Genomics Helps Predict Best Treatment

[tousled1]

It’s been known for some time that breast cancer is not just one disease, but a grouping of several different subtypes, including those that overexpress the protein HER2 and those that are fueled from hormones. Building on that knowledge, recently developed technology now allows for a broad population-based molecular analysis of the disease, which promises to give oncologists the ability to choose the best treatment for each individual patient based on a tumor’s genomic expression.

Genomic expression data from breast cancer has already identified four distinct subtypes (luminal A and luminal B, which are both estrogen receptor-positive but differ in prognosis; HER2-positive; and basal type, which are negative for both hormone and HER2). Each subtype responds differently to each type of drug.

In a presentation on the genomic approaches to identifying a specific breast cancer subset and choosing an appropriate treatment, Joe Gray, PhD, from the Lawrence Berkeley National Laboratory, presented data from a multi-institutional effort to identify specific markers of each subset.

Dr. Gray and colleagues were able to expose a number of cell culture models to an even larger number of drug agents which could slow down or stop cell growth. By correlating the genomic characteristics of each specific cell model to how they react to a variety of these drug agents, scientists were able to map out what molecular variables were associated with a response to each agent.

To translate this research into the clinic, markers are selected, such as HER2. A test was created and confirmed by Dr. Gray’s group and compared with the more standard test using FISH (fluorescent in vitro hybridization). Both tests used data from a study looking at Taxol with or without Tykerb (lapatinib), a HER2-targeted agent, in newly diagnosed metastatic breast cancer.

When the two tests were compared, both Dr. Gray’s genomic test as well as a predictive HER2-associated 6-gene response signature developed by his group, were as sensitive as HER2-FISH in predicting response to Tykerb. —Zach Moore, PhD

[gdpawel]

The Microarray (gene chips) is a device that measures differences in gene sequence, gene expression or protein expression in biological samples. Microarrays may be used to compare gene or protein expression under different conditions, such as cells found in cancer.

Hence the headlong rush to develop tests to identify molecular predisposing mechansims whose presence still does not guarantee that a 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 agents of the same class.

Genetic profiles are able to help doctors determine which patients will probably develop cancer, and those who will most likely relapse. However, it cannot be suitable for specific treatments for individual patients.

In the new paradigm of requiring a companion diagnostic as a condition for approval of new targeted therapies, the pressure is so great that the companion diagnostics they’ve approved often have been mostly or totally ineffective at identifying clinical responders (durable and otherwise) to the various therapies.

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. Targeting one pathway may not be as effective as targeting multiple pathways in a cancer cell.

Another challenge is to identify for which patients the targeted treatment will be effective. Tumors can become resistant to a targeted treatment, or the drug no longer works, even if it has previously been effective in shrinking a tumor. Drugs are combined with existing ones to target the tumor more effectively. Most cancers cannot be effectively treated with targeted drugs alone. Understanding “targeted” treatments begins with understanding the cancer cell.

If you find one or more implicated genes in a patient's tumor cells, how do you know if they are functional (is the encoded protein actually produced)? If the protein is produced, is it functional? If the protein is functional, how is it interacting with other functional proteins in the cell?

All cells exist in a state of dynamic tension in which several internal and external forces work with and against each other. Just detecting an amplified or deleted gene won't tell you anything about protein interactions. Are you sure that you've identified every single gene that might influence sensitivity or resistance to a certain class of drug?

Assuming you resolve all of the preceeding issues, you'll never be able to distinguish between susceptibility of the cell to different drugs in the same class. Nor can you tell anything about susceptibility to drug combinations. And what about external facts such as drug uptake into the cell?

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 culture method, which exists today 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.

Source: Cell Function Analysis