T cell attacking cancer cells*
Dose finding designs driven by
immunotherapy outcomes with
Application to a metastatic
melanoma phase I trial
Elizabeth Garrett-Mayer, PhD
Cody Chiuzan, MS
Hollings Cancer Center, MUSC
SRCOS June 2012
* 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
1.0
Assumption of classic dose finding
designs in oncology
DLT =
doselimiting
toxicity
0.8
0.6
0.4
0.2
0.0
Probability of Outcome
Response
DLT
1
2
3
4
Dose Level
5
6
7
Immunotherapies in cancer

Immunotherapies often are expected to be nontoxic
◦ 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 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 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 wellfounded

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 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 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 IL2 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 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 30 days post-infusion”
Rosenberg results
6 responses in 20 patients
“There was no correlation between the number of cells administered
and 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 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 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
 𝑦𝑖 = % CD3 cells at follow−up compared to baseline
 Standard linear regression model*:
log(𝑦𝑖 ) = 𝛽1 + 𝛽2 𝐼 𝑑𝑖 = 2 + 𝛽3 𝐼 𝑑𝑖 = 3 +
… + 𝛽𝐾 𝐼 𝑑𝑖 = 𝐾



Define 𝑝𝑗 = estimated persistence (%) at dose j
Define 𝜋𝑗 =
𝑝𝑗
𝑘 𝑝𝑘
or 𝜋𝑗 =
* log link here. others could be used.
𝑝𝑗
𝑘 𝑝𝑘
After 2K patients,
adaptively randomize

For the next patient, randomize to doses j = 1,…,K
based on 𝜋𝑗

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 doses
Precision estimates for doses with highest persistence
will be improved
Dose 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:
y ( y / x ) L

Allocation Function: g ( x , y ) 
,  1, L  2
 y ( y / x )   L

k
k
k
K

j
j 1
Target Allocation Proportion:
k
pˆ j

k
pˆ k
j
j
, k  1,..., K , K  3
Simulations

Total N=25
◦ 2 at each of five dose levels
◦ 15 allocated by adaptive randomization or balanced
allocation

Five true models.
True Models Considered
linear
curvilinear
flat
plateau
quadratic
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:
𝜇𝑖 ~𝐵𝑒𝑡𝑎 𝛼, 𝛽
𝑦𝑖 ~𝐵𝑖𝑛(100, 𝑢𝑖 )

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
persistence
estimation 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 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
◦ 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?
Acknowledgements




Cody Chiuzan, MS (PhD candidate)
Mike Nishimura, PhD
Supported 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 Tcell 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.
◦
◦
◦
Contact info
Elizabeth Garrett-Mayer
garrettm@musc.edu
Cody Chiuzan
chiuzan@musc.edu
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.