What may limit the effectiveness of Avastin is that there are multiple ways by which tumors can evolve that are independent of VEGF and independent of angiogenesis. Tumors can acquire a blood supply by three different mechanisms: angiogenesis; co-option of existing blood vessels; and vasculogenic mimicry. All must be inhibited to consistently starve tumors of oxygen.

Instead of growing new blood vessels, tumor cells can just grow along existing blood vessels. This process, called co-option, cannot be stopped with drugs that inhibit new blood vessel formation. Some types of cancers form channels that carry blood, but are not actual blood vessels. Drugs that target new blood vessel formation also cannot stop this process, called vasculogeneic mimicry. The realization is that starving tumors by shutting off their blood flow requires that all three mechanisms be addressed.

It could be vastly more important to measure the net effect of all processes (systems) instead of just individual molecular targets (like VEGF). The cell is a system, an integrated, interacting network of genes, proteins and other cellular constituents that produce functions. You need to analyze the systems' response to drug treatments, not just one or a few targets or pathways.

There are many pathways to the altered cellular (forest) function, hence all the different "trees" which correlate in different situations. Improvement can be made by measuring what happens at the end (the effects on the forest), rather than the status of the indiviudal trees.

VEGF-targeted drugs are poorly-predicted by measuring the preferred target VEGFR. They can be well-predicted by measuring the effect of the drug on the function of live cells.

Many of these fine drugs (and Avastin is a miracle drug for the few) cry out for validated clinical biomarkers as pharmacodynamic endpoints and with the ability to measure multiple parameters in cellular screens to help set dosage and select people likely to respond. Many molecular diagnostics approved often have been mostly or totally ineffective at identifying clinical responders to various therapies.

If you find one or more implicated proteins 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 protein 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? You're not going to accomplish this using genetic tests.

Improving cancer patient diagnosis and treatment through a combination of cellular and gene-based testing will offer predictive insight into the nature of an individual's particular cancer and enable oncologists to prescribe treatment more in keeping with the heterogeneity of the disease. The biologies are very different and the response to given drugs is very different.

The major obstacle in controlling cancer drug prices is the widespread inappropriate use of anti-cancer drugs. As the increasing numbers and types of anti-cancer drugs are developed, oncologists become more and more likely to misuse them in their practice. There is seldom a "standard" therapy which has been proven to be superior to any other therapy. What may work for one, may not work for another.