Personalized Cancer Medicine Is Here, NOW!

[gdpawel]

As we enter the era of "personalized" medicine, it is time to take a fresh look at how we evaluate treatments for cancer patients. More emphasis is needed matching treatment to the patient. 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.

Findings presented at the 41st Annual Meeting of the European Society for Clinical Investigation in Uppsala, Sweden and the Annual Meeting of the American Assoication for Cancer Research (AACR) in San Diego, CA concluded that "functional profiling" with cell-based assays is relevant for the study of both "conventional" and "targeted" anti-neoplastic drug agents (anti-tumor and anti-angiogenic activity) in primary cultures of "fresh" human tumors.

Cell-based Assays with "cell-death" endpoints can show disease-specific drug activity, are useful clinical and research tools for "conventional" and "targeted" drugs, and provide unique information complementary to that provided by "molecular" tests. There have been more than 25 peer-reviewed publications showing significant correlations between cell-death assay results and patient response and survival.

Many patients are treated not only with a "targeted" therapy drug like Tarceva, Avastin, or Iressa, but with a combination of chemotherapy drugs. Therefore, existing DNA or RNA sequences or expression of individual proteins often examine only one compenent of a much larger, interactive process. The oncologist might need to administer several chemotherapy drugs at varying doses because tumor cells express survival factors with a wide degree of individual cell variability.

There is a tactic of using biopsied cells to predict which cancer treatments will work best for the patient, by taking pieces of live "fresh" tumor tissue, applying different chemotherapy treatments to it, and examining the results to see which drug or combination of drugs does the best job killing the tumor cells. A cell-based assay test 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).

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, resulting in a cellular response to the drug, measuring the interaction of the entire genome. No matter which genes are being affected, 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. They don'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.

As 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.

The funtional profiling technique makes the statistically significant association between prospectively reported test results and patient survival. It can correlate test results that 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.

This could help solve the problem of knowing which patients can tolerate costly new treatments and their harmful side effects. These "smart" drugs are a really exciting element of cancer medicine, but do not work for everyone, and a pre-test to determine the efficacy of these drugs in a patient could be the first crucial step in personalizing treatment to the individual.

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)

[gdpawel]

Some of the most effective and expensive cancer drugs, dubbed "smart drugs" for their ability to stop tumors by targeting key drivers of cancer cell growth, are not effective in some patients. In two related studies, Yale School of Medicine researchers examined one such driver, the EGF receptor (EGFR), and found that a decoy receptor might be limiting the amount of drug that gets to the intended target.

"We know that smart drugs like Cetuximab are not always effective in the cancer cells they're supposed to target because there are no positive predictive markers for selecting the patients who will benefit from treatment with EGFR-targeted therapies, including EGFR itself," said lead author Nita Maihle, professor in the Departments of Obstetrics, Gynecology & Reproductive Sciences and of Pathology at Yale School of Medicine. "Why would a patient be given an expensive drug if it doesn't work? Our studies provide new insight into this paradoxical EGFR testing conundrum."

In a study published recently in the journal Cancer, Maihle and her team isolated a protein from human blood that looks like EGFR, but is actually a closely related variant called serum sEGFR. They showed that Cetuximab binds equally as well to serum sEGFR as it does to the intended EGFR cancer target.

Those study results showed that sEGFR might act as a decoy receptor in the blood of cancer patients, tying up Cetuximab and therefore limiting the amount of Cetuximab that actually gets to the intended target.

Such limitations may, in part, provide an explanation for the failure of two large phase III clinical trials on Cetuximab in colorectal cancer patients, since serum sEGFR concentrations are highly variable in cancer patients. These studies suggest that serum sEGFR should be measured and considered prior to treatment with Cetuximab. Other research has supported this concept by showing that serum sEGFR concentration changes in response to treatment with Cetuximab.

In their second related study, published online in the current issue of the journal Biochemistry, Maihle and her team show that newly developed reagents to measure sEGFR in blood and other human tissues can detect a second unrelated cell surface protein in tumor cells: alpha-5 integrin.

"This important finding suggests that the naturally occurring sEGFR protein may play a complex role in cell adhesion and migration - two cellular processes important in the spread of cancer," said Maihle, who is a member of Yale Cancer Center. "Together these studies demonstrate an unanticipated level of complexity in EGFR signaling and assay development, and suggest new ways to overcome current challenges associated with clinical testing for this important cancer target."

Notes:

The studies were funded by the National Cancer Institute, Susan G. Komen for the Cure, and the Marsha Rivkin Center for Ovarian Cancer Research.

Other authors on the Biochemistry study include Jason A. Wilken, Andre T. Baron, Ramsey A. Foty and Daniel J. McCormick.

Source: Yale University

These so-called "smart drugs" focus their effects on specific, identifiable processes occurring within cancer cells. The new "targeted" drugs are highly promising in that they "sometimes" provide benefit to patients who have failed traditional therapies. However, they do not work for everyone, they often have unwanted side effects, and they are all extremely expensive. 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.

There was a predictive test (functional cytometric profiling) developed that holds the key to solving some of the problems confronting this high-price 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. Not only is it an important predictive test, 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.

But how does one get ASCO and others to understand this and allow its judicious use? They have single-handedly done more over the past 20 years to keep assay-testing (pre-testing) technology under a bushel basket and out of the public light. It has hurt literally hundreds of thousands of patients. We'd be much further along and technology would have improved, even more accurate. New treatments would have been discovered and targeted immediately to the people who could most benefit from them. This has been one great lost of opportunity in clinical cancer research.

[gdpawel]

Although Tarceva, Iressa and Erbitux kill cells containing a normal but overactive EGFR (epidermal growth factor recpetor) molecule, only small molecule Tarceva and Iressa kills lung cancer cells containing a mutated EGFR molecule.

The monocolonal antibody (large molecule) drug Erbitux has little effect on the mutant signal, because it strikes at a different part of the EGFR molecule. It involves a normal, not mutant, EGFR molecule.

While those with EGFR mutations would benefit from Tarceva or Iressa, others without EGFR mutations would benefit from Erbitux.

In this setting, to inhibit the mutant receptor, you need to inhibit the domain of the EGFR molecule that lies within the cell, as opposed to the domain that lies outside the cell.

This study's lead author states "there are no positive predictive markers for selecting the patients who will benefit from treatment with EGFR-targeted therapies, including EGFR itself."

Why hasn't there been any progress at all in drug selection through the use of molecular diagnostics and biomarkers? Simply put, they do not work.

Little progress has been made in identifying which therapeutic strategies are likely to be effective for individual patients by molecular prognostic and predictive markers.

It was hoped that any patient with cancer would have their tumor biopsied and profiled. The profile would then be displayed as a unique genetic signature, which would in turn predict which therapy would most likely work.

However, gene-expression signatures are not ready for prime time. Genetic testing cannot do this for the individual.

All an EGFR mutation study can tell you is whether or not the cancer cells are "potentially" susceptible to this mechanism of attack. It cannot tell you if Tarceva will actually work for your cancer cells, or not, or if Erbitux other EGFR-inbititing drug will.

EGF-targeted drugs are poorly-predicted by measuring the ostansible target (EGFR), but can be well-predicted by measuring the effect of the drugs on the function of "live" cells. The functional profiling platform can actually integrate all the gene expression into one convenient test result.

Source: Cell Function Analysis