Mutation Patterns Predict Drug Sensitivity in Acute Myeloid Leukemia.

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Clinical cancer research : an official journal of the American Association for Cancer Research


Humans; Leukemia, Myeloid, Acute; Mutation; Drug Resistance, Neoplasm; Antineoplastic Agents; Single-Cell Analysis; Machine Learning; High-Throughput Nucleotide Sequencing; Male; washington; isb


PURPOSE: The inherent genetic heterogeneity of acute myeloid leukemia (AML) has challenged the development of precise and effective therapies. The objective of this study was to elucidate the genomic basis of drug resistance or sensitivity, identify signatures for drug response prediction, and provide resources to the research community.

EXPERIMENTAL DESIGN: We performed targeted sequencing, high-throughput drug screening, and single-cell genomic profiling on leukemia cell samples derived from patients with AML. Statistical approaches and machine learning models were applied to identify signatures for drug response prediction. We also integrated large public datasets to understand the co-occurring mutation patterns and further investigated the mutation profiles in the single cells. The features revealed in the co-occurring or mutual exclusivity pattern were further subjected to machine learning models.

RESULTS: We detected genetic signatures associated with sensitivity or resistance to specific agents, and identified five co-occurring mutation groups. The application of single-cell genomic sequencing unveiled the co-occurrence of variants at the individual cell level, highlighting the presence of distinct subclones within patients with AML. Using the mutation pattern for drug response prediction demonstrates high accuracy in predicting sensitivity to some drug classes, such as MEK inhibitors for RAS-mutated leukemia.

CONCLUSIONS: Our study highlights the importance of considering the gene mutation patterns for the prediction of drug response in AML. It provides a framework for categorizing patients with AML by mutations that enable drug sensitivity prediction.

Clinical Institute