Omics, Biomarkers, Personalized Medicine:A New Era, or More of the Same?

1. Omics, Biomarkers, Personalized Medicine:
A New Era, or More of the Same?
Klaus Lindpaintner
Roche Genetics/Roche Center for Medical Genomics

2. 2
Differential drug efficacy
Same symptoms
Same findings
Same disease (?)
Same Drug….
Different Effects
?
Genetic Differences
Possible Reasons:
Non-Compliance…
Drug-drug interactions…
Chance…
Or….

3. 3
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

4. 4
Pharmacogenetics and Personalized
Medicine
An altogether new concept?
• Knowledge of inter-individual differences wrt metabolism as old as
civilization: 6th century B.C. Pythagoras observes
that 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

5. 6
Bridging a Historical Divide
protein
RNA
DNA
protein
RNA
DNA
protein
RNA
DNA
protein
RNA
DNA
cell-biology cell-biology
drugs
tissue / organ physiology-pathology
clinical diagnosis
molecular diagnosis

6. 7
Pharmacogenetics, Pharmacogenomics
Glossary 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 genomes (patients)
• focus: 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 genomes (patients)
• focus: patient variability
• Pharmacogenomics (2):
• a tool for compound selection/drug discovery
• many drugs – one genome (inbred animal/chip)
• focus: compound variability
*as conceptualized by Motulsky (1957), Vogel (1959), Kalow (1962) and
endorsed in the 2003 Nuffield Council’s Report on Pharmacogenetics

7. 8
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

8. 9
Omeprazole response rate and CYP2C19
0
10
20
30
40
50
60
70
80
90
100
gastric ulcer duodenal ulcer
B/B – FAST
A/B
A/A – SLOW
response
frequency
(%)

9. Drug metabolism
Inheriteddifferences affectdrug effects

10. 11
Pharmacogenetics = molecular DD
Case Study: Herceptin®
Low HER2
High HER2
Bimodal response:
2/3 of patients: addition of Herceptin® to chemoRx
 no benefit
1/3 of patients: addition of Herceptin® to chemoRx
 50% survival time increased by factor 1.5 (20 29 weeks)

11. 12
Xeloda® (capcitabine)
Patient stratification based on enzyme patterns
S: susceptible
R: refractory
Xeloda susceptibility vs tumor TP/DPD
in 24 xenografts
(dThdPase/DPD)
TP/DPD
100
10
1
0.1
P = 0.0015
S R
Xeloda
5-DFUR 5-FU inactive
metabolites
TP DPD
TS

12. 13
Biomarkers
What’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

13. 14
0
10
20
30
40
50
60
70
80
response
(%)
individual patients
31%
0
10
20
30
40
50
60
70
80
response
(%)
individual patients
43%
22%
FDA benchmark: 35% improvement/response
Caveat 2 “Responders” & “Non-
Responders”
RealityCheck

14. 15
health
outcome
Mutation
SNPs in other genes
Environment
intermediate
phenotype
health
outcome
intermediate
phenotype
Single Gene Disease
Deterministic … possible stigma
Heritability: h2 ≈ 1

15. 16
Monogenic
CCD
Common Complex
Diseases
Diseases

16. 17
health
outcome
SNP
SNPs in other genes
Environment
intermediate
phenotype
health
outcome
intermediate
phenotype
Common Complex Disease
health
outcome
Mutation
SNPs in other genes
Environment
intermediate
phenotype health
outcome
intermediate
phenotype
Single Gene Disease
Probabilistic, not deterministic - no reason for stigma.

17. 18
Complex Common Disease:
Nature and Nurture
genes
environment
Hemo-
philia
CF
HD
MVA
GSW
Lung cancer
tobacco --- asbestos
P450
Stroke MI
AD Diabetes
Asthma
Colon,
breast
Cancer
P53, BRCA
nutrition
ApoE4

18. 19
Heritability estimates in CCD
Disorder or phenotype Heritability h2
Preeclampsia 0.2-0.35
NIDDM 0.26-0.50
Hypertension 0.28-0.73
Osteoarthritis 0.3-0.46
Stroke 0.32
Asthma 0.36-0.47
Obesity 0.4-0.7
Depression 0.41-0.66
Other dementia 0.43
Blood pressure 0.5
BMI 0.5-0.7
Rheumatoid arthritis 0.53-0.65
Death from heart disease 0.55
Coronary heart disease 0.56
IGT 0.61
SLE 0.66
Alzheimer’s (sporadic) 0.72
Protracted/recurrent otitis media 0.72

19. 20
Malignancy Heritability h2
Thyroid 0.53 (0.52–0.53)
Endocrine glands 0.28 (0.27–0.28)
Breast 0.25 (0.23–0.27)
Testis 0.25 (0.15–0.37)
Cervix invasive 0.22 (0.14–0.27)
Melanoma 0.21 (0.12–0.23)
Nervous system: age <15 years 0.13 (0.06–0.20)
Colon 0.13 (0.12–0.18)
Cervix in situ 0.13 (0.06–0.15)
Rectum 0.12 (0.08–0.13)
Nervous system 0.12 (0.10–0.18)
Non-Hodgkin lymphoma 0.10 (0.08–0.10)
Leukemia: age <15 years 0.09 (0.09–0.16)
Lung 0.08 (0.05–0.09)
Kidney 0.08 (0.07–0.09)
Urinary bladder 0.07 (0.02–0.11)
Leukemia 0.01 (0.00–0.01)
Stomach 0.01 (0.01–0.06)
Czene et al, Int J Cancer 99:260; 2002
Heritabilityestimates in cancer

20. 21
Medical Progress: Evolution or
Revolution?
…Genetics
Clinical expertise
Classical epidemiology
Differential diagnosis
Risk assessment - prevention
Historic Drivers of Medical Progress
More differentiated, molecular understanding of pathology and drug action
Clinical Disease Definition
Clinical Diagnosis
Molecular Disease Definition
Molecular Diagnosis
in-vitro Diagnostics

21. 22
Tuberculosis Heart Failure
Cancer
HER-2-negative (2/3) HER-2-positive (1/3)
Cytostatics Cytostatics + humMAb
Tuberculosis Heart Failure
Cancer
Antibacterials Cytostatics ACE Inhibitors
Consumption
Phlebotomy
Mean survival 3 yrs
Mean survival 7 yrs
Breast Ca
Colon Ca
Lung Ca

22. 23
Pharmacogenetics vs. other Markers
Ausefuldistinction?
* alteration germ-line in origin – heritable
DNA
mRNA
primary
protein
processed
protein,
small
molecule
response to
medicine
Normal
DNA*
mRNA*
primary
protein*
processed
protein,
small
molecule*
altered
response to
medicine*
Pharmaco-
genetics
DNA *
mRNA*
primary
protein*
processed
protein,
small
molecule*
altered
response to
medicine*
Pharmaco-
genomics
DNA
mRNA*
primary
protein*
processed
protein,
small
molecule*
altered
response to
medicine*
Pharmaco-
genomics
DNA
mRNA
primary
protein*
processed
protein,
small
molecule*
altered
response to
medicine*
Pharmaco-
proteomics
DNA
mRNA
primary
protein
processed
protein,
small
molecule*
altered
response to
medicine*
Pharmaco-
metabonomics
* alteration somatic – acquired (environment, life-style)

23. 24
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

24. 25
Biomarker tests in medical practice
Twosetsofconsiderations
• 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 illness
Default: “don’t treat in error”;
If in doubt: “don’t treat”

25. 26
EGFR Mutants
Much ado about…?

26. 27
EGRF-R variants
Colocation with ATP-binding domain

27. 28
Regulators are Taking Note

28. 29
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

29. 30
• Gefitinib (IRESSA) Response in Caucasians 10%
Prevalence of variants in Boston patients 2/25
(NEJM)
• Gefitinib (IRESSA) Response in Japanese 28%
Prevalence of variants in Japanese patients 26%
(Science)
• Erlotinib (TARCEVA) Monotherapy in NSCLS
EGFR Mutratoin prevalence 12%
Response Rate 42%
EGF-R variants and Drug Response

30. 31
Analytical Performance: Metrology
Aything butstraight-forward
• Precision
• Repeatability
under same conditions, precision in a series of measurement
in the same run; and
• Reproducibility
under 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.

31. 32
Biomarker tests in medical practice
Two 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 illness
Default: “don’t treat in error”;
If in doubt: “don’t treat”

32. 33
Analytical performance
Thedirty (notso)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

33. 34
unambiguous wt
wt vs. mut
wt vs. mut vs. artifact
wt?
wt vs. mut?
unambiguous known mut
known 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 sequencing
Sometimes,farfromit…

34. 35
EGFR mutation analysis analytical performance
Thedirty(notso)littlesecret
• 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)

35. 36
EGFR-Mutations, Erlotinib, and Survival
The picture is more complex…

36. 37
Biomarker tests in medical practice
Twosetsofconsiderations
• 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 illness
Default: “don’t treat in error”;
If in doubt: “don’t treat”

37. 38
Optimizing Sensitivity vs. Specificity
TargetProductProfileDefinitionisEssential
sensitivity
1-specificity
0% 100%
0%
100%
Note: Sliding the ROC-cutoff value may be more difficult with (categorical) genotype
data than with other (quantitative) biomarker data

38. 39
+ response - response
+ test true positive  false positive
- test  false negative true negative
Efficacy marker: High sensitivity
+ adverse event - adverse event
+ test true positive  false positive
- test  false negative true negative
Safety marker: High specificity
+ response - response
+ test true positive  false positive
- test  false negative true negative
Efficacy marker: High specificty
+ adverse event - adverse event
+ test true positive  false positive
- test  false negative true negative
Safety marker: High sensitivity
Less serious illness: don’t prescribe inappropriately
Serious illness: don’t withhold inappropriately
Biomarker performance
UpanddowntheROCcurve

39. 40
Case-in-point: Herceptin/HerCepTest
Thesearchfornewbiomarkers–anditsimplications
+ response - response
+Her2
test
20
true +
10
false +
30
66% response among
treated Her2+
- Her2
test
0
false -
70
true -
70
presumed 0% response
among Her2-
(NB: anecdotal data)
Sensitivity:
true+/(true+ + false-)
20/(20+0)=1
100% sensitive
Specificity:
true-/(true- + false+)
70/70+10=0.875
88% specific
100
+ response - response
+ new
BM test
16
true +
2
false +
18
88% response among
treated Her2+/BM+
- new
BM test
4
false -
8
true -
12
33% response among
Her2-/BM-
Sensitivity:
true+/(true+ + false-)
16/(16+4)=0.8
80% sensitive
Specificity:
true-/(true- + false+)
8/1+9=0.9
80% (98%*) specific
30
+ response - response
+ new BM
test
19
true +
5
false +
24
79% response among
treated Her2+/BM+
- new BM
test
1
false -
5
true -
7
16% response among
Her2-/BM-
Sensitivity:
true+/(true+ + false-)
19/(19+1)=0.95
95% sensitive
Specificity:
true-/(true- + false+)
5/5+5=.5
50% (94%*) specific
30
Status 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

40. 41
Total Patients without
deficient TPMT-allele
Patients with one or two
deficient TPMT-alleles
Reference
n n % n %
25 20 80 5 20 Black et al. 1998
17 16 94 1 6 Naughton et al. 1999
7 4 57 3 43 Ishioka et al. 1999
15 14 93 1 7 Dubinsky et al. 2000
41 29 70 12 30 Colombel et al. 2000
8 6 75 2 25 Ando et al. 2001
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

41. 42
“Exhaustive pharmacogenetic research efforts have
narrowed your niche market down
to Harry Finkelstein of Newburg Heights here.”
Economic considerations
How far is segmentation of markets feasible?

42. 43
Emergence of sub-critically small
segments
A 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

43. 44
The Tightening Reimbursement Climate
Biomarker strategies may be essential
Strategy 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)

44. 45
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
BUT
we will have increasingly better medicines

45. 46
No 1-on-1 custom tailoring,
but towards a much better fit …
38 40
39 39½39¾
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

46. Without information,
the doctor cannot act.
With information,
he cannot but act.

47. 48
HL Mencken’s Law
Every complex problem
has a simple solution.
And it is always wrong.