# Autophagy and Lipid Metabolism as a Therapeutic Target for Overcoming Drug Resistance in Acute Myeloid Leukemia

## TL;DR

This paper explores how autophagy and lipid metabolism could be targeted to treat drug-resistant acute myeloid leukemia.

## Contribution

The paper introduces a novel approach combining lipidomics, AI, and biomarkers to target autophagy in AML.

## Key findings

- Autophagy supports leukemic stem cell survival and chemoresistance in AML.
- Lipophagy-driven fatty acid oxidation is a metabolic vulnerability in leukemic stem cells.
- Non-coding RNAs modulate autophagy networks to reinforce therapy resistance in AML.

## Abstract

Acute myeloid leukemia (AML) remains a therapeutically challenging malignancy due to high relapse rates driven by leukemic stem cells (LSCs) and adaptive resistance mechanisms. Emerging evidence positions autophagy as a central regulator of AML pathobiology, exerting context-dependent effects that suppress leukemogenesis during disease initiation yet sustain LSC survival and chemoresistance in established AML. Mechanistically, autophagy integrates mitochondrial quality control, lipid droplet turnover, and metabolic rewiring to support oxidative phosphorylation, particularly under hypoxic bone marrow conditions. Lipophagy-driven fatty acid oxidation has emerged as a key metabolic vulnerability distinguishing LSCs from normal hematopoietic stem cells. Furthermore, non-coding RNAs critically modulate autophagy networks, reinforcing therapy resistance. Preclinical and clinical studies demonstrate that both inhibition and activation of autophagy may yield therapeutic benefit depending on genetic context, mutational landscape, and disease stage. We propose that integrating multi-omics approaches, particularly lipidomics, with artificial intelligence and machine learning will enable precise identification of autophagy-dependent AML subsets. Rational, biomarker-guided modulation of autophagy may overcome resistance while preserving normal hematopoiesis, offering a path toward personalized metabolic targeting in AML.