He advocates adopting the Bayesian methodology, a statistical approach that is more in line with how science works. It is used routinely in physics, geology and other sciences. And he has put the approach to the test at M.D. Anderson, where more than 100 cancer-related phase I and II clinical trials were being planned or carried out using the Bayesian approach.

The main difference between the Bayesian approach and the frequentist approach to clinical trials has to do with how each method deals with uncertainty, an inescapable component of any clinical trial. Unlike frequentist methods, Bayesian methods assign anything unknown a probability using information from previous experiments. The Bayesian methods make use of the results of previous experiments, to do continuous updating as information accrues, whereas the frequentist approaches assume we have no prior results.

Doctors want to be able to use biomarkers to determine who is responding to what medication and look at multiple potential treatment combinations. They want to be able to treat a patient optimally depending on the patient's disease characteristics. Cancer is a diverse disease and what works to treat one person's disease may not work for another.

Clinical trials test the efficacy (not the accuracy) of a drug. The efficacy of a drug is to produce a desired effect, which is tumor response (shrinkage). Single arm clinical trials provide the tumor response evidence that is the basis for approving new cancer drugs. The Bayesian methology can bring some much-needed "accuracy" to the forefront of clinical trials.

Clearly, more effective cancer therapies are desperately needed, and after 30 years of investigation aimed at intensified multi-agent chemotherapy, we should look for other avenues of study. In an era of ever-increasing numbers of partially effective cancer therapeutics, there is an obvious need for more accurate methologies. We cannot afford any more 'trial-and-error' treatments.

http://www.mdanderson.org/departments/newsroom/display.cfm?id=f90987e4-8ebb-4e10-8ed282ec15dd38e9&method=displayfull&pn=00c8a30f-c468-11d4-80fb00508b603a14

The Bayesian method turns this calculation around. That is, it tries to calulate the probability of C, given that E has occurred. Baye's Theorem is useful and reasonably well accepted for some applications such as testing whether the assumptions of probability are valid. For instance, if you flip 100 coins in the air at once, and only get tails 5 times, you have to assume that they aren't "fair" coins. The whole idea of it all, is to get more accuracy out of analysis.

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. The theoretical expectations for cell death assays, based on Bayes' Theorem, are overall specificity for drug resistance of 0.92 and an overall sensitivity of 0.72.

Thus, the absolute probability of response with assay "sensitive" and "resistant" drugs varies according to the overall prior response probability in the patient population. Which means assay "resistant" patients have a below average probability of response and assay "sensitive" patients have an above average probability of response. Treatment with assay "sensitive" drug(s) is more likely to be associated with a favorable outcome than treatment with assay "resistant" drug(s).

Cell death assays are broadly applicable to a wide range of human neoplasms, ranging from low response rate tumors (like pancreatic cancer and Cholangiocarcinoma) to high response rate tumors (like acute lymphoblastic leukemia, breast cancer and ovarian cancer). In cases where more than one acceptable regimen exists, the physician can select the regimen containing the most favorable drugs and avoid the regimen containing the most unfavorable drugs.

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).

Bayes' theorem describes the relationship between the accuracy of a predictive test (post-testing probability) and the overall incidence of what is being tested (pre-testing probability).

Bayes' theorem indicates that laboratroy assays will be accurate in the prediction of clinical drug resistance in tumors with high overall response rates in assays that are extremely specific for drug resistance (>99% specificity).

Post-test probability of response is independent of pre-test (expected) probability of response. Once identified, post-test response probabilities vary according to both assay results and pre-test reponse probabilities, precisely according to predictions based on Bayes' theorem. This allows the construction of a monogram for determining assay-predicted probability of response.

Assay Results and Bayes' Theorem: http://weisenthal.org/figure06.htm

Using a Cell Culture Assay (in vitro apoptosis) for choosing cancer drugs is no different than a marker like estrogen receptor or CD20 or a gene expression pattern. They are all markers. One is a structural marker, the other is a functional marker. A Cell Culture Assay is a "functional" biomarker. A functional biomarker provides information about the biomarker uptake rate in tumor cells or on tumor cell surfaces through fluorescence intensity changes.

As with any other laboratory test, the determination of the efficacy of cell culture assay tests is based on comparisions of laboratory tests with patient response (clinical correlations). The hypothesis to be tested with clinical correlations is that above-average drugs effects in the assays correlate with above-average drug effects in the patient, as measured by both response rates and patient survival.

Patients with test results in the "sensitive" range were more likely to respond than the total patient population as a whole. Conversely, patients with test results in the "resistant" range were less likely to respond than the patient population as a whole. On average, patients with assays in the "sensitive" range were 3.5-fold more likely to respond than patients with assays in the "resistant" range.

Targeted treatments take advantage of the biologic differences between cancer cells and healthy cells by "targeting" faulty genes or proteins that contribute to the growth and development of cancer. Many times these drugs are combined with chemotherapy, biologic therapy (immunotherapy), or other targeted treatments.

Understanding targeted treatments begins with understanding the cancer cell. Every tissue and organ in the body is made of cells. In order for cells to grow, divide, or die, they send and receive chemical messages. These messages are transmitted along specific pathways that involve various genes and proteins in a cell.

Targeted treatments fight cancer by correcting or modifying defective pathways in a cancer cell. In healthy cells, each pathway is tightly controlled. For instance, healthy cells are allowed to divide into new cells, and damaged cells are destroyed. However, in cancerous cells, certain points in the pathway become disrupted, usually through a genetic mutation (change in form).

Serious consequences to the cell may result from these mutations, depending on which pathway is affected. For example, suppose a cell develops a mutation that causes it to continue dividing into new cells? In other words, the signal is always on. If the signal never turns off, the cells that keep growing may eventually form a tumor.

The most appealing idea behind targeted drug therapy is that cancerous cells are destroyed and healthy cells are spared, resulting in fewer side effects of treatment. In contrast, traditional chemotherapy destroys both the cancer cells and the healthy cells, and does not have any mechanism to distinguish between them.

Because many cancer cells use similar pathways, the same drug could be used to treat one person's breast cancer and another person's lung cancer, as long as each tumor contained similar targets. This is why many of these treatments are being used in a variety of cancer types. Gleevec is used to treat both leukemia and a rare stomach tumor, called gastrointestinal stromal tumor (GIST).

Although targeted therapy is appealing, it is more complex than meets the eye. Cancer cells often have many mutations in many different pathways, so even if one route is shut down by a targeted treatment, the cancer cell may be able to use other routes. In other words, cancer cells have "backup systems" that allow them to survive. The result is that the drug does not shrink the tumor as expected. One approach to this problem is to target multiple pathways in a cancer cell.

Another challenge is to identify for which patients the targeted treatment will be effective. When Iressa is used in patients with lung cancer, researchers discovered that only patients whose tumors contained specific mutations responded to this drug.

Finally, tumors can become resistant to a targeted treatment. This means that the drug no longer works, even if it has previously been effective in shrinking a tumor. To solve this problem, new drugs are being designed or combined with existing ones to target the tumor more effectively.

The introduction of targeted drugs has not been accompanied by specific "predictive tests" allowing for a rational and economical use of these drugs. However, given the technical and conceptual advantages of cell culture analysis, together with its performance and the modest efficacy of therapy prediction on analysis of genome expression, there is reason for a renewal in the interest of cell culture assays (functional biomarker) for optimized use of medical treatment of malignant disease.

There should be an immediate recognition that matchmaking between cancer and cancer treatment is one area in cancer research and treatment which is deserving of much greater attention and utilization. There should be an inclusive effort to study and utilize technologies which are based on both the sub-cellular (molecular) level and at the cellular (cell function/cell culture) level.