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Dose finding designs driven by immunotherapy outcomes with Application to a metastatic melanoma phase I trial
Elizabeth Garrett-Mayer, PhDCody Chiuzan, MSHollings Cancer Center, MUSCSRCOS June 2012
T cell attacking cancer cells*
* Azgad, The Cutting Edge, 2011
Dose Finding in Medical Research
In cancer research, usually small studies: 6 – 30 patients is the norm◦ not ethical to use ‘healthy volunteers’◦ few eligible patients◦ small expectation of efficacy
Due to ethical concerns, you must try doses sequentially Algorithmic designs are most common: 3 patients per
dose and use predefined escalation and de-escalation rules.
Rules are defined by a binary measure of toxicity: bad toxicity vs. no or acceptable toxicity.
Model based designs arrived on the scene in 1990◦ most use toxicity as the outcome◦ driven by assumed monotonic association between both
dose and toxicity dose and efficacy
Assumption of classic dose finding designs in oncology
Pro
babi
lity
of O
utco
me
Dose Level1 2 3 4 5 6 7
0.0
0.2
0.4
0.6
0.8
1.0
ResponseDLT
DLT = dose-limitingtoxicity
Immunotherapies in cancerImmunotherapies 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
levelUnnecessary overdosing would be costly
◦ actual monetary costs◦ possible 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 with immunotherapy outcomesUsually 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: 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 lymphodepleting chemotherapy preparative regimen.
PI’s: Mike Nishimura and David ColePart 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 tumor of the patient, which are trained to attack the cancerous cells.
◦ These T cells are referred to as tumor-infiltrating lymphocytes (TILs)
Expansion: TILs are multiplied in vitro These T cells are then transferred back into the patient
along with IL-2 to act as a growth factor for T cells
Cartoon version*
Strategy towards adoptive cell transfer with genetically modified T cells. 1) Extractions of T cells from the patient. 2) Transfection of an rationally optimized TCR in those cells using a viral vector. 3) Optional expansion. 4) Lymphodepletion of the patient. 5) Reinfusion of the modified T cells to the patient. Adapted from Olivier Michielin: http://www.nccr-oncology.ch/scripts/index.aspx?idd=134
Original Trial DesignSubjects 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 DesignDesire to explore each dose levelSafety concerns suggested dose escalation
necessarySignificant 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 30 days 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 promise
Rosenberg shows weak association between dose of cells and response
Do not want to assume monotonicity.
Selection of optimal dose is not obvious
Practical GoalsMake 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 approachA 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 K 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
Standard linear regression model*:
log(
Define = estimated persistence (%) at dose j Define or
* log link here. others could be used.
After 2K patients, adaptively randomize
For the next patient, randomize to doses j = 1,…,K based on
Fit model above based on updated persistence outcome.
Repeat until total sample size of N achieved.
Theorized benefitsMore patients will be allocated to doses with
higher persistenceBetter 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? mean?◦ select dose so that most patients will have certain
level of persistence?
Model Comparisons We compared our adaptive model to the equal allocation and doubly
adaptive biased coin (DBCD) designs.
Equal allocation (balanced) design: randomization to achieve equal sample size per dose
For the DBCD, the first 2K patients were equally allocated to K doses; the assignments of the remaining patients were made using the following allocation function and target allocation proportions:
1
( / )Allocation Function: ( , ) , 1, 2
( / )
ˆTarget Allocation Proportion: , 1,..., , 3
ˆ
k k k
k K
j j jj
j
kk
y y x Lg x y L
y y x L
pk K K
p
SimulationsTotal N=25
◦ 2 at each of five dose levels◦ 15 allocated by adaptive randomization or
balanced allocationFive true models.
True Models Considered
curvilinear
plateau quadratic
linear flat
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:
So far, we’ve used two sets of variance assumptions◦ constant variance across doses vs. larger variance near
persistence of 50%◦ small vs. large variance in Beta (v=0.002, v=0.01)
Reasonable assumptions, yet◦ not completely consistent with fitted model◦ allows robustness to misfit to be evaluated
Results: Allocation to doses (large V)
Results: estimated persistence (large V)
What we learned (so far)we can allocate more patients to doses with higher
persistenceestimation at the doses with higher persistence is
marginally improved◦ less bias, greater precision◦ depends on level of variance assumed (work in
progress!)◦ square root vs. no square root does not have much effect
on results.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 progressChoosing 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◦ The main cause is represented by the nonimmune
systemic toxicities and autoimmunity triggerred by IL2◦ 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?
AcknowledgementsCody Chiuzan, MS (PhD candidate)Mike Nishimura, PhDSupported by:
◦ NIH/NCI P01 CA54778-01◦ NIH/NCI P30 CA138313-02
References:◦ Johnson L. A., Morgan R. A., Dudley M. E., Rosenberg S. A., Gene Therapy with human
and mouse T-cell receptors mediates cancer regression and targets normal tissues expressing cognate antigen. Blood, 2009, Vol. 114, No. 3.
◦ Duval L., Schmidt K., Fode K., Jensen J., Nishimura M., Adoptive Transfer of Allogeneic Cytotoxic T Lymphocytes Equipped with a HLA-A2 Restricted MART-1 T-Cell Receptor: A Phase I Trial in Metastatic Melanoma. Clinical Cancer Research 12: 1229-1236.
◦ Feifang Hu, Li-Xin-Zhang, Asymptotic Properties of Doubly Adaptive Biased Coin Designs for Multitreatment Clinical Trials. The Annals of Statistics, 2004, Vol. 32, No. 1, 268-301.
◦ Lanju Zhang, Rosenberger W. F., Response-Adaptive Randomization for Clinical Trials with Continuous Outcomes. Biometrics 62, 2006, 562-569.
◦ Liangliang Duan, Feifang Hu, Doubly Adaptive Biased Coin Designs with Heterogeneous Responses. Journal of Statistical Planning and Inference 139, 2009, 3220-3230.
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.
