Dose finding designs driven by immunotherapy outcomes with Application to a metastatic melanoma phase I trialElizabeth 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 finding1. Perform an algorithmic design to identify safe

doses2. 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 specificSAFETY 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.

Contact infoElizabeth Garrett-Mayer

garrettm@musc.edu

Cody Chiuzanchiuzan@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.