Large Acute Myeloid Leukemia Dataset Could Help Identify Targeted Treatments

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There were 11 genes identified in BeatAML — called in at least 1% of patients — that were not originally recognized in previous AML sequencing studies.
There were 11 genes identified in BeatAML — called in at least 1% of patients — that were not originally recognized in previous AML sequencing studies.

Researchers have gathered a large dataset detailing the molecular makeup of tumor cells from 562 patients with acute myeloid leukemia, according to a study published in Nature.1 The dataset included information on the mutational patterns of the genes of these patients and outlined which identified mutations responded to interventions with targeted therapies.

The study included initial findings from the BeatAML program. The initial cohort included 672 tumor specimens from 562 patients. Specimens were assessed using whole-exome sequencing, RNA sequencing, and analyses of ex vivo drug sensitivity.

The final variant list revealed between 1 and 80 somatic variants per patient, with a median of 13 variants per patient. The 33 most commonly mutated genes in BeatAML were similar to the most commonly identified mutated genes in The Cancer Genome Atlas. However, there were 11 genes identified in BeatAML that were called in 1% or more of patients that were not observed in previous AML sequencing studies.

Mutations in several genes, most notably TP53 or ASXL transcriptional regulator 1 (ASXL1), were shown to cause a “broad pattern of drug resistance.”1 Specifically, there was a significant association between mutations in FLT3, NPM1, and DNMT3A and sensitivity to ibrutinib.

The researchers also identified several novel co-occurrences of mutations and expression clusters that were seen across the cohort. For example, for ibrutinib, co-occurrences included a co-expression cluster of 345 genes that correlated with drug sensitivity and frequently co-occurred with FLT3-ITD, which also correlated with drug sensitivity.

“These data have all been made publicly available through the NIH/NCI dbGaP and Genomic Data Commons resources, and we have developed tools to facilitate user-interfacing with the dataset,” the researchers wrote. “We hope and expect that this public data release will stimulate further use of the data, such that novel findings can be derived and turned into new clinical approaches for treatment of AML.”

Disclosure: Funding for this study was supported by various grants, and the authors report financial support and funding from pharmaceutical companies. For a full list of disclosures, please refer to the original study.

Reference

  1. Tyner JW, Tognon CE, Bottomly D, et al. Functional genomic landscape of acute myeloid leukaemia [published online October 17, 2018]. Nature. doi: 10.1038/s41586-018-0623-z

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