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Omics, Biomarkers, Personalized Medicine:
A New Era, or More of the Same?
Klaus LindpaintnerRoche Genetics/Roche Center for Medical Genomics
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Differential drug efficacy
Same symptomsSame findingsSame disease (?)
Same Drug….
Different Effects
?Genetic Differences
Possible Reasons: Non-Compliance…
Drug-drug interactions… Chance…
Or….
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Pharmacotherapy: State-of-the-Art
Group Incomplete/absent efficacy AT2-antag 10-25% SSRI 10-25% ACE -I 10-30% Beta blockers 15-25% Tricycl. AD 20-50% HMGCoAR-I 30-70% Beta-2-agonists 40-70% • Inter-individual differences in drug
efficacy
• Significant incidence of serious adverse effects among elderly hospitalized patients (US)
Serious 6.7% 2 M cases Lethal 0.3% 100,000 cases
JAMA 98;279:1200
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Pharmacogenetics and Personalized MedicineAn altogether new concept?• Knowledge of inter-individual differences wrt metabolism as
old as civilization: 6th century B.C. Pythagoras observesthat ingestion of fava beans is harmful to some individuals yet innocuous to others
• Finding the optimal treatment for every patient is as old as medicine: differential diagnosis
• Tailoring treatments to drug-specific test results is nothing new. Example: antibiotics
• Gram-positive bacteria: e.g. penicillin derivatives• Gram-negative bacteria: e.g. aminoglycosides• M. tuberculosis: isoniazid/rifampin/pyrazinamide
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Bridging a Historical Divide
proteinRNADNA
proteinRNADNA
proteinRNADNA
proteinRNADNA
cell-biology cell-biology
drugs
tissue / organ physiology-pathology
clinical diagnosis
molecular diagnosis
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Pharmacogenetics, PharmacogenomicsGlossary of Terms
• Pharmacogenetics:• a concept to provide more patient/disease-specific health care*• based on the effects of inherited (or acquired) genetic variants• assessed primarily by sequence determination (or single gene
expression)• one drug – many genomesone drug – many genomes (patients) • focus: patient variabilityfocus: patient variability
• Pharmacogenomics (1):• a concept to provide more patient/disease-specific health care• based on the effects acquired (or inherited) genetic variants• assessed primarily by expression profiles (many mRNAs)• one drug – many genomesone drug – many genomes (patients) • focus: patient variabilityfocus: patient variability
• Pharmacogenomics (2):• a tool for compound selection/drug discovery• many drugs – one genome many drugs – one genome (inbred animal/chip)• focus: compound variabilityfocus: compound variability
*as conceptualized by Motulsky (1957), Vogel (1959), Kalow (1962) and endorsed in the 2003 Nuffield Council’s Report on Pharmacogenetics
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2 Major Classes of Pharmacogenetics – Both Resulting in Patient Stratification
• Strictly affecting drug response – not predictive of disease risk: “Differentiating people” (“classical” pgx: Archibald Garrod)
• Pharmacokinetics (not only M, but also AADE)• Pharmacodynamics• Has not had much impact
• Related to molecular subclass of clinical diagnosis: “Differentiating disease” (“molecular differential diagnosis”)
• Inherently linked to disease mechanism/prognosis• Likely increasing impact in indications where we begin to treat
causally – oncology, inflammatory disease
• Both are conceptually rather different (and arguably the second should not be included) but have practically the same consequence:Patient stratification according to novel, DNA-based parameters
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Omeprazole response rate and CYP2C19
0
10
20
30
40
50
60
70
80
90
100
gastric ulcer duodenal ulcer
B/B – FAST A/BA/A – SLOW
resp
onse
freq
uenc
y (%
)
Drug metabolism Inherited differences affect drug effects
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Pharmacogenetics = molecular DDCase Study: Herceptin®
Low HER2
High HER2
Bimodal response:2/3 of patients: addition of Herceptin® to chemoRx no benefit1/3 of patients: addition of Herceptin® to chemoRx 50% survival time increased by factor 1.5 (20 29 weeks)
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Xeloda® (capcitabine)Patient stratification based on enzyme patterns
S: susceptibleR: refractory
Xeloda susceptibility vs tumor TP/DPDin 24 xenografts
(dTh
dPas
e/D
PD) TP/DPD
100
10
1
0.1
P = 0.0015
S R
Xeloda
5-DFUR 5-FU inactive metabolites
TP DPDTP DPD
TSTS
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BiomarkersWhat’s new – and why now?
• Availability of powerful, highly parallel new screening methods (omics) makes looking for new biomarkers a reasonable proposition.
• Paradigm shift(?): maturation of these basic cell and molecular biology tools makes them newly applicable to later-stage R&D• Opportunities: personalized medicine• Challenges: technical, scientific (clinical-
epidemiological) economical, ethical
• CAVEAT 1: Association ≠ Causality• Good news and bad news
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0
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20
30
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50
60
70
80
resp
onse
(%)
individual patients
31%
0
10
20
30
40
50
60
70
80
resp
onse
(%)
individual patients
43%
22%
FDA benchmark: 35% improvement/response
AN
Caveat 2 “Responders” & “Non-Responders”Reality Check
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healthhealthoutcomeoutcomeMutationMutation
SNPs in other genesEnvironment
intermediateintermediatephenotypephenotype
healthhealthoutcomeoutcome
intermediateintermediatephenotypephenotype
Single Gene Disease
Deterministic … possible stigmaHeritability: h2 ≈ 1
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MonogenicCCDCommon Complex
Diseases
Diseases
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healthhealthoutcomeoutcome
SNPSNP
SNPs in other genesEnvironment
intermediateintermediatephenotypephenotype
healthhealthoutcomeoutcome
intermediateintermediatephenotypephenotype
Common Complex Disease
healthhealthoutcomeoutcomeMutationMutation
SNPs in other genesEnvironment
intermediateintermediatephenotypephenotype healthhealth
outcomeoutcomeintermediateintermediatephenotypephenotype
Single Gene Disease
Probabilistic, not deterministic - no reason for stigma.
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Complex Common Disease:Nature and Nurture
genes
environment
Hemo-philia
CFHD
MVAGSW
Lung cancertobacco --- asbestos
P450
Stroke MIAD Diabetes
Asthma
Colon,breastCancer
P53, BRCAnutrition
ApoE4
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Heritability estimates in CCD
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Czene et al, Int J Cancer 99:260; 2002
Heritability estimates in cancer
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Medical Progress: Evolution or Revolution?
……GeneticsGenetics
Clinical expertise
Classical epidemiology
Differential diagnosis
Risk assessment - prevention
Historic Drivers of Medical Progress
More differentiated, molecularmolecular understanding of pathology and drug action
Clinical Disease DefinitionClinical Disease DefinitionClinical DiagnosisClinical Diagnosis
MolecularMolecular Disease Definition Disease DefinitionMolecularMolecular Diagnosis Diagnosisin-vitroin-vitro Diagnostics Diagnostics
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Tuberculosis Heart FailureCancer
HER-2-negative (2/3) HER-2-positive (1/3)
Cytostatics Cytostatics + humMAb
Tuberculosis Heart FailureCancer
Antibacterials Cytostatics ACE Inhibitors
ConsumptionPhlebotomy
Mean survival 3 yrsMean survival 7 yrs
Breast Ca Colon Ca
Lung Ca
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Pharmacogenetics vs. other MarkersA useful distinction?
* alteration germ-line in origin – heritable
DNA
mRNA
primaryprotein
processedprotein,small
molecule
response to
medicine
Normal
DNA*
mRNA*
primaryprotein*
processedprotein,
smallmolecule*
alteredresponse
tomedicine*
Pharmaco-genetics
DNA *
mRNA*
primaryprotein*
processedprotein,
smallmolecule*
alteredresponse
tomedicine*
Pharmaco-genomics
DNA
mRNA*
primaryprotein*
processedprotein,
smallmolecule*
alteredresponse
tomedicine*
Pharmaco-genomics
DNA
mRNA
primaryprotein*
processedprotein,
smallmolecule*
alteredresponse
tomedicine*
Pharmaco-proteomics
DNA
mRNA
primaryprotein
processedprotein,
smallmolecule*
alteredresponse
tomedicine*
Pharmaco-metabonomics
* alteration somatic – acquired (environment, life-style)
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Pharmacogenetics and beyond: Biomarkers• Key concept:
More targeted medicines (“personalized medicine”)• More effective• Safer• More cost-effective (?)
• Based on a better understanding of inter-individual differences among patients
• Inherited (the “classical” pharmacogenetics)• Acquired (“flavors” of disease, underlying molecular
heterogeneity of any one clinical diagnosis: molecular differential diagnosis)
• Paradigm: carry out specific test that point to one or another medicine as optimal for the patient before prescribing it. What does not matter: Nature of test (DNA, RNA, protein, other) What does matter: Information content
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Biomarker tests in medical practiceTwo sets of considerations• Test performance
• Analytical performance – QC and accreditation of labs• Clinical performance
• Clinical validity – retrospective/observation studies• Clinical utility – prospective intervention trials
• Note: Prior probability: critical for test performance, esp. screens (sensitivity/specificity, PPV/NPV)
• Nature of illness• Serious (life-threatening) illness
Default: ”don’t withhold in error”; If in doubt: “treat”
• Less serious illnessDefault: “don’t treat in error”; If in doubt: “don’t treat”
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EGFR MutantsMuch ado about…?
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EGRF-R variants Colocation with ATP-binding domain
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Regulators are Taking Note
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Interpretation? Consequences? • NEJM
• 8/9 responders + for mutation• 7/7 non-responders – for mutation• 2 of 25 untreated + for mutation• Pre-testing will increase response rate to 100%
among those who test +• Pre-testing will result in denial of treatment to 11%
of who would responders
• Pao et al, MSKCC (PNAS)• 7/10 responders + for mutation• 8/8 non-responders – for mutation• 4/81 NSCLC smokers + for mutation• 7/15 non-smoker, adeno-Ca + for mutation
• Pre-testing will result in denial of treatment to 30%
of who would be responders
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• Gefitinib (IRESSA) Response in Caucasians 10%
Prevalence of variants in Boston patients2/25
(NEJM)
• Gefitinib (IRESSA) Response in Japanese28%Prevalence of variants in Japanese patients
26%(Science)
• Erlotinib (TARCEVA) Monotherapy in NSCLSEGFR Mutratoin prevalence12%Response Rate42%
EGF-R variants and Drug Response
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Analytical Performance: MetrologyAything but straight-forward• Precision
• Repeatability under same conditions, precision in a series of measurement in the same run; and
• Reproducibilityunder different conditions, which are usually specified, e.g. day-to-day or lab-to lab
• Trueness • the closeness of agreement of an average value from
a large series of measurements with a "true value" or an accepted reference value.
• Numerical value: bias• Accuracy –
• referring to a single measurement and comprising both random and systematic influences.
• Numerical value: total error of measurement.
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Biomarker tests in medical practiceTwo sets of considerations• Test performance
• Analytical performance – QC and accreditation of labs• Clinical performance
• Clinical validity – retrospective/observation studies• Clinical utility – prospective intervention trials
• Note: Prior probability: critical for test performance, esp. screens (sensitivity/specificity, PPV/NPV)
• Nature of illness• Serious (life-threatening) illness
Default: ”don’t withhold in error”; If in doubt: “treat”
• Less serious illnessDefault: “don’t treat in error”; If in doubt: “don’t treat”
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Analytical performanceThe dirty (not so) little secret
• Multiple complex variables:• Tissue heterogeneity• Limited sample quantity and quality (FFPE)• LCDM/macro-dissection commonly necessary• PCR-pre-amplification• 4 exons x 2 amplification runs each
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unambiguous wtwt vs. mut
wt vs. mut vs. artifactwt?wt vs. mut?
unambiguous known mutknown mut vs. new mut vs. both?
mut?
wt vs. mut vs. artifact?
known mut vs. new mut vs. both vs. indet?
unambiguous new mut new mut?wt?known mut?new mut?
unambiguous unknown
Analytical performance: EGFR sequencingSometimes, far from it…
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EGFR mutation analysis analytical performanceThe dirty (not so) little secret• Multiple complex variables:
• Tissue heterogeneity• Limited sample quantity and quality (FFPE)• LCDM/macro-dissection• PCR-pre-amplification• 4 exons x 2 amplification runs each
• How to deal with “drop-outs”?• How to deal with non-replicated mutations – artifact
or quantitative manifestation of relative abundance of mutation?
• None of current publications disclose this difficulty• Own experience – using different “calling”
algorithms:• Algorithm 1: 6.1% (13 mut / 200 wt / 94 indeterminate)• Algorithm 2: 7.5% (15 mut / 186 wt / 106 indeterminate)• Algorithm 3: 9.9% (23 mut / 210 wt / 74 indeterminate)
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EGFR-Mutations, Erlotinib, and SurvivalThe picture is more complex…
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Biomarker tests in medical practiceTwo sets of considerations• Test performance
• Analytical accuracy – QC and accreditation of labs• Clinical performance
• Clin validity – retrospective/observation studies• Clinical utility – prospective intervention trials
• Note: Prior probability: critical for test performance, esp. screens (sensitivity/specificity, PPV/NPV)
• Nature of illness• Serious (life-threatening) illness
Default: ”don’t withhold in error”; If in doubt: “treat”
• Less serious illnessDefault: “don’t treat in error”; If in doubt: “don’t treat”
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Optimizing Sensitivity vs. SpecificityTarget Product Profile Definition is Essentialsensitivity
1-specificity0% 100%
0%
100%
Note: Sliding the ROC-cutoff value may be more difficult with (categorical) genotype data than with other (quantitative) biomarker data
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Efficacy marker: High sensitivity
Safety marker: High specificity
Efficacy marker: High specificty
Safety marker: High sensitivity
Less serious illness: don’t prescribe inappropriately
Serious illness: don’t withhold inappropriately
Biomarker performanceUp and down the ROC curve
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Case-in-point: Herceptin/HerCepTestThe search for new biomarkers – and its implicationsStatus quo,
66% success rate no potential responder denied Rx
Add-on-BM scenario 1 78% success rate 5% of would-be responders denied Rx
Add-on-BM scenario 2 88% success rate 20% of would-be responders denied Rx
*Specificity of combined Her2 and new BM tests
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Not all that glitters is gold: TPMT
Thiopurine-treated patients with adverse drug reactions
sensitivity positive test predicts, but negative tests by no means excludes SAE
299 negative tests for every one positive test
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“Exhaustive pharmacogenetic research efforts have narrowed your niche market down
to Harry Finkelstein of Newburg Heights here.”
Economic considerationsHow far is segmentation of markets feasible?
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Emergence of sub-critically small segmentsA self-limited proposition• Retrospectively:
Given biomedical variance, biomarker-defined segments are unlikely to be recognizable unless they represent a significant share of the overall patient population.
• Prospectively:Small segments known to exist will either not be addressed for lack of business case, or under Orphan Drug Guidelines
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The Tightening Reimbursement ClimateBiomarker strategies may be essentialStrategy Life-months Incr.
QALYs Incr. Cost
UK £ Incr
Cost/QUALY UK £
No test Chemo-Rx alone 28.02 1.28 26,919 21,030
Positive HerCep Test Chemo-Rx and Herceptin 29.30 1.36 33,376 24,541
No test Chemo-Rx and Herceptin 29.41 1.37 49,211 35,920
Elkin et al; J Clin Oncol 2004; 22:854 ff($/£ conv. rate 1/1/2003, not PPP-adjusted)
NB: National Institute for Clinical Excellence’s (NICE) threshold for approving reimbursement through NHS believed to be ~UK £ 30,000 per QUALY (quality-adjusted life year)
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Biomarkers – likely outcome:• The concept applies potentially to most compounds
• It will in fact, however, become reality only for some/few compounds… but we will have to look at all to find the few!
• (We will likely see more examples of “pathology-related” biomarker-based stratification (Herceptin-paradigm) that advance efficacy; and most likely in oncology and inflammatory/autoimmune disease)
• Multifactorial algorithms likely to emerge, rather than simple, one-variable models – but highly complex algorithms unlikely.
• Essential: Define Target-Product-Profile
• Key: Modesty, Realism, robust Optimism:we will not have perfect medicines
BUTwe will have increasingly better medicines
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No 1-on-1 custom tailoring, but towards a much better fit …
38 4039 39½ 39¾ 377/8
Remember: All medical decisions/knowledge are based on group-derived (aggregate) data analysis.“Data” on individuals (Harry Finkelstein) are anecdotal and(largely) medically/clinically meaningless
Without information, the doctor cannot act.
With information, he cannot but act.
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HL Mencken’s Law
Every complex problem
has a simple solution.
And it is always wrong.