Screening anticancer compounds: how it’s really done
I’ve commented a number of times about stories in the papers where researchers have, basically, dripped herbal tea on cancer cells in a Petri dish and found it killed them (the cells, not the researchers). Screening anticancer compounds this way does work, it’s just that it doesn’t work very often, and when it does work it’s just the first step of years of likely failure.
Derek Lowe, at In The Pipeline, writes today about a research paper looking for things that might kill cancer stem cells. These are a tiny fraction of cells in a tumour, but are thought to be responsible for a lot of the treatment resistance and relapse. The researchers couldn’t work with actual cancer stem cells, which aren’t available in large quantities, so they used imitations produced by gene knockout. They screened 300 000 compounds from a large collection maintained by the National Institutes of Health. About 3000 killed the imitation stem cells. They then threw out all the compounds known to be highly toxic to normal cells, and those that repeatedly show up in screens for interesting properties. The problem with the latter group is that either they cheat (ie, they interfere with the assays being used) or they really do so many different things that they probably won’t be practical tools.
Of the remaining 2200 compounds that killed the imitation stem cells, nearly all of them also killed the original cancer cells that didn’t have the gene knockout, so however they might work it’s probably nothing useful for cancer stem cells. Finally, they rechecked the results with independent samples of the compounds (since if you have a collection of 300 000 compounds, no matter how careful you try to be, they aren’t all going to be what they say on the tin).
After all of this, they ended up with two compounds that appear to be selectively toxic against cancer stem cells. These aren’t drugs, even potentially, but they should be useful for finding out more about the biological differences in cancer stem cells and that, in turn, may lead to new treatments.
Thomas Lumley (@tslumley) is Professor of Biostatistics at the University of Auckland. His research interests include semiparametric models, survey sampling, statistical computing, foundations of statistics, and whatever methodological problems his medical collaborators come up with. He also blogs at Biased and Inefficient See all posts by Thomas Lumley »