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Leukemia Dataset Match Therapies to Patients

Published: 2018-10-17 |

Source: Howard Hughes 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 Druker, a Howard Hughs Medical Institute investigator at Orgaon 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 very quickly get answers to "Is this a good drug?" Or, "Is there a patient population my drug can work in?" Druker says. He and colleagues report the work October 17, 2018, in the journal Nature.

Developing effective therapies for AML has been challenging because the molecular factors that drive the disease vary significantly among patients. Researchers have identified at least 11 genetic classes of AML and uncovered thousands of different mutations among patients' cancer cells. Targeted cancer therapies, which attempt to eliminate cancer cells by exploiting their specific molecular vulnerabilities, may only work when given to patients whose AML has the right molecular features. Until now, researchers haven't had a clear map to identify the best candidates for specific treatments.

The new study reports initial findings from the BeatAML program, which is now moving into a clinical trial. Druker's team, which includes collaborators at 11 academic medical centers and 11 pharmaceutical and biotechnology companies, collected and analyzed 672 samples of cancer cells from 562 patients. The team's work, which was also supported by the Leukemia and Lymphoma Society, includes the complete DNA sequence of each sample's protein-coding genes, as well as profiles of gene activity. The team also assessed how tumor cells from 409 of the samples responded to each of 122 different targeted therapies.

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