Imaging gene expression

In one of my first posts I commented on the challenge of obtaining samples for microarray and other biomarker work. Getting samples for microarrays is at best difficult, painful to the patient and only a little dangerous to them; in many cases the samples are simply unobtainable. Getting a broad range of samples from multiple sites, or a time series is going to be very rarely feasible.

With this backdrop, a recent paper in Nature Biotechnology is quite stunning. Indeed, it is a bit of a surprise that it didn't show up in the mother ship or Science: the paper is well written, audacious in design and shows very nice results.

Using actual liver cancer patients the paper correlates contrast-enhanced CT (aka CAT) imaging features to gene expression patterns detected by microarrays using samples from the same patients. While these patients had to go through biopsies, the approach holds out the hope of calibrating imaging assays for future use.

The imaging-microarray connections have many intriguing possibilities. Some of the linked microarray patterns have clear therapeutic associations, such as cell cycle genes and VEGF. Such an imaging approach might, with much further validation, enable appropriate selection of therapeutic agents -- such as Avastin to target VEGF.

The paper also notes the challenges that lie ahead. The choice of liver cancer was no accident: liver tumors tend to be large and well-vascularized, making them straightforward to image using CT. Some of the imaging features found are generic to tumors, but others have some degree of liver specificity. Expression program to image feature mappings may vary from tumor to tumor.

One potential side-effect of this study would be to increase biopharma interest in liver cancer. Liver cancer is a scourge outside of the Western world (perhaps driven by food-borne toxins) but is not in the top of deadly cancers in the U.S. According to some 2002 figures from the American Cancer Society, liver cancer in the U.S. is about 17K new cases and about 15K fatalities -- a horrible toll, but far less than 160K annual lung cancer deaths. One big attraction for companies is potential payoff, but another is the potential for accelerated development decisions. Being able to subset patients based matching drug mechanism to biology inferred from imaging is potentially a powerful means to do that.