Experimental Design and Statistical Considerations in Translational Cancer Research (in 15 minutes) Elizabeth Garrett-Mayer, PhD Associate Professor of Biostatistics and Epidemiology

 Phase I studies  Taking markers into the clinic Two Parts

 Historically, DOSE FINDING study  Classic Phase I objective:  “What is the highest dose we can safely administer to patients?”  Translation: Kill the cancer, not the patient  Assumes monotonic relationship between  dose and toxicity  dose and efficacy Phase I Trial Design

Classic Phase I Assumption: Efficacy and toxicity both increase with dose DLT = dose- limiting toxicity

Classic Phase I approach: Algorithmic Designs Treat 3 patients at dose K 1.If 0 patients experience DLT, escalate to dose K+1 2.If 2 or more patients experience DLT, de-escalate to level K-1 3.If 1 patient experiences DLT, treat 3 more patients at dose level K A.If 1 of 6 experiences DLT, escalate to dose level K+1 B.If 2 or more of 6 experiences DLT, de-escalate to level K-1

“Novel” Phase I approaches  Continual reassessment method (CRM) (O’Quigley et al., Biometrics 1990)  Many changes and updates in 20 years  Tends to be most preferred by statisticians  Other Bayesian designs (e.g. EWOC) and model-based designs (Cheng et al., JCO, 2004, v 22)  Other improvements in algorithmic designs  Accelerated titration design (Simon et al. 1999, JNCI)  Up-down design (Storer, 1989, Biometrics)

CRM: Bayesian Adaptive Design  Dose for next patient is determined based on toxicity responses of patients previously treated in the trial  After each cohort of patients, posterior distribution is updated to give model prediction of optimal dose for a given level of toxicity (DLT rate)  Find dose that is most consistent with desired DLT rate  Modifications have been both Bayesian and non-Bayesian.

New paradigm: Targeted Therapy How do targeted therapies change the early phase drug development paradigm?  Not all targeted therapies have toxicity  Toxicity may not occur at all  Toxicity may not increase with dose  Targeted therapies may not reach the target of interest  Implications for study design: Previous assumptions may not hold  Does efficacy increase with dose?  Endpoint (DLT) may no longer be appropriate  Should we be looking for the MTD?  What good is phase I if the agent does not hit the target?

Possible Dose-Toxicity & Dose-Efficacy Relationships for Targeted Agent

 A study that correlates a “marker” with disease  What is a marker?  An innate characteristic of a tumor or tissue  Examples What is a Correlative Study? MarkerPSA Estrogen receptor SUV from PET KIT mutation Disease Prostate cancer Breast cancer Many cancers GIST

 Prognostic marker:  Predicts outcome (independent of therapy)  Predictive marker:  Predicts response to therapy  Can be used for  Treatment assignment  Treatment stratification in clinical trials  Surrogate endpoint (?)  Targeted therapy development  Diagnosis What is it good for?

Mitotic Rate: Prognostic Marker DeMatteo et al, Cancer, 112: Figure 3. Recurrence-free survival in 127 patients with completely resected localized gastrointestinal stromal tumor (GIST) based on mitotic rate

Disease-free survival. Gennari A et al. JNCI J Natl Cancer Inst 2007;100:14-20 © The Author Published by Oxford University Press. HER-2: Predictive Marker

 Analytical development  Measurement, logistics etc  Clinical development  Sample collection, storage, processing  “Retrospective” connection with outcome  Clinical validation  “Prospective “ connection with outcome Lifecycle of a marker

Statistical issues during analytical development  Reproducibility  Repeat the measurement on the same sample multiple times under otherwise identical conditions  Suppose binary marker, twice measured  Results can be summarized in a fourfold (2x2) table  Statistical Significance?  not good enough!  p<0.05 shows there is a trend  need strong agreement, not just a trend

Continuous Measurements DO NOT RELY ON P-VALUES!!

 Correlate marker(s) with the outcome on a cohort of patients  Many issues relate to bias  Case/control selection  Quality/Processing  Over-fitting/Lack of validation Clinical development of a marker

 A systematic difference between what we think we observe and what we actually observe  The more “haphazard” the data collection process, the more chances of bias creeping in  Buyer beware: Commercial Tissue Microarrays  Why is bias a problem?  Cannot be “quantified” (within a study)  Does not diminish with increasing sample sizes What is bias?

 Use the same data to develop/fine-tune a marker (or model) and evaluate its characteristics  Most obvious with multivariable analyses (gene signatures etc)  Might happen in seemingly innocuous circumstances  Choosing a cutpoint  Not reporting negative markers  VALIDATION!!!  “cross-validation”: statistical approaches that use the same data but account for double-dipping  true validation:  repeat the study in a new but similar population  apply the “model” to a new dataset and test its prediction accuracy Double dipping

 All sorts of biases crept in  Patients with tissue are unlikely to be a random sample  No real inclusion/exclusion criteria  Possibly looked at many markers, many subsets and many thresholds  Build your marker into a clinical trial Be critical of your results

 Start as secondary endpoints in a Phase I or II trial  If Phase I, might be better to have an MTD-cohort and limit the correlative studies to that cohort  If Phase II and an expensive/invasive marker, consider a two-stage design where marker will be measured only in the second stage Incorporating markers into clinical trials