Functional Tumor Cell Profiling

[gpawelski]

Researchers have seen that whether a tumor was a breast tumor, prostate tumor, lung tumor or colon tumor, it didn't correlate to how the cancers interacted with standard anticancer drugs. Their findings suggest that traditional cancer treatments, which have established different drug regimens for lung, prostate or ovarian cancer, for example, should be replaced with therapies that use drugs deemed to be of highest benefit based on the tumor's pharmacologic profile. Treatment choice would be determined by how each patient's tumor reacts to anticancer drugs, regardless of the tumor's anatomical origin.

The drug effect is independent of where the tumor came from in the body. Under current treatment selection methods virtually no chemotherapeutic drug has been successful in more than 50 percent of patients with advanced cancer. But instead of considering a drug that works only ten percent of the time a failure, it would be better to consider such a drug effective for one in ten tumors and to search for the agents among the current arsenal of chemotherapeutic drugs that will work for the rest. Having a good tumor-drug match not only would improve survival rates, it would be cost-effective, and the high cost of the newer cancer therapies reinforces the necessity of choosing the right therapy the first time around.

The introduction of new "targeted" drugs has not been accompanied by specific predictive tests allowing for a rational and economical use of the drugs. Given the technical and conceptual advantages of Cell Culture Assays together with their performance and the modest efficicay of therapy prediction on analysis of genome expression, there is reason for a renewal in the interest for these for optimized use of medical treatment of malignant disease.

Clinical study results published at the annual meeting of the American Society of Clinical Oncology (ASCO) show that a new laboratory test, called EGFRx™, has accurately identified patients who would benefit from treatment with the molecularly-targeted anti-cancer therapies. The finding is important because the EGFRx™ test, which can also be applied to many emerging targeted cancer drugs, could help solve the growing problem of knowing which patients should receive costly, new treatments that can have harmful side-effects and which work for some but not all cancer patients who receive them. The test can discriminate between the activity of different targeted drugs and identify situations in which it is advantageous to combine the targeted drugs with other types of cancer drugs.

The new test relies upon what is called "Functional Profiling" in which living tumor cells are removed from an individual cancer patient and exposed in the laboratory to the new drugs. A variety of metabolic and apoptotic measurements are then used to determine if a specific drug was successful at killing the patient's cancer cells. The whole cell profiling method differs from other tests in that it assesses the activity of a drug upon combined effect of all cellular processes, using combined metabolic and morphologic endpoints. Other tests, such as those which identify DNA or RNA sequences or expression of individual proteins often examine only one component of a much larger, interactive process.

The whole cell profiling method makes the statistically significant association between prospectively reported test results and patient survival. Using the EGFRx™ Assay and the "functional profiling" method, can correlate test results which are obtained in the lab and reported to physicians prior to patient treatment, with significantly longer or shorter overall patient survival depending upon whether the drug was found to be effective or ineffective at killing the patient's tumor cells in the laboratory.

Over the past few years, researchers have put enormous efforts into genetic profiling as a way of predicting patient response to targeted therapies. However, no gene-based test has been described that can discriminate differing levels of anti-tumor activity occurring among different targeted therapy drugs. Nor can an available gene-based test identify situations in which it is advantageous to combine a targeted drug with other types of cancer drugs. So far, only whole cell profiling has demonstrated this critical ability.

Not only is this an important predictive test that is available "today," but it is also a unique tool that can help to identify newer and better drugs, evaluate promising drug combinations, and serve as a "gold standard" correlative model with which to develop new DNA, RNA, and protein-based tests that better predict for drug activity.

These "targeting" drugs are expensive, costing patients and insurance carriers $5,000 to $7,000 or more per month of treatment. Patients, physicians, insurance carriers, and the FDA are all calling for the discovery of predictive tests that allow for rational and cost-effective use of these drugs.

The whole cell profiling approach, holds the key to solving some of the problems confronting a healthcare system that is seeking ways to best allocate available resources while accomplishing the critical task of matching individual patients with the treatments most likely to benefit them.

Genomic testing is not the answer, without cell culture analysis. In developing a program to discover gene expression microarrays, which predict for responsiveness to drug therapy, the way to identify informative gene expression patterns is to have a gold standard and that cell culture assays are by far the most powerful, efficient, useful gold standard to have.

The 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, which improves quality, serves as control, and assesses dose response in vitro (includes newly-emergent drug combinations).

http://weisenthalcancer.com/Patient%20P ... kFacts.htm

[gpawelski]

Functional profiling (whole cell profiling) with cell culture assays for targeted drug therapy

There was a recent study describing correlations between cell culture assay results (cell death in response to Iressa exposure) and survival of 31 patients with non-small cell lung cancer who had received extensive prior chemotherapy. These correlations were based on the actual assay results which had been reported, in real time, prospectively to the doctors who had ordered the assay laboratory tests. There were striking correlations between test results and patient survival (not just response).

By inhibiting anti-apoptosis with Iressa (or even Tarceva), the cells undergo apoptosis and die. And it is detected at the whole cell level in the cell culture assays and reported out - prospectively - that this correlates strikingly with patient survival. Not only is it a very important predictive test, but it is a unique tool for identifying newer, better drugs, testing drug combinations, and serving as a "gold standard" to develop new DNA, RNA, and protein-based tests of drug activity.

EGF-targeted drugs (Iressa, Tarceva, Erbitux) are poorly-predicted by measuring the ostensible target (EGFR), but can be well-predicted by measuring the effect of the drugs on the function of "live" cells. Epidermal Growth Factor (EGF) is a receptor on many normal tissues/cells, and also on many cancer cells. It is a growth hormone, locally secreted by cells. It attaches to a receptor on the cell membrane called Epidermal Growth Factor Receptor (EGFR). It then activates so-called signalling pathways within the cell, a cascade of biochemical events, including phosphorylation of proteins, leading to cell growth/proliferation/division. One type of an enzyme which is involved in the pathway which is involved in protein phosphorylation is called tyrosine kinase.

Iressa (gefitinib) or Tarceva (erlotinib) induced cell death in short term fresh tumor cultures predicts for long term patient survival in previoulsy-treated non-small cell lung cancer.

Sub-category: Non-Small Cell Lung Cancer

Category: Lung Cancer

Meeting: 2006 ASCO Annual Meeting

Abstract No: 17117

Author (s): L. M. Weisenthal

Abstract: Gefitinib (GEF) may act by inhibiting anti-apoptotic signals transduced by mutant EGFR kinase (Science 305:1163,04). Cell culture assays with cell death endpoints could be informative for GEF activity.

Methods: We tested 568 biopsies of fresh human tumors (TUM) with 2 concentrations of GEF (22 and 11 µg/ml) for 96 hrs, each with 2 separate cell death endpoints (DISC and MTT). Results classified as resistant (RES), intermediate (INT), or sensitive (SEN) based on means and standard deviations of training set data, reported prospectively to 3 different physicians: surgeon, pathologist, and oncologist. Assay evaluability rate > 90%.

Results: Based on overall % control cell death, the following TUM showed (on average) no greater RES or SEN than the universe of 568 assays: NSCLC (n = 72), colon (33), breast (106), ovarian (109), melanoma (23), pancreatic (20), endometrial (12). The following showed (on avg) significantly greater RES: soft tissue sarcomas (n = 24), carcinoid/islet (16), renal (15), and mesothelioma (. For NSCLC, there was no avg difference between female (32) vs male (35) or untreated (34) vs previously treated (38). For 32 unRxd pts with survival data, there was no significant difference in overall surv for 20 pts with prospectively reported GEF RES (GR) assays vs 12 pts with SEN or INT (GSI) assays. For 31 pts with prior chemoRx (med surv = 155 days), there was significant survival disadvantage for 14 pts with prospectively reported GR vs 17 pts with GSI (median 85 vs 380 days, P2 < 0.0001, HR 3.7; 95% C.I. 2.6-19). For pts with known post-assay Rx, there were 7 pts with GSI subsequently receiving GEF or erlotinib (ERLOT), with med surv = 485 days; 9 pts with GSI not receiving GEF or ERLOT, med surv = 135 days; 10 pts with GR not receiving GEF or ERLOT, med surv = 76 days, and 3 pts with GR receiving GEF or ERLOT, med surv = 75 days. Survival of group of 7 pts was significantly greater than those of groups of 9, 10, and 3 pts (P2 = 0.037, P2 < 0.0001, and P2 = 0.0008, respectively.

Conclusions: GEF-induced cell death in cultures of fresh TUM from prev-treated NSCLC pts may identify pts with favorable prognosis, particularly when treated with GEF or ERLOT.

The assay is the only test 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, which improves quality, serves as control, and assesses dose response in vitro (includes newly-emergent drug combinations).

http://weisenthal.org/ex_targeted_egfr_kinase.pdf

[gpawelski]

The "forest and trees" analogy can explained the fact that conventional chemo treatments try to kill "all" cancerous cells (along with non-cancerous cells). The whole forest of cells. The new "targeted" drugs go after a "pathway" within or on cancerous cells. Hence the "trees" instead of the "forest."

With "functional" cell-based assays, the "forest" is looked at and not the "trees." There are many pathways to altered cellular (forest) function (hence all the different "trees" which correlate in different situations). The "functional" profiling technique of cell-death assays, measures what happens at the end (the effects on the forest), rather than the status of the individual trees. Cancer is a complex disease and needs to be attacked on many fronts.

Cancer therapy needs to be thought of "outside the box" with "personalized" treatments for "individual" patients, and requires a combination of novel diagnostics and therapeutics. If "some" drugs are working for "some" people (not average populations), then obviously there are others out there who would also benefit. Who are those that would benefit? All the more reason to test the tumor first.

A cell culture assay with "functional" profiling, using a cell-death endpoint, can help see what treatments will not have the best opportunity of being successful (resistant) and identify drugs that have the best opportunity of being successful (sensitive). Cell "function" analysis doesn't claim to have a perfect model, but all retrospective studies have documented that killing cells in the test tube does correlate with dead cancer cells in the patient.

"Funtional" profiling measures the response of the tumor cells to drug exposure. Following this exposure, they measure both cell metabolism and cell morphology. The integrated effect of the drugs on the whole cell (forest), resulting in a cellular response to the drug, measuring the interaction of the entire genome. No matter which genes are being affected (trees), "functional" profiling is measuring them through the surrogate of measuring if the cell is alive or dead.

For example, the epidermal growth factor receptor (EGFR) is a protein on the surface of a cell. EGFR inhibiting drugs certainly do target specific genes, but even knowing what genes the drugs target doesn't tell you the whole story. Both Iressa and Tarceva target EGFR protein-tyrosine kinases. But all the EGFR mutation or amplificaton studies can tell us is whether or not the cells are potentially susceptible to this mechanism of attack.

It doesn't tell you if Iressa is better or worse than Tarceva or other drugs which may target this. There are differences. The drugs have to get inside the cells in order to target anything. So, in different tumors, either Iressa or Tarceva might get in better or worse than the other. And the drugs may also be inactivated at different rates, also contributing to sensitivity versus resistance.

In an example of this testing, researchers have tested how well a pancreatic cancer patient can be treated successfully with a combination of drugs commonly used to fight lung, pancreatic, breast and colorectal cancers. The pre-test can report prospectively to a physician specifically which chemotherapy agent would benefit a cancer patient. Drug sensitivity profiles differ significantly among cancer patients even when diagnosed with the same cancer. One-size-does-not-fit-all.

[gpawelski]

In chemotherapy selection, Gene and Protein testing examine a single process within the cell or a relatively small number of processes. The aim is to tell if there is a theoretical predisposition to drug response.

Whole Cell Functional Profiling tests not only for the presence of genes and proteins but also for their functionality, for their interaction with other genes, proteins, and processes occurring within the cell, and for their response to anti-cancer drugs.

Genes create the blueprints for the production of proteins within the cell. A protein is a molecule that makes a cell behave in a certain way. It does so by interacting with other proteins in a complex series of steps.

The goal of Gene testing is to look for patterns of normal and abnormal gene expression which could suggest that certain proteins might or might not be produced within a cell. However, just because a gene is present it does not mean that an associated protein has been produced.

Protein testing goes one step further by testing to see if the relevant protein actually has been produced. However, even Protein testing cannot tell us if a protein is functional or how it will interact with other proteins in the presence of anti-cancer drugs.

Gene and Protein testing involve the use of dead, formaldehyde preserved cells that are never exposed to chemotherapy drugs. Gene and Protein tests cannot tells us anything about uptake of a certain drug into the cell or if the drug will be excluded before it can act or what changes will take place within the cell if the drug successfully enters the cell.

Gene and Protein tests cannot discriminate among the activities of different drugs within the same class. Instead, Gene and Protein tests assume that all drugs within a class will produce precisely the same effect, even though from clinical experience, this is not the case. Nor can Gene and Protein tests tell us anything about drug combinations.

"Whole Cell" Functional Tumor Cell Profiling tests living cancer cells. Functional Tumor Cell Profiling assesses the net result of all cellular processes, including interactions, occurring in real time when cancer cells actually are exposed to specific anti-cancer drugs. Functional Tumor Cell Profiling can discriminate differing anti-tumor effects of different drugs within the same class. Functional Profiling can also identify synergies in drug combinations.

Gene and Protein tests are better suited for ruling out "inactive" drugs than for identifying "active" drugs. When considering a cancer drug which is believed to act only upon cancer cells that have a specific genetic defect, it is useful to know if a patient's cancer cells do or do not have precisely that defect.

Although presence of a targeted defect does not necessarily mean that a drug will be effective, absence of the targeted defect may rule out use of the drug. Of course, this assumes that the mechanism of drug activity is known beyond any doubt, which is not always the case.

Although Gene and Protein testing currently are limited in their reliability as clinical tools, the tests can be important in research settings such as in helping to identify rational targets for development of new anti-cancer drugs.

As you can see, just selecting the right test to perform in the right situation is a very important step on the road to personalizing cancer therapy.

http://weisenthalcancer.com/Patient%20P ... tients.htm