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Predictive biomarker validation in practice: Lessons from real trials

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Title: Predictive biomarker validation in practice: Lessons from real trials


1
Predictive biomarker validation in practice
Lessons from real trials
  • Daniel Sargent, PhD
  • Division of Biomedical Statistics and Informatics
  • Mayo Clinic, Rochester MN
  • U Penn Conference, April 29, 2009

2
Prognostic Marker
  • Single trait or signature of traits that
    separates different populations with respect to
    the risk of an outcome of interest in absence of
    treatment or despite non targeted standard
    treatment

Prognostic
No treatment or Standard, non targeted treatment
Marker Marker
3
Predictive Marker
  • Single trait or signature of traits that
    separates different populations with respect to
    the outcome of interest in response to a
    particular (targeted) treatment

Predictive
No treatment Or Standard
Targeted Treatment
Marker Marker
4
Predictive Goal Curable Disease
LOW RISK
INT RISK
HIGH RISK
Control
New Treatment
5
Randomized Controlled Trial (RCT) for predictive
marker validation
  • Goal Determine which treatment will work for
    which patient
  • Vital Patients treated with treatment choices
    in question must be comparable
  • Only true assurance Patients randomized between
    treatments in question

6
Example Lack of randomization
  • Observational study of 656 consecutive patients
  • Tested association of biomarker with chemotherapy
    benefit
  • Appears pts with marker get big therapy benefit
    (compare dotted lines)
  • Problem Non-randomized Treated pts median 13
    years younger than untreated!

Elsaleh, Lancet 2000
7
Phase III Trial Designs
  • Retrospective Validation
  • Prospective Validation
  • Enrichment Designs
  • All-comers or Unselected Designs
  • Adaptive Analysis Designs

8
Retrospective/Prospective Validation
  • Test a marker by treatment interaction effect
    utilizing data collected from previously
    conducted randomized controlled trial (RCT)
    comparing therapies for which a marker is
    proposed to be predictive
  • Reasonable when
  • a prospective RCT is ethically impossible based
    on results from previous trials, and/or
  • a prospective RCT is not logistically feasible
    (large trial and long time to complete).
  • Feasible and timely

9
Retrospective/Prospective Validation
  • Samples must be available on a large majority of
    patients to avoid selection bias in the patients
    that have or do not have the samples.
  • Hypotheses, analyses techniques, patient
    population, and precise algorithm for assay
    techniques must be stated prospectively
  • All marker subgroup analyses have to be stated
    upfront, with appropriate sample size
    justification
  • If replicated, this should be considered
    acceptable for full marker validation

10
Single-Arm Studies Support the Hypothesis for
KRAS as a Biomarker for EGFr Inhibitors
Objective Response N () Objective Response N ()
Reference Treatment (panitumumab or cetuximab) No of patients (WTMT) MT WT
A. Liévre, et al. (AACR Proceedings, 2007) cmab CT 76 (4927) 0 (0) 24 (49)
S. Benvenuti, et al. (Cancer Res, 2007) pmab or cmab or cmab CT 48 (3216) 1 (6) 10 (31)
W. De Roock, et al. (ASCO Proceedings, 2007) cmab or cmab irinotecan 113 (6746) 0 (0) 27 (40)
D. Finocchiaro, et al. (ASCO Proceedings, 2007) cmab CT 81 (4932) 2 (6) 13 (26)
F. Di Fiore, et al. (Br J Cancer, 2007) cmab CT 59 (4316) 0 (0) 12 (28)
S. Khambata-Ford, et al. (J Clin Oncol, 2007) cmab 80 (5030) 0 (0) 5 (10)
WT, wild type MT, mutant cmab, cetuximab CT,
chemotherapy pmab, panitumumab
11
KRAS Analysis of a Phase 3, Randomized,
Controlled Trial Comparing Panitumumab vs Best
Supportive Care (BSC) in Colorectal Cancer
Hypothesis The treatment effect of
panitumumab monotherapy is larger in patients
with wild-type KRAS compared to patients with
mutant KRAS
11
  • Randomization stratification
  • ECOG score 0-1 vs. 2
  • Geographic region Western EU vs. Central
    Eastern EU vs. Rest of World

Van Cutsem, Peeters et al. JCO. 2007251658-1664.
12
Objectives and Analysis Methodology
  • Primary Objective
  • To assess if the effect of panitumumab on
    progression-free survival (PFS) was significantly
    greater in patients with wild-type KRAS compared
    to patients with mutant KRAS
  • Secondary Objectives
  • To assess whether panitumumab improves PFS
    compared with BSC alone in patients with
    wild-type KRAS
  • To assess whether panitumumab improves OS
    compared with BSC alone in patients with
    wild-type KRAS

Test for a PFS effect among all randomized
patients at a 5 level
Test for quantitative PFS effect interaction,
i.e., wild-type effect gt mutant
p 0.05
p gt 0.05
Compare PFS in wild-type KRAS subset
STOP
p 0.05
p gt 0.05
Compare OR OS in wild-type KRAS subset
STOP
13
KRAS Evaluable Pts (92 of population) PFS by
Treatment
Median In Weeks
Mean In Weeks
1
.
0
Events/N ()
0
.
9
191/208 (92)
8.0
15.4
Pmab BSC
9.6
BSC Alone
209/219 (95)
7.3
0
.
8
0
.
7
HR 0.59 (95 CI 0.480.72)
0
.
6
Proportion with PFS
0
.
5
0
.
4
0
.
3
0
.
2
0
.
1
0
.
0
0
2
4
6
8
1
0
1
2
1
4
1
6
1
8
2
0
2
2
2
4
2
6
2
8
3
0
3
2
3
4
3
6
3
8
4
0
4
2
4
4
4
6
4
8
5
0
5
2
Weeks
Patients at Risk
1
9
7
1
8
8
1
7
8
1
0
6
7
9
7
1
6
4
5
5
5
0
4
9
4
9
3
7
2
9
2
5
2
4
1
9
1
5
1
5
1
5
1
2
9
9
7
6
6
2
0
8
Pmab BSC
2
0
0
1
6
8
1
4
2
7
5
4
2
3
4
2
5
2
3
1
9
1
6
1
4
1
4
1
0
1
0
1
0
1
0
9
8
6
6
5
4
4
4
3
2
1
9
BSC Alone
14
Mutant KRAS Subgroup PFS by Treatment
Median In Weeks
Mean In Weeks
1.0
Events/N ()
0.9
76/84 (90)
7.4
9.9
Pmab BSC
0.8
10.2
BSC Alone
95/100 (95)
7.3
0.7
HR 0.99 (95 CI 0.731.36)
0.6
Proportion with PFS
0.5
0.4
0.3
0.2
0.1
0.0
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
34
36
38
40
42
44
46
48
50
52
Weeks
Patients at Risk
Pmab BSC
78
76
72
26
10
8
6
5
5
5
5
4
4
4
4
2
2
2
2
2
2
2
1
1
1
84
91
77
61
37
22
19
10
9
8
6
5
5
4
4
4
4
4
4
3
3
3
2
2
2
2
100
BSC Alone
15
Wild-type KRAS Subgroup PFS by Treatment
p lt 0.0001 for quantitative-interaction test
comparing PFS log-HR (pmab/BSC) between KRAS
groups
16
CRYSTAL trial first-line mCRC
Cetuximab FOLFIRI Cetuximab IV 400 mg/m2 on
day 1, then 250 mg/m2 weekly irinotecan
(180mg/m2) 5-FU (400 mg/m2 bolus 2400 mg/m2
as 46-hr continuous infusion) FA every 2 weeks
EGFR-expressing metastatic CRC
R
FOLFIRI Irinotecan (180 mg/m2) 5-FU (400
mg/m2 bolus 2400 mg/m2 as 46-hr continuous
infusion) FA every 2 weeks
  • Stratification factors
  • Regions
  • ECOG PS
  • Populations
  • Randomized patients n1217
  • Safety population n1202
  • ITT population n1198

Van Cutsem E et al, ASCO 2007
17
CRYSTAL trial Primary endpoint PFS
Van Cutsem E et al, ASCO 2007
18
KRAS evaluable population
1198 subjects (ITT)
587 subjects analyzed for KRAS mutation status
540 (45) subjects KRAS evaluable population
348 (64.4) KRAS wild-type
192 (35.6) KRAS mutant
Group A 105 (54.7)
Group B 87 (45.3)
Group A 172 (49.4)
Group B 176 (50.6)
171 subjects with events (49.1)
101 subjects with events (52.6)
FOLFIRI
Cetuximab FOLFIRI
19
PFS KRAS wild-type
Van Cutsem, NEJM 2009
20
PFS KRAS mutant
Van Cutsem, NEJM 2009
21
KRAS conclusions
  • Marker identified in single arm trials after
    non-targeted phase III trials completed
  • Initial targeting marker wrong EGFR expression
  • Prospective specification of KRAS analysis plan
  • Multiple retrospective trials provided very
    consistent results
  • Research clinical communities convinced all
    ongoing trials of EGRF inhibitors modified to
    enroll only KRAS WT patients

22
Phase III Trial Designs
  • Retrospective Validation
  • Prospective Validation
  • Enrichment Designs
  • All-comers or Unselected Designs
  • Adaptive Analysis Designs

23
Enrichment Designs
  • Screens patients for the presence or absence of a
    marker or a panel of markers, AND
  • Only includes patients in the clinical trial who
    either have or do not have a certain marker
    characteristic or profile
  • Paradigm Not all patients will benefit from the
    study treatment under consideration
  • Understand the safety, tolerability and clinical
    benefit of a treatment in the subgroup of the
    patient population defined by a specific marker
    status

24
Enrichment Designs
  • Appropriate when
  • Mechanism of drug action is known
  • Assay is reliable
  • Compelling preliminary evidence suggesting that
    patients with or without that marker profile do
    not benefit from the treatments in question
  • Needs fewer overall randomized patients compared
    to an untargeted design

Simon, CCR 2005
25
Enrichment Designs
  • SIMPLE, BUT
  • Need real time method for assessing patients who
    are / are not likely to respond
  • End up screening all patients anyway so maybe
    not a real time saver!
  • Sample size for screening/randomization depends
    on
  • Accuracy of the assay
  • Prevalence of the marker

26
Enrichment Design - Example
  • HER2 as a marker for Herceptin in Breast Cancer
    (BC)
  • Trastuzumab (Herceptin) is currently approved for
    treatment of HER2 positive BC patients in the
    adjuvant setting
  • Based on improvement in disease free survival
    from a combined analysis of 2 national intergroup
    adjuvant BC trials (NSABP B-31, NCCTG N9831)
  • Both trials utilized an enrichment design
    strategy of allowing only HER2 positive BC
    patients, based on preliminary evidence
  • Enrichment strategy was advantageous here
  • only approximately 20 of women are HER2 positive
  • if truly no benefit of Herceptin in 80 of women
    deemed HER2 negative, a much larger sample size
    would have been required to establish
    statistically significant results in an
    unselected study

27
Using markers to restrict trial eligibility
success Her 2 Breast Cancer
Romond, NEJM 2005
28
Using markers to restrict trial eligibility
beware
  • What about Herceptin in Her2- breast cancer?
  • New Data No difference in benefit based on
    strength of HER2
  • After 10 years, may need new study of Herceptin
    in Her2- patients

Paik, ASCO 2007
29
Enrichment Design - Example
  • Enrichment strategy MAYBE not so successful? ?
  • High degree of discordance between central and
    local testing for FISH and IHC
  • Post-hoc central testing for HER2 expression
    suggests patients with tumors negative for FISH
    and less than IHC 3 staining also derived
    benefit from Herceptin
  • Patients deemed HER2 negative not enrolled onto
    the trials, so cannot fully establish the
    predictive utility of HER2

30
Enrichment Design - Example
  • While the enrichment strategy did -
  • Clearly and quickly define an effective
    treatment for a subset of patients
  • It did not answer -
  • Questions regarding the predictive utility of
    HER2 due to the issues of assay reproducibility
    and inclusion of only biomarker defined subgroups
  • An unselected design, allowing for both HER2
    positive and negative patients, may have helped
    provide these answers in a definitive and
    ultimately more timely manner.

31
Semi-Enrichment Design N0147
R A N D O M I Z E
mFOLFOX6
Wild type K-ras
P R E R E G I S T E R
mFOLFOX6 Cetuximab
Stage 3 Colon Cancer (N 3768)
Centralized K-ras analysis
R E G I S T E R
Adjuvant therapy per primary oncologist Report
therapy given Annual status through year 8
Mutant K-ras
32
Unselected Designs
  • Subset Analysis, if overall effect is not
    significant
  • Marker based strategy design
  • Randomize subjects to treatment either based on
    or independent of the marker status
  • Marker by treatment interaction design
  • Use the marker status as a stratification factor
    when randomizing subjects to treatment
  • All patients of a specific disease type and
    stage are eligible for the clinical trial,
    regardless of their actual marker status

33
Unselected Design Marker Based Strategy
Marker Level (-)
Treatment A
Marker Based Strategy
Marker Level ()
Treatment B
Register
Randomize
Test Marker
Treatment A
Non Marker Based Strategy
Randomize
Treatment B
Sargent et al., JCO 2005
34
(No Transcript)
35
Unselected Design Upfront Stratification by
Marker status
Treatment A
Marker Level (-)
Randomize
Treatment B
Register
Test Marker
Treatment A
Marker Level ()
Randomize
Power trial separately within marker groups
Treatment B
Sargent et al., JCO 2005
36
Marker by Treatment Interaction Design EGFR as a
marker for Erlotinib in Lung Cancer
  • Randomized trials in unselected patients with
    advanced non small cell lung cancer (NSCLC) have
    demonstrated
  • A small survival advantage for erlotinib-treated
    patients and a trend toward improved survival for
    gefitinib-treated patients (two epidermal growth
    factor receptor (EGFR) tyrosine kinase (TK)
    inhibitors)
  • Patients with EGFR tumors by IHC, FISH and
    mutations appear to derive more benefit from
    erlotinib than patients with EGFR- tumors
  • So, why not use an enrichment strategy design for
    validation?

37
FISH May Predict Survival Benefit of EGFR-TKIs
Subset Analyses
ISEL FISH
BR.21 FISH
100
100
80
80
60
60
Survival,
Survival,
40
40
HR0.44 (0.23, 0.82) P .008
HR0.61 (0.36, 1.04) P .07
20
20
0
0
4
8
12
16
6
12
18
30
24
Months
Months
BR.21 FISH -
ISEL FISH -
100
100
80
80
60
60
Survival,
Survival,
HR0.85 (0.48, 1.51) P .59
HR1.16 (0.81, 1.64) P .42
40
40
20
20
0
0
24
4
8
12
16
6
12
18
30
Months
Months
  • BR.21 FISH Interaction p0.10
  • ISEL FISH interaction p0.04

38
FISH May NOT? Predict Survival Benefit of
EGFR-TKIs INTEREST Subset Analysis (Trial N
1466)
1.00
Probability of progression-free survival
EGFR FISH n158
EGFR FISH- n179
0.8
0.6
0.4
0.2
0.00
0
4
8
12
16
20
24
32
0
4
8
12
16
20
24
28
32
28
36
40
36
40
At risk
Months
Months
Gefitinib
77
27
10
4
3
3
2
0
80
21
7
3
1
1
0
0
0
2
0
0
0
0
Docetaxel
81
28
5
3
1
0
0
0
99
32
8
2
1
0
0
0
0
0
0
0
0
0
Gefitinib
Docetaxel
Gefitinib
Docetaxel
N Events
77 68
81 74
N Events
80 73
99 83
HR (95 CI) 0.84 (0.59, 1.19) p0.3343
HR (95 CI) 1.30 (0.93, 1.83) p0.1229
39
MARVEL - Marker Validation for Erlotinib in Lung
Cancer
Initial Registration
Strata
Randomize
EGFR FISH ( 30)
Erlotinib
2nd line NSCLC with specimen
FISH Testing
Pemetrexed
EGFR FISH - ( 70)
Erlotinib
Pemetrexed
1196 patients
957 patients
Primary To evaluate whether there are
differences in progression free survival
between erlotinib and pemetrexed within the FISH
positive and FISH negative subgroups Secondary T
o evaluate whether there are differences in
overall survival between erlotinib and pemetrexed
within the FISH positive and FISH negative
subgroups
40
Summary Predictive Biomarkers
  • Proof of principle established
  • Translation to clinical utility will require
  • Prospective planning
  • Independent validation
  • Data from both retrospective and in some cases
    prospective trials
  • Decision between targeted vs. unselected
    eligibility is trial specific
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