 ± 1.02 Endothelin receptor type B Neuroligin 4, Y-linked Transmembrane protein 158 Zinc finger, BED-type containing 2 Phosphodiesterase 3A, cGMP-inhibited LFNG O-fucosylpeptide 3-beta-N-acetylglucosaminyltransferase Matrix-remodelling associated 5 Tryptase alpha/beta 1 Sparc/osteonectin 5.29 ± 1.06 3.96 ± 1.47 7.46 ± 1.08 5.72 ± 0.48 4.55 ± 1.56 6.16 ± 1.48 6.00 ± 0.75 5.44 ± 1.33 6.72 ± 0.23 3.09 ± 1.52 1.82 ± 1.05 5.51 ± 1.15 4.10 ± 1.43 2.67 ± 0.85 4.45 ± 0.64 4.55 ± 1.16 3.44 ± 0.36 5.18 ± 0.78 Down-regulated genes 206701_x_at 207703_at 213338_at 219836_at 206389_s_at 215270_at 209596_at 210084_x_at 202363_at EDNRB NLGN4Y TMEM158 ZBED2 PDE3A LFNG MXRA5 TPSAB1 SPOCK1 • P.VALUE: Calculated using t-test • FOLD CHANGE: ratio between log2 expression level at Time 1 versus Time 0 Immunohistochemistry showed higher NEP glomerular expression in CyA+MPA compared to CyA+AZA and CyA only groups A B p=0.4 E p=0.03 p=0.02 C D % stained area 70 60 p=0.9 50 40 30 20 10 0 DD P.value calculated by pairwise comparison t-test CsA only CsA+AZA CsA+EC-MPS Immunohistochemistry showed higher NEP tubular expression in CyA+MPA compared to CyA+AZA and CyA only groups A B E p=0.01 p=0.02 40 C D % stained area p=0.04 30 p=0.6 20 10 0 DD P.value calculated by pairwise comparison t-test CsA only CsA+AZA CsA+EC-MPS Neutral endopeptidase (NEP) Also known as Neprilysin, Membrane metallo-endopeptidase (MME), CD10, CALLA • NEP is a glycoprotein that is particularly abundant in kidney, where it is present on glomerular epithelial cells and brush border of proximal tubules McIntosh GG et al. Am J Pathol. 1999;154(1):771999;154(1):77-82 • NEP is a zinc-dependent metalloprotease enzyme that cleaves peptides at the ammino-side of hydrofobic residues and inactivates in vivo several peptide hormones (Angiotensin I, Angiotensin II, Atrial natriuretic factor, Neurotensin, ecc.) Ervin G et al. FASEB J. 1989; 3(2): 145145-51 TLR2 plays a role in the activation of human resident renal stem/progenitor cells Sezione di Nefrologia, Dialisi e Trapianto, DETO “Policlinico”, Università degli Studi di Bari Sallustio F et al., FASEB J. 2010 Feb;24(2):514-25. Recent results obtained in humans suggest that progenitors, localized at the urinary pole of the Bowman’s capsule, can initiate the replacement and regeneration of glomerular, as well as tubular, epithelial cells. May the cells which constitute the reservoir of the Bowman ’ s capsule modify their expression while migrating down to the injured tubules? Gene expression profile Six gene clusters have been identified on the basis of differential gene expression. Clusters A and E discriminate MSCs from the renal cell lines; Clusters F and B discriminate ARPCs when compared to the MSCs and RPTECs; Clusters C and D distinguish the stem/progenitor cells from RPTECs. A The secret for a Microarray study success? RNA (from Tissue and/or cell sample) FACILITY CORE EACH STEP IS IMPORTANT + DATASET ….on the contrary Genomics, Proteomics & Systems Biology Genomics Proteomics Systems Biology 1990 1995 2000 2005 2010 2015 2020 Systems biology: the scientific challenge of the 21th century The study of the mechanisms underlying complex biological processes as integrated systems of many interacting components. Systems biology involves (1) collection of large sets of experimental data, (2) proposal of mathematical models that might account for at least some significant aspects of this data set, (3) accurate computer solution of the mathematical equations to obtain numerical predictions, and (4) assessment of the quality of the model by comparing numerical simulations with the experimental data Reductionist Approach Holistic Approach Sauer U et al. Science 2007, 316: 550-1 Grazie per l’attenzione