Dataset Could Help Match Therapies to Patients
Published: 2019-10-17 |
Source: Howard Hughs Medical Institute
After years of work, researchers are releasing a massive dataset detailing the molecular makeup of tumor cells from more than 500 patients with an aggressive blood cancer called acute myeloid leukemia (AML). The dataset includes how hundreds of individual patients' cells responded to a broad panel of drugs in laboratory screens.
It is the largest cancer dataset of its kind and could rapidly advance clinical trials evaluating potential AML treatments, says Brian Drucker, a Howard Hughes Medical Institute investigator at Oregon Health & Science University (OHSU) who led the work with his colleague Jeffrey Tyner, also from OHSU.
Using a new online data viewer, researchers can now find out in minutes what kinds of targeted therapies are most effective against specific subsets of AML cells. "People can get online, search our database, and quickly get answers to "Is this a good drug?" or "Is there a patient population my drug can work in?" Drucker says. He and colleagues report the work October 17, 2018, in the journal Nature.
"The real power comes when you start to integrate all that data," Drucker says. "You can analyze what drug worked and why it worked." That, he says is the foundation for planning clinical trials to test new therapies in the patients who are most likely to respond.
In their own analysis, Drucker and his colleagues have identified a set of three genetic mutation that may make AML patients good candidates for treatment with ibrutinib. That drug is currently approved for the treatment of some other types of blood cancer. AML cells carrying all three mutations are significantly more sensitive to ibrutinib than cells with just one or two of those mutations, the team found.
Clinical trials will be needed to evaluate ibrutinib's effects in this group of patients--but tying the mutation trio to a potential drug sensitivity showcases how the new dataset can help researchers. "We're just starting to scratch the surface of what we can do when we analyze the data, "Drucker says