Microbubbles and photoacoustic probes energize cancer researchers

The Canary Foundation’s eighth year of activities was marked with a symposium held at Stanford University May 25-27, 2010. The Canary Foundation focuses on the early detection of cancer, specifically lung, ovarian, pancreatic, and prostate cancer, in a three-step process of blood tests, imaging, and targeted treatment.

Imaging advances: microbubbles and photoacoustic probes
Imaging is an area that continues to make advances. One exciting development is the integration of multiple technologies, for example superimposing molecular information onto traditional CT scans. Contemporary scans may show that certain genes are over-expressed in the heart, for example, but obscure the specific nodule (tumor) location. Using integrins to bind to cancerous areas may allow their specific location to be detected (4 mm nodules now, and perhaps 2-3 mm nodules as scanning technologies continue to improve).

Other examples of integrated imaging technologies include microbubbles, which are gassy and can be detected with an ultrasound probe as they are triggered to vibrate. Similarly, photoacoustic probes use light to perturb cancerous tissue, and then sound detection tools transmit the vibrations. Smart probes are being explored to detect a variety of metaloproteases on the surface of cancer cells, breaking apart and entering cancer cells where they can be detected with an ultrasound probe.

Systems biology approaches to cancer
Similar to aging research, some of the most promising progress points in cancer research are due to a more systemic understanding of disease, and the increasing ability to use tools like gene expression analysis to trace processes across time. One example is being able to identify and model not just one, but whole collections of genes that may be expressed differentially in cancers, seeing that whole pathways are disrupted, and the downstream implications of this.

Cancer causality
Also as in aging research, the 'chicken or the egg' problem arises as multiple things that go wrong are identified, but which happens first, and causality, is still unknown. For example, in ovarian cancer, where there are often mutations in the p53 gene, and gene rearrangements and CNV (copy number variation; different numbers of copies of certain genes), but which occurs first and what causes both is unknown.

Predictive disease modeling
There continues to be a need for models that predict clinical outcome, and serve as accurate representations of disease. DNA and gene expression, integrated with traits and other phenotypic data in global coherent datasets could allow the ability to build probabilistic causal models of disease. It also may be appropriate to shift to physics/accelerator-type models to manage the scale of data now being generated and reviewed in biomedicine.