Giant Acute Myeloid Leukemia Data Set Offers a Leap Forward in Matching Therapies to Patients

Researchers at Oregon Health and Science University (OHSU) have created a giant data set from more than 500 patients with acute myeloid leukemia (AML), which includes the molecular details of tumor cells and how they responded to various drugs.

The team, led by Brian Druker, MD, and Jeffrey W. Tyner, PhD, are releasing the data, which can be used with a new online viewer, so that fellow researchers can learn quickly which targeted therapies will work on which types of AML cells. The findings were reported Wednesday in Nature.

“People can go 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 said in a statement.

Druker is best known as the developer of imatinib mesylate (Gleevec), which transformed chronic myeloid leukemia from a fatal to a chronic disease. In 2013, Druker joined the Leukemia and Lymphoma Society to announce a partnership with OHSU for Beat AML, an initiative to understand the various mutations and drivers of this aggressive blood cancer.

The Nature article reports the results of that effort, which mapped 672 tumor specimens from 562 patients. Researchers used whole-exome sequencing, RNA sequencing, and analyses of ex vivo drug sensitivity, and it discovered some mutations not previously found.

“We show that the response to drugs is associated with mutational status, including instances of drug sensitivity that are specific to combinatorial mutational events,” the authors wrote. “Integration with RNA sequencing also revealed gene expression signatures, which predict a role for specific gene networks in the drug response.”

About 20,000 people are diagnosed with AML in the United States each year, most of them are older adults. AML has the poorest prognosis of any blood cancer, with 5-year survival rates for patients over age 60 still less than 10%.

As the researchers noted, using targeted therapies in AML is difficult because of the limited number of agents and the complex mutational patterns across the condition—AML is actually described as a series of diseases. The process uncovered 11 genetic classes of AML and thousands of mutations; a given therapy may work only if the patient has the right molecular match—but until now it was not easy to figure out how to make that connection.

“The real power comes when you start to integrate all that data,” Druker said. “You can analyze what drug worked and why it worked.”

Based on this knowledge, which examined how 409 tumor cells reacted to 122 different therapies, Beat AML now moves to a clinical trial in within Druker’s research team.

Reference

Tyner JW, Tognon CE, Bottomly D, et al. Functional genomic landscape of acute myeloid leukaemia. [published online October 17, 2018]. Nature.