Biomarker-driven Phase III Clinical
Trials: Practical & Design Considerations
David R. Gandara, MD
University of California, Davis
Comprehensive Cancer Center
1
Transition from Empiric to Personalised Cancer Therapy (Biomarkerdriven)
Empiric Therapy
Biomarker-driven &
Personalised Therapy
Tumour Molecular Profiling to identify Drug
Targets (adequate tumour tissue)
Drugs Against the
Molecular Targets
Predictive Biomarkers
for the Drugs
Clinical trial designs that define the
activity of the drug against its target
(measured by a biomarker)
Gandara et al: Clin Lung Cancer, 2012
2
Evolution of NSCLC Subtyping from Histologic to Molecular-based
NSCLC
as one
disease
Li, Gandara et al: JCO 2013 (adapted from Pao et al)
3
Need for Paradigm Shift in Targeted Therapy Clinical Trial Design (Presumes
Biomarker Potential)
“All Comer” Phase III Design Adding Targeted Therapy
to Chemotherapy
Standard
Therapy
M+
M-
“All Comers”
Exp Therapy
(Targeted Agent
or
Standard + Targeted)
M+
M+
M-
M+
M-
M-
• When marker not known or not validated (analytical)
• Marker (if known) can be retrospectively assessed
• Cautionary tale: Most Phase III “all comer” trials in NSCLC targeted
therapy fail
• May be random differences in marker+ and marker- proportions per arm
Gandara et al: Clin Lung Cancer, 2014
4
Prognostic vs Predictive Biomarkers
Prognostic Marker
• Information about disease
outcome independent of
treatment
• Example: EGFR mutation in
NSCLC
• Mutation + : better prognosis
• Mutation - : worse prognosis
Predictive Marker
• Information on disease outcome
related to a specific treatment
• Example: EGFR mutation in
NSCLC
• Mutation + : ~70% probability of
response to EGFR TKI therapy
• Mutation - : <5% probability of
response to EGFR TKI therapy
Some biomarkers are both prognostic & predictive
Only predictive biomarkers can be used to indicate “which patients
should be treated with which drug” (a targeted therapy)
Predictive biomarkers can also identify patients who may be
harmed by “targeted therapy”
5
Possible Outcome Scenarios:
Marker+ vs MarkerM+ : marker positive, marker value > cut point
M - : marker negative, marker value < cut point
0.4
T1: Standard Therapy
T2: New (Experimental) Therapy
-0.5
Cut-off
0.0 0.5 1.0
Marker Value
0.0
0.0
0.4
0.8
Scenario 2
0.8
Scenario 1
1.5
-0.5
0.8
0.4
Cut-off
0.0 0.5 1.0
Marker Value
Scenario 1: Biomarker is neither prognostic
nor predictive
Predictive Markers:
Scenario 2: T2 benefits M+ pts, but not M- pts
0.0
0.8
0.0
-0.5
1.5
Scenario 4
0.4
Scenario 3
Cut-off
0.0 0.5 1.0
Marker Value
1.5
-0.5
Cut-off
0.0 0.5 1.0
Marker Value
Scenario 5
0.8
M-
M+
1.5
Scenario 3: T2 benefits M+ & M- pts, but effect
on M+ pts is greater
0.0
0.4
Scenario 4: T2 benefits M+ pts but is harmful to
M- pts (total interaction)
-0.5
Cut-off
0.0 0.5 1.0
Marker Value
1.5
Hoering, Crowley et al: CCR, 2008
Scenario 5: Prognostic Marker (no predictive value)
6
Met IHC for Use as a Companion Diagnostic
Intensity of Met IHC staining in tumour cells scored in 4 categories (0-3+)
Negative (0)
Met Dx
Negative
Weak (1+)
(0-3+)
Moderate (2+)
Strong (3+)
Met Dx
Positive
‘Met Diagnostic Positive’ was defined as majority (≥50%) of tumour cells
with moderate or strong staining intensity (2+ or 3+)
52% of patients enrolled were ‘Met Diagnostic Positive’
Spigel et al: WCLC 2011
7
Phase II Trial of Erlotinib ± MetMab: PFS & OS
Met
High
Met
Low
Targeted agents can do harm in the wrong patient population.
8
Classic RCT Design (Unselected): Phase III Trials of Chemotherapy
±Targeted Agent* in 1st-line Therapy of Advanced Stage NSCLC
Target
Agent
Survival Benefit
MMPs
Prinomastat, others
No
EGFR TKI
Gefitinib or erlotinib
No
Farnesyl transferase (RAS)
Lonafarnib
No
PKCα
ISIS 3521
No
RXR
Bexarotene
No
VEGFR (TKI)
Sorafenib
No
VEGF (Mab)
Bevacizumab
Yes
EGFR (Mab)
Panitumumab
No
TLR9 agonist
PF-351
No
EGFR (Mab)
Cetuximab
IGR1-R
Figitumumab
No
VDA
ASA-404
No
Yes**
*In combination with platinum-based chemotherapy versus chemotherapy
**EGFR IHC positive
From Gandara et al: Clin Lung Cancer, 2012
9
Integrated New Drug−New Biomarker
Development Paradigm
From Gandara et al: Clin Lung Cancer, 2012
10
Validation of Prognostic & Predictive Biomarkers
Analytical Validation vs Clinical Validation
Analytical Validation
Reproducibility from day-day, performerperformer and batch-batch
Linearity
Recovery
Specificity
Crossreacitvity of related substances
Interference of RF and HAMAs
Monitor calibration curves
Precision/accuracy
Detection limit
Acceptance criteria for assay runs (IQC)
Normal values, cut-off values
Biological variation, age and gender
Clinical Validation
Retrospective analysis of “all
comer” trials
Retropsective analysis of
“biomarker-selected” trials
Prospective analysis of
“biomarker-selected” trials
Prospective analysis of
“biomarker-selected versus
standard comparison” trials
Prospective analysis of “predictive
biomarker validation” trials
Gandara et al: CAPR Workshop, April 2011
11
Importance of Patient Selection (Enrichment):
Without Selection, a Positive Treatment Effect May Be Missed
(Courtesy of Gwen Fyfe)
100% of patients
show treatment effect
Mathematical
Modeling
50% of patients
show treatment effect
25% of patients
show treatment effect
12
Biomarker-driven Clinical Trial Designs (Selected)
RANDOMISATION
Examples:
Trastuzumab in Breast CA
EGFR TKIs in EGFR MT+ NSCLC
Example:
SLCG trial of
ERCC1-driven therapy
Example:
NCI-MARVEL trial
R
A
N
D
O
M
I
S
E
A
S
S
I
G
N
M
E
N
T
RANDOMISE
Gandara et al: NCI CAPR Workshop, April 2011
13
Phase III Embedded Biomarker Testing & Validation
Standard Tx
+ placebo
(N=324)
Standard Tx
+ New Drug
(N=324)
Begin
enrollment
Stage 1: Marker training
-Randomly select ½ the pts
-Test markers
-Define predictive markers
Complete
accrual
(N=648)
Marker
identified
Stage 2: Marker validation
-Classify remaining ½ pts by
marker
-Unblind outcome
Marker not
identified
From J Heymach
Full study
unblinded
Biomarker
analysis
dropped
Primary
Endpoint:
Improved OS
Overall study
α=0.04
Co-Primary
Endpoint:
Improved OS
Marker+ group
α=0.01
Primary
Endpoint
(No biomarker):
Improved OS
Overall study
α=0.05
14
Adaptive Strategy Design
15
BATTLE (Adaptive Design)
Enrollment into BATTLE Umbrella Protocol N=253
Core Biopsy N=239
Biomarker Profile and Adaptive Randomisation
EGFR
KRAS
VEGF RXR/Cyclin D1
Erlotinib (58)
Equal N=25
Adaptive N=33
Sorafenib (98)
Equal N=26
Adaptive N=72
Vandetanib (52)
Equal N=23
Adaptive N=29
All-
Erlotinib + Bexarotene (36)
Equal N=21
Adaptive N=15
Primary Endpoint: 8-week disease
control rate; 30% assumed N=164
16
Strategies for Optimising Development of New Therapies:
Single Agents vs Combinations vs Sequential (Advanced Stage)
Single Agent
New
Drug
Combo
New
Drug
Chemo
Combo
New
Drug
Targeted
Therapy
Sequential
New
Drug
Sequential
Targeted
Therapy
or Chemo
Targeted
Therapy
New Drug
maintenance
Adapted from Gandara et al: IASLC LALCA 2014
17
Strategies for Optimising Development of New Therapies:
Single Agents vs Combinations vs Sequential (Advanced Stage)
Single Agent
PD-L1
Combo
PD-L1
Platinum
Chemo
Combo
PD-L1
Erlotinib
Sequential
PD-L1
Sequential
Targeted
Therapy
or Chemo
Targeted
Therapy
PD-L1
maintenance
Adapted from Gandara et al: IASLC LALCA 2014
18
Checkpoint Immunotherapy vs Chemotherapy:
Trial Designs
“All Comer”
Design
Advanced NSCLC
(tumour collected
for analysis)
“Marker Positive”
Design
Advanced NSCLC
PD-L1 positive
R
A
N
D
O
M
I
S
E
R
A
N
D
O
M
I
S
E
Checkpoint Immunotherapy
Chemotherapy
Checkpoint Immunotherapy
Chemotherapy
19
Measurement of PD-L1 in Cancer/NSCLC by IHC
Tumour Type
*Nearly all human tumours include a subset
that expresses PD-L1
Spigel et al: #8008 ASCO 2013.
20
Lack of Correlation of Response/Survival with Nivolumab Therapy & PD-L1
Expression with Anti-PD-L1 Antibodya
1% Staining
Positive
aFigure
Neg. Control Ab
5% Staining
Positive
Neg. Control Ab
from Antonia SJ, et al. WCLC 2013. Poster P2.11-035. PD-L1 staining is shown in archival tumour tissue
• PD-L1 expression measured in archival pretreatment tumour (including >1 year old)
• Responses in PD-L1+ & PD-L1– tumours were similar; ORRs 15% (5/33) vs 14% (5/35)
• PD-L1 expression not associated with better OS
‒ Median OS was 7.8 mo vs 10.5 mo in PD-L1+ and PD-L1– tumours
PD-L1 expression was measured using the automated BMS/Dako IHC assay based on the anti-PD-L1 monoclonal antibody (clone 28-8). Positive
staining with this assay is defined as tumour cell membrane staining at any intensity, analysed with cut-off values of 1% and 5% in a minimum
number of 100 evaluable cells
Rizvi et al: IASLC LALCA, August 2014
21
Phase III Trials of PD-1 Therapy Compared to Docetaxel in 2nd/3rd-Line
Advanced/Metastatic NSCLC
“All Comers” Strategy:
(PD-L1+ & PD-L1-)
Nivolumab Phase III Trials
Stage IIIB/IV Squam (017) NSCLC
Non-Squamous (057) NSCLC
Docetaxel
75 mg/m2 IV
Q3W
Nivolumab
3 mg/kg IV
Q2W
Treat until progression or
unacceptable toxicity or
withdrawal of consent
Overall Survival (OS)
Marker-Positive Strategy:
PD-L1+
Pembrolizumab Phase III Trial
Stage IIIB/IV NSCLC
Docetaxel
75 mg/m2 IV
Q3W
Pembro
2 mg/kg IV
Q3W
Pembro
10 mg/kg IV
Q3W
Treat until progression or
unacceptable toxicity or
withdrawal of consent
Overall Survival (OS)
CheckMate 017: Squamous
CheckMate 057: Non-Squamous
22
Nivolumab in Squamous Lung Cancer (CHECKMATE-017): Overall
Survival (OS)
Median OS (mo)
Nivolumab: 9.2
Docetaxel: 6.0
HR = 0.59 (95% Cl: 0.44-0.79; P=0.00025)
Trial was stopped early because an assessment conducted by the independent Data Monitoring Committee
(DMC) concluded that the study met its endpoint, demonstrating superior overall survival in patients
receiving nivolumab compared to the control arm
Opdivo® (nivolumab) prescribing information. Available at: http://packageinserts.bms.com/pi/pi_opdivo.pdf
23
A Phase III Study (CheckMate 017) of Nivolumab (Nivo;
Anti-Programmed Death-1) vs Docetaxel (Doc) in
Previously Treated Advanced or Metastatic Squamous
(SQ) cell Non-small Cell Lung Cancer (NSCLC)
Spigel et al: ASCO 2015
CheckMate 017: Nivolumab vs Docetaxel in
Advanced SQ NSCLC
Trial 017: “Kinetics of the Survival Curves”
• Primary Endpoint of improved OS was met, with early separation of KM curve
• Secondary Endpoint of improved PFS also met, with late separation of KM curve
Spigel et al: ASCO 2015
CheckMate 017: Nivolumab vs Docetaxel in
Advanced SQ NSCLC
• Significantly higher Response Rate with Nivolumab: 20% vs 9%
• Prolonged Duration of Response with Nivolumab (63% with ongoing response)
Spigel et al: ASCO 2015
CheckMate 017: Nivolumab vs Docetaxel in
Advanced SQ NSCLC
No apparent prognostic effect of PD-L1 expression.
Survival benefit of nivolumab was independent of PDL1 expression levels
in the SQ lung cancer trial.
CheckMate 017: Nivolumab vs Docetaxel in
Advanced SQ NSCLC
Survival benefit of nivolumab was independent of PDL1 expression levels
in this trial in SQ lung cancer
Spigel et al: ASCO 2015
Phase III, Randomized Trial (CheckMate 057)
of Nivolumab (NIVO) versus Docetaxel (DOC)
in Advanced Non-squamous (non-SQ) Non-small Cell
Lung Cancer (NSCLC)
• Stage IIIB/IV non-SQ NSCLC
NIVO
• Prior maintenance therapy with
pemetrexed, bevacizumab, or
erlotinib alloweda
• Prior TKI therapy allowed for
known ALK translocation or
EGFR TKI for known EGFR
mutation
• PS 0–1
N = 582
Randomize 1:1
• 1 prior PT-combination
3 mg/kg IV Q2W
until PD or
unacceptable
toxicity
n = 292
DOC
75 mg/m2 IV Q3W
until PD or
unacceptable
toxicity
n = 290
Paz-Ares et al: LBA109 ASCO 2015
Endpoints
Primary
• OS
Secondary
• Investigator-assessed
ORR
and PFS
• Efficacy by tumor PDL1 expression level
• Quality of life (LCSS)
CheckMate 057 in Non-SQ NSCLC: OS & PFS
Trial 057: “Kinetics of Survival Curves”
Cross-over & subsequent separation for both OS & PFS
For PFS, cross-over occurs late: median 2.3 mos (Nivo) vs 4.2 mos (Doc)
Treatment Effect on OS in Predefined Subgroups
Overall
N
Unstratified HR (95% CI)
582
0.75 (0.62, 0.91)
339
200
43
0.81 (0.62, 1.04)
0.63 (0.45, 0.89)
0.90 (0.43, 1.87)
319
263
0.73 (0.56, 0.96)
0.78 (0.58, 1.04)
179
402
0.64 (0.44, 0.93)
0.80 (0.63, 1.00)
458
118
0.70 (0.56, 0.86)
1.02 (0.64, 1.61)
82
340
160
1.18 (0.69, 2.00)
0.66 (0.51, 0.86)
0.74 (0.51, 1.06)
Age Categorization (years)
<65
≥65 and <75
≥75
Gender
Male
Female
Baseline ECOG PS
0
≥1
Smoking Status
Current/Former Smoker
Never Smoked
EGFR Mutation Status
Positive
Not Detected
Not Reported
These data suggest that perhaps Nivo
is less effective in EGFR-mutated cancers.
Note: EGFR-mutated cancers generally
carry low mutational load
0
NIVO
1
2
DOC
31
CheckMate 057: Tumor Response
ORR, n (%) [95% CI]
NIVO
N = 292
DOC
N = 290
56 (19) [15, 24]
36 (12) [8.8, 17]
1.7 (1.1, 2.6)
0.0246
Estimated odds ratio (95% CI)
P-value
Best overall response, n (%)
CR
PR
SD
PD
Unable to determine
4 (1.4)
52 (18)
74 (25)
129 (44)
33 (11)
1 (0.3)
35 (12)
122 (42)
85 (29)
47 (16)
2.1 (1.2, 8.6)
2.6 (1.4, 6.3)
Median DOR,a mos (range)
17.2 (1.8, 22.6+)
5.6 (1.2+, 15.2+)
Ongoing response,b n (%)
29 (52)
5 (14)
Median time to response,a mos (range)
Response rate not so different between Nivo & Doc in Non-SQ NSCLC
Contrasts with trial 017 in SQ.
But duration of response much longer with Nivo.
32
CheckMate 057: Nivo vs. Doc in advanced NonSquamous NSCLC: OS by PD-L1 Expression
OS benefit correlates with PD-L1 expression in this Non-SQ trial.
Paz-Ares et al. ASCO 2015, LBA109.
Contrasts with trial 017 in SQ.
CheckMate 057: OS and PFS by PD-L1 Levels
Perspective on CheckMate Phase III Trials
Two positive randomized phase III trials of nivolumab vs. docetaxel,
but very different “Kinetics of Survival Curves”
Trial 017: Squamous Cell Carcinoma
Trial 057: Non-Squamous Cell Carcinoma
“Kinetics of Survival Curves (OS)”
Trial 017 SQ: Early & continuous separation
Trial 057 Non-SQ: Cross-over but subsequent separation
déjà vu:
Cross-over in PFS in CheckMate057 & IPASS
CheckMate057: Nivo vs Chemo2
IPASS: Gefitinib vs Chemo2
“Kinetics of Survival Curves (PFS)”
Trial 057 Non-SQ & IPASS: Cross-over & subsequent separation
This pattern likely represents 2 distinct patient populations
(Biomarker-driven)
Distinct Populations: EGFR-mutated in IPASS; PD-L1+ in Trial 057
1Paz-Ares
et al: ASCO 2015;
2
Mok TS et al. N Engl J Med 2009
Possible Outcome Scenarios:
Marker+ vs Marker- Applied to CheckMate 017 & 057
M+ : marker positive, marker value > cut point
M - : marker negative, marker value < cut point
0.4
T1: Standard Therapy
T2: New (Experimental) Therapy
-0.5
Cut-off
0.0 0.5 1.0
Marker Value
0.0
0.0
0.4
0.8
Scenario 2
0.8
Scenario 1
1.5
-0.5
0.8
0.4
Cut-off
0.0 0.5 1.0
Marker Value
Scenario 1: Biomarker is neither prognostic
nor predictive
Predictive Markers:
Scenario 2: T2 benefits M+ pts, but not M- pts
0.0
0.8
0.0
-0.5
1.5
Scenario 4
0.4
Scenario 3
Cut-off
0.0 0.5 1.0
Marker Value
1.5
-0.5
Cut-off
0.0 0.5 1.0
Marker Value
Scenario 5
0.8
M-
M+
1.5
Scenario 3: T2 benefits M+ & M- pts, but effect
on M+ pts is greater
0.0
0.4
Scenario 4: T2 benefits M+ pts but is harmful to
M- pts (total interaction)
-0.5
Cut-off
0.0 0.5 1.0
Marker Value
1.5
Scenario 5: Prognostic Marker (no predictive value)
Hoering, Crowley et al: CCR, 2008
37
Magnitude of Genomic Derangement Is Greatest
in Lung Cancer
n=109 81
64
38
316
100
17
82
Mutations
Per Mb DNA
28
119
21
40
20
Carcinogen-induced Cancers
100 / Mb
Ovarian, Breast,
Prostate Cancers
Adenoca
1 / Mb
Squamous
10 / Mb
Hematologic &
Childhood Cancers
0.1 / Mb
?
?
Adapted from The Cancer Genome Atlas Project: Govindan & Kondath et al Nature 2013
38
Relationship of Mutational Load to Checkpoint Immunotherapy
Efficacy
Champiat et al: OncoImmunology 2014
39
Phase III Study of Anti-PD-L1 Agents Compared to Platinum Chemotherapy in 1stLine Advanced NSCLC (PD-L1+)
Key Feature
• Requires PD-L1 biomarker status
• Only PD-L1+ are eligible (Marker+ strategy)
BMS-558 (Nivolumab)
CheckMate 026—Phase III Trial
Stage IIIB/IV NSCLC
N=495
Platinum
Doublet
Q3W
Nivolumab
3 mg/kg IV
Q2W
MK-3475 (Pembrolizumab)
KeyNote 024—Phase III Trial
Stage IIIB/IV NSCLC
N=300
Platinum
Doublet
Q3W
MK-3475
200 mg IV
Q3W
Treat until progression or
unacceptable toxicity or
withdrawal of consent
Treat until progression or
unacceptable toxicity or
withdrawal of consent
Progression-Free Survival (PFS)
Progression-Free Survival (PFS)
40
Will These PD-1/PD-L1 Agents End Up Being
“Untargeted Use of Targeted Therapy”
“None of the recent trials was
based on selecting patients
whose tumors were producing
the targets”
David R. Gandara, MD, from
The Economist, June 2007
Only time will tell
“Untargeted use of targeted therapy”
41