First Results of Beat AML Master Trial Published

Initial results from the Leukemia & Lymphoma Society–led Beat AML Master Trial, a ground-breaking study evaluating several novel targeted therapies for acute myeloid leukemia (AML), were recently reported in Nature.

Importantly, the investigators announced the creation of a collective dataset that can be used for clinical, genomic, transcriptomic, and functional analyses of AML. The dataset is accessible through the National Institutes of Health (NIH)/National Cancer Institute (NCI) database of Genotypes and Phenotypes (dbGaP) and Genomic Data Commons (GDC) resources, and the team developed user-interface tools to help others harness its capability. Researchers hope that the public release of their data will contribute to the discovery of new AML treatments.

“The implementation of targeted therapies for AML has been challenging because of the complex mutational patterns within and across patients, as well as a dearth of pharmacologic agents for most mutational events,” wrote the authors, led by Jeffrey W. Tyner, PhD, of the departments of Cell and Developmental and Cancer Biology at Oregon Health & Science University in Portland.

Cytogenetic and sequencing analyses have identified 11 or more genetic classes of AML. Moreover, 20-plus subsets can be assigned when cell differentiation states of the leukemic blasts are considered. In AML, previous research has shown that a number of recurrent cytogenetic events and somatic mutations are of prognostic importance.

In the current study, the investigators examined a cohort of 672 primary tumor biopsies taken from 562 patients. They analyzed specimens via whole-exome sequencing, RNA sequencing, and analyses of ex-vivo drug sensitivity. In the process, they discovered novel AML mutational events not yet elucidated.

“We present a cohort of specimens from patients with AML for which we have performed detailed clinical annotations, genomic and transcriptomic analyses and ex vivo drug sensitivity studies, and we provide analytical approaches for data integration,” the authors wrote.

Each dataset compiled by the researchers provided further insight into the biology of AML, as well as new translational approaches. Integration of the datasets uncovered new markers and mechanisms of drug sensitivity and resistance, which warrant future study.

“Integration with RNA sequencing also revealed gene expression signatures, which predict a role for specific gene networks in the drug response,” the researchers wrote.

In an interview with Cancer Network, Eunice S. Wang, MD, a professor of oncology at the University of Buffalo Jacobs School of Medicine and Biomedical Sciences in Buffalo, NY, expounded on the strengths and limitations of the current study.

“The authors here provide one of the most extensive datasets ever published and made publicly available.  Human AML samples from 562 patients were exhaustively profiled and evaluated for mutational status, gene expression, and drug sensitivity. Based on these data, the researchers have generated some provocative information on novel therapeutic targets and predictive drug sensitivity based on specific molecular signatures and in vitro drug assays. This study is highly informative and the databases provided here likely mined by investigators for years in the search for new drug targets and agents,” she said.

Wang continued, “However, Beat AML is characterized by some important limitations. For instance, all of these experiments are performed in vitro and are not validated using any in vivo models. Given the known importance of the bone marrow microenvironment to AML biology and therapy resistance, the ability of these data presented in Beat AML to predict true clinical responses in actual patients is uncertain. Lastly AML samples profiled in Beat AML were processed as whole AML samples and do not differentiate between subsets of potentially very biologically different AML populations, for example, leukemic stem vs. bulk cells.”

Catherine Lai, MD, MPH, director of leukemia and an assistant professor at Georgetown University Medical Center's Lombardi Comprehensive Cancer Center, put the future clinical implications of the study into perspective.

“This information helps us understand and predict potential combinations of drugs that can be used in AML patients based on their mutations. It also gives insight on what drugs to avoid based on mutation status,” she said. “FDA approvals for targets drugs in AML-for example, midostaurin, ivosidenib, and enasidenib-have changed how we manage these patients and have allowed for more options, but unfortunately are still limiting since these drugs only target a single mutation, and most AML patients have multiple mutations. In my opinion, understanding how to use the right combination of drugs will be the next hurdle we need to jump.”