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Title: Patient Selection Biomarkers in Drug Development: A first step towards individualized therapy Michae


1
Patient Selection Biomarkers in Drug
DevelopmentA first step towards individualized
therapyMichael OstlandGenentech
BioOncologyApril 21, 2005
2
Outline
  • Background
  • Some Challenges of a Drug Development Program
    that includes Biomarkers
  • Decision Making
  • Logistical and Technical
  • Wide-scale Screening of Potential Biomarkers
  • Examples
  • Discussion

3
Background
  • Very rough summary of normal drug development

4
Background (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)

5
What 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.

6
Background (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.

7
Background (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

8
Challenges
  • Decision Making
  • Logistical and Technical
  • Screening Potential Biomarkers

9
Decision 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.

10
Typical 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

11
Decisions with a Biomarker
There are seven possible decisions about Phase
III when dose and biomarker questions are part of
the development
12
Phase II Trial w/ Biomarker Dose
  • Design with retrospective Dx testing

Chemo Placebo
Enrolled Patients
randomize
Chemo low dose
Chemo high dose
13
Logistical 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.

14
Wide-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)

15
Screening 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.

16
Screening 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.

17
Screening 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

18
Ranking 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.

19
Ranking 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)

20
Results
  • 72 genes identified at the 10 FDR
  • Bioinformatics analysis follow-up ongoing
  • Modeling and proper interpretation require
    collaboration between clinical scientists,
    statisticians, and bioinformaticians

21
Example 2 Tarceva LungA Drug w/ Biomarker
Information in Label
22
Tarceva 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

23
BR21 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)
24
EGFR 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?

25
BR.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.

26
Resulting 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.
27
Figure 3 Survival in EGFR Positive Patients
28
Figure 4 Survival in EGFR Negative Patients
29
Figure 5 Survival in EGFR Unmeasured Patients
30
Conclusions
  • 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

31
Acknowledgments
  • Xiaolin Wang
  • Ben Lyons
  • Gracie Lieber
  • Cheryl Jones
  • Alex Bajamonde
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