The value of an anticancer drug lies in its ability to relieve patients symptoms and prolong their lives. How well it does this is the ultimate question posed in testing new agents or regimens.
For a variety of reasons, many promising drugs fall by the wayside on the long march from the chemists bench to the oncologists armamentarium: What devastates tumor cells in vitro leaves them undaunted in vivo. What works in mice fails in men. Or the cure proves more dreadful than the disease.
Most costly, in terms of money, effort, and time wasted, are drugs or combinations of drugs that first appear powerful in their ability to shrink tumors, but wash out in a randomized clinical trial. Randomized trials often require hundreds or thousands of patients and years of follow-up to yield clear evidence of clinical benefit.
"Its very common to see response rates in phase II trials that dont hold up in phase III," said Richard Pazdur, M.D., director of the Division of Oncologic Drug Products at the U.S. Food and Drug Administration. And sometimes a drug or regimen that produces good response rates based on tumor reduction in phase II trials ends up showing little or no medical benefit in phase III trials.
In this issue of the Journal (p. 1601), a group of investigators led by T. Timothy Chen, Ph.D., of the University of Maryland at Baltimore, introduce a statistical model they hope will help form a more solid basis for mounting randomized trials. They analyzed data from extensive-stage small-cell lung cancer (SCLC) trials, but the method could apply to other cancers, particularly those with short survival times.
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They found that in phase II trials, median survival better predicted survival in phase III trials than did response rate, the standard phase II outcome measure. "All the successful treatments for extensive SCLC have a predicted power of greater than 0.55 based on our model," Chen said, which suggests that this number might be a reasonable cutoff point for determining whether the regimen should advance to a phase III trial.
Statisticians at NCI and the cooperative clinical trials groups could apply the model to historical data, creating prior probability distributions in a variety of diseases based on all available results, Chen added. For diseases with longer survival, such as breast or prostate cancer, the specific model used in the SCLC analysis is less likely to be useful because the time needed to accumulate information is longer, Chen said. But he noted that Bayesian analysis could also be applied to response rates.
FDAs Pazdur, in a related editorial in this issue (p. 1552), discusses the uncertain value of response rates. He notes that in a recent meta-analysis (The Lancet, July 29, pp. 3738), researchers for the Meta-Analysis Group In Cancer found that in colorectal cancer trials, phase II response rates did predict phase III survival.
In an interview, Pazdur emphasized that response rate is not an ideal outcome measure. Problems with response rates include the fact that investigators often are notor cannot beadequately blinded to treatment assignment, leading to potential bias. Measurement of tumor sizetypically done by CT or MRI imagingis inexact, so that a precise determination of response is difficult. Further bias may creep in if a trial protocol does not "prespecify" which lesions are to be measured for response.
"Inaccuracies and biases can be introduced by measurement techniques and their interpretations," he said. For instance, "Researchers may not prospectively measure lesions on the CAT scans or other tests. After identifying the lesions that respond, then investigators call them the measurable lesions."
On the other hand, survival rates in phase II trials can be easily confounded. They could be attributable to nontreatment factors such as selection bias among the patients. Even within a disease stage, patients with less advanced disease and better performance status (a measure of symptoms) are likely to survive longer.
"If a patient lives 15 months with a disease, part of the survival duration would be due just to the natural history of the disease and part of it may be due to the effect of the therapy," Pazdur said. "Whereas with response rate, usually the total effect is attributed to the therapy itself."
Furthermore, he writes in the editorial, "Patients who survive a sufficient duration to have . . . a response will have a predictably longer survival than other patients will, even if the therapy has no effect on survival."
FDA evaluates response rates in a more complex way than the single percentage figure commonly used to summarize them, Pazdur said. First, they are composites of complete and partial responses. How long the response lasts is important. Responses must have a minimum duration to qualify, but a response lasting 6 months is more impressive than one lasting 6 weeks. And where the responses are observed matters, too: shrinking a tumor is more clinically significant when it is in a hard-to-treat visceral site such as the liver than, for example, on the skin.
The agency is interested in improving the predictive potential of phase II data because such data could be useful in making decisions on accelerated approval.
Also, new classes of drugs, such as angiogenesis inhibitors, may not shrink tumors like conventional cytotoxic drugs, said Richard L. Schilsky, M.D., an oncologist at the University of Chicago and chairman of the Cancer and Leukemia Group B.
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A potentially more satisfactory approach is to use biomarker end points.
"If you think your drug is supposed to hit a specific target molecule in the cell, one way of gauging its potential efficacy is to demonstrate in a clinical study that youre actually hitting the target in the tumor tissue," Schilsky said. But this approach depends on making correct assumptions about a drugs mechanism of action and being able to measure its interaction with a target accurately.
Another tack researchers are taking, he said, is randomized design of phase II trials so that time to disease progression following the experimental treatment can be assessed in the context of an appropriate control group. Here again, measuring or even defining progression is far from precise. Most important, he said, "We dont know if, when an agent delays progression of a tumor, its going to result either in better survival or improvement in quality of life for patients."
Added Pazdur, "Theres no perfect answer."
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