[gdpawel]

Next-generation sequencing (NGS) technologies have come a long way since 1977 when Frederick Sanger developed chain-termination sequencing, but are they ready for prime time in drug selection?

Researchers have realized that cancer biology is driven by signaling pathways. Cells speak to each other and the messages they send are interpreted via intracellular pathways known as signal transduction. Many of these pathways are activated or deactivated by phosphorylations on select cellular proteins.

Sequencing the genome of cancer cells is explicitly based upon the assumption that the pathways - network of genes - of tumor cells can be known in sufficient detail to control cancer. Each cancer cell can be different and the cancer cells that are present change and evolve with time.

Although the theory behind inhibitor targeted therapy is appealing, the reality is more complex. Cancer cells often have many mutations in many different pathways, so even if one route is shut down by a targted treatment, the cancer cell may be able to use other routes.

In other words, cancer cells have "backup systems" that allow them to survive. The result is that the drug does not affect the tumor as expected. The cancer state is typically characterized by a signaling process that is unregulated and in a continuous state of activation.

In chemotherapy selection, genotype analysis (genomic profiling) examines a single process within the cell or a relatively small number of processes. All a gene mutation study can tell is whether or not the cells are potentially susceptible to a mechanism of attack. The aim is to tell if there is a theoretical predisposition to drug response.

It doesn't tell you the effectiveness of one drug (or combination) or any other drug which may target this in the individual. There are many pathways to altered cellular function. Phenotype analysis (functional profiling) measures the end result of pathway activation or deactivation to predict whether patients will actually respond (clinical responders).

It measures what happens at the end, rather than the status of the individual pathway, by assessing the activity of a drug (or combinations) upon combined effect of all cellular processes, using combined metabolic and morphologic endpoints, at the cell population level, measuring the interaction of the entire genome.

Should oncologists begin using deep genome sequencing in their clinical practice? At the annual meeting of the European Society for Medical Oncology, two key opinion leaders battled it out over this topic in a debate.