Transcript Personalized Medicine

Personalized Medicine - Genomics
Maria Judit Molnar
2014
The Personal Genome
Project is a long term, large
cohort study
Aims to sequence and
publicize the complete
genomes and medical
records of 100,000
volunteers, in order to enable
research into personal
genomics and personalized
medicine.
It was initiated by Harvard
University in 2005.
Personal Genome Project
• The individuals agree to make their genome and
their health records public.
• „volunteers… willing to share their genome
sequence and many types of personal information
with the research community and the general
public,
• Aim: to understand genetic and environmental
contributions to human traits.”
The project publish the
• genotype (the full DNA sequence),
• phenotype: medical records, various
measurements, MRI images, etc.
• all data are within the public domain
• made available over the Internet so that
researchers can test various hypotheses about
the relationships among genotype, environment
and phenotype.
Risks
• Curiosity may be just suspicion co-opted by
endorphins. I had no idea what I was blundering
into. But I figured I could start learning now about
privacy and public good, research and
entrepreneurship, risk and susceptibility – all the
dangers of knowing the full story – or I could bump
up against them later, along with the rest of
unwitting humanity.
Richard Powers
Dealing with bad news
• We know what happens to people who do get the
worst news. They don’t sink into despair or throw
themselves off bridges; they handle it perfectly
well. Most of us cope using some combination of
denial resignation and religion.
Steven Pinker
Genotype and phenotype
• When the connection between the ACTN3 gene and
muscle type was discovered, parents and coaches
started swabbing the cheeks of children so they
could steer the ones with the fast-twitch variant
into sprinting and football.
Steven Pinker
Some variants predicting severe
effects in the PGP-10
Some variants predicting severe effects in the PGP-10
Participant
Variant
Putative effect
PGP5 (hu9385BA)
PKD1-R4276W Autosomal dominant polycystic kidney disease
PGP6 (hu04FD18)
MYL2-A13T
Hypertrophic cardiomyopathy
PGP9 (hu034DB1)
SCN5A-G615E
Long QT syndrome
PGP10 (hu604D39) PKD2-S804N
Autosomal dominant polycystic kidney disease
PGP10 (hu604D39) RHO-G51A
Autosomal dominant retinitis pigmentosa
PGP Harward
PGP Canada
PGP UK
Risk-Benefit Ratio
The roots
„ It’s far more important to know what person
the disease has than what disease the
person has.”
Hippocrates (BC. 400)
The paradigm of the classic treatments
Trial and
error
Symptom
Diagnosis
Treatment
Dosage
Non specific
Non selectiv
Uniformized
Phenotype
Does not evaluate the different therapeutic response the blockbuster concept
The medicine in the XX. century
• „One fits to all”
• The target is the disease
• Evidence based medicine
– statistical approach using the rule of large numbers,
resulting in statistically meaningful conclusions
“The dose makes the poison.”
But differently for genetically
different individuals
Paracelsus
(1493-1541)
The revolution of the molecular
biology:
Right Disease
Right Patient
Right Drug
Right Time
Ineffective therapies – waste money
Hypertension Drugs 10-30%
ACE Inhibitors
$390 million – $1.2 billion
Heart Failure Drugs 15-25%
Beta Blockers
$345 million – $575 million
Anti Depressants 20-50%
SSRIs
$2.3 billion – $5.8 billion
Cholesterol Drugs 30-70%
Statins
$3.8 billion – $8.8 billion
Asthma Drugs 40-70%
Beta-2-agonists
$560 million – $1.0 billion
The results in 2013
• The most drugs are not or partially effective in the
60% of the treated patients
• Side effects are responsible for
– 100,000 death
– 2 million hospitalisations
– 100 billion USD cost for healthcare in USA
– 50%- of the cases is related genetics
Personalized Medicine:
The Answer?
Definition
The use of information and data from a patient’s genotype and
phenotype (level of gene expression and/or clinical
information) to:
– stratify disease
– select a medication
– provide a therapy
– initiate a preventative measure that is particularly suited to that
patient at the time of administration
Personalized Medicine is an emerging practice of medicine that
uses an individual's genetic profile to guide decisions made in
regard to the prevention, diagnosis, and treatment of disease
Focus on the clinical needs!
“Bench to Bedside”
“Bedside to Bench to Bedside”
However genomic determinate the potential biological and
physiological reactions of the individual, we can not miss the
analysis of the environmental effects.
The bigest weakness of the clinic nowadays is the lack of the exact
diagnosis and the inapropriate determination of the stadium of
the disease.
The classical therapy:
Uniformisation
Observation
Treatment
Uncertain
respond
Independently from the heterogeneity of the population try to get in large cohorts
positiv results/risk ratio with the treatment (clinical utility)
Targeted therapy:
Differenciate, diagnostics and drug co-development
Observation
Testing
(Biomarker)
Treatment
Predicted
respond
Targeted therapies help by identificatioon of the patients with the best respond and less side
effects
Biomarkers are such diagnostic tools, wich may predict the therapeutic respond to a certain
drug
The key drivers of the paradigm change
in the healthcare - 2013
Healthcare pressure:
Risk / benefit ratio
Economical pressure:
Cost / benefit ratio
New Technologies:
Expanding possibilities
Needs of highly differentiated healthcare, which effects the
health of the person and society
Only the really innovative medicine is justified
Innovative ~ Personalized, Differentiated
The Power of Information - Moore’s law
Computer processing power is doubling every 18 months
Amount of data is doubling every 18 months
Power of technology
Technological improvement
• Genomic revolution of the end of the 20.th century
– Completing the Human Genom Project (2000)
• „Only” 25 thousand genes – vs 100 thousand
– Computed genotyping, DNA microarray
– „$1000 Genom”
• „Nobody expected”:
–
–
–
–
–
25thousand genes – 9 million SNP
The function of 30% of the genes is uncleared
The role of deletions, duplications, CNVs
Microsatellite polymorphisms
Epigenetic
Forrás: Jose de Leon, Pharm Res 59 (2009) 81-89 alapján
PM impacts diagnostic categories
A new era in genomics medicine?
• Human genome project
• Direct-to-consumer genomics
• Intellectual property disputes
– Catalona
– Myriad Genetics
– Henrietta Lacks
• Personal Genome Project
Drug discovery paradigm shift:
a problem or an opportunity?
• Ever increasing demand for safer medicine
• Stark realization that drug discovery is expensive and slow
– shrinking budgets, consolidation, outsourcing
• Current drug inventory is large, diverse and possibly has a lot
more to offer than was initially thought
• Increasing availability of genomic data and tools to
use/understand it
Genomic data in the patient care
Monogenic vs Complex Disorders
Monogenic Disorders: Success story
Complex disorders: limited success rate
Age related macula degeneration
Apolipoprotein E Genotype and
Alzheimer Disease
• Metaanalysis of 40 study
• 5.930 patient and 8.607 control
A mutation in APP protects against Alzheimer’s disease and
age-related cognitive decline and Alzheimer Disease
Thorlakur Jonsson et al.
Nature 2012; 488, 96–99 (02 August 2012) doi:10.1038/nature11283
A coding mutation (A673T) in the APP gene protects against Alzheimer’s disease
This substitution results in an approximately 40% reduction
in the formation of amyloidogenic peptides in vitro.
The change of disease concept
Traditional: reductionist, one single factor
Causal factor
Disease
New conception: multifactorial
Basic
risk
Preclinical
progression
Environmental
factors
Disease
onset
Disease
progression
Irreversible
changes
New Disease Concept
Monogenic disease
Environment
Other SNPs
Intermedier
intermediate
phenotype
Mutation
Egészségre
Effect
on the
gyakorolt
health
hatás
Complex, polygenic, multifactorial disease
Environment
SNP combinations
Other SNPs
Köztes
Intermedier
fenotípus
phenotype
Effect
on the
Egészségre
health
gyakorolt
hatás
The old paradigm: Treatment of the disease
60
Switch drug again
Disease severity
50
40
30
Switch drug
Select drug
Diagnosis
20
10
0
1. n.év 2. n.év
Time
Reactive medical care
To effective health management
35
Disease severity
30
Right Drug
25
20
Monitoring
Diagnosis/Prognosis
15 Predisposition Screening
10
5
Time
0
1. n.év 2. n.év
Efficient medical care
Social expectations
• Cheaper, more effective drug development
Forrás: Business Insights: Expanding Applications of Personalized Medicine, 2009
Social expectations
Scruples
•
•
•
•
Healthpolitical questions
Regulatory issues
Financing aspects
Insurance consequence
– USA: Genetic Information Nondiscrimination Act (2008)
• Ethical questions
– How to sell the test laymens?
FDA prohibited to sell the test
What will likely happen??
Personalized medicine will involve pharmacogenomic
treatment approaches that transcend the „one-size-fits-all”
approach
Personalized medicine will focus on keeping people well and
treating disease at its earliest stages!
Laboratory medicine will lead the way!
„Disease signatures” comprised of hundreds or thousands of
data point will be the biomarkers of the future
Drug companies will develope their markets around
interventional treatments for „disease signatures”!!
The POTENTIAL for Personalized Medicine
A „Wellness” Vision
• A new comprehensive and integrated approach to wellness –
prevention of chronic disease, early detection of disease risk and
individualized treatment plans
• Predictive toxicology for new drug candidates – ability to predict
which individuals will benefit and those who might be most at risk for
experiencing serious side-effects
Healthy
Routine
Comprehensive
Health
Status
Monitoring
Pre-disease
New
diagnostics
Disease
prediction
Preventative
therapies
Diseased
Recovering
Earlier
disease
detection
New
diagnostics
Personalized
treatment
New
interventional
therapies
Accurate
disease
diagnosis
Informed
treatment
decisions
•Improved economics of
disease screening
•Reduced occupational
exposures
Real-time
Disease
Reoccurrence
Monitoring
•More timely therapy
•Reduced unnecessary
referrals
•People adopting healthier lifestyles
•Timely medical interventions
•Timely testing of environmental exposures
•Reduced hospitalizations
•More efficient treatment plans
•Improved outcomes
The POTENTIAL for Personalized Medicine
Increased Healthcare Quality and Reduced Costs (?)
Predict and prevent chronic diseases
Keep people out of the hospital
Eliminate adverse drug events
Improve drug development
Create new markets
The POTENTIAL for Personalized Medicine
Transform Healthcare Markets
Today
Tomorrow
HC markets on numbers of
sick people might be
treated with a new drug
Metric
Morbidity and mortality
rates
Outcome
People suffer and die from
chronic and preventable
diseases with multiple
hospitalizations
HC markets based on numbers of
people with preventable
diseases
Multiplex
biomarkers
to predict
and guide
treatment of
early
chronic Dz
Metric
Number of people positive for valid
predictive biomarkers
Outcome
• New era of interventional
therapeutics
• People will live healthier, painfree lives and die of old age or
trauma with minimal
hospitalizations
We Can’t do This Now!!
Current Personalized Medicine
Approaches Limited To:
Pharmacogenomics
Electronic Health Records
Great Start –
But does not yet address the all technologies
required for prediction and prevention
State of the Art in HC Measurement
Technologies
Despite Major Progress over the Last 25 Years, Healthcare
Measurement Technological Capabilities is Limited to:
Digitalizing medical records
Measuring a few serum biomarkers
Identifying simple genetic defects/differences
Imaging gross anatomical features and detect major changes
Imaging some disease-associated molecular mechanism
Comparing mRNA expression patterns between healthy and diseased cells
Statistical analysis of research for evidence-based medicine
The Personalized Medicine Gap
The lack of adequate measurement technology limits
the vision for personalized medicine
We simply do not have the tools to measure the
biochemical details of the human body with the
resolution needed to fully-realize the amazing
potential of personalized medicine
Need to Know the Root Cause of Chronic
Disease
But…
Human Cells are Extremly Complex
Diseases are the result of perturbations
in complex biomolecular networks
PM in the clinical practice
Prevention
BRCA1/2 - Breast and ovarian tu.
prophyilactic tamoxifen and surgery
Effectivity
Oncology
Herceptin – breast cancer
Cetuximab – colon tumor
Rare disease: cystic fibrosis
Ivacaftor G551D mutation in CFTR gene
Safety
VKOR/CYP2C9 – warfarin dosing
Multiplex Tests are Already Starting
to Have an Impact
OncoType DX
Analyzes by qPCR, mRNA expression of a panel of 21 genes within a tumor to
determine a Recurrence Score
MammaPrint
Microarray-based prognostic breast cancer mRNA expression profiling test of 70 genes
AlloMap
qPCR-based expression profile of 11 genes to assist physicians in managing heart
transplant patients for potential organ rejection
Tissue of Origin
Microarray technology considers 15 common malignant tumor types, including
bladder, breast,and colorectal tumors based on mRNA expression on 1,550 genes
A kihivások
New Technologies for Determination of
„Disease Signatures”
Changes in biomolecular networks indicative of the onset or progression of disease
Normal Human System
100 trillion cells
6 billion basepairs
ABNORMALITIES
30,000 genes
10 million different proteins
100,000’s of molecular events
50 organs and organ systems
Multiplex Measurements
Computer Integration
•Increased Drug Pipeline
Discovery Decisions
•Improved Diagnostics
•New Predictive Biomarkers
•Decrease Adverse Events
•Improve Clinical Outcome
•Fewer Errors & Misdiagnoses
•Predict Disease Onset
•Prevent Disease
•Reduce Health Care Costs
Clinical
Decisions
Cell- or Tissuespecific Disease
Probablity Score
Changes due to P4 Medicine
• More innovative, patient-centered, proactive medicine that
will be predictive, preventive, personalized and participatory
rather than reactive
• The role of physician is changing
• Patients increasingly need „coaches” to help them dealing
with complexity of „data clouds”, monitor their health and
wellness
• Broadening the definition of patient (not only limited to
thick persons)
• Social media and e-health will influence the healthcare
Fears from the P4
• Payer: increasing expenses?
• Physician: decreasing margin?
• Patient: certain drugs are inaccessible?
• Authorities: how to deal the complex situation?
• Diagnostic lab: more test with bed financing?
• Industry:
– Narrowing market?
– New financing strategy?
?
Ethics
Too soon for
conclusions
New ideas about self,
privacy, medicine, and
freedom
Conclusion
We tend to overestimate the effect of
technology in the short run and
underestimate the effect in the long run
Amara’s Law
Figuring out how to use that information to
improve your medical care is personalized
medicine's next great challenge