A study led by scientists from Harvard Medical School reveals "hidden" variability in how tumor cells are affected by anticancer drugs, offering new insights on why patients with the same form of cancer can have different responses to a drug. The results were published in Nature Communications today.
Based on analyses of over 600 drug and breast cancer cell pairings, researchers showed that, for some cells, drug exposure can cause significant changes in gene expression—indicating the successful action of a drug on its target—without affecting cell growth or survival. This appears to be caused by adaptive resistance mechanisms, which, when identified, can be blocked by other drugs given in combination.
"Our findings suggest new ways of tackling the still-difficult task of working out which patients should receive which drug and how drugs should be combined to maximize therapeutic benefit," said senior study author Peter Sorger, the Otto Krayer Professor of Systems Pharmacology.
In collaboration with the Broad Institute of Harvard and MIT, Sorger and colleagues collected gene expression data from six genetically diverse breast cancer cell lines, which were each exposed to 109 small-molecule drugs at multiple dosages and time points. Cell growth and survival were assessed in parallel using a method developed by the Sorger lab that corrects for growth rate variations and makes large-scale comparisons of drug response more accurate.
In total, the researchers identified almost 8,000 gene expression signatures that they compared across several dimensions of drug response, including by cell line, drug class, biological pathway, cellular function and more.
The team found that cell lines appeared to respond to drugs in two broad patterns. One group of drugs—primarily targeting machinery involved in the cell cycle, protein chaperoning or DNA repair—elicited similar patterns of response across all cell lines.
A second group triggered responses that were specific to cell type. Different cell types respond to these drugs in qualitatively different ways. Such drugs largely targeted the signaling pathways that are disrupted by oncogenic mutations.
"We find that drug-sensitive tumor cells respond in similar ways to some classes of drugs and very different ways to other classes of drugs," Sorger said. "This was unexpected and suggests fundamental but unrecognized differences in drug action in tumor cells from different individuals having the same disease—in this case, breast cancer."
While gene expression in most drug-cell line pairings correlated with cell growth and survival, a small subset, roughly 3%,, had a unique pattern of response. In these cases, drug exposure caused significant changes in tumor cells' gene expression profile but had no lasting physical effect.
"Our study is not a recipe for generating effective drug combinations, but it can help narrow down candidates and identify which drugs might be most promising when tested in combination," Hafner said. "In order to go beyond targeting an oncogene and just hoping for the best, we need to understand the actual biological effects of drugs inside cells. That's how we can have better and smarter use of drugs."