The Foundations of Personalized Medicine · Pancreatitis as a Model for Personalized Medicine...
Transcript of The Foundations of Personalized Medicine · Pancreatitis as a Model for Personalized Medicine...
The Foundationsof
Personalized MedicineJeremy 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
TAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAAC
CCTAACCCAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCCTAACCCTAACCCTAACCCTAACCCTAACCTAACCCTAACCCTAACCCTAA
CCCTAACCCTAACCCTAACCCTAACCCTAACCCCTAACCCTAACCCTAAACCCTAAACCCTAACCCTAACCCTAACCCTAACCCTAACCCCAACCCCAAC
CCCAACCCCAACCCCAACCCCAACCCTAACCCCTAACCCTAACCCTAACCCTACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCCTAACCCC
TAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCCTAACCCTAACCCTAACCCTAACCCTCGCGGTACCCTCAGCCGGCCCGCCCGCCCGGG
TCTGACCTGAGGAGAACTGTGCTCCGCCTTCAGAGTACCACCGAAATCTGTGCAGAGGACAACGCAGCTCCGCCCTCGCGGTGCTCTCCGGGTCTGTGCT
GAGGAGAACGCAACTCCGCCGTTGCAAAGGCGCGCCGCGCCGGCGCAGGCGCAGAGAGGCGCGCCGCGCCGGCGCAGGCGCAGAGAGGCGCGCCGCGCCG
GCGCAGGCGCAGAGAGGCGCGCCGCGCCGGCGCAGGCGCAGAGAGGCGCGCCGCGCCGGCGCAGGCGCAGAGAGGCGCGCCGCGCCGGCGCAGGCGCAGA
CACATGCTAGCGCGTCGGGGTGGAGGCGTGGCGCAGGCGCAGAGAGGCGCGCCGCGCCGGCGCAGGCGCAGAGACACATGCTACCGCGTCCAGGGGTGGA
GGCGTGGCGCAGGCGCAGAGAGGCGCACCGCGCCGGCGCAGGCGCAGAGACACATGCTAGCGCGTCCAGGGGTGGAGGCGTGGCGCAGGCGCAGAGACGC
AAGCCTACGGGCGGGGGTTGGGGGGGCGTGTGTTGCAGGAGCAAAGTCGCACGGCGCCGGGCTGGGGCGGGGGGAGGGTGGCGCCGTGCACGCGCAGAAA
CTCACGTCACGGTGGCGCGGCGCAGAGACGGGTAGAACCTCAGTAATCCGAAAAGCCGGGATCGACCGCCCCTTGCTTGCAGCCGGGCACTACAGGACCC
GCTTGCTCACGGTGCTGTGCCAGGGCGCCCCCTGCTGGCGACTAGGGCAACTGCAGGGCTCTCTTGCTTAGAGTGGTGGCCAGCGCCCCCTGCTGGCGCC
GGGGCACTGCAGGGCCCTCTTGCTTACTGTATAGTGGTGGCACGCCGCCTGCTGGCAGCTAGGGACATTGCAGGGTCCTCTTGCTCAAGGTGTAGTGGCA
GCACGCCCACCTGCTGGCAGCTGGGGACACTGCCGGGCCCTCTTGCTCCAACAGTACTGGCGGATTATAGGGAAACACCCGGAGCATATGCTGTTTGGTC
TCAGTAGACTCCTAAATATGGGATTCCTGGGTTTAAAAGTAAAAAATAAATATGTTTAATTTGTGAACTGATTACCATCAGAATTGTACTGTTCTGTATC
CCACCAGCAATGTCTAGGAATGCCTGTTTCTCCACAAAGTGTTTACTTTTGGATTTTTGCCAGTCTAACAGGTAAGGCCCTGGAGATTCTTATTAGTGAT
TTGGGCTGGGGCCTGGCCATGTGTATTTTTTTAAATTTCCACTGATGATTTTGCTGCATGGCCGGTGTTGAGAATGACTGCGCAAATTTGCCGGATTTCC
TTTGCTGTTCCTGCATGTAGTTTAAACGAGATTGCCAGCACCGGGTATCATTCACCATTTTTCTTTTCGTTAACTTGCCGTCAGCCTTTTCTTTGACCTC
TTCTTTCTGTTCATGTGTATTTGCTGTCTCTTAGCCCAGACTTCCCGTGTCCTTTCCACCGGGCCTTTGAGAGGTCACAGGGTCTTGATGCTGTGGTCTT
CATCTGCAGGTGTCTGACTTCCAGCAACTGCTGGCCTGTGCCAGGGTGCAAGCTGAGCACTGGAGTGGAGTTTTCCTGTGGAGAGGAGCCATGCCTAGAG
TGGGATGGGCCATTGTTCATCTTCTGGCCCCTGTTGTCTGCATGTAACTTAATACCACAACCAGGCATAGGGGAAAGATTGGAGGAAAGATGAGTGAGAG
CATCAACTTCTCTCACAACCTAGGCCAGTAAGTAGTGCTTGTGCTCATCT...
Chromosome 1
“The” Human Genome Sequence
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“The” Human Genome Sequence
...CCCAGCTGCCAGCAGGCGGGCGTGCTGCCAGTACACCTTGAGCAAGAGGACCCTGCAATGTCCGTAGCTGCCAGCAGGCGGCGTGCCACCACTATAC
AGTAAGCAAGAGGACCCTGCAGTGCCCCGGCGCCACGAGGGGGCGGTGGCCACCACTCTAAGCAAGAGAGCCCTGCAGTTGCCCTAGTCGCCAGCAGGGG
GCGCCCTGGCACAGCACCGTGAGCAAGCGGGTCCTGTAGTGCCCGGCTGCAAGCAAGGGGCGGTCGATCCCGGCTTTTCGGATTACTGAAGTTCCACCCG
TCTCTGCGCCGCGCCGCCGTGACGTGAGTTTCTGCGCGTGCACGGCGCCCCCGCACCCCCCCGCCCCCAGCCCGGCGCCGTGCGACTTTGCTCCTGCAAC
ACACGCACCCCCAACCCCCGCCCGTAGGCGTGCGTCTCTGCGCCTGCGCCACGCCTCCACCCCTGGACGCGCTAGCATGTGTCTCTGCGCCTGCGCCGGC
GCGGCGCGCCTCTCTGCGCCTGCGCCGGCGCGGCGCGCCTCTCTGCGCCTGCGCCGGCGCGGCGCGCCTCTCTGCGCCTGCGCCGGCGCGGCGCGCCTCT
CTGCGCCTGCGCCGGCGCGGCGCGCCTCTCTGCGCCTGCGCCGGCGCGGCGCGCCTCTCTGCGCCTGCGCCGGCGCGGCGCGCCTCTCTGCGCCTGCGCC
GGCGCGGCGCGCCTCTCTGCGCCTGCGCCGGCGCGGCGCGCCTTTGCGACGGCCGAGTTGCGTTCTCGTCAGCACAGAGCGGCAGAGCACCGCGAGGGCG
GAGCTGCGTTGTCCTCTGCACAGATTTCGGTGGTACTGCGAAGGCGGAGCAGAGTTCTCCTCAGGTCAGACCCGGGCGGGCGGGCTGAGGGTACCGCGAG
GGCGGAGCTGCGTTCTGCTCAGTACAGACCTGGGGGTCACCGTAAAGGTGGAGCAGCATTCCCCTAAGCACAGACGTTGGGGCCACTGACTGGCTTTGGG
ACAACTCGGGGCGCATCAACGGTGAATAAAAATGTTTCCCGGTTGCAGCCATGAATAATCAAGGTGAGAGACCAGTTAGAGCGGTTCAGTGCGGAAAACG
GGAAAGCAAAAGCCCCTCTGAATGCTGCGCACCGAGATTCTCCCAAGGCAAGGGGAGGGGCTGCATTGCAGGGTCCACTTGCAGCGTCGGAACGCAAATG
CAGCATTCCTAATGCACACATGATACCCAAAATATAACACCCACATTCCTCATGTGCTTAGGGTGAGGGTGAGGGTTGGGGTTGGGGTTGCGGTTGGGGT
TGGGGTTGGGGTTGGGGTTGGGGTTAGGGTTTGGGTTTAGGGTTGGGGTAGGGGTAGGGGTGGGGTTGGGGTTGGGGTTGGGGTTGGGGTTAGGGGTTGG
GGTTGGGGTTGGGGTTGGGGTTGGGGTTAGGGTTAAGGGTTAGGGTTAGGGGTTAGGGGTTAGGGTTGGGGTTGGGGTTAGGGTTAGGGTAGGGTTAGGG
TTAGGGTTAGGGGTTAGGGGTTAGGGTAGGGTTAGGGTGAGGGTGAGGGTGAGGGTGAGGGTGAGGGTGAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTA
GGGGTTAGGGGTTAGGGTTAGGGTTAGGGGTTAGGGGTTAGGGTTAGGGTTAGGGGTTAGGGTTAGGGTTAGGGGTTAGGGGTTAGGGGTTAGGGGTTAG
GGTAGGGTAGGGTAGGGTAGGGAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTT
AGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGG
TTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTGAGGGTTAGGGTTAG
GGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTT
AGGGTTAGGGGTTAGGGGTTAGGGGTTAGGGGTTAGGGGTTAGGGGTTAGGGTTAGGGTTAGGGTTAGGGTGTGGTGTGTGGGTGTGTGTGGGTGTGGTG
TGTGTGGGTGTGGTGTGTGGGTGTGGGTGTGGGTGTGGGTGTGTGGGTGTGGTGTGTGGGTGTGGT
Y Chromosome
DNA Sequencing Technology
• 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
Human DNA Sequence Variation
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 1:10 1:16
1:10 1:500,000 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 factorsDavid 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 Contributorsto Ideopathic Pancreatitis
• SPINK1 (Serine Protease Inhibitor, Kazal Type 1)
– Inhibition of activated trypsin
• CTRC (Chymotrypsin C)
– Cleavage of activated trypsin
• CFTR (Cystic Fibrosis TransmembraneConductance Regulator)
– Contributor to secretion leading to flushing of pancreatic ducts
GWAS Studies
• Studies of ideopathic pancreatitis > Raregenetic 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
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
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
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
Ethical Considerations
• Personalized Medicine has many associated ethical considerations
– Privacy
– Patient autonomy
– Informed Consent
– Other issues
The Database of Genotypes and Phenotypes
“Anonymous” DNA Sequences Can Sometimes be Identified
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
• 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
Personalized Medicine
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
www.ipm.pitt.edu
Thanks
• Personalized Medicine Task Force– Ivet Bahar– Mike Barmada– Mike Becich– Takis Benos– Rebecca Crowley
Jacobson– Nancy Davidson– Robert Edwards– Phil Empey– Arjun Hattiangadi– John Kellum– Adrian Lee
– John Maier
– George Michalopoulos
– Yuri Nikiforov
– Lisa Parker
– Aleks Rajkovic
– Steve Reis
– Steve Shapiro
– Dietrich Stephan
– Lans Taylor
– Jerry Vockley
– David Whitcomb
– Nathan Yates
Grazie!
Domande?