[gdpawel]

The headlong rush to develop pre-tests (companion diagnostics) to identify molecular predisposing mechanisms still does not guarantee that a cancer drug will be effective for an "individual" patient. Nor can they, for any patient or even large group of patients, discriminate the potential for clinical activity among different cancer agents of the same class.

The drug discovery model over the last three years or so has been limited to one gene/protein, one target, one drug. The "cell" is a system, an integrated, interacting network of genes, proteins and other cellular constituents that produce functions. You need to analyse the systems' response to drug treatments, not just one target or pathway.

With all the hoopla of decoding the human genome in 2000, sparked hopes that a new era of tailored medicine was just around the corner. However, uncovering the genetic differences that determine how a person responds to a drug, and developing tests, or biomarkers, for those differences, is proving more challenging than ever. As a result, patients with cancer are still being prescribed medicines on a trial-and-error basis.

The key to understanding the genome is understanding how cells work. The ultimate driver is "functional" pre-testing (is the cell being killed regardless of the mechanism) as opposed to "target" pre-testing (does the cell express a particular target that the drug is supposed to be attacking).

While a "target" test tells you whether or not to give "one" drug, a "functional" pre-test can find other compounds and combinations and can recommend them from the one test.

The core of "functional" testing is the cell, composed of hundreds of complex molecules that regulate the pathways necessary for vital cellular functions. If a "targeted" drug could perturb any one of these pathways, it is important to examine the effects of the drug within the context of the cell. Both genomics and proteomics can identify potential new thereapeutic targets, but these targets require the determination of cellular endpoints.

Cell-based "functional" pre-testing is being used for screening compounds for efficacy and biosafety. The ability to track the behavior of cancer cells permits data gathering on functional behavior not available in any other kind of testing.

Gene profiling tests, important in order to identify new therapeutic targets and thereby to develop useful drugs, are still years away from working successfully in predicting treatment response for "individual" patients. Perhaps this is because they are performed on dead, preserved cells that were never actually exposed to the drugs whose activity they are trying to assess.

It will never be as effective as the cell "function" methodology, which has existed for the last twenty years and is not hampered by the problems associated with gene expression tests. That is because they measure the net effect of all processes within the cancer, acting with and against each other in real-time, and it tests "living" cells actually exposed to drugs and drug combinations of interest.

It would be more advantageous to sort out what's the best "profile" in terms of which patients benefit from this drug or that drug. Can they be combined? What's the proper way to work with all the new drugs? If a drug works extremely well for a certain percentage of cancer patients, identify which ones and "personalize" their treatment. If one drug or another is working for some patients then obviously there are others who would also benefit. But, what's good for the group (population studies) may not be good for the individual.

Patients would certainly have a better chance of success had their cancer been chemo-sensitive rather than chemo-resistant, where it is more apparent that chemotherapy improves the survival of patients, and where identifying the most effective chemotherapy would be more likely to improve survival above that achieved with "best guess" empiric chemotherapy through clinical trials.

It may be very important to zero in on different genes and proteins. However, when actually taking the "targeted" drugs, do the drugs even enter the cancer cell? Once entered, does it immediately get metabolized or pumped out, or does it accumulate? In other words, will it work for every patient?

All the validations of this gene or that protein provides us with a variety of sophisticated techniques to provide new insights into the tumorigenic process, but if the "targeted" drug either won't "get in" in the first place or if it gets pumped out/extruded or if it gets immediately metabolized inside the cell, it just isn't going to work.

To overcome the problems of heterogeneity in cancer and prevent rapid cellular adaptation, oncologists are able to tailor chemotherapy in individual patients. This can be done by testing "live" tumor cells to see if they are susceptible to particular drugs, before giving them to the patient. DNA microarray work will prove to be highly complementary to the parellel breakthrough efforts in targeted therapy through cell function analysis.

As we enter the era of "personalized" medicine, it is time to take a fresh look at how we evaluate new medicines and treatments for cancer. More emphasis should be put on matching treatment to the patient, through the use of individualized pre-testing.

Upgrading clinical therapy by using drug sensitivity assays measuring "cell death" of three dimensional microclusters of "live" fresh tumor cell, can improve the situation by allowing more drugs to be considered. The more drug types there are in the selective arsenal, the more likely the system is to prove beneficial.