1. The Era of Personalised Healthcare: Designing Clinical Studies with Biomarkers
Ugochi Emeribe, PhD

2. Outline Background Classic Designs using Biomarkers
concepts, and retrospective analyses of Gefitinib (Iressa) trial
Classic Designs using Biomarkers
Classifier, Prognostic and Predictive Biomarkers
Sample Size Calculations
Validating Biomarkers
surrogate Biomarkers
Conclusions

3. SPECIAL REPORT-Big Pharma's global guinea pigs Chicago Tribune Monday, May 9, 2011 8:13 AM CDT
As drug treatments become more targeted, scientists are unraveling how small genetic variations may make one medicine suitable for a particular group of people.
AstraZeneca's lung cancer drug Iressa, for example, failed to help Western patients overall in tests but proved much more effective in Asians -- a discovery that has shed valuable new light on ways of tackling the disease worldwide. “We are starting to understand ethnic differences through the responses seen in global trials. By cherishing our genetic diversity we can identify biomarkers like the one for Iressa. That is really exciting.” says Dr. David Kerr, president of the European Society for Medical Oncology.

4. What is Personalized Health Care?
Matching individual patient characteristics with drugs that produce better outcomes for that patient
Perfect Medicine
Effective in all patients!
The same dose for every patient!
No side effects!
Real Medicines
Effective only in some patients
Dose varies for different patients
Some patients may develop adverse events
Herceptin is seen as the poster child for PHC
But a classic example of a drug development that did not start with PHC in mind is Gefitinib

5. Retrospective Analyses of Gefitinib Trials
EGFR Mutation- first thought to be predictive was actually prognostic
EGFR Gene Amplification- first thought to be prognostic was actually predictive

6. EGFR mutation status I
0.0
0.2
0.4
0.6
0.8
1.0
Progression free survival time (months) 2 4 6 8 10 12
Proportion event free
Gefitinib 250/500mg and EGFR M+
(n = 14)
Gefitinib 250/500mg and EGFR M–
(n = 65)
Survival time (months)
Median TTP for EGFR mutation +ve cases was longer (116 days, range ), than that for mutation -ve cases (57 days, range )
There was no impact on OS

7. EGFR gene amplification
FISH: technique for measuring increased EGFR gene copy
FISH +
FISH -
N=114, E=68 Cox HR=0.61 (0.36, 1.04) p=0.07
N=256, E=157 Cox HR=1.16 (0.81, 1.64) p=0.42
Proportion surviving
1.0
1.0
Gefitinib
Gefitinib
0.8
Placebo
0.8
Placebo
0.6
0.6
0.4
0.4
EGFR gene copy number was measured by fluorescent in situ hybridization (FISH). Samples with high polysomy or gene amplification were scored as FISH-positive. In total, samples from 370 patients were successfully analyzed for FISH. Approximately one third of patients were FISH-positive.
Patients with high EGFR gene copy number (FISH positive) appeared to achieve better survival outcomes with gefitinib compared to placebo than patients with low EGFR copy number (FISH negative), as indicated by the statistically significant interaction test p value of 0.04.
In FISH-positive patients, the risk of death was 39% lower among patients receiving gefitinib compared with placebo (HR=0.61), however this effect was not statistically significant (p=0.07).
An interaction test compares positive patients to negative patients to see if their treatment effects are different.
In a survival analysis such as this, the hazard ratio measures the treatment effect. So the interaction test is assessing if the hazard ratio in positive patients is different to the hazard ratio in negative patients. If there was no difference, then by implication the subgroups are not behaving any differently to the overall population and there would be no justification for regarding the subgroups separately.
Median overall survival among patients with high EGFR gene copy number was 4.5 months for patients receiving placebo and 8.3 months for gefitinib-treated patients.
In FISH-negative patients there was no significant difference in survival between gefitinib and placebo (HR=1.16, p=0.42).
Response rates were also higher (16.4%) in FISH positive compared to FISH negative (3.2%) patients.
0.2
0.2
0.0
0.0 2 4 6 8 10 12 14 16 2 4 6 8 10 12 14 16
Time (months)
Time (months)
Interaction test: p=0.04

8. FISH positive status and clinical characteristics
% of FISH positive patients 60 50 40 30 20 10
There was some relationship between FISH positive status and clinical characteristics (eg, FISH positive status was more frequent in adenocarcinomas than in other histologies), but this relationship was much less pronounced than for EGFR mutations.
No. patients with evaluable samples:
Adeno
Other
Never
Ever
Asian origin
Other
Female
Male
156
214 48
322 11
359
117
253
Histology
Smoking
Ethnicity
Gender

9. Conclusions from Gefitinib trials
FISH+ status is the biomarker which is the strongest predictor of Gefitinib benefit on OS
Patients who are FISH- are unlikely to benefit from Gefitinib therapy.
Therefore, EGFR amplification is a predictive marker for benefit with Gefitinib therapy.

10. Definitions
Clinical Endpoint (or Outcome) : A characteristic or variable that reflects how a patient feels, functions, or how long a patient survives.
Biomarker (or Biological marker): A characteristic objectively measured as an indicator of normal biologic or pathogenic process, or pharmacologic responses to a therapeutic intervention.
measured once before treatment
Types of Biomarkers
Prognostic
Predictive

11. Prognostic vs Predictive
Prognostic markers indicate that clinical outcome is independent of treatment.
Stage of disease is a prognostic marker for survival outcome.
Predictive biomarkers show treatment effect on the clinical endpoint.
High Her-2 gene copy number in advanced breast cancer is predictive for the effect of Herceptin.
Statistically, a predictive marker is a marker that interacts with treatment “significantly.”

13. …and will adversely impact on power

14. …and will adversely impact on power

15. …therefore it is obvious that a biomarker targeted approach to drug development will lead to smaller, more secure and more successful developments
losers will be dropped early and winners taken forward, resulting in more successful drug development…

16. How about this?
100 treatment resistant patients are offered a new drug
70 respond and 30 do not.
How do we interpret this experiment?

17. Which is the correct interpretation?
A - Treatment works for 70% of patients 100% of the time and for 30% of patients 0% of the time.
Or…
B - Treatment works in 100% of patients 70% of the time.

18. Which is the correct interpretation?
A - No within patient variability –patients are deterministically responders or non responders
B - Within patient variability –drug has some effect in all patients, but patients vary in their response –sometimes they respond, sometimes they don’t

19. What does this mean?
In most situations, it is impossible to know if patients respond deterministically
To know for sure requires repeat administration of drug (and control) in within-patient crossover trials
However, such trials are impossible in many settings, especially oncology, so that there is little choice but to assume interpretation A.

20. Suppose biomarker target identified in patients treated with drug, show target +vepatients do better than target –vepatients
% surviving or progression-free
Time

21. …suppose the same is true for patients treated with control, target +vepatients do better than target –vepatients
% surviving or progression-free
Time

22. This is an example of a prognostic biomarker
Patients with the biomarker do better than those without it irrespective of the treatment they receive
This biomarker is not predictive for the effect of drug over control
Using this biomarker as a basis for patient selection is unlikely to result in a positive outcome for drug

23. Predictive vs. Prognostic

24. Biomarker +vepatients treated with drug do better than biomarker +vepatients treated with control
% surviving or progression-free
Time

25. This is an example of a predictive biomarker biomarker
+vepatients do better when treated with drug than when treated with control
biomarker –vepatients do less well on both drug and control

26. So, we need to stratify on receptor status and then randomize to drug and no drug to assess the true potential of a drug

27. Therefore, data from a properly designed Phase II trial could be used to assess the true value of receptor status

28. How about designing late Phase trials?

29. Just an example C E Effect +ve (25%) 6 months 12 months 0.50 –ve (75%)
1.0
All patients
7.5 months
0.80
No. required to screen
No. required to enroll
All patients
1000
+ve (25%)
117
468
1median follow-up of 18 months assumed

30. To validate biomarkers……
Sensitivity
Pr(test +ve/true +ve)
Specificity
Pr(test –ve/true –ve)
Positive Predictive Value
Pr(true +ve/test +ve)

31. An imperfect test lessens the advantage of a biomarker strategy
Sens, Spec C E
Effect size
No. Required to enroll
No. required to screen
100%, 100% 6 12
0.50
117
468
95%, 75%
9.4
0.64
260
613
75%, 95% 11
0.55
149
663
75%, 75% 9
0.68
317
845

32. Is a selected design still best?
Anyway, assume we have the perfect test, what happens if there is some modest effect in –ve pts?
Is a selected design still best?

33. Even a small effect in biomarker –vepts erodes the advantage of a biomarker strategy
6 months
12 months
0.50
–ve (75%)
7.5 months
0.80
All patients
8.7 months
0.69
No. required to screen
No. required to enroll
All patients
384
+ve (25%)
117
468
Effect in –vepts = 1/3 effect in +vepts

34. Likely the relationship between treatment effect and biomarker level is continuous, reflecting underlying biology

35. In a biomarker strategy we would need to be very confident that
we had a very good test
the biomarker ‘-ve’ population achieved no or very little benefit from treatment

36. In late phase development, testing across the population offers some advantages

37. Power the trial for an “interaction”
“Is there any statistical evidence that the treatment effect in +vepts is different to the treatment effect in -ve pts?”
If “Yes”, valid to look at +ve and -ve groups separately.
Possibility of labeling in (+) pts.
If “No”, then there is no statistical rationale for looking at +ve and –ve patients separately.
Compare treatments in the overall population, irrespective of biomarker status.

38. An example
Treatment A is either better or worse than treatment B (qualitative interaction)
Treatment HR = 0.74
Interaction HR = HR(+vepts) / HR(-vepts)
= 0.48 / 2.85 = 0.17
If interaction effect size is better that treatment effect size
Than interaction is highly significant

39. Power of Interaction Test
Interaction test has very low power
So, validation of predictive biomarker is more complicated ---due to limited power of the interaction test
It is known that as inflation factor for total sample size decreases, so does interaction effect size in relation to overall treatment effect size.
Therefore, inflation factor is required to increase the sample size to ensure interaction test has the same power as the original sample size calculated for overall treatment effect.

40. Design can provide confirmatory evidence in either all patients or the subset of biomarker +ve patients

41. Alternatively, patient selection adaptive designs can identify those patients most likely to benefit
Interim
Continuance
Randomize

42. The need for surrogate endpoints
In many settings, the primary clinical endpoint takes large, long term trials
Breast cancer recurrence, cardiac events, osteoporotic fracture, death from prostate cancer
To reduce time and expense and to bring effective medicines to patients quickly requires use of surrogate endpoints

43. Statistical definition of Surrogacy
“A response variable for which the test of the null hypothesis of no relationship to the treatment groups under comparison is also a valid test of the corresponding hypothesis based on the true endpoint.” by Prentice, (1989)

44. The Problem with Prentice
Criteria based on Prentice’s definition are problematic
Cannot prove the null
The ‘%’ effect retained is not a true percentage, and CI for the ‘%’ effect retained is usually very wide
Cannot realistically expect 100% of a drug effect on OS to be explained by a direct effect on the disease itself.

45. Newer Approaches to Surrogacy
That reliably predict drug effect on a later clinical outcome (e.g. OS or PFS) given the effect of drug on some earlier endpoint.
Buyse and Molenbergs (2000, 2002) provided a meta-analytic methodology for doing just this.
Unlike Prentice, this approach does not require proving the null nor the presence of a significant treatment effect.

46. Strong evidence of surrogacy: Relation between tumor response to first-line chemotherapy and survival in advanced colorectal cancer: a meta-analysis
R2=0.97

47. Using methodology to quantitate uncertainty in prediction Ovarian cancer

49. Conclusions
There should be an assurance that selected test for biomarker is correct. So, validation of biomarkers in early in drug development is imperative.
Treatment interaction effect has to be factored in sample size calculation for late phase studies.

50. Backup Slides

51. FISH: technique for measuring increased EGFR gene copy
Fluorescent In Situ Hybridisation (FISH) is a technique for measuring increased EGFR gene copy number
A piece of synthetic DNA labelled
with a fluorescent tag binds to the EGFR gene. A probe to the gene CEP7 labelled with a second probe acts as a reference
The normal situation
‘balance disomy’
EGFR probe
Control probe
‘balanced polysomy’
‘gene amplification’
Cappuzzo et al 2005

52. EGFR gene copy number (FISH) in ISEL trial
Pattern
Patients n=370 (%)
Disomy
Low trisomy
High trisomy
Low polysomy
High polysomy
Gene amplification
15.7
24.1
2.2
27.3
17.0
13.8
Note: Categories in blue above represent those considered FISH+