When to Use Assay Testing

There is a common misconception that chemosensitivity-resistance assays are only useful for patients in the relapsed state once they have failed conventional first line therapy. This assertion is wrong on several levels.

1. First, the best outcomes in cancer medicine are known to occur with first line therapies. The selection of the most active, least toxic drug or combination should be the goal of every physician at the time of initial therapy. As CSRAs have well established performance characteristics (sensitivity and specificity), their positive predictive accuracy (the likelihood that a patient with a sensitive assay will respond to the clinical treatments selected) are highest when they are applied in the first line setting.
2. Secondly, on theoretical grounds, exposure to randomly selected chemotherapeutics, many of which are mutagens, may select for or induce drug resistance, diminishing the likelihood of a good outcome in second line or subsequent therapy.
3. Finally, the introduction of active targeted agents provides patients the opportunity to receive first line therapies that do not carry the side effects and toxicities of classic cytotoxic chemotherapies. Our experience with first line Erlotinib in non-small cell lung cancer today provides response rates that exceed those associated with patients selected based on EGFr mutation or overexpression. Furthermore, the selection of candidates for combined targeted agents, e.g. EGFR & VEGF inhibitors, etc. provides a growing opportunity to introduce novel combinations into the first line setting. The growing cost and potential toxicity of some of these agents make the application of accurate selective methodologies increasingly crucial.

First line chemotherapy provides patients their best opportunity for a good outcome. There is no rationale for exposing patients to randomly selected toxic and potentially ineffective therapy when clinically validated selective methodologies can be applied in the first line as well as second line setting and beyond.

The Clinical Applications of Functional Profiling CSRA: Lung Cancer

Lung cancer, with 215,000 new diagnoses and 162,000 deaths in the United States each year, represents the leading cause in cancer death in the US. Average response rates of 30 percent and median survivals of 12 months remain static over decades. The five-year survival of 15 percent for this disease has not changed in 50 years.

While better surgical techniques and the introduction of new drugs have improved the one-year survival from 35 percent to 41 percent in the last 20 years, this has not had a major impact on overall survival.

Few diseases offer the opportunity to meaningfully improve cancer survival like lung cancer. A 25 percent improvement in survival in lung cancer would be the numerical equivalent of curing breast cancers outright. Recognizing this opportunity, we have made lung cancer a principal focus of the functional profiling platform. Our current IRB approved clinical protocol in non-small cell lung cancer applies our functional profiling platform to select from among the commercially available FDA approved compendium listed drugs, in metastatic non-small cell lung cancer. The results of our study have been submitted to national meetings. To date, our approach has more than doubled the objective response rate to 69 percent, improved the time to progression, provided over one-third of our stage IV patients the capacity to undergo definitive radiation or surgery, and extended the lives of some stage IV patients to five or more years. By exploring the first line use of the newer targeted agents, many patients are achieving durable responses without ever being exposed to classic chemotherapeutic drugs. It is our intent to make these laboratory analyses available to all newly diagnosed and relapsed lung cancer patients.

The Clinical Applications of Functional Profiling CSRA: Ovarian Cancer

Ovarian cancer is the leading cause of cancer death for gynecologic malignancies, afflicting 22,000 women in the US each year. The majority of ovarian cancer patients are diagnosed with advanced disease requiring chemotherapy. Experience shows that these patients typically respond well to available drugs, yet there has been no improvement in five-year survival for this disease in decades. And the standard of care — platinum plus taxane — has not changed in more than 15 years.

However, the clinical responsiveness of ovarian cancer renders it an ideal candidate for functional profiling. Using functional profiling, we were the first to use platinum and Gencitabine to treat this disease, showing its efficacy even in platinum-resistant patients. Clinical responses — many very durable — have been observed even in the most heavily pre-treated patients. While there are many drugs active in this disease, microarray gene platforms have been unable to meaningfully distinguish subsets of patients and improve therapy selection. These limitations of DNA-based techniques are not shared by functional profiling, which has the unique capacity to examine complex biological systems in their native state. By incorporating the interaction of tumor cells with their stroma, vasculature and inflammatory elements, functional profiling has been shown to provide highly validated predictive information.

Using functional profiling, we are now exploring novel drug combinations and the introduction of signal transduction inhibitors into the management of advanced ovarian cancer. The failure of large cooperative group clinical trials (like the GOG182) to improve clinical outcomes in the first line setting can now be seen as a failed paradigm of patient randomization. Using functional profiling to examine untreated ovarian cancers, we have shown that no standard combination is best for the majority of patients. Instead, patients manifest unique patterns of sensitivity and resistance that can only be recognized through the individualization of treatment. Functional profiling has the capacity to match patients to available drugs and combinations, thereby improving the odds of good response and minimizing exposure to ineffective and toxic drugs.

Chemosensitivity-Resistance Assay as Functional Profiling

Modern cancer research can be divided into three principal disciplines based upon methodology:

1.     Genomic — the analysis of DNA sequences, single nucleotide polymorphisms (SNPs), amplifications and mutations to develop prognostic and, to a limited degree, predictive information on cancer patient outcome.

2.     Proteomic — the study of proteins, largely at the level of phosphoprotein expressions.

3.     Functional — the study of human tumor explants isolated from patients to examine the effects of growth factor withdrawal, signal transduction inhibition and cytotoxic insult on cancer cell viability.

Contrary to analyte-based genomic and proteomic methodologies that yield static measures of gene or protein expression, functional profiling provides a window on the complexity of cellular biology in real-time, gauging tumor cell response to chemotherapies in a laboratory platform. By examining drug induced cell death, functional analyses measure the cumulative result of all of a cell’s mechanisms of resistance and response acting in concert. Thus, functional profiling most closely approximates the cancer phenotype.  Insights gained can determine which drugs, signal transduction inhibitors, or growth factor inhibitors induce programmed cell death in individual patients’ tumors. Functional profiling is the most clinically validated technique available today to predict patient response to drugs and targeted agents.