The Foundations of Personalized Medicine Jeremy M. Berg Pittsburgh Foundation Professor and Director, Institute for Personalized Medicine University of Pittsburgh
“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
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”
1990-2003: 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)
“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...
“The” Human Genome Sequence Skip next 12.4 Million Slides
“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
DNA Sequencing Technology
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)
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
Blocks of Linkage Disequilibrium
Complex Traits • Influenced by both genes (usually many) and environment • Heritability can be inferred from studies of twins (identical and fraternal)
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
Genome-Wide Association Studies What are the odds of these patterns occurring by chance?
Genome-Wide Association Studies 1:23 1:16 1:10 1:500,000 1:10 1:10
The Genomics of Eye Color
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
Acute vs Chronic Pancreatitis David Whitcomb, MD, PhD
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
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
Trypsin Autolysis • Trypsin can be inactivated by proteolysis by trypsin and chymotrypsin
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)
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
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)
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
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
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