Hopefully, the University of Utah Huntsman Cancer Institute's genetic test will go the same way as other commercialized multigene prognostic and predictive tests, like Oncotype DX and MammaPrint, in telling physicians which high risk breast cancer patients will likely benefit from chemotherapy (identify patients who are likely to have a recurrence if treated with surgery alone) and which ones do not need to be unnecessarily exposed to toxic chemotherapy cocktails.

The Breast Bioclassifier is a genetic analysis that scans thousands of genes to identify patterns of gene activity in individual tumors that indicate a patient is likely to suffer a recurrence of disease. It is based on RNA microarry analysis and is an appropriate application of a genomic strategy to estimate prognosis, by identifying early stage cancer patients who may benefit from adjuvant chemotherapy.

These microarray-based tests measure 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.

If these genetic tests find a patient to be at high-risk, then a suitable platform can be used to utilize cell-based tests to better select a repertoire of available drugs to improve the efficacy of chemotherapy. None of the genetic tests can predict clinical responders. Further cell-based pre-tests can help see what treatments have the best opportunity of being successful.

The headlong rush to develop tests to identify molecular predisposing mechanisms whose presence still does not guarantee that a drugs (or combinations of drugs) 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.

Microarray profiling tests, important in order to identify new therapeutic targets and thereby to develop useful drugs, still cannot work successfully in predicting treatment response for individual patients. Perhaps this is because they are performed on formalin-fixed, paraffin-embedded tissues or fresh-frozen tissue samples or unfrozen samples stored in RNA-preserving solution that were never actually exposed to the drugs whose activity they are trying to assess.

It will never be as effective as the cell-based "function" method, which exists today and is not hampered by the problems associated with gene or protein expression tests. That is because functional methodology measures the net effect of all processes within the cancer, acting with and against each other in real-time, and it tests living (fresh) cells actually exposed to drugs and drug combinations of interest.

The key to understanding the genome is understanding how cells work. The ultimate driver is a functional assay (is the cell being killed regardless of the mechanism) as opposed to a target assay (does the cell express a particular target that the drug is supposed to be attacking). While a target assay tells you whether or not to give one drug, a functional assay can find other compounds and combinations and can recommend them from the one assay.

The core of the functional assay 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. Because genomics are far too limited in scope to encompass the vagaries and complexities of human cancer biology, these targeted therapies require the determination of cellular endpoints. Cell-based functional assays are being used for screening compounds for efficacy and biosafety.