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

Skip next 12.4 Million Slides

“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?