New tool reveals individual tumor cell reactions to therapy
Researchers have developed a method that uses extended time-lapse automated imaging to provide the accurate, detailed picture of cell behavior needed to understand tumor response to drugs. The new tool, which tracks single cancer cells over time, may improve personalized cancer therapy by predicting tumor response and allowing combinations of targeted therapies to be tested in an individual patient's tumor.
In personalized cancer treatment, a tumor is analyzed for a set of mutations, which are then matched to agents designed to address these alterations. “The genetics are well understood, the clinical effect is understood, and the chemistry behind the therapy is understood,” affirmed Vito Quaranta, MD, in a statement issued by Vanderbilt University Medical Center in Nashville, Tennessee, where Quaranta, one of the creators of the new technology, is a professor of cancer biology. “But there is a missing piece: Believe it or not, what is actually not understood is how cells respond to these drugs; what is actually happening.”
Although the prevailing view has been that targeted therapies kill all the cells harboring a particular mutation, a drug will not affect all cells the same way, even in the unlikely event that the tumor is composed entirely of genetically identical cells. “Some of these cells may die, some may just stop dividing and sit there [in a state known as quiescence], and some may keep dividing, but more slowly,” Quaranta explained.
To be able to see such cell behavior in detail, Quaranta and colleagues combined powerful automated, time-lapse microscopy with analytical tools and software they had developed, and applied the whole package to capture the behavior of lung cancer cells every 6 to 10 minutes for up to 10 days.
As the team reported in the journal Nature Methods, the targeted therapy erlotinib, a tyrosine kinase inhibitor, killed some lung tumor cells as expected. However, other tumor cells became quiescent. The investigators even observed that the drug had different effects on cells that arose from the same parental cell.
“These cells are clearly genetically identical—as identical as they can possibly be, because one cell just divided into two—but you get completely different responses,” pointed out study coauthor Darren Tyson, PhD, a research assistant professor in the cancer biology department at Vanderbilt. “This suggests that there are other things besides genetics that have to be taken into account.”
Tyson added that the quiescent cells presumably are at the root of tumor recurrence, although more studies are needed to explore that possibility.
The investigators hope to take the technology into small clinical trials to test whether it can predict a patient's response to cancer therapy. Such information could help foretell how long a tumor will respond to a given treatment before the cancer recurs, help determine which patients will require more aggressive treatment, and help in testing drug combinations that will induce the most effective responses within a tumor.