DOSE FINDING DESIGNS DRIVEN BY IMMUNOTHERAPY OUTCOMES: A PRACTICAL CONSIDERATIONS IN A METASTATIC MELANOMA PHASE I TRIAL Elizabeth Garrett-Mayer, PhD Cody Chiuzan, MS Hollings Cancer Center Medical University of South Carolina Charleston, SC
DOSE FINDING DESIGNS DRIVEN BY IMMUNOTHERAPY OUTCOMES: A PRACTICAL CONSIDERATIONS IN A METASTATIC MELANOMA PHASE I TRIAL
Elizabeth Garrett-Mayer, PhDCody Chiuzan, MSHollings Cancer CenterMedical University of South CarolinaCharleston, SC
BACKGROUND
Immunotherapies often are expected to be non-toxic anecdotally, they are often not
There is not strong rationale to assume that the highest tolerable dose is the optimal dose
Standard algorithmic and model-based designs for dose finding based on binary measures of toxicity are inappropriate for identifying the optimal dose
Efficacy-driven dose finding is more relevant, although safety concerns need to be incorporated
BACKGROUND
Immunotherapy approaches can be ridiculously expensive cost may increase exponentially by dose level
Unnecessary overdosing would be costly actual monetary costspossible non-monotonicity of association between dose
and response safety needs to be considered
CURRENT STATUS
Most immunotherapy trials in cancer use a two step approach to dose finding
1. Perform an algorithmic design to identify safe doses
2. collect immunological data and “explore” it to see if there appears to be an optimal dose
Optimal dose?
we imagine there will be a clear plateau in the association between dose and outcome.
unrealistic and simplistic due to small sample sizes at each dose
unrealistic and simplistic due to heterogeneity across patients
CHALLENGES OF IMMUNOTHERAPY OUTCOMES
Usually continuous
target levels often not known
heterogeneity across patients
Often not well-defined or described prior to the trial.
There is not a clear link between clinical outcomes and the immunology “target”
Assumption of monotonicity is not well-founded
MOTIVATING PROJECT
“Transfer of Genetically Engineered Lymphocytes in Melanoma Patients - A Phase I Dose Escalation Study”
Objective 1 To establish the recommended phase II dose of autologous T cell receptor (TCR) transduced T cells when administered with low dose IL-2 to stage IV melanoma patients following a non-myeloablative and lymphodepletingchemotherapy preparative regimen.
Objective 2: To evaluate biologic and immunologic parameters associated with the adoptively transferred TCR transduced T cells, including auditory and visual changes.
Objective 3: To determine if systemic infusion of TCR gene modified autologous T cells can mediate objective clinical responses in stage IV melanoma patients.
PI’s: Mike Nishimura and David Cole Part of P01 Program Project Grant (funded Aug 1, 2011)
ADOPTIVE T-CELL TRANSFER
Tumor Infiltrating Lymphocytes (TIL) Tumor infiltrating lymphocytes are white blood cells that have left
the bloodstream and migrated into a tumor. They are an important prognostic factor in melanoma,higher levels being associated with a better
outcome.
Adoptive cell transfer uses T cell-based cytotoxic responses to attack cancer cells. T cells that have a natural or genetically engineered reactivity to a patient's cancer are generated in vitro and then transferred back into the cancer patient
This can be achieved by taking T cells that are found with the tumour of the patient, which are trained to attack the cancerous cells.
These T cells are referred to as tumor-infiltrating lymphocytes (TIL) are then encouraged to multiply in vitro using high concentrations of IL-2, anti-CD3 and allo-reactive feeder cells.
These T cells are then transferred back into the patient along with exogenous administration of IL-2 to further boost their anti-cancer activity.
BRIEF DESCRIPTION OF APPROACH T Cell Receptor (TCR) Modified T Cells
“Genetically engineered lymphocytes”
This approach involves
identifying and cloning the TCR genes from tumor reactive T cell clones (human or mouse).
constructing retroviral vectors capable of introducing these genes into normal cells
genetically modifying the patients PBL-derived T cells or hematopoietic stem cells ex vivo.
genes encoding TCRs are engineered into retroviral vectors
these are then used to transduce autologous peripheral lymphocytes
these gene modified autologous cells are then returned to the patient.
BRIEF DESCRIPTION OF RATIONALE
Redirect the specificity of normal T cell to recognize a variety of antigens
There are several advantages to treating patients with cells that have been engineered to express TCR genes. The vectors represent an “off the shelf” reagent that could be used to treat any
patient that expresses the antigen and MHC molecules recognized by the TCR.
This approach does not rely on the patients TCR repertoire and precursor frequency.
The unique sequences within theTCR enable us to monitor the persistence, localization, and frequency of these genetically engineered cells using clone specific PCR primers. This ability to monitor patients based on the presence of the transduced TCR will enable us to understand more about the behavior of tumor-reactive T cells in cancer patients.
ORIGINAL TRIAL DESIGN
Subjects will receive a single infusion of autologous bulk TIL 1383I TCR transduced T cells supported with low dose IL-2.
Four cohorts of 3 patients will be treated with increasing doses of TIL 1383I TCR transduced T cells cohort 1: 2x108 TIL 1383I TCR transduced T cells
cohort 2: 5x108 TIL 1383I TCR transduced T cells
cohort 3: 2x109 TIL 1383I TCR transduced T cells
cohort 4: 5x109 TIL 1383I TCR transduced T cells
EXPERIMENTAL DESIGN
Desire to explore each dose level Safety concerns suggested dose escalation necessary Significant accrual concerns: N=18 over 2 years. Single center ‘3+3’ Data analysis at the end to identify best dose based on
immunologic parameters
IMMUNOLOGIC PARAMETERS?
Difficult to get them to “commit” to a quantitative definition.
Basis was paper by Johnson, Rosenberg et al. (2009) “% Persistence of T cells” In a related trial, the binary endpoint of persistence was
defined as “20% or greater TIL 1383I TCR transduced CD8+
T cells in the CD3+ T cell fraction of the subject’s PBMC 30days post-infusion”
ROSENBERG RESULTS6 responses in 20 patients
“There was no correlation between the number of cells administeredand the likelihood of a clinical response, with some responding patients receiving a log fewer cells than others.”
BETTER DESIGN?: ASSIGN PATIENTS TO DOSES SHOWING MORE PROMISERosenberg shows weak
association between dose of cells and response
Do not want to assume monotonicity.
Selection is optimaldose or RP2D not obvious
PRACTICAL GOALS
Make it easy to implement
relatively few assumptions
estimation can be done using standard software
flexibility to different outcomes
fold change (e.g., genetic marker)
% persistence (e.g., immunology)
absolute count (e.g., pharmacokinetics; CTCs)
Make it easy to understand
clinician ‘buy-in’
statistician ‘buy-in’(?)
ADAPTIVE RANDOMIZATION APPROACH
A basic scenario:
K doses of interest
outcome is persistence at 2 weeks (or 30 days?)
for accrual reasons: 2 weeks preferred
for link to clinical outcomes: 30 days may be preferred
treat two patients at each dose, escalating from dose 1 to 5
Implement rules to disallow doses if not safe (e.g., 2 DLTs)
Continue enrolling to a total of N patients using adaptive randomization
AFTER 2K PATIENTS, ADAPT RANDOMIZATION
Estimate % persistence at each dose using data from first 2*K patients
%CD3cellsatfollow upcomparedtobaseline Standard linear regression model*:
log(2 3 …
Define ̂ = estimated persistence (%) at dose j
Define ∑
* log link here. others could be used.
AFTER 2K PATIENTS, ADAPTIVELY RANDOMIZE
For the next patient, randomize to doses j = 1,…,Kbased on
Fit model above based on updated persistence outcome.
Repeat until total sample size of N achieved.
THEORIZED BENEFITS
More patients will be allocated to doses with higher persistence
Better inferences will be made regarding optimal dosesPrecision estimates for doses with highest persistence will
be improvedDose selection for RP2D will be improved compared to
balanced design
EVALUATING THE RESULTS
Number of patients treated per dose level
Estimated persistence per dose level
Accuracy of dose selection: what is the best dose?
Three types of criteria:
dose with maximum persistence
minimum dose with persistence of at least X%
highest persistence prior to plateau (defined by increase of <P% between doses).
Incorporating uncertainty into dose selection based on median persistence per dose?
select dose so that most patients will have certain level of persistence?
SIMULATIONS
Adaptive design vs. Balanced Design
Balanced: randomization to achieve equal sample size per dose
Total N=25
2 at each of five dose levels
15 allocated by adaptive randomization or balanced allocation
Four true models
Two levels of variance considered
TRUE MODELS CONSIDERED
SIMULATION SETUP
Persistence can range from 0% to 100% (technically can be greater, but very unlikely)
Persistence is generated from beta-binomial where between patient heterogeneity is controlled by beta distribution and within patient heterogeneity by N:
~ ,~ 100,
Reasonable assumptions, yet not completely consistent with fitted model
allows robustness to misfit to be evaluated
ASSUMED VARIANCE ACROSS PATIENTS
1 2 3 4 5
020
4060
8010
0Variance=0.01
Dose Level
Per
sist
ence
(%)
1 2 3 4 5
020
4060
8010
0
Variance=0.05
Dose Level
Per
sist
ence
(%)
RESULTS: ALLOCATION TO DOSES (V=0.01)
RESULTS: ESTIMATED PERSISTENCE (V=0.01)
RESULTS: PRECISION (SE)
WHAT WE LEARNED (SO FAR) we can allocate more patients to doses with higher persistence
estimation at the doses with higher persistence is improved
less bias
greater precision
when there is no dose response we maintain essentially the average properties as the balanced design
N of 25 is not very big.
We are only considering 15 patients in adaptive portion.
larger sample sizes provide greater improvements compared to balanced design.
DOSE SELECTION: WORK IN PROGRESS
choosing the best dose
’eyeball test’ vs. a quantitative approach?
incorporating clinical outcomes into dose selection
current approach (so far) addresses dose assignment
dose selection may incorporate both persistence and clinical outcomes (and the association between persistence and clinical outcome)
defining a plateau is application specific
SAFETY CONSTRAINTS
doses may become ‘disqualified’ if there are adverse events at those dose levels
relatively easy insertion: will likely have similar effects on balanced and adaptive approaches.
LOTS MORE TO CONSIDER many more scenarios! lag time:
14 days (or 30 days) to measure persistence in this situation. if there is rapid accrual, randomization probability will not be updated as frequently and design
will lean more towards balanced.
transformations: choice of link function for deriving randomization probabilities will be context specific dose selection will have a similar issue Should we consider using ranks?
other outcomes drop-outs/inevaluables
there is the reality of patients who drop out or whose follow-up measures are inevaluable
accounting for uncertainty in the model: quite a few ways to go. shall we be Bayesian?