New Clinical Study Shows Test That Doubles Chemotherapy Response Rate
Using only FDA-approved, standard lung cancer drugs available to all oncologists, a process of laboratory selection provided a 64.5 percent response rate - more than double the national average of 30 percent (p = 0.00015), well established in medical literature. More importantly, the median overall survival of 21.3 months was nearly two-fold longer than the best results of 13.5 months reported for non-assay based standard treatments. Strikingly, among the stage IV (metastatic) patients, there are several who remain alive approaching eight years since diagnosis.
According to a Phase II clinical trial conducted by investigators at Rational Therapeutics and the Memorial Care Todd Cancer Institute, Long Beach, CA, and published in the October issue of Anticancer Research, functional cytometric profiling of programmed cell death doubles the response rate and improves time-to-progression and survival in patients with advanced lung cancer. According to Dr. Robert A. Nagourney, lead investigator, this study confirms the ability of a laboratory test to accurately predict drug activity for individual cancer patients.
The article, titled "Functional Profiling to Select Chemotherapy in Untreated, Advanced or Metastatic Non-Small Cell Lung Cancer," describes results achieved in patients who received first-line chemotherapy based on their phenotype analysis. Functional profiling provides a window into the dynamic process by which human tumor cells respond to therapy. By capturing cells within their natural microenvironment, human biology is recreated in the laboratory. Statistical analyses enable researchers to establish “levels” of certainty. Reported as “p-values,” these metrics offer the reader levels of statistical significance indicating that a given finding is not simply the result of chance. A p-value equal to 0.1 (1 in 10) means that the findings are 90 percent likely to be true with a 10 percent error. A p-value of 0.05 (1 in 20) tells the reader that the findings are 95 percent likely to be true. While a p-value equal to 0.01 (1 in 100) tells the reader that the results are 99 percent likely to be true. For an example in real time, this paper is reporting in lung cancer literature that doubled the response rate for metastatic disease compared with the national standard. The results achieved statistical significance where p = 0.00015. That is to say, that there is only 15 chances out of 100,000 that this finding is the result of chance. The biomarker-based paradigm will require us to consider the level of evidence necessary to declare true activity. Daniel J. Sargent, PhD, Professor of Cancer Research at the Mayo Clinic, tells us that it may become impossible to perform traditional trials with requirements to achieve a P-value less than 0.05, high statistical power, and an OS advantage. When the patient population becomes small, we’re going to have to consider either other endpoints or other statistical philosophies. Should we use a Bayesian strategy, in which we borrow information from other clinical trials to help make decisions? Or do we loosen the P-value requirements, that a P-value of less than 0.1 or 0.2, for example, be considered a sufficient level of evidence for activity? These are active areas of research that need to be fully considered as we enter this era of truly personalized therapy with patient populations that are becoming smaller and smaller. I do know that the Bayesian method is no stranger to the functional profiling platform. It’s what gives credit to the accuracy of the assay tests.
The absolute predictive accuracy of cell culture assay tests varies according to the overall response rate in the patient population, in accordance with Bayesian principles. The actual performance of assays in each type of tumor precisely match predictions made from Bayes’ Theorem. Bayes’ Theorem is a tool for assessing how probable evidence makes some hypothesis. It is a powerful theorem of probability calculus which is used as a tool for measuring propensities in nature rather than the strength of evidence (Solving a Problem in the Doctrine of Changes). Sources: Anticancer Research October 2012 vol. 32 no. 10; 4454-4460 Daniel J. Sargent, PhD, is the Ralph S. and Beverly E. Caulkins Professor of Cancer Research at the Mayo Clinic Cancer Center in Rochester, Minnesota, and Group Statistician for the Alliance for Clinical Trials in Oncology. "Commentary on clinical endpoints, validation of surrogate endpoints and biomarkers in oncology clinical trials." http://ar.iiarjournals.org/content/32/10/4453.abstract?sid=eb8c3504-bee5-4756-a1a8-1ae7c0d554dc