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?