Genetics and Genomics -- Impact... - Stanford...

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

10

20

30

40

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


Single Gene Disease

Deterministic … possible stigmaHeritability: h2 ≈ 1

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MonogenicCCDCommon Complex

Diseases

Diseases

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SNPSNP





Common Complex Disease

healthhealthoutcomeoutcomeMutationMutation


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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• Analytical performance – QC and accreditation of labs• Clinical performance

• Clinical validity – retrospective/observation studies• Clinical utility – prospective intervention trials





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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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• Analytical accuracy – QC and accreditation of labs• Clinical performance

• Clin validity – retrospective/observation studies• Clinical utility – prospective intervention trials





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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.

Genetics and Genomics -- Impact... - Stanford...

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