Statistical Challenges in the Design of Late-Stage Cancer Immunotherapy Studies

Mick, Rosemarie; Chen, Tai-Tsang

doi:10.1158/2326-6066.cir-15-0260

Cited by 47 publications

(57 citation statements)

References 55 publications

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“…This indicates deviations from the most commonly used standard model, with proportional hazards assumption that the hazard ratio of the investigational arm versus the control arm is constant over time. The delay in benefit may lead to substantial loss of statistical power, thus reducing the ability to detect the effect if the trial uses conventional calculation of required events based on proportional hazards assumption (77–79). …”

Section: Traditional Endpointsmentioning

confidence: 99%

Immuno-oncology Trial Endpoints: Capturing Clinically Meaningful Activity

Anagnostou

Yarchoan

Hansen

et al. 2017

Clinical Cancer Research

127

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Immuno-oncology (I-O) has required a shift in the established paradigm of toxicity and response assessment in clinical research. The design and interpretation of cancer clinical trials has been primarily driven by conventional toxicity and efficacy patterns observed with chemotherapy and targeted agents, which are insufficient to fully inform clinical trial design and guide therapeutic decisions in I-O. Responses to immune-targeted agents follow nonlinear dose–response and dose–toxicity kinetics mandating the development of novel response evaluation criteria. Biomarker-driven surrogate endpoints may better capture the mechanism of action and biological response to I-O agents and could be incorporated prospectively in early-phase I-O clinical trials. While overall survival remains the gold standard for evaluation of clinical efficacy of I-O agents in late-phase clinical trials, exploration of potential novel surrogate endpoints such as objective response rate and milestone survival is to be encouraged. Patient-reported outcomes should also be assessed to help redefine endpoints for I-O clinical trials and drive more efficient drug development. This paper discusses endpoints used in I-O trials to date and potential optimal endpoints for future early- and late-phase clinical development of I-O therapies.

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Section: Traditional Endpointsmentioning

confidence: 99%

Immuno-oncology Trial Endpoints: Capturing Clinically Meaningful Activity

Anagnostou

Yarchoan

Hansen

et al. 2017

Clinical Cancer Research

127

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“…In this paper, we consider the special case that there is only one change point τ (ie, a fixed delay ends at τ ) and the hazard ratios are r 1 = 1 and r 2 = r before and after the delay, respectively. In this case, the model reduces to

S_{1} (t) = \{\begin{matrix} S_{0} false(t false), & t \leq τ \\ S_{0} {false(τ false)}^{1 - r} \cdot S_{0} {false(t false)}^{r}, & t \geq τ . \end{matrix}

The above 2‐piece PPHM was used in Xu et al to derive an optimal weighted log‐rank test, when a fixed delay time was present but not a cure rate. Proportional hazards cure rate modelTo incorporate a cure rate into the PHM, a PHCRM was proposed in Kim and Gray and applied in Chen and Mick and Chen . The PHCRM assumes that patients taking the control are potentially cured from the cancer with a probability of π 0 .…”

Section: Piece‐wise Proportional Hazards Cure Rate Model With Random mentioning

confidence: 99%

“…However, the PH cure model assumes a proportional hazard ratio only for the conditional survival functions of the noncured patients and does not specify any relationship between the cure rates of the 2 groups. In the PH cure model, the survival function for the experimental treatment group is

S_{1} false(t false) = π_{1} + false(1 - π_{1} false) S_{0}^{*} {false(t false)}^{r} .

Piece‐wise proportional hazards cure rate model with fixed delay timeTo incorporate both a cure rate and a fixed delay time, a PPHCRM was applied in Chen and Mick and Chen by combining the PPHM and PHCRM. The model assumes that the survival function S 0 ( t ) for the control group is the same as in the PHCRM

S_{0} (t) = π_{0} + (1 - π_{0}) S_{0}^{*} (t) .

All patients taking the experimental treatment are assumed to have the same fixed delay time τ , after which the hazard ratio ( r ≠ 1) is constant over time.…”

Section: Piece‐wise Proportional Hazards Cure Rate Model With Random mentioning

confidence: 99%

“…As shown in Figure of the Kaplan‐Meier plot for metastatic melanoma, ipilimumab started to take clinical effect from Month 6, and the overall survival curve of the combination arm reached plateau around Month 24, suggesting a potential delay time of about 6 months and a potential cure rate of about 20%. In such case, the study design based on the proportional hazards (PH) model assuming exponential distribution may lead to reduced study power and/or unexpected long study duration . New statistical models are required to better fit the data, and new statistical tests are needed to provide more power in detecting the treatment effect of the immunotherapy.…”

Section: Introductionmentioning

confidence: 99%

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Weighted log‐rank test for time‐to‐event data in immunotherapy trials with random delayed treatment effect and cure rate

Liu

Chu

Rong

2018

Pharmaceutical Statistics

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A cancer clinical trial with an immunotherapy often has 2 special features, which are patients being potentially cured from the cancer and the immunotherapy starting to take clinical effect after a certain delay time. Existing testing methods may be inadequate for immunotherapy clinical trials, because they do not appropriately take the 2 features into consideration at the same time, hence have low power to detect the true treatment effect. In this paper, we proposed a piece-wise proportional hazards cure rate model with a random delay time to fit data, and a new weighted log-rank test to detect the treatment effect of an immunotherapy over a chemotherapy control. We showed that the proposed weight was nearly optimal under mild conditions. Our simulation study showed a substantial gain of power in the proposed test over the existing tests and robustness of the test with misspecified weight. We also introduced a sample size calculation formula to design the immunotherapy clinical trials using the proposed weighted log-rank test.

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“…Immunotherapy, including checkpoint inhibitors, cancer vaccines, adoptive cell immunotherapy, and strategies that exploit chimeric antigen receptor engineered T cells, is rapidly emerging as a promising modality for different types of cancers (1). The early success of immune checkpoint inhibition, such as targeted therapy against cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), along with programmed death-1 (PD-1) (2, 3) and the PD-1 ligand, PD-L1 (4), has drawn much attention (5).…”

Section: Introductionmentioning

confidence: 99%

Modified breast cancer model for preclinical immunotherapy studies

Katsuta

DeMasi

Terracina

et al. 2016

Journal of Surgical Research

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Background Interest in immunotherapy for breast cancer is rapidly emerging and applicable animal models that mimic human cancer are urgently needed for preclinical studies. This study aimed to improve a technique for orthotopic inoculation of syngeneic breast cancer cells to be used as a preclinical animal model for immunotherapy. Materials and methods We utilized our previously reported murine model of orthotopic cancer cell inoculation under direct vision and compared the efficiency of tumorigenesis with tumor cells suspended in either PBS or Matrigel containing varying numbers of cells. As a model for immune rejection, murine BALB/c derived 4T1-luc2 breast cancer cells were inoculated orthotopically into both BALB/c and C57BL/6 mice. Results Matrigel suspended cells formed larger tumors with higher efficiency than PBS suspended cells. The maximum volume of Matrigel that could be inoculated without spillage was 20 μl and 30 μl in the #2 and #4 mammary fat pads, respectively. Tumor take rates increased as the injected cell number increased. In this immune rejection model, there were no significant differences in tumor weight between the strains up to Day 7, after which tumor weight decreased in C57BL/6 mice. Bioluminescence in C57BL/6 mice was also significantly less than in BALB/c mice and increased up to Day 7, then swiftly decreased thereafter. Conclusions This improved technique of innoculating murine breast cancer cells utilizing bioluminescence technology may be useful in evaluating the efficacy of tumor regression mediated by immune responses, as shown by an allogeneic response in C57BL/6 mice.

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Statistical Challenges in the Design of Late-Stage Cancer Immunotherapy Studies

Cited by 47 publications

References 55 publications

Immuno-oncology Trial Endpoints: Capturing Clinically Meaningful Activity

Immuno-oncology Trial Endpoints: Capturing Clinically Meaningful Activity

Weighted log‐rank test for time‐to‐event data in immunotherapy trials with random delayed treatment effect and cure rate

Modified breast cancer model for preclinical immunotherapy studies

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