The Foundations of Personalized Medicine
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1 The Foundations of Personalized Medicine Jeremy M. Berg Pittsburgh Foundation Professor and Director, Institute for Personalized Medicine University of Pittsburgh
2 Personalized Medicine Physicians have treated patients based on their individual characteristics since before Hippocrates Modern technologies (genomic and other) enable characterization of individuals at unprecedented levels of resolution The goal of Personalized Medicine is to harvest these data to aid in disease prevention and treatment with benefit both to patients and society
3 Personalized Medicine Different Subfields Complex Diseases Cancer Perinatal Diagnosis Pharmacogenomics Common Themes DNA sequencing and other technologies Complexity but links to existing knowledge Big Data
4 : The Human Genome Project Over 3 Billion Unique Base Pairs Distributed Across 23 Pairs of Chromosomes Sequence finished in 2003 though international effort (under budget and ahead of schedule with some competition from a private company)
5 The Human Genome Sequence Chromosome 1 TAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAAC CCTAACCCAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCCTAACCCTAACCCTAACCCTAACCCTAACCTAACCCTAACCCTAACCCTAA CCCTAACCCTAACCCTAACCCTAACCCTAACCCCTAACCCTAACCCTAAACCCTAAACCCTAACCCTAACCCTAACCCTAACCCTAACCCCAACCCCAAC CCCAACCCCAACCCCAACCCCAACCCTAACCCCTAACCCTAACCCTAACCCTACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCCTAACCCC TAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCCTAACCCTAACCCTAACCCTAACCCTCGCGGTACCCTCAGCCGGCCCGCCCGCCCGGG TCTGACCTGAGGAGAACTGTGCTCCGCCTTCAGAGTACCACCGAAATCTGTGCAGAGGACAACGCAGCTCCGCCCTCGCGGTGCTCTCCGGGTCTGTGCT GAGGAGAACGCAACTCCGCCGTTGCAAAGGCGCGCCGCGCCGGCGCAGGCGCAGAGAGGCGCGCCGCGCCGGCGCAGGCGCAGAGAGGCGCGCCGCGCCG GCGCAGGCGCAGAGAGGCGCGCCGCGCCGGCGCAGGCGCAGAGAGGCGCGCCGCGCCGGCGCAGGCGCAGAGAGGCGCGCCGCGCCGGCGCAGGCGCAGA CACATGCTAGCGCGTCGGGGTGGAGGCGTGGCGCAGGCGCAGAGAGGCGCGCCGCGCCGGCGCAGGCGCAGAGACACATGCTACCGCGTCCAGGGGTGGA GGCGTGGCGCAGGCGCAGAGAGGCGCACCGCGCCGGCGCAGGCGCAGAGACACATGCTAGCGCGTCCAGGGGTGGAGGCGTGGCGCAGGCGCAGAGACGC AAGCCTACGGGCGGGGGTTGGGGGGGCGTGTGTTGCAGGAGCAAAGTCGCACGGCGCCGGGCTGGGGCGGGGGGAGGGTGGCGCCGTGCACGCGCAGAAA CTCACGTCACGGTGGCGCGGCGCAGAGACGGGTAGAACCTCAGTAATCCGAAAAGCCGGGATCGACCGCCCCTTGCTTGCAGCCGGGCACTACAGGACCC GCTTGCTCACGGTGCTGTGCCAGGGCGCCCCCTGCTGGCGACTAGGGCAACTGCAGGGCTCTCTTGCTTAGAGTGGTGGCCAGCGCCCCCTGCTGGCGCC GGGGCACTGCAGGGCCCTCTTGCTTACTGTATAGTGGTGGCACGCCGCCTGCTGGCAGCTAGGGACATTGCAGGGTCCTCTTGCTCAAGGTGTAGTGGCA GCACGCCCACCTGCTGGCAGCTGGGGACACTGCCGGGCCCTCTTGCTCCAACAGTACTGGCGGATTATAGGGAAACACCCGGAGCATATGCTGTTTGGTC TCAGTAGACTCCTAAATATGGGATTCCTGGGTTTAAAAGTAAAAAATAAATATGTTTAATTTGTGAACTGATTACCATCAGAATTGTACTGTTCTGTATC CCACCAGCAATGTCTAGGAATGCCTGTTTCTCCACAAAGTGTTTACTTTTGGATTTTTGCCAGTCTAACAGGTAAGGCCCTGGAGATTCTTATTAGTGAT TTGGGCTGGGGCCTGGCCATGTGTATTTTTTTAAATTTCCACTGATGATTTTGCTGCATGGCCGGTGTTGAGAATGACTGCGCAAATTTGCCGGATTTCC TTTGCTGTTCCTGCATGTAGTTTAAACGAGATTGCCAGCACCGGGTATCATTCACCATTTTTCTTTTCGTTAACTTGCCGTCAGCCTTTTCTTTGACCTC TTCTTTCTGTTCATGTGTATTTGCTGTCTCTTAGCCCAGACTTCCCGTGTCCTTTCCACCGGGCCTTTGAGAGGTCACAGGGTCTTGATGCTGTGGTCTT CATCTGCAGGTGTCTGACTTCCAGCAACTGCTGGCCTGTGCCAGGGTGCAAGCTGAGCACTGGAGTGGAGTTTTCCTGTGGAGAGGAGCCATGCCTAGAG TGGGATGGGCCATTGTTCATCTTCTGGCCCCTGTTGTCTGCATGTAACTTAATACCACAACCAGGCATAGGGGAAAGATTGGAGGAAAGATGAGTGAGAG CATCAACTTCTCTCACAACCTAGGCCAGTAAGTAGTGCTTGTGCTCATCT...
6 The Human Genome Sequence Skip next 12.4 Million Slides
7 The Human Genome Sequence Y Chromosome...CCCAGCTGCCAGCAGGCGGGCGTGCTGCCAGTACACCTTGAGCAAGAGGACCCTGCAATGTCCGTAGCTGCCAGCAGGCGGCGTGCCACCACTATAC AGTAAGCAAGAGGACCCTGCAGTGCCCCGGCGCCACGAGGGGGCGGTGGCCACCACTCTAAGCAAGAGAGCCCTGCAGTTGCCCTAGTCGCCAGCAGGGG GCGCCCTGGCACAGCACCGTGAGCAAGCGGGTCCTGTAGTGCCCGGCTGCAAGCAAGGGGCGGTCGATCCCGGCTTTTCGGATTACTGAAGTTCCACCCG TCTCTGCGCCGCGCCGCCGTGACGTGAGTTTCTGCGCGTGCACGGCGCCCCCGCACCCCCCCGCCCCCAGCCCGGCGCCGTGCGACTTTGCTCCTGCAAC ACACGCACCCCCAACCCCCGCCCGTAGGCGTGCGTCTCTGCGCCTGCGCCACGCCTCCACCCCTGGACGCGCTAGCATGTGTCTCTGCGCCTGCGCCGGC GCGGCGCGCCTCTCTGCGCCTGCGCCGGCGCGGCGCGCCTCTCTGCGCCTGCGCCGGCGCGGCGCGCCTCTCTGCGCCTGCGCCGGCGCGGCGCGCCTCT CTGCGCCTGCGCCGGCGCGGCGCGCCTCTCTGCGCCTGCGCCGGCGCGGCGCGCCTCTCTGCGCCTGCGCCGGCGCGGCGCGCCTCTCTGCGCCTGCGCC GGCGCGGCGCGCCTCTCTGCGCCTGCGCCGGCGCGGCGCGCCTTTGCGACGGCCGAGTTGCGTTCTCGTCAGCACAGAGCGGCAGAGCACCGCGAGGGCG GAGCTGCGTTGTCCTCTGCACAGATTTCGGTGGTACTGCGAAGGCGGAGCAGAGTTCTCCTCAGGTCAGACCCGGGCGGGCGGGCTGAGGGTACCGCGAG GGCGGAGCTGCGTTCTGCTCAGTACAGACCTGGGGGTCACCGTAAAGGTGGAGCAGCATTCCCCTAAGCACAGACGTTGGGGCCACTGACTGGCTTTGGG ACAACTCGGGGCGCATCAACGGTGAATAAAAATGTTTCCCGGTTGCAGCCATGAATAATCAAGGTGAGAGACCAGTTAGAGCGGTTCAGTGCGGAAAACG GGAAAGCAAAAGCCCCTCTGAATGCTGCGCACCGAGATTCTCCCAAGGCAAGGGGAGGGGCTGCATTGCAGGGTCCACTTGCAGCGTCGGAACGCAAATG CAGCATTCCTAATGCACACATGATACCCAAAATATAACACCCACATTCCTCATGTGCTTAGGGTGAGGGTGAGGGTTGGGGTTGGGGTTGCGGTTGGGGT TGGGGTTGGGGTTGGGGTTGGGGTTAGGGTTTGGGTTTAGGGTTGGGGTAGGGGTAGGGGTGGGGTTGGGGTTGGGGTTGGGGTTGGGGTTAGGGGTTGG GGTTGGGGTTGGGGTTGGGGTTGGGGTTAGGGTTAAGGGTTAGGGTTAGGGGTTAGGGGTTAGGGTTGGGGTTGGGGTTAGGGTTAGGGTAGGGTTAGGG TTAGGGTTAGGGGTTAGGGGTTAGGGTAGGGTTAGGGTGAGGGTGAGGGTGAGGGTGAGGGTGAGGGTGAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTA GGGGTTAGGGGTTAGGGTTAGGGTTAGGGGTTAGGGGTTAGGGTTAGGGTTAGGGGTTAGGGTTAGGGTTAGGGGTTAGGGGTTAGGGGTTAGGGGTTAG GGTAGGGTAGGGTAGGGTAGGGAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTT AGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGG TTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTGAGGGTTAGGGTTAG GGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTT AGGGTTAGGGGTTAGGGGTTAGGGGTTAGGGGTTAGGGGTTAGGGGTTAGGGTTAGGGTTAGGGTTAGGGTGTGGTGTGTGGGTGTGTGTGGGTGTGGTG TGTGTGGGTGTGGTGTGTGGGTGTGGGTGTGGGTGTGGGTGTGTGGGTGTGGTGTGTGGGTGTGGT
8 DNA Sequencing Technology
9 Human DNA Sequence Variation Unrelated individuals are (on average) ~99.5% identical in DNA sequence Single base variations (single nucleotide polymorphisms, SNPs) Variable numbers of copies of repeated sequences (copy number variations, CNVs)
10 Human DNA Sequence Variation 99.5% Identical means 0.5% different 0.5% X 3 billion base pairs = 15 million differences Not all differences are independent Not all differences are meaningful
11 Blocks of Linkage Disequilibrium
12 Complex Traits Influenced by both genes (usually many) and environment Heritability can be inferred from studies of twins (identical and fraternal)
13 Genome-Wide Association Studies Identify a trait for which information is available from a moderate to large population of diverse individuals Test genetic markers from across the human genome to look for specific markers that vary between individuals with the same pattern as the trait Identify genes that are adjacent to the genetic markers as candidates for contributing to the variation in the trait
14 Genome-Wide Association Studies What are the odds of these patterns occurring by chance?
15 Genome-Wide Association Studies 1:23 1:10 1:16 1:10 1:500,000 1:10
16 The Genomics of Eye Color
17 Pancreatitis as a Model for Personalized Medicine Applied to Complex Diseases Inflammation of the pancreas Acute pancreatitis (30/100,000/year) Recurrent acute pancreatitis Chronic pancreatitis (8/100,000/year) Risk Factors Heavy alcohol use Smoking Gall stones Genetic factors David Whitcomb, MD, PhD
18 Acute vs Chronic Pancreatitis David Whitcomb, MD, PhD
19 Hereditary Pancreatitis Some families show very high risk of pancreatitis Autosomal dominant inheritance Variations mapped to chromosome 7q35 Mutations discovered in PRSS1 gene encoding cationic trypsinogen
20 Trypsinogen Activation Inactive precursor (zymogen) of digestive protease trypsin Trypsin cleaves after basic (lysine, arginine) residues Trypsinogen activated by cleavage of Lys6-Ile7 bond by enteropeptidase Can be autoactivated by trypsin
21 Trypsin can be inactivated by proteolysis by trypsin and chymotrypsin Trypsin Autolysis
22 Variations Associated with Hereditary Pancreatitis Different families have different variations e.g. R122H N29I A16V D19A D22G K23R E79K Gain of function (increased auto-activation, resistance to autolysis)
23 Other Genetic Contributors to Ideopathic Pancreatitis SPINK1 (Serine Protease Inhibitor, Kazal Type 1) Inhibition of activated trypsin CTRC (Chymotrypsin C) Cleavage of activated trypsin CFTR (Cystic Fibrosis Transmembrane Conductance Regulator) Contributor to secretion leading to flushing of pancreatic ducts
24 GWAS Studies Studies of ideopathic pancreatitis > Rare genetic variations that contribute to pancreatitis risk Gene-wide association studies should reveal common variations that may contribute 2 stage GWAS study (676 cases, 4507 controls; 910 cases, 4170 controls)
25 GWAS Studies Two loci identified on chromosomes 7q34 and Xq22.3 The locus on chromosome 7 appears to be in the PRSS1-PRSS2 gene cluster The locus on the X chromosome appears to be in the CLDN2 gene encoding claudin-2, a membrane protein found in tight junctions
26 GWAS Studies The variant in the PRSS1-PRSS2 cluster does not, in general, affect the amino acid sequence of trypsinogen Rather, the variant is in the promoter region and appears to be associated with higher levels of gene expression
27 GWAS Studies The variant in CLDN2 appears to affect localization of claudin-2 within pancreatic acinar cells The presence of a risk allele on the X chromosome may contribute to the higher prevalence of pancreatitis in males over females Additional genes with risk alleles are being discovered by other methods
28 Gene X Environment Interactions Not all risk alleles are associated with environmental factors such as alcohol use in the same manner For example, the CLDN2 variant is more closely associated with alcohol-related pancreatitis than is the PRSS1-PRSS2 variant
29 A Vision for Personalized Medicine Applied to Pancreatitis When a patient presents with an initial case of acute pancreatitis Determine the patients genotype with regard to key genes Stratify patients according to risk of progression calculated by computational models that include genetic, environmental, and clinical factors Treat high risk patients more aggressively than patients with lower risk
30 Ethical Considerations Personalized Medicine has many associated ethical considerations Privacy Patient autonomy Informed Consent Other issues
31 The Database of Genotypes and Phenotypes
32 Anonymous DNA Sequences Can Sometimes be Identified
33 Incidental Findings Whole exome and whole genome methods are becoming less expensive and more effective than single gene approaches American College of Medical Genetics and Genomics recommended returning results for 56 genes for which actionable information can be inferred from known or expected pathogenic variants
34
35 Personalized Medicine Personalized Medicine depends on genome sequencing and other technologies but is MORE Family history Individualized screening Ethical considerations Implementation of knowledge/evidence Data collection/analytics to drive improvements
36 Some Challenges NextGen sequence information quality and critical use Correlating genotype and phenotype The influence of differences in genomic background Data overload Data sharing Regulatory issues Technology Ethics Identification of clinical questions that are amenable to Personalized Medicine approaches
37
38 Thanks Personalized Medicine Task Force Ivet Bahar John Maier Mike Barmada George Michalopoulos Mike Becich Yuri Nikiforov Takis Benos Lisa Parker Rebecca Crowley Aleks Rajkovic Jacobson Steve Reis Nancy Davidson Steve Shapiro Robert Edwards Dietrich Stephan Phil Empey Lans Taylor Arjun Hattiangadi Jerry Vockley John Kellum David Whitcomb Adrian Lee Nathan Yates
39 Grazie! Domande?
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