An, Ming‐Wen

6520 Background: We previously reported that alternative cutpoints and alternate categorical metrics to RECIST standards provided no meaningful improvement in OS prediction. Here, we sought to identify and validate continuous, longitudinal TM-based metrics for predicting OS. Methods: Data from 13 trials (1739 patients with sufficient imaging-based measurements from breast, lung or colon cancer) were randomly split (60:40) into training and validation datasets and landmarked at 12-weeks (w). Multiple candidate metrics that capture tumor curve trajectory were considered: first slope (or % change) from baseline to 6 w, last slope (or % change) from 6 w to 12 w, and indicator of increase (<5% (includes no increase or a decrease) vs. ≥5%) from the prior assessment (baseline to 6 w, 6 w to 12 w). Multivariable Cox models stratified by study and number of baseline target lesions (<3 vs. >3) and adjusted for baseline tumor burden (ratio of the sum of lesions/number of lesions) were fit for each tumor type. Predictive ability was assessed via concordance (c)-index (0.5: no association to 1.0: perfect association), and hazard ratios/p-values (HR/p). Results: Patients whose tumor size increased by at least 5% from the prior assessment, as well as whose tumors showed a positive slope (or % change) from 6 to 12 w (indicating tumor growth) had worse OS. The first slope (or % change) metric was less consistent, with an initial decrease in slope suggesting worse OS in some cases. Predictive ability of these metrics varied across tumor type with poorest prediction in Breast (Table). Conclusions: Continuous TM-based metrics performed no better than categorical RECIST metrics for OS prediction. [Table: see text]

With the launch of the National Cancer Institute’s Precision Medicine Initiative in 2015, there has been a shift to trial designs that tailor health care solutions to individual patients by using a screening platform and by moving away from the one-trial/one-biomarker-at-a-time approach. To make precision medicine a reality, it is critical to identify and validate potential biomarkers to help select patients who will truly benefit from a targeted therapy. In this article, we discuss five trial designs: enrichment, umbrella, basket, subgroup, and window of opportunity. For each trial design, we describe the design characteristics, use ongoing or completed trials as case studies, provide any recent advances to the trial design, and discuss advantages and disadvantages of each design.

34 Background: Phase II clinical trials aim to identify promising experimental regimens for further testing in phase III trials. Testing targeted therapies with predictive biomarkers mandates efficient trial designs. Current biomarker-based trial designs, including the enrichment, all-comers, and adaptive designs, randomize patients to receive treatment or not throughout the entire duration of the trial. Recognizing the need for randomization yet acknowledging the possibility of promising but nonconclusive results after a preplanned interim analysis (IA), we propose a two-stage phase II design that allows for the possibility of direct assignment (i.e., stop randomization and assign all patients to the experimental arm in stage II) based on IA results. Methods: Using simulations, we compared properties of the direct assignment option design to a 1:1 randomized phase II design and assessed the impact of the timing of IA (after 33%, 50%, or 67% of accrual) and number of IA (one versus two with option for direct assignment at the first and second) over a range of response rate ratios (between 1.0 and 3.0). Results: Between 12% and 30% of the trials (out of 6,000 simulated trials) adopt direct assignment in stage II, with direct adoption depending on the treatment effect size and specified type I error rate (TIER). The direct assignment option design has minimal loss in power (<1.8%) and minimal increase in T1ER (<2.1%) compared to a 1:1 randomized design. The maximum loss in power across possible timings of IA was <1.2%. For the direct assignment option design, there was a 20%-50% increase in the number of patients treated on the experimental (vs. control) arm for the 1 IA case, and 40%-100% increase for the 2 IA case. Conclusions: Testing predictive biomarkers in clinical trials requires new design strategies. In the spectrum of phase II designs from adaptive to balanced randomized all-comers or enrichment designs, the direct assignment design provides a middle ground with desirable statistical properties that may appeal to both clinicians and patients.

Purpose Phase II clinical trials inform go/no-go decisions for proceeding to phase III trials, and appropriate end points in phase II trials are critical for facilitating this decision. Phase II solid tumor trials have traditionally used end points such as tumor response defined by Response Evaluation Criteria for Solid Tumors (RECIST). We previously reported that absolute and relative changes in tumor measurements demonstrated potential, but not convincing, improvement over RECIST to predict overall survival (OS). We have evaluated the metrics by using additional measures of clinical utility and data from phase III trials. Methods Resampling methods were used to assess the clinical utility of metrics to predict phase III outcomes from simulated phase II trials. In all, 2,000 phase II trials were simulated from four actual phase III trials (two positive for OS and two negative for OS). Cox models for three metrics landmarked at 12 weeks and adjusted for baseline tumor burden were fit for each phase II trial: absolute changes, relative changes, and RECIST. Clinical utility was assessed by positive predictive value and negative predictive value, that is, the probability of a positive or negative phase II trial predicting an effective or ineffective phase III conclusion, by prediction error, and by concordance index (c-index). Results Absolute and relative change metrics had higher positive predictive value and negative predictive value than RECIST in five of six treatment comparisons and lower prediction error curves in all six. However, differences were negligible. No statistically significant difference in c-index across metrics was found. Conclusion The absolute and relative change metrics are not meaningfully better than RECIST in predicting OS.

Background. A phase II design with an option for direct assignment (stop randomization and assign all patients to experimental treatment based on interim analysis, IA) for a predefined subgroup was previously proposed. Here, we illustrate the modularity of the direct assignment option by applying it to the setting of two predefined subgroups and testing for separate subgroup main effects.Methods. We power the 2-subgroup direct assignment option design with 1 IA (DAD-1) to test for separate subgroup main effects, with assessment of power to detect an interaction in a post-hoc test. Simulations assessed the statistical properties of this design compared to the 2-subgroup balanced randomized design with 1 IA, BRD-1. Different response rates for treatment/control in subgroup 1 (0.4/0.2) and in subgroup 2 (0.1/0.2, 0.4/0.2) were considered.Results. The 2-subgroup DAD-1 preserves power and type I error rate compared to the 2-subgroup BRD-1, while exhibiting reasonable power in a post-hoc test for interaction.Conclusion. The direct assignment option is a flexible design component that can be incorporated into broader design frameworks, while maintaining desirable statistical properties, clinical appeal, and logistical simplicity.

Purpose We sought to test and validate the predictive utility of trichotomous tumor response (TriTR; complete response [CR] or partial response [PR] v stable disease [SD] v progressive disease [PD]), disease control rate (DCR; CR/PR/SD v PD), and dichotomous tumor response (DiTR; CR/PR v others) metrics using alternate cut points for PR and PD. The data warehouse assembled to guide the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 was used. Methods Data from 13 trials (5,480 patients with metastatic breast cancer, non–small-cell lung cancer, or colorectal cancer) were randomly split (60:40) into training and validation data sets. In all, 27 pairs of cut points for PR and PD were considered: PR (10% to 50% decrease by 5% increments) and PD (10% to 20% increase by 5% increments), for which 30% and 20% correspond to the RECIST categorization. Cox proportional hazards models with landmark analyses at 12 and 24 weeks stratified by study and number of lesions (fewer than three v three or more) and adjusted for average baseline tumor size were used to assess the impact of each metric on overall survival (OS). Model discrimination was assessed by using the concordance index (c-index). Results Standard RECIST cut points demonstrated predictive ability similar to the alternate PR and PD cut points. Regardless of tumor type, the TriTR, DiTR, and DCR metrics had similar predictive performance. The 24-week metrics (albeit with higher c-index point estimate) were not meaningfully better than the 12-week metrics. None of the metrics did particularly well for breast cancer. Conclusion Alternative cut points to RECIST standards provided no meaningful improvement in OS prediction. Metrics assessed at 12 weeks have good predictive performance.

Abstract Biomarkers are critical to targeted therapies, as they may identify patients more likely to benefit from a treatment. Several prospective designs for biomarker-directed therapy have been previously proposed, differing primarily in the study population, randomization scheme, or both. Recognizing the need for randomization, yet acknowledging the possibility of promising but inconclusive results after a stage I cohort of randomized patients, we propose a 2-stage phase II design on marker-positive patients that allows for direct assignment in a stage II cohort. In stage I, marker-positive patients are equally randomized to receive experimental treatment or control. Stage II has the option to adopt “direct assignment” whereby all patients receive experimental treatment. Through simulation, we studied the power and type I error rate of our design compared with a balanced randomized two-stage design, and conducted sensitivity analyses to study the effect of timing of stage I analysis, population shift effects, and unbalanced randomization. Our proposed design has minimal loss in power (&lt;1.8%) and increased type I error rate (&lt;2.1%) compared with a balanced randomized design. The maximum increase in type I error rate in the presence of a population shift was between 3.1% and 5%, and the loss in power across possible timings of stage I analysis was less than 1.2%. Our proposed design has desirable statistical properties with potential appeal in practice. The direct assignment option, if adopted, provides for an “extended confirmation phase” as an alternative to stopping the trial early for evidence of efficacy in stage I. Clin Cancer Res; 18(16); 4225–33. ©2012 AACR.