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Using Benchmarks Based on Historical Survival Rates for Screening New Therapies for Stage IV Melanoma Patients

The Melanoma Program of the Abramson Cancer Center; Department of Biostatistics & Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, PA

DuPont Guerry, Keith Flaherty

The Melanoma Program of the Abramson Cancer Center; Department of Medicine, Division of Hematology/Oncology, University of Pennsylvania School of Medicine, Philadelphia, PA

For the great majority of patients diagnosed with stage IV melanoma, prognosis is poor and death comes quickly. After three decades of clinical trials in metastatic melanoma, systemic therapy has failed to alter the natural history of this disease. Counterbalancing this is the promise of new therapeutic agents developed through a deepening understanding of the molecular circuitry and immunobiology of melanoma.^1,2

The article by Korn et al³ in this issue of the Journal of Clinical Oncology presents an alternative framework for evaluating phase II trials in melanoma with the aim of making fewer errors in identifying candidate therapies worthy of phase III testing. There are two aspects of this approach that differ from standard phase II trial designs. First, 1-year overall survival (OS) rates and 6-month progression-free survival (PFS) rates are proposed as outcomes rather than tumor response. These end points also have been explored in trials of patients with glioblastoma multiforme, where the disease is similarly aggressive and therapies are similarly inactive.⁴ The 6-month PFS rate has been adopted for use as a clinical end point for phase II trials conducted in the brain tumor cooperative groups. Second, a trial-specific benchmark, calculated retrospectively at the end of the trial, is used in a decision rule to move an agent forward. This proposed benchmark is tailored for a specific trial. It depends on historical rates within the prognostic classes defined by Korn et al and on the numbers of patients actually accrued in each prognostic class.

Korn et al³ took advantage of the lack of progress in therapeutic development in melanoma to pool data from 2,100 patients from 70 arms of 42 Southwest Oncology Group trials done between 1975 and 2005 who were treated with a variety of agents and regimens, some of which appeared sufficiently promising to proceed to phase III trials. However, no phase III trial in melanoma has ever demonstrated an improvement in OS, including those with objective response rates so high that some investigators doubted the equipoise of trials that randomized patients to the historical but inactive standard therapy, dacarbazine. The authors’ meta-analysis based on 1,278 patients provided an estimate of the 1-year OS rate (25.5%) and 1-year OS rates for 24 prognostic classes (ranging from 5.5% to 63.8%) defined by four statistically significant independent prognostic factors: three patient factors (performance status, presence of visceral metastasis, sex) and one trial factor (exclusion of patients with brain metastasis). Their analysis confirmed the findings of the 2001 analysis by the American Joint Committee on Cancer staging committee that site of metastatic disease is an important prognostic factor for patients with metastatic melanoma.⁵ However, the most important prognostic factor that they identified, performance status, is one that all oncologists recognize but which the current American Joint Committee on Cancer staging system does not take into account. The strength of the association between performance status and both OS and PFS clearly justifies its inclusion as an important prognostic factor for stage IV melanoma patients.

To facilitate future use of the information from the authors’ prognostic model presented in Table 3, the number of patients within each prognostic class should be made publicly available in addition to the OS rates. These data are essential for the computation of the 1-year OS rate when one or more of the prognostic factors are not available. In addition, these data can be used to compare the characteristics of patients at specific sites versus those patients included in the meta-analysis.

In the proposed framework, the decision to advance a therapeutic intervention to a phase III trial depends on the difference between a trial's benchmark (based on historical 1-year OS) and the observed 1-year OS rate. The standard parameter of interest, the true 1-year OS rate for (all) stage IV patients, is replaced with one that is tailored, providing more flexibility to reflect site-specific patient characteristics or inclusion/exclusion criteria for the specific therapeutic trial. This is an elegant and important use of a prognostic model. Its use with a decision rule defined for a specific clinical trial balances tailoring (to a specific patient population) with generalizability (survival rates within prognostic classes from a meta-analysis).

Sample size is a key element of the design of all phase II trials. The authors suggest three approaches. The first uses standard procedures and computes sample sizes based on a site-specific, historical 1-year OS rate; the second seems arbitrary and uses 35% for the 1-year OS rate; and the third is done within the context of survival analysis using a historically derived survival curve (in Appendix A). Oddly, the first two approaches do not use any information from the authors’ meta-analysis or any trial-specific features such as trial eligibility criteria. In the first approach, for example, why not use the 25.5% 1-year OS rate from the meta-analysis of the phase II trial data as a benchmark rather than a site-specific rate? A sample size computed using either of the first two approaches could differ from the one that would be computed for a trial-specific benchmark, potentially resulting in either an underpowered or an overpowered trial on which the decision to move the therapy forward is made.

Sample sizes for phase II studies should be large enough to have sufficient power to detect clinically meaningful differences in the parameter of interest—in this case, the trial's benchmark. Larger sample size may protect against misspecification of the true values of the parameters but it potentially adds significant costs (human and monetary) to the trial. The challenge in the design of a trial that uses the proposed benchmark is that the benchmark is not defined until all the patients have been enrolled onto the trial. An alternative approach for a phase II trial using a benchmark is a stratified design with defined strata that are relevant to the therapy under study, possibly using fewer than four of the authors’ prognostic factors. In this case, the benchmark and optimal sample size can be computed before the start of the trial. Or, historical rates could be used to get a preliminary sample size. A sample size reassessment⁶ could then be done halfway into the trial based on information on patients who had enrolled. Additional investigation of these approaches is needed to provide guidance on the best way to design a phase II trial using benchmarks. We also note that although Korn et al propose the use of a benchmark in single-arm phase II trials, this approach could also be used in the design for and evaluation of randomized phase II trials.

The authors state that this study's objective was to facilitate using OS or PFS, an intriguing option also recently explored in colorectal cancer,⁷ given that objective tumor response rates in phase II trials historically have not reliably predicted meaningful survival benefits. However, the greatest deficiency in treating patients with disseminated melanoma is the absence of effective therapy. It is time to reconsider the paradigm that governed the evaluation of the previous generation of therapies. It may or may not be the case that molecularly targeted signal transduction inhibitors and novel immunotherapies will result in prolonged disease stabilization as their dominant effect, as Korn et al suggest. However, it is clear that in an era of new treatment options and with decreasing financial support for the cooperative groups, more efficient designs and increased statistical rigor in the selection of therapies for phase III testing are necessities. The study by Korn and colleagues is a step forward.

AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST

The author(s) indicated no potential conflicts of interest.

AUTHOR CONTRIBUTIONS

Conception and design: Phyllis A. Gimotty, Keith Flaherty

Manuscript writing: Phyllis A. Gimotty, DuPont Guerry, Keith Flaherty

Final approval of manuscript: Phyllis A. Gimotty, DuPont Guerry, Keith Flaherty

ACKNOWLEDGMENTS

Supported by Specialized Program of Research Excellence (SPORE) on Skin Cancer (Grant No. CA-093372).

REFERENCES

1. Amaravadi RK, Flaherty KT: Targeted therapy for metastatic melanoma. Clin Adv Hematol Oncol 5:386-394, 2007[Medline]

2. Antonia S, Mule JJ, Weber JS: Current developments of immunotherapy in the clinic. Curr Opin Immunol 16:130-136, 2004[CrossRef][Medline]

3. Korn EL, Liu PY, Lee SJ, et al: Meta-analysis of phase II cooperative group trials in metastatic stage IV melanoma: Determining progression-free and overall survival for future phase II trials. J Clin Oncol 26:527-534, 2008[Abstract/Free Full Text]

4. Ballman KV, Buckner JC, Brown PD, et al: The relationship between six-month progression-free survival and 12-month overall survival end points for phase II trials in patients with glioblastoma multiforme. Neuro Oncol 9:29-38, 2007[Abstract/Free Full Text]

5. Balch CM, Buzaid AC, Soong SJ et al: Final version of the American Joint Committee on Cancer staging system for cutaneous melanoma. J Clin Oncol 19:3635-3648, 2001[Abstract/Free Full Text]

6. Desseaux K, Porcher R: Flexible two-stage design with sample size reassessment for survival trials. Stat Med 26:5002-5013, 2007[CrossRef][Medline]

7. Tang PA, Bentzen SM, Chen EX, et al: Surrogate end points for median overall survival in metastatic colorectal cancer: Literature-based analysis from 39 randomized controlled trials of first-line therapy. J Clin Oncol 25:4562-4568, 2007[Abstract/Free Full Text]

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