Title: Patient Selection Biomarkers in Drug Development: A first step towards individualized therapy Michae
1Patient Selection Biomarkers in Drug
DevelopmentA first step towards individualized
therapyMichael OstlandGenentech
BioOncologyApril 21, 2005
2Outline
- Background
- Some Challenges of a Drug Development Program
that includes Biomarkers - Decision Making
- Logistical and Technical
- Wide-scale Screening of Potential Biomarkers
- Examples
- Discussion
3Background
- Very rough summary of normal drug development
4Background (2)
- Considerable inter-patient variability in
treatment benefit is the norm. For many highly
effective drugs, many patients wont benefit at
all - There are several examples of drugs/indications
where some of this variability is account for - Study of clinical prognostic factors
- Study of clinical predictive factors
- PK differences from C-P450 enzymes
- Drugs targeted to a patient sub-population
defined by a specific molecular biomarker (often
related to the MOA of the drug) -
5What is a Biomarker?
- A biomarker is a characteristic that is
objectively measured and evaluated as an
indicator of normal biologic processes,
pathogenic processes, or pharmacologic responses
to a therapeutic intervention (Biomarkers
Definitions Working Group). - Our interest is in the identification of
baseline-measured biomarkers that may be
predictive of clinical benefit following
subsequent therapy.
6Background (4)
- Identification of molecular biomarkers may allow
drugs to be targeted to treat those patients who
will benefit most from the therapy. Increased
benefit could come from increased efficacy and/or
decreased toxicity.
7Background (5)
- Individualized therapy has enormous
implications for the health care system and
pharmaceutical market place - Less trial-and-error in prescribing
- Smaller pool of eligible patients
- Greater benefit in these patients could mean
- Great competitive advantage
- Easier to get re-imbursement from insurers
8Challenges
- Decision Making
- Logistical and Technical
- Screening Potential Biomarkers
9Decision Making
- Phase II trials are used to get an early
indication of efficacy, further assess safety,
and identify a promising dosing regimen. - The overall objective is (usually) to allow a
decision whether to proceed into lengthy and
costly phase III trials to confirm efficacy. - Assessing a potential biomarker adds another
layer of complexity to the decision-making
process.
10Typical Oncology Randomized Phase II Trial
Chemo Placebo
Compare safety and efficacy (usually tumor
response rate or time to disease progression)
among the three arms.
Enrolled Patients
randomize
Chemo low dose drug
Chemo high dose drug
- Design the study so we are likely to have
adequate information to choose among three
possible decisions - Proceed to phase III with low dose drug
- Proceed to phase III with high dose drug
- Do not proceed to phase III at this time
11Decisions with a Biomarker
There are seven possible decisions about Phase
III when dose and biomarker questions are part of
the development
12Phase II Trial w/ Biomarker Dose
- Design with retrospective Dx testing
Chemo Placebo
Enrolled Patients
randomize
Chemo low dose
Chemo high dose
13Logistical Technical Issues
- Identification of a potential biomarker
- Timely development and technical validation of a
commercially viable assay (Dx test) - Acquiring usable patient tissues with proper
informed consent from clinical trials - Dealing with indeterminate assay results
- Clinical validation of the predictive value of
the Dx test, including CDRH regulations.
14Wide-scale Screening of Potential Biomarkers
- Technologies such as DNA microarrays allow
simultaneous assaying of thousands of potential
biomarkers. - Approach Assay samples from a randomized
clinical trial identify markers where assay
response is associated with treatment benefit. - Examine functional data on the identified markers
- Test a small number of the most promising markers
in a second trial (ideally prospectively)
15Screening Details
- Rank all markers using a statistical model that
models clinical outcome as a function of each
marker (one at-a-time), treatment group, and
other clinical covariates (if appropriate). - Determine a cut-off that accounts for sampling
variability multiplicity - Estimate the magnitude of the association between
biomarker and treatment effect - Work with Bioinformatics group to interpret,
refine, and iterate.
16Screening Details (2)
- Account for multiplicity using a False Discovery
Rate(FDR) controlling procedure (Benjamini
Hochberg Storey). - FDR is defined as the expected proportion of
rejected hypotheses that are mistakenly rejected
(falsely discovered). - Less conservative than FWER controlling
procedures, so may be more appropriate for
hypothesis generation.
17Screening Example
- 60 patients with refractory ovarian cancer were
randomized equally to standard chemo with or
without experimental drug X. - 42 had usable tissue samples that were run on
Affymetrix Microarrays with 40K mRNA probes. - Primary endpoint was duration of PFS
- Prior response to first line regimen of
platinum-based chemotherapy is a known prognostic
factor
18Ranking Genes
- For each of J biomarkers, fit a univariate Cox-PH
model. Let represent the hazard function
for the kth patient in the model for the jth
biomarker - where Tk is treatment indicator, Xkj is the (log)
expression measure for the jth gene in the kth
subject, and greek letters are unknown
parameters. - Zk is an indicator that the kth subject was known
to be resistant to platinum-based chemotherapy at
the time of randomization.
19Ranking Genes (2)
- Let Tj be the usual Wald test statistic of
- Following the development of Dudoit et al (2003),
calculate unadjusted p-values, pj, with a
permutation procedure. Then calculate adjusted
p-values - Where r1,, rJ is a sequence that puts the
unadjusted p-values in ascending order. - Then selecting genes with provides
strong control of the FDR (Benjamini and
Hochberg, 1995)
20Results
- 72 genes identified at the 10 FDR
- Bioinformatics analysis follow-up ongoing
- Modeling and proper interpretation require
collaboration between clinical scientists,
statisticians, and bioinformaticians -
21Example 2 Tarceva LungA Drug w/ Biomarker
Information in Label
22Tarceva Phase III Study BR.21 in NSCLC
- BR.21 (NCI Canada, OSIP) Tarceva monotherapy vs.
placebo in chemotherapy-relapsed (2nd/3rd line)
NSCLC - The primary endpoint was survival. Secondary
endpoints were tumor response, tumor response
duration, progression-free survival, QoL, and to
correlate the expression of EGFR levels with
outcomes. - 731 patients were randomized 21 to Tarceva or
placebo. - Designed to detect a 33 improvement in overall
survival with 90 power
23BR21 All patients
1.00
Tarceva Median 6.7 mo (n488) Placebo Median
4.7 mo (n243)
Total N731
0.75
1-yr Survival 31 1-yr Survival 21
0.50
Survival Distribution Function
0.25
0.00
0
5
10
15
20
25
30
Survival Time (Months)
24EGFR IHC and benefit from EGFR TKI
- Rationale Tumors which express target should be
more likely to respond than tumors which dont - Does the assay actually identify subgroups with
differential benefit? - Can tumors which dont express the target
benefit from treatment?
25BR.21 Survival by EGFR IHC status(data from 33
of patients)
- EGFR values using presence of staining in 10 or
more cells as positive/negative cut point - 53
were EGFR positive - EGFR IHC () survived significantly longer when
treated with Tarceva vs. placebo in BR.21 - EGFR (-) showed no evident survival benefit with
treatment in BR.21 but confidence interval for
the EGFR(-) subset is wide.
26Resulting Label Content on EGFR IHC
Relation of Results to EGFR Protein Expression
Status (as Determined by Immunohistochemistry)
Analysis of the impact of EGFR expression status
on the treatment effect on clinical outcome is
limited because EGFR status is known for only 238
study patients (33). EGFR status was ascertained
for patients who already had tissue samples prior
to study enrollment. However, the survival in the
EGFR tested population, and the effect of TARCEVA
were almost identical to that in the entire study
population, suggesting that the tested population
was a representative sample. A positive EGFR
expression status was defined as having at least
10 of cells staining for EGFR in contrast to the
1 cut-off specified in the DAKO EGFR pharmDx
kit instructions. The use of the pharmDx kit has
not been validated for use in non-small cell lung
cancer. TARCEVA prolonged survival in the EGFR
positive subgroup (N 127 HR 0.65 95 CI
0.43 0.97) (Figure 3) and the subgroup whose
EGFR status was unmeasured (N 493 HR 0.76
95 CI 0.61 0.93) (Figure 5), but did not
appear to have an effect on survival in the EGFR
negative subgroup (N 111 HR 1.01 95 CI
0.65 1.57) (Figure 4). However, the confidence
intervals for the EGFR positive, negative and
unmeasured subgroups are wide and overlap, so
that a survival benefit due to TARCEVA in the
EGFR negative subgroup cannot be excluded.
27Figure 3 Survival in EGFR Positive Patients
28Figure 4 Survival in EGFR Negative Patients
29Figure 5 Survival in EGFR Unmeasured Patients
30Conclusions
- Patient selection via biomarkers promises to
reshape the industry and patient care - There are interesting and challenging problems
for drug development with a Dx test, and
statisticians will play an important role in
meeting these challenges - Regulators are keen to get information in the
drugs label on possible biomarkers
31Acknowledgments
- Xiaolin Wang
- Ben Lyons
- Gracie Lieber
- Cheryl Jones
- Alex Bajamonde