Assay Results and Bayes' Theorem

[gpawelski]

Donald Berry, Ph.D., professor and chair of the Department of Biostatistics and Applied Mathematics at M.D. Anderson Cancer Center stated in the January 2006 issue of Nature Reviews Drug Discovery, the statistical method used nearly exclusively to design and monitor clinical trials today (the frequentist method) is so narrowly focused and rigorous in its requirements that it limits innovation and learning.

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.

The Bayesian method is no stranger to the technology of Cell Culture Assay Testing, a "functional" biomarker. In fact, it is what gives credit to the accuracy of assay tests. The method has to do with "conditional probability." The probability that event E (an effect) and C (a cause) will both occur is the product of the event C occurring, times the conditional probability of an event E occuring (remember that in elementary statistics?). An example: The chances of being hit by a truck and bleeding to death is the product of the probability of being hit by a truck and the probability of bleeding to death if you get hit by a truck. Well, so what?

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

[gpawelski]

In statistics, a confidence interval (CI) is a particular kind of interval estimate of a population parameter and is used to indicate the reliability of an estimate. It is an observed interval (i.e it is calculated from the observations), in principle different from sample to sample, that frequently includes the parameter of interest, if the experiment is repeated. How frequently the observed interval contains the parameter is determined by the confidence level or confidence coefficient. (the standard level of rigor required by any scholastic journal).

A confidence interval with a particular confidence level is intended to give the assurance that, if the statistical model is correct, then taken over all the data that might have been obtained, the procedure for constructing the interval would deliver a confidence interval that included the true value of the parameter the proportion of the time set by the confidence level. More specifically, the meaning of the term "confidence level" is that, if confidence intervals are constructed across many separate data analyses of repeated (and possibly different) experiments, the proportion of such intervals that contain the true value of the parameter will approximately match the confidence level; this is guaranteed by the reasoning underlying the construction of confidence intervals.

A confidence interval does not predict that the true value of the parameter has a particular probability of being in the confidence interval given the data actually obtained.(An interval intended to have such a property, called a credible interval, can be estimated using Bayesian methods; but such methods bring with them their own distinct strengths and weaknesses).

With regard to cell-based analysis, data show conclusively that patients benefit both in terms of response and survival from drugs and drug combinations found to be "active" in the assay even after treatment failure with several other drugs, many of which are in the same class, and even with combinations of drugs found to have low or no activity as single agents but which are found in the assay to produce a synergistic and not merely an additive anti-tumor effect.

Patients treated with assay-sensitive drugs have a 1.44-fold greater probability of response (95% confidence interval 1.36 to 1.52) than the population taken as a whole, while patients treated with assay-resistant drugs have only about one-fourth the probability of response (relative risk = 0.23, 95% confidence interval 0.18 - 0.29), relative to the study population taken as a whole.

The overall study population had a 56% response rate. Patients treated with assay "sensitive" drugs had an 81% response rate. Patients treated with assay "resistant" drugs had a 13% response rate. Dealing only with solid tumors, the overall study population had a 45% response rate. With a "sensitive" assay, there was an 80% response rate. With a "resistant" assay, there was an 8.6% response rate. With solid tumors, the average advantage to receiving treatment with an assay "sensitive" regimen compared with an assay "resistant" regimen was a 9.3-fold advantage.

In other words, patients receiving a drug that tested "sensitive" were 1.44 times [i.e. 44%] more likely to respond compared to all patients treated in studies, while patients testing "resistant" were 0.23 as likely to respond as all patients. Patients receiving treatment with drugs testing "sensitive" enjoyed a 6-fold advantage (1.44/0.23 = 6.23) over patients treated with drugs testing "resistant."

This data includes both patients with solid tumors (e.g., breast cancer, lung cancer) and hematological (blood system) tumors (e.g. leukemia, lymphoma). In the case of solid tumors only, the advantage to receiving sensitive versus resistant drugs was 9.3 fold. In the case of breast cancer, it was more than 10-fold. Furthermore, patients receiving "sensitive" drugs were shown in many studies to enjoy significantly longer durations of survival than patients treated with "resistant" drugs.

Patients treated with a "positive" (sensitive) drug would respond 79.1% of the time, while patients treated with a "negative" (resistant) drug would respond only 12.6% of the time. Once again, there would be a huge advantage to the patient to receive a "positive/sensitive" drug, compared to a "negative/resistant" drug.

Sources:

Cree IA, Kurbacher CM, Lamont A, et al. A prospective randomized controlled trial of ATP-based tumor chemosensitivity assay-directed chemotherapy versus physicians choice in patients with recurrent platinum-resistant ovarian cancer. BMC Cancer. 2003; 3:19.

Weisenthal Cancer Group, Huntington Beach, CA and Departments of Clinical Pharmacology and Oncology, Uppsala University, Uppsala, Sweden. Current Status of Cell Culture Assay Testing May, 2002.

Functional profiling with cell culture-based assays for kinase and anti-angiogenic agents Eur J Clin Invest 37 (suppl. 1):60, 2007

Functional Profiling of Human Tumors in Primary Culture: A Platform for Drug Discovery and Therapy Selection (AACR: Apr 2008-AB-1546)

[gpawelski]

Highly Specific Prediction of Antineoplastic Drug Resistance With an In Vitro Assay Using Suprapharmacologic Drug Exposures

David H. Kern and Larry M. Weisenthal

Abstract

Bayes' theorem has been used to describe the relationship between the accuracy of a predictive test (posttest probability) and the overall incidence of what is being tested (pretest probability). Bayes' theorem indicates that laboratory assays will be accurate in the prediction of clinical drug resistance in tumors with high overall response rates (e.g., previously untreated breast cancer) only when the assays are extremely (>98%) specific for drug resistance. We developed a highly specific drug-resistance assay in which human tumor colonies were cultured in soft agar and drugs were tested at high concentrations for long exposure times. Coefficients for concentration x time exceeded those reported in contemporaneous studies by about 100-fold. We reviewed 450 correlations between assay results and clinical response over an 8-year period. Results were analyzed by subsets, including different tumor histologies, single agents, and drug combinations. Extreme drug resistance (an assay result ≥ SD below the median) was identified with greater than 99% specificity. Only one of 127 patients with tumors showing extreme drug resistance responded to chemotherapy. This negligible post-test probability of response was independent of pretest (expected) probability of response. Once this population of patients with tumors showing extreme drug resistance had been identified, posttest response probabilities for the remaining cohorts of patients varied according to both assay results and pretest response probabilities, precisely according to predictions based on Bayes' theorem. This finding allowed the construction of a nomogram for determining assay-predicted probability of response.

J Natl Cancer Inst 82:582–588, 1990

http://jnci.oxfordjournals.org/content/ ... c61b00d3e9

There is a striking relationship between assay results and pre-test/post-test response probabilities which were precisely in accord with predictions from Bayes' Theorem (Kern and Weisenthal, J Natl Cancer Inst 82:582-8,'90; Weisenthal and Kern, Oncology 5:93-103,'91; Weisenthal, L.M. Developments in Oncology; Kluwer Academic Publishers, Dordrecht. 64:103-50,91)

[gpawelski]

When it comes to diagnostic tests in cancer medicine, the "Holy Grail" is clinical correlations.

It shows that a diagnostic test correlates with clinical outcomes. It doesn't show that doing the test made a difference. What that would take would be a clinical trial in which you performed a test on half the patients and didn't perform it on the other half and half got treated with the knowledge of test results and half got treated without knowledge of test results. Then you show that patients who got the test did better than those who didn't. That has never been done - with ER, PR, Her2/neu, BCR-ABL, C-KIT, CD-20, bacterial C&S, and panels of immunohistochemical stains for subclassifying tumors.

All of these tests are used to select chemotherapy in precisely the same manner as cell culture assay tests are used. Also, diagnostic imaging studies (CT, MRI and PET scans), performed for the purpose of monitoring the size of the tumor to determine if it is shrinking or growing with chemotherapy. The purpose of which is to determine if chemotherapy with specific drugs should be continued or changed to different drugs. They are also used as an aid in making clinical decisions about the choice of chemotherapy. None of these have been trialed.

The standards used to judge the utility of laboratory and radiographic tests have always been (1) acceptable "accuracy" of clinical correlations and (2) clinical utility, in the judgement of the physician ordering the test.

Various cell-based functional profiling labs have compiled a compelling collection of both retrospective and prospective correlative analyses that strongly support the clinical utility of this methodology.

Weisenthal LM, Marsden JA, Dill PL, Macaluso CK. A novel dye exclusion method for testing in vitro chemosensitivity of human tumors. Cancer Res l1983;43: 749-57.

Blackman KE, Fingert HJ, Fuller AF, Meitner PA. The fluorescent cytoprint assay in gynecological malignancies and breast cancer. Methodology and results. Contrib Gynecol Obstet l1994;19: 53-63.

Sargent J, Elgie A, Taylor CG, Wilson J, Alton P, Hill JG. The identification of drug resistance in ovarian cancer and breast cancer: application of the MTT assay. Contrib Gynecol Obstet l1994;19: 64-75.

Weisenthal, L. M. Patel,N., Rueff-Weisenthal, C. (2008). "Cell culture detection of microvascular cell death in clinical specimens of human neoplasms and peripheral blood." J Intern Med 264: 275-287, 2008. doi: 10.1111/j.1365-2796.2008.01955.x

Weisenthal, L., Lee,DJ, and Patel,N. (2008). Antivascular activity of lapatinib and bevacizumab in primary microcluster cultures of breast cancer and other human neoplasms. ASCO 2008 Breast Cancer Symposium. Washington, D.C.: Abstract # 166.

Functional profiling with cell culture-based assays for kinase and anti-angiogenic agents Eur J Clin Invest, Volume 37 (suppl. 1):60, 2007

Nagourney, R.A. Functional Profiling of Human Tumors in Primary Culture: A Platform for Drug Discovery and Therapy Selection (AACR: Apr 2008-AB-1546)

Journal of Clinical Oncology, 2006 ASCO Annual Meeting Proceedings Part I. Vol 24, No. 18S (June 20 Supplement), 2006: 17117

Apoptosis and angiogenesis: two promising tumor markers in breast cancer (review). Wu J. Academic Department of Biochemistry, Royal Marsden Hospital, London, U.K. Anticancer Res. 1996 Jul-Aug;16(4B):2233-9.

Staib,P.et al. Br J Haematol 128 (6):783-781, March 2005

Robert A. Nagourney, Paula Bernard, Federico Francisco, Ryan Wexler, Steve Evans, Rational Therapeutics, Long Beach, CA. Proceedings of AACR - Volume 52 - April 2011.

Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 1764.

[gpawelski]

Robert Nagourney, M.D., PhD.

In some ways, the slow adoption of these techniques - compared with Europe and Asia - does reflect the relative conservatism of American medicine. We have been slow to adopt acupuncture and incorporate diet and lifestyle changes into medical therapy, despite their manifest importance. We are often slower to improve drugs, even when they establish clinical utility in well-conducted foreign trials. So, there may indeed be a component of late adoption and conservatism.

However, The re-importation of technologies is not only seen in the medical community. The early adoption of transistor technology by the Japanese despite their development by American inventors; the late adoption of robotics and fuzzy logic by Americans; and our tardiness in adopting smaller, more fuel-efficient automobiles all illustrate this point. But, the most vexing hurdle of all has been the dismissal by mostly university-based investigators who have weighed in against the adoption of human tissue tests for the prediction of response to chemotherapeutics.

These investigators - who, in aggregate, provide care to less than 10 percent of the cancer patients in need - have an inordinate amount of influence upon the application of novel technologies. In what can only be viewed as a sour grapes phenomenon, many of these physicians even tried to apply early forms of human tumor study in their own labs and medical centers.

The utter failure of the clonogenic assay in the 70s and 80s and related growth-based technologies, poisoned these academics and closed their minds to newer developments based on the modern discoveries of apoptosis and other forms of programmed call death. When we, and our colleagues, reported discoveries using these more modern endpoints, the academic community turned a deaf ear. As our data improved, they dug in their heels. And when the data rose to the level of the best peer reviewed journals in the field, the critics became ever more vocal.

We can now thank these "scientists" for putting the United States behind Europe and Asia in the adoption of these important methodologies. While patients in America must struggle with their physicians to get ex-vivo analyses conducted, children in Europe with leukemia have immediate access to these tests. Adults in England with leukemia can all request these assays, German patients regularly take advantage of assay methodologies. And the Japanese often apply related techniques for the treatment of their solid tumors.

Not unlike robotics, total quality management and fuel-efficient automobiles, the Americans (who invented in vitro chemosensitivity testing) will again be importing the technology that they are responsible for developing."

Dr. Nagourney is medical and laboratory director at Rational Therapeutics, Inc., in Long Beach, California, and an instructor of Pharmacology at the University of California, Irvine School of Medicine. He is board-certified in Internal Medicine, Medical Oncology and Hematology.

[gpawelski]

Robert A. Nagourney, M.D., PhD.

Scientific proof is rarely proof, but instead our best approximation. Beyond death and taxes, there are few certainties in life. That is why investigators rely so heavily on statistics.

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. To wit, 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, we are just reporting a paper in the 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.

Today, many laboratories offer tests that claim to select candidates for treatment. Almost all of these laboratories are conducting gene-based analysis. While there are no good prospective studies that prove that these genomic analyses accurately predict response, this has not prevented these companies from marketing their tests aggressively. Indeed, many insurers are covering these services despite the lack of proof.

So let’s examine why these tests may encounter difficulties now and in the future. The answer to put it succinctly is Type I errors. In the statistical literature, a Type I error occurs when a premise cannot be rejected. The statistical term for this is to reject the “null” hypothesis. Type II errors occur when the null hypothesis is falsely rejected.

Example: The scientific community is asked to test the hypothesis that Up is Down. Dedicated investigators conduct exhaustive analyses to test this provocative hypothesis but cannot refute the premise that Up is Down. They are left with no alternative but to report according to their carefully conducted studies that Up is Down.

The unsuspecting recipient of this report takes it to their physician and demands to be treated based on the finding. The physician explains that, to his best recollection, Up is not Down. Unfazed the patient, armed with this august laboratory’s result, demands to be treated accordingly. What is wrong with this scenario? Type I error.

The human genome is comprised of more than 23,000 genes: Splice variants, duplications, mutations, SNPs, non-coding DNA, small interfering RNAs and a wealth of downstream events, which make the interpretation of genomic data highly problematic. The fact that a laboratory can identify a gene does not confer a certainty that the gene or mutation or splice variant will confer an outcome. To put it simply, the input of possibilities overwhelms the capacity of the test to rule in or out, the answer.

Yes, we can measure the gene finding, and yes we have found some interesting mutations. But no we can’t reject the null hypothesis. Thus, other than a small number of discreet events for which the performance characteristics of these genomic analyses have been established and rigorously tested, Type I errors undermine and corrupt the predictions of even the best laboratories. You would think with all of the brainpower dedicated to contemporary genomic analyses that these smart guys would remember some basic statistics.

Statistical reasoning in clinical trials: hypothesis testing (American Journal of Emergency Medicine 02/1988; 6(1):52-61)

Hypothesis testing is based on certain statistical and mathematical principles that allow investigators to evaluate data by making decisions based on the probability or implausibility of observing the results obtained.

However, classic hypothesis testing has its limitations, and probabilities mathematically calculated are inextricably linked to sample size.

Furthermore, the meaning of the p value frequently is misconstrued as indicating that the findings are also of clinical significance.

Finally, hypothesis testing allows for four possible outcomes, two of which are errors that can lead to erroneous adoption of certain hypotheses:

1. The null hypothesis is rejected when, in fact, it is false.

2. The null hypothesis is rejected when, in fact, it is true (type I or alpha error).

3. The null hypothesis is conceded when, in fact, it is true.

4. The null hypothesis is conceded when, in fact, it is false (type II or beta error).

[gpawelski]

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).

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."

[gpawelski]

There are a family of assays based on the concept of total cell kill, or cell-death occurring in the entire popluation of tumor cells.

A fresh specimen is obtained from a viable neoplasm. The specimen is most often a surgical specimen from a viable solid tumor. Less often, it is a malignant effusion, bone marrow, or peripheral blood specimen containing "tumor" cells. These cells are isolated and then cultured in the continuous presence or absence of drugs, most often for 3 to 7 days. At the end of the culture period, a measurement is made of cell injury, which correlates directly with cell-death. There is evidence that the majority of available anticancer drugs may work through a mechanism of causing sufficient damage to trigger so-called programmed cell-death or apoptosis.

Some patients may not have easily-accessible tumors (needle biopsies do not gather enough specimen), making it difficult to harvest a large enough sample (200mg or 10mm in size). The tests are most reliable before a tumor has been exposed to chemotherapy. However, after a patient fails a previous chemotherapy treatment, the test still can be done once a patient waits at least four weeks.

There are four endpoint measurements of cell-death that have been applied:

1. DISC assay. The delayed loss of cell membrane integrity.

2. MTT assay. The loss of mitochondrial Krebs cycle activity.

3. ATP assay. The loss of cellular ATP.

4. Caspase 3/7 assay. Directly measures key apoptosis expression markers.

The DISC assay is the only assay that involves direct visualization of the cancer cells at endpoint. This allows for accurate assessment of drug activity, discriminates tumor from non-tumor cells, and provides a permanent archival record. Originators of the MTT and ATP assays modeled assay conditions on the DISC assay. The use of complementary tests improves accuracy and provides quality control. Also, certain drugs cannot be tested reliably in all assay systems. Use of different tests with different mechanisms helps to overcome this.

These four endpoints can and do, in most cases, produce valid and reliable measurements of cell-death, which correlate very well with each other on direct comparisons of the different methods. This is not surprising any more than should the fact that auscultating heart sounds, observing spontaneous breathing, palpating a carotid pulse, measuring core body temperture, and recording an electroencelphalogram or electrocardiogram are all good and reliable methods of determing patient death.

Different investigators have favored different cell-death endpoints, depending on the laboratory and clinical situation. What is important is that each of the cell-death endpoints do give essentially the same results (except in the case of isolated drugs like taxanes and 5FU). So, it is entirely reasonable and proper to consider as a whole the clinical validation data which has been published over the last 20 years, using the above four endpoints.

Cell-death assays are not intended to be scale models of chemotherapy in the patient, anymore than the barometric pressure is a scale model of the weather. But it's always more likely to rain when the barometer is falling than when it is rising, and chemotherapy is more likely to work in the patient when it kills the patient's cancer cells in the laboratory. It is no different than any other medical test in this regard.

Not all patients will have the same response to the same chemotherapy. Special laboratories can test tumor samples from individual patients to see which chemotherapy drugs have the best likelihood of killing tumor cells and optimizing survival. The results provide medical and surgical oncologists with patient-specific tumor information that may provide additional insight when determing the appropriate course of treatment for a patient.

Assay-testing focuses on the unique characteristics of a particular cancer. The test results help the physician to determine which anti-cancer drugs are "likely" to be effective against a particular cancer. The assay test also helps the physician to determine which anti-cancer drugs are "unlikely" to affect a cancerous tumor, which can help to avoid toxic and possibly ineffective therapy.

The tests have a specifity of 0.92 and a sensitivity of 0.71, which means that a treatment regimen "not" resistant in the assays is 7-9 fold more likely to work than is a treatment regimen which "is" resistant in the assays, and evaluability rates (the ability to perform the assay on a specimen) are >95%. A preponderance of evidence would indicate that it would be worthwhile to consider the assay results in drug selection.

Literature Citation:

Functional profiling with cell culture-based assays for kinase and anti-angiogenic agents Eur J Clin Invest 37 (suppl. 1):60, 2007

Functional Profiling of Human Tumors in Primary Culture: A Platform for Drug Discovery and Therapy Selection (AACR: Apr 2008-AB-1546)

http://www.cancertest.org/wp-content/up ... 3_1984.pdf

World renowned Oncologists are challenging the cancer industry to recognize a Chemo-Screening test (CSRA) that takes the "guesswork" out of drug selection. One of the reasons medical oncologists dont like in vitro chemosensitivity tests is that it may be in direct competition with the randomized controlled clinical trial paradigm.

http://vimeo.com/72389724