Cancers are far more variable on a molecular level than they appear under the microscope. Still, we continue to treat most cancers based on their appearance – an approach nearly as crude as giving a blood transfusion to everyone who complains of fatigue. Today, however, emerging genomic and computational technologies are transforming cancer research. That research, in turn, promises to transform cancer treatment as well.
An "N of 1" approach
The Blau Lab is establishing the infrastructure to treat a small number of highly motivated cancer patients as individual experiments: in scientific parlance, an "N of 1." Vast amounts of data will be analyzed from each patient's tumor to predict which proteins should be targeted in order to destroy the cancer. Each patient would then receive a drug regimen tailored to his or her tumor. In this kind of single-subject approach, there is no control group against which to compare the response of the experimental subject. Instead, each patient would serve as his or her own control, using a technique called serial molecular monitoring. In this technique, the patient would receive a drug designed to block a particular target protein. A biopsy would then be performed to confirm whether the drug had worked and the target had indeed been blocked. In this way, researchers would track the tumor's molecular response to treatment through repeated biopsies (a requirement that may eventually be replaced by sampling blood). One of the most important advantages of serial molecular monitoring is that it would reveal strategies that tumors adopt to evade therapy, possibly uncovering new targets of opportunity.
This patient-centered approach represents a dramatic departure from traditional oncology. Because these novel patient-specific combinations of drugs could have unforeseen side effects, the methodological, regulatory, and ethical framework for cancer research would need to be reconsidered from the ground up. Therapies that appear to be effective would be validated in small trials involving other patients with similar molecular profiles. Unsuccessful therapies could be analyzed to refine our understanding of tumor biology and drug mechanisms. The N of 1 approach may not hit immediate home runs. However, the extensive body of knowledge generated from each patient should, upon aggregation with data from other patients, enable us to tell from a blood sample which patients will respond to particular drugs. In time, we hope to be able to stop tumors by anticipating the escape routes they are likely to take.
Although this approach will be very expensive for early adopters, technology costs are falling at exponential rates (think of Moore's Law for integrated circuits). Eventually, the approach should lead to dramatic reductions in health-care costs. For one thing, the approach allows us to administer these expensive drugs only to patients for whom they are most likely to work. For another, the costs of the approach will go down and – and scalability increase – once expected advances in technology allow us to use simple blood draws rather than repeated tumor biopsies.
Although many significant challenges remain, the primary roadblock to implementing the single-subject approach is financial. Will insurance companies or federal agencies pay to test this new paradigm for cancer treatment? Will patients with difficult-to-treat cancers (and the means to fund their own treatment) be willing to take on this grand experiment – for their own benefit, potentially, and for the benefit of other patients? We believe that it's time to find out.