Abstract A49: Characterizing the order of mutation acquisition in acute myeloid leukemia using large-scale single-cell sequencing
Authors: Schwede Matthew , Jahn Katharina , Miles Linde A , Bowman Robert L , Kuipers Jack , Robinson Troy M , Ediriwickrema Asiri , Gentles Andrew J , Levine Ross , Beerenwinkel Niko , Takahashi Koichi , Majeti Ravindra 
Published: May 2023

Introduction: Acute myeloid leukemia (AML) has a poor prognosis, despite aggressive therapies and a recent expansion in the array of treatments. Treatment is often determined by mutations, which risk-stratify a patient’s leukemia or can identify mutations that serve as therapeutic targets. Although common mutations in AML have been extensively studied, less research has formally characterized the order by which mutations tend to occur and how this order relates to properties of the disease.

Methods: We leveraged published, single-cell DNA sequencing data from three institutions to model the clonal evolution of AML. Sequencing had been performed using targeted sequencing panels covering 19 to 37 genes, and driver mutations were identified using conservative filters. Mutation trees were created using Single Cell Inference of Tumor Evolution (SCITE). Clonal evolution patterns, including across clinical timepoints, were identified and compared to patient characteristics, disease phenotype, and outcomes. The BeatAML dataset was leveraged to explore associations between mutation order and gene expression.

Results: Using 835 driver mutations from 276 samples and 209 patients, we identified 223 total unique mutation patterns. Branched evolution primarily involves FLT3 and RAS pathway mutations, whereas certain mutation pairs, such as NPM1 and FLT3, always occurred linearly in the same clone. Although some mutation pairs, such as those related to DNA methylation versus the RAS pathway, tended to occur in a specific order, several cases exhibited atypical orderings. Early signaling gene mutations were associated with younger patient age and increased signaling mutation homozygosity while NRAS-first cases were associated with increased monocyte counts. NRAS-first cases were also associated with distinct gene expression patterns in the BeatAML dataset. Paired diagnosis and relapse samples revealed novel associations between mutations gained at relapse and their clonal context, and these analyses supported a relative fitness advantage for signaling mutations in clones containing mutations in NPM1 or genes affecting DNA methylation.

Conclusions: Clonal evolution in AML can be reconstructed at scale using single-cell DNA sequencing data, and different mutation acquisition patterns are associated with distinct leukemia and patient characteristics, despite cases having similar co-mutation patterns.

Citation Format: Matthew Schwede, Katharina Jahn, Linde A Miles, Robert L Bowman, Jack Kuipers, Troy M Robinson, Asiri Ediriwickrema, Andrew J Gentles, Ross Levine, Niko Beerenwinkel, Koichi Takahashi, Ravindra Majeti. Characterizing the order of mutation acquisition in acute myeloid leukemia using large-scale single-cell sequencing [abstract]. In: Proceedings of the AACR Special Conference: Acute Myeloid Leukemia and Myelodysplastic Syndrome; 2023 Jan 23-25; Austin, TX. Philadelphia (PA): AACR; Blood Cancer Discov 2023;4(3_Suppl):Abstract nr A49.