So you have a precursor cell that's triggered to differentiate by giving it some hormone. And you take staged samples of the thing as it differentiates, and we watch genes wax and wane in their activities during the course of that differentiation.
That poses a problem of what do you do with all that data. Right now what people are doing largely is cluster analysis, which is to say they take the chip data from a bunch of different cell types and try to cluster the genes that are expressed to a high level versus those that are expressed to a low level. And in effect that's simply a different way of recognizing cell types, but now at the molecular level. And there's nothing wrong with doing that, but there's nothing functional about it in the sense that if you're interested in finding out that gene A turns on gene B, and gene B -- when it's on -- turns off gene C. The way people are going about analyzing it doesn't lead in the direction of answering those questions.
SA: Why is that?
Because they're just recognizing patterns. Which is useful for diagnostic purposes and treatment purposes, but it's not the way to use the data to find what the regulatory circuits are.
SA: What could you do once you discover the circuitry?
First of all, you've just broadened the target range for the drug industry. Suppose a given gene makes an enzyme, then perhaps the enzyme's a nicely drugable target and you can make a molecule that enhances or inhibits the activity of the enzyme. But something you could do instead would be to turn on or off the gene that makes the enzyme. By finding the circuitry in and around a gene of medical interest, you've just expanded the number of drugable targets, so that you can try to modulate the activity of the genetic network rather than impinging upon the product of the gene.
Also, anything along the lines of diagnostics, if I know patterns of gene activities and regulatory circuitries, I can test to see the difference between a normal cell type and hepatic cancer cell -- a liver cancer cell. That is obviously useful diagnostically and therapeutically.
The biggest and longest-term consequences of all of this is uncovering the genetic regulatory network that controls cell development from the fertilized egg to the adult. That means that in the long run, we're going to be able to control cell differentiation and induce cell death, apoptosis. My dream is the following: 10 or 20 years from now, if you have prostatic cancer, we will be able to give drugs that will induce the cancer cells to differentiate in such a way that they will no longer behave in a malignant fashion, or they'll commit suicide by going into apoptosis. Then we'll also be able to cause tissue regeneration so that if you happen to have lost half of your pancreas, we'll be able to regenerate your pancreas. Or we'll be able to regenerate the beta cell islets in people who have diabetes. Afterall, if we can clone Dolly the sheep and make a whole sheep from a single cell, and if we now have embryonic stem cells, what we need are the chemical inductive stimuli that can control pathways of differentiation so that we can cause tissue to regenerate much more at will than we can now. I think that's going to be a huge transformation in biomedicine.
SA: What part does bioinformatics play in achieving this?
Most cancer cells are monoclonal; that means they're all derived from some single cell. And most cancer cells when they differentiate are leaky in the sense that they give rise to normal cell types as well as to cancer cells. This is a fact known to oncologists, which is not part of our therapeutic regimen right now. Cancer cells give rise to both normal and cancer cell types when the cancer stem cell, which is maintaining monoclonal line, is proliferating. What if we could take that cancer cell, give it chemical signals that induce it to differentiate into normal cell types -- we would be treating the cancer cell not by killing cells, but by using jujitsu on them and diverting them to be normal cell types. This already works with vitamin A and certain cancer cells, so there's already a precedent for it.