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Personalized Medicine - Genomics
Maria Judit Molnar 2014
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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.
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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.”
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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.
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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
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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
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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
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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 RHO-G51A Autosomal dominant retinitis pigmentosa
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PGP Harward PGP UK PGP Canada
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Risk-Benefit Ratio
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The roots „ It’s far more important to know what person the disease has than what disease the person has.” Hippocrates (BC. 400) Asdd date
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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
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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
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“The dose makes the poison.” But differently for genetically
different individuals The revolution of the molecular biology: Right Disease Right Patient Right Drug Right Time Paracelsus ( )
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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
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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
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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
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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.
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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
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The key drivers of the paradigm change
in the healthcare Healthcare pressure: Risk / benefit ratio Economical pressure: Cost / benefit ratio New Technologies: Expanding possibilities In summary, when we look at the three drivers of change in healthcare today, the most obvious answer is to invest in Personalised Healthcare. At Roche, we are addressing the need for highly differentiated medicines through systematic implementation of PHC approaches. PHC approaches help us to focus on the true value of our clinically differentiated medicines so that patients benefit from enhanced efficacy and greater safety. This kind of innovation that targets a medicine to specific patients will be recognized and rewarded by the healthcare community. Needs of highly differentiated healthcare, which effects the health of the person and society Only the really innovative medicine is justified Innovative ~ Personalized, Differentiated
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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
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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) alapján
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PM impacts diagnostic categories
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A new era in genomics medicine?
Human genome project Direct-to-consumer genomics Intellectual property disputes Catalona Myriad Genetics Henrietta Lacks Personal Genome Project
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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
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Genomic data in the patient care
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Monogenic vs Complex Disorders
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Monogenic Disorders: Success story
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Complex disorders: limited success rate
Age related macula degeneration
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Apolipoprotein E Genotype and Alzheimer Disease
Metaanalysis of 40 study 5.930 patient and control
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Thorlakur Jonsson et al.
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: /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.
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The change of disease concept Environmental factors
Traditional: reductionist, one single factor Causal factor Disease New conception: multifactorial Basic risk Preclinical progression Disease onset Disease progression Irreversible changes Environmental factors
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intermediate phenotype
New Disease Concept Monogenic disease Other SNPs Environment Mutation Egészségre gyakorolt hatás Effect on the health intermediate phenotype Intermedier phenotype Complex, polygenic, multifactorial disease Other SNPs Environment Effect on the health Egészségre gyakorolt hatás Intermedier phenotype Köztes fenotípus SNP combinations
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The old paradigm: Treatment of the disease
Switch drug again Switch drug Select drug Diagnosis Disease severity Time Reactive medical care
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To effective health management
Right Drug Monitoring Diagnosis/Prognosis Disease severity Predisposition Screening Time Efficient medical care
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Social expectations Cheaper, more effective drug development
Forrás: Business Insights: Expanding Applications of Personalized Medicine, 2009
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Social expectations
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Scruples Healthpolitical questions Regulatory issues Financing aspects
Insurance consequence USA: Genetic Information Nondiscrimination Act (2008) Ethical questions How to sell the test laymens? The Act prohibits group health plans and health insurers from denying coverage to a healthy individual or charging that person higher premiums based solely on a genetic predisposition to developing a disease in the future. The legislation also bars employers from using individuals' genetic information when making hiring, firing, job placement, or promotion decisions 44
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FDA prohibited to sell the test
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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”!!
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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 Pre-disease Diseased Recovering Earlier disease detection New interventional therapies New diagnostics Disease prediction Preventative therapies Personalized treatment Informed treatment decisions Routine ComprehensiveHealth Status Monitoring New diagnostics Accurate disease diagnosis Real-time Disease Reoccurrence Monitoring Improved economics of disease screening Reduced occupational exposures More timely therapy Reduced unnecessary referrals More efficient treatment plans Improved outcomes Timely medical interventions Reduced hospitalizations People adopting healthier lifestyles Timely testing of environmental exposures
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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
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The POTENTIAL for Personalized Medicine Transform Healthcare Markets
Today 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 Tomorrow HC markets based on numbers of people with preventable diseases Metric Number of people positive for valid predictive biomarkers Outcome New era of interventional therapeutics People will live healthier, pain-free lives and die of old age or trauma with minimal hospitalizations Multiplex biomarkers to predict and guide treatment of early chronic Dz
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We Can’t do This Now!!
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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
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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
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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
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Need to Know the Root Cause of Chronic Disease But…
Human Cells are Extremly Complex
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Diseases are the result of perturbations
in complex biomolecular networks
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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
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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
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A kihivások
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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 billion basepairs 30,000 genes million different proteins ,000’s of molecular events organs and organ systems ABNORMALITIES Multiplex Measurements Computer Integration Discovery Decisions Increased Drug Pipeline Improved Diagnostics New Predictive Biomarkers Decrease Adverse Events Disease Signatures Cell- or Tissue-specific Disease Probablity Score Improve Clinical Outcome Fewer Errors & Misdiagnoses Predict Disease Onset Prevent Disease Reduce Health Care Costs Clinical Decisions
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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
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? 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? ?
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Ethics Too soon for conclusions
New ideas about self, privacy, medicine, and freedom
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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
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