The Foundations of Personalized Medicine Jeremy M. Berg - - PowerPoint PPT Presentation

The Foundations

• f

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

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

• f 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 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
• f 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

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?