High content clinical trials involve dense sample collection and complex analyses from small patient numbers. They are important for early drug development and evaluation, addressing biological questions about target and pathway inhibition. Successful high content trials require standardized assays and infrastructure across sites, as well as collaboration between multiple institutions. Challenges include developing new science and technologies, building collaborative partnerships, and establishing operational and informatics systems for specimen and data management.

1. High Content Clinical Trials – Design and
Infrastructure
Janet Dancey, MD, FRCPC
Program Leader, High Impact Clinical Trials, Ontario Institute for Cancer Research
Director, Clinical Translational Research, NCIC Clinical Trials Group
Clinical Trial Design for the 21st Century
Vancouver British Columbia March 2nd 2011

2. Types of Trials
• High Impact (correlation with clinical outcome)
Multi-institutional
Fewer samples, complex analyses
E.g. phase 2 trials and phase 3 trials, population studies
Require standardization across sites and/or more robust assays
Address clinical-biological correlations, more likely to have clinical impact
• High Content (Dense sample collection/analyses)
Single/Oligo-institutional trials
Multiple samples (number and type), complex analyses
e.g. Phase 1 trials to assess novel agent
Important for early development/evaluation
Address biological questions: target/pathway inhibition
2

3. Changes in Clinical Trials
Pre-Clinical
Develop- Phase I Phase II Phase III
ment
Scarcity of drug
discovery
Pre-
Biomarker – Proof of
Clinical Phase II-III – Proof of
mechanism Commercialization
Develop- principle (Predictive
(Pharmacodynamic Biomarkers)
ment
Biomarkers)
Abundance of drug
discovery
Adapted from Eli Lilly and Company, Lillian Siu 3

4. Trial Designs and Modifications
Trial Phase Purpose Biomarkers Modifications
0 Define dose Target modulation Normal Volunteers
Selected agents PK Pre-surgical
I Metastatic Dose/schedule Target Inhibition Expanded cohorts to
PK evaluate target ,
Toxicity toxicity or screen
Activity activity
II Metastatic Activity Predictive markers Randomized
III Metastatic Clinical benefit Predictive markers Subset analyses
III Adjuvant Clinical benefit Predictive Subset analyses
Prognostic
4

5. Phase 1 Trials: Considerations
• Primary goal: To identify an appropriate dose/schedule for
further evaluation
Small
• Design principles: patient
Maximize safety
numbers
Minimize patients treated at biologically inactive doses
Optimize efficiency
• Study population: Heterogenous
Patients for whom no standard therapy
Refractory
Tumours
Expect target modulation but not anti-tumour activity
5

6. Where/when do biomarkers play a role?
Target Versus Toxic Effects
1.0
Off Target Toxicity Target Effect in Tumour
Probability of Effect
Target Toxicity
Target Toxicity
Dose/Concentration/Exposure
6

7. PLX4032, a V600EBRAF kinase inhibitor: correlation of
activity with PK and PD in a phase I trial.
Puzanov, K. L. J Clin Oncol 27:15s, 2009 (suppl; abstr 9021)
Patients pERK pERK KI67 KI67 PK Imaging
PRE PRE µM*h
4 range range range range mean PD (4)
50-100, 10-40, 20-60%, 5-25%, AUC0-
median median median median 24h ~
60; 11 45%; 12.5% 126
5-fold 4-fold µM*h
2 70 2 30 -50% 3-5% 500 - PR (1)
35-fold 10-fold 1000 PET (2)
Target Pathway Tumor
7

8. Phase I Predictive Markers
Target Drug Test Phase I ORR (%)
PARP Olaparib (AZD2281; KU- BRCA1/2 9/21 (44%) Ovary,
0059436) breast, prostate
Hedgehog GDC-0449 Mutation 18/33 (56%) Basal
SMO (PTCH/SMO) Cell
EML4-ALK PF-02341066 Translocation 20/31 (61%) Lung
BRAFV600E PLX4032 (RG7204) Mutation 19/27 (70%)
Melanoma
Fong et al NEJM, 2009; von Hoff et al NEJM 2009; Kwak et al ECCO/ESMO
2009: Chapman et al ECCO/ESMO 2009;
8

9. Biomarker Designs for Late Phase Clinical Trial
• Target Selection or Enrichment Designs
• Unselected or All-comers designs
Marker by treatment interaction designs (biomarker
stratified design)
Adaptive analysis designs
Sequential testing strategy designs
Biomarker-strategy designs
• Hybrid designs

10. Types of Trials – Stratified Medicine
Molecular Analysis Study Rx
pop.
Requirements –
CLIA/GLP Laboratory,
Fast analysis of patient samples
Smaller number of patients enrolled in trial
Whole population
Rx
Molecular Analysis
Requirements –
Larger number of patients enrolled in trial,
GLP – like assay/laboratory
Is there a strong hypothesis and compelling rationale?
Is there a validated assay?
NOTE: The population size screened does not change 10

11. Challenges to Designing Trials to Prove
Personalized Medicine
• Contingent on the following assumptions:
Drug(s): Are effective in modulating target(s) of
interest
Biomarker (Mutations): Are functional “drivers” -
activating or inactivating and there is no effect in the
biomarker negative group
Resistance mechanisms do not set in fast enough that
override any antitumor activity
11

12. Target Selection/Enrichment Designs
If we are sure that the therapy will not work in Marker-
negative patients
AND
We have an assay that can reliably assess the Marker
THEN
We might design and conduct clinical trials for Marker-
positive patients or in subsets of patients with high
likelihood of being Marker-positive

13. IPASS-Schema
East Asian
Never smoker/light
R Gefitinib
former smoker A 250 mg daily
Pulmonary N
Adenocarcinoma D
No prior treatment O
M Paclitaxel 200 mg/m2
I
Carboplatin AUC 5-6
Z
E
1° Endpoint PFS
2° EGFR Biomarker 13
Mok et al N Engl J Med 2009;361:947-57

14. IPASS-Gefitinib or Carboplatin–Paclitaxel in Pulmonary
Adenocarcinoma.
Mok et al N Engl J Med 2009;361:947-57 14

15. Prospective/Retrospective Design
• Well-conducted randomized controlled trial
• Prospectively stated hypothesis, analysis techniques,
and patient population
Prospective
• Predefined and standardized assay and scoring system
• Upfront sample size and power calculation
• Samples collected during trial and available on a large
majority of patients to avoid selection bias
• Biomarker status is evaluated after the analysis of
clinical outcomes Retrospective
• Results are confirmed by independent RCT(s)
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16. Marker-based Strategy Design
Marker-Guided Randomized Design
Randomize To Use Of Marker Versus No Marker Evaluation
Control patients may receive standard or be randomized
M+ New Drug
Marker Determined
Treatment
Control
Randomize
All Patients
New Drug
Randomize Treatment
Control
OR
Standard Treatment Control
• Provides measure of patient willingness to follow marker-assigned therapy
• Marker guided treatment may be attractive to patients or clinicians
• Inefficient compared to completely randomized or randomized block design
16

17. Example: ERCC1: Customizing Cisplatin Based on Quantitative
Excision Repair Cross-Complementing 1 mRNA Expression
Cobo M et al. J Clin Oncol; 25:2747-2754 2007
• 444 chemotherapy-naïve patients with stage IIIB/IV NSCLC enrolled,
• 78 (17.6%) went off study before receiving chemotherapy, due insufficient tumor for
ERCC1 mRNA assessment.
• 346 patients assessable for response: Objective response was 39.3% in the control
arm and 50.7% in the genotypic arm (P = .02).
17

18. Trial Designs With Biomarker Stratification
• Restricting to 1 tumour type and 1 mutation
Multiple examples
– BRAF – melanoma
– EML4-ALK – Lung cancer
– HER2 - Breast
• Inclusion of multiple mutations/biomarkers with tumour-
focused question:
A few examples
– BATTLE - NSCLC
– I-SPY 2 – Locally Advanced Breast Cancer
• Inclusion of multiple tumour types with mutation-focused
question
Emerging studies proposed
– ALK, PI3K
18

19. One Tumour/One Mutation
• Restricting to 1 tumour type and 1 mutation
Multiple examples
– BRAF – melanoma
– EML4-ALK – Lung cancer
– HER2 - Breast
Unless data are compelling and there is a well
characterized assay this design is risky and restrictive
(e.g. BRAF mutation in melanoma),
Logistics are formidable but can be overcome
19

20. Multiple Tumours with One Mutations
• Inclusion of multiple tumour types with mutation-
focused question
Emerging studies proposed
– ALK, PI3K, BRAF, etc
Facilitates accrual but
– Same mutation may have different degrees of functionality
in different tumor types (continue to stratify by histology
and mutation)
– Different mutations of the same gene may confer different
sensitivities
20

21. MDACC Experience with Mutation Directed
Therapy
• Phase I trial patients from Oct 08 to Nov 09
• 217 pts tested for PIK3CA mutations:
25 pts (11.5%) harbour PIK3CA mutations
21% in endometrial, 17% in ovarian; 17% in CRC; 14% in
breast; 13% in cervical and 9% in SCCHN
Of these 25 pts, 17 pts were treated with PI3K-AKT-mTOR
pathway inhibitor
6/17 pts (35%) achieved PR
15/241 pts (6%) without PIK3CA mutations treated on
same protocols responded
Janku et al. Mol Cancer Ther 2011
21

22. Multiple Markers within One Histology
• Inclusion of multiple biomarkers with tumour-
focused question:
A few examples
– BATTLE - NSCLC
– I-SPY 2 – Locally Advanced Breast Cancer
Need to get different drugs from multiple pharma
companies, big sample size
Complex collaborations
Large, multi-center trial
22

23. BATTLE (Biomarker-based Approaches of Targeted
Therapy for Lung Cancer Elimination)
• Patient Population: Stage IV recurrent NSCLC
• Primary Endpoint: 8-week disease control rate [DCR]
• 4 Targeted Treatments
• 11 Markers
• 200 patients
• 20% type I error rate and 80% power for DCR > 30%
Zhou X, Liu S, Kim ES, Lee JJ. Bayesian adaptive design for targeted therapy
development in lung cancer - A step toward personalized medicine (In press, Clin
Trials,
Trials, 2008).
23

24. Four Molecular Pathways and
Four Putative Targeted Therapies in NSCLC:
EGFR K-ras / B-raf VEGF/VEGFR RXR/Cyclin D1
Erlotinib Sorafenib ZD6474 Erlotinib + Bexarotene
Biomarker Profiles: 24 = 16 marker groups
16 mark groups x 4 treatments = 64 combinations
24

25. Kim et al. AACR 2010 25

26. Phase 2 – I-SPY-2
Breast Cancer Patients, candidates for neoadjuvant therapy
26

27. I-SPY2 Neoadjuvant Trial
27

28. “Druggable” Mutations
45
40
35
30
25
20
15
10
5
0
Breast Ovary CRC NSCLC Melanoma
PIK3CA PTEN AKT1 BRAF KRAS NRAS
Courtesy of P. Bedard
28

29. Trials of the (near) Future
Multiple Multiple Multiple Drugs Issues
Histologies Mutations
Breast EGFR Scientific
Lung RAF
Colon MEK Methodological
Melanoma PI3K
Glioblastoma AKT Regulatory
Etc CDK4
Operational
Etc Etc
etc Etc
Cultural
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30. Translation
• Successful translation of science into innovative therapies requires
more and better science
integration of target, agent and test discovery and development
better management of supporting activities, such as specimen and
data management and collaboration for the trial and its conduct in the
clinics
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31. Gaps in Drug Development
Preclinical Clinical Approval and
Drug Discovery
Development Development Marketing
Phase I, II, III
More intelligent and coordinated biomarker research
Better Preclinical More
understandi models that efficient
ng of better
clinical trial
oncogenic predict for
safety and designs and
pathways
and their efficacy methods
potential for
therapeutic
targeting
Better Science, Collaboration, Coordination, Precompetitve Space
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32. Biomarker Development & Application
Group 4 markers – Clinical Application –
Determine economics, laboratory proficiency for broad clinical application Knowledge
Translations
Preclinical To Clinical Translation and Application
Group 3 markers – Clinical Validation
Test in an established or defined clinical setting, drug, therapy;
Multiple sites with ability to accrue a large number of patients.
Choose biomarker/assay that can be used across sites
Choose a drug/clinical setting with clear cut evidence of efficacy so can understand
clinical correlations with biomarker;
Outcomes serve as a baseline for evaluating new assays, therapies, interventions or Late Clinical
new biomarkers after evaluating the biomarker with established agents Evaluation
Commercialization
Collect data for cost effectiveness as well as clinical outcomes
Group 2 markers – Clinical Proof of Concept
Proof of concept in humans but requires specialized centres due to specimen, assay,
technology requirements
2a: evaluate potential to move to group 3 Early Clinical
2b: likely will stay specialized due to specific requirements
Determine if sufficient clinical evidence to justify moving to group 3
Evaluation
Group 2 biomarker pipeline: safety, early clinical data,
preclinical rationale, assay standardization, feasibility.
Group 1 - Exploratory Markers Laboratory
Pre-clinical evidence is promising. More direct interrogation of pathways/biology at
mechanistic level in mouse model and other pre-clinical models
Translational
Need organized effort to chose potential “winners’ that should be selected to move into Research
humans
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33. On the Next Clinical Trial
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34. Challenges
• Research & Development
• Collaborations
• Regulatory
• Commercial / Economics
• Societal
Addressing the above to enable high content trials requires systems changes
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35. High Content Time: What we need
• Science and Technology Development
Translate best science with the best chance of clinical impact
Move toward quantitative assays/imaging
• Collaborations
Reward teams
Build partnerships multidisciplinary, multi-institutional, multi-organizational
collaborations
Inter-institutional organization and communication
• Operations and infrastructure
Core – administration, structure, organization, informatics, education, data
quality
Support development/optimization of assays and tests;
HQP to ensure standardization, regulatory compliance
Quality control for specimen collection, storage and analysis and data
– Reduce variability across samples, patients and time
– Improve biomarker interpretation
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36. My Biases and Beliefs
• The integration of biospecimens with reliable clinical data is critical
• Highest quality biospecimens are collected on standardized protocols for
prespecified purpose(s) and maintained in central facilities with
appropriate quality control/quality assurance.
• Highest quality clinical data are collected in randomized controlled clinical
trials.
• Highest quality biomarker studies are evaluated in clinical trials
well supported hypothesis
well evaluated assays
appropriate biospecimens
with results correlated to appropriate clinical outcomes with statistical design
that provides certainty in the results.
• The specific resources to conduct high quality biospecimen research must
be available.
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37. My Biases and Beliefs
• Clinical research (and life) is a series of compromises some
of which are worth making and some of which are not.
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