Back

Stress Granules Underlie Acute Myeloid Leukemia Stem Cell Survival and Stress Adaptation

Tajik, A.; Tsao, E.; Jahangiri, S.; Seale, B.; Yee, B. A.; Naritomi, J. T.; Balde, Z.; Cathelin, S.; Chahi, A. K.; Li, L.; Chen, H. T.; Wong, N.; Liu, L.; Joshi, P.; Moreira, S.; McCloskey, C. W.; Khan, S.; Rothamel, K. L.; Boutzen, H.; Bansal, S.; Zeng, A. G. X.; Aigner, S.; Lu, Y.; Dick, J. E.; Kislinger, T.; Khokha, R.; Minden, M. D.; Gingras, A.-C.; Yeo, G. W.; Hope, K. J.

2025-01-17 cancer biology
10.1101/2025.01.14.632811 bioRxiv
Show abstract

The link between cancer maintenance and an ability to sustain continued growth through stresses conferred by the cancer state itself is growing. However, there are significant gaps in our understanding of how this stress is managed, particularly at the level of cancer initiating cells. Here, we identify proteins comprising the dynamic, stress-adaptive ribonucleoprotein complexes known as stress granules (SG) to be enriched among the factors essential for leukemic stem cell (LSC)-driven leukemic propagation. Focusing on core SG nucleator G3BP1, we dissect the role of SGs in human acute myeloid leukemia (AML), their targetability, and the mechanisms they govern to uncover a novel propensity for AML, and in particular LSC-enriched fractions, to prime the expression of SG components, form SGs with greater fidelity and to be reliant on their establishment and continued integrity for LSC maintenance. We further unveil the transcript and protein interactome of G3BP1 in the AML context and show that consolidated control of innate immune signaling, and apoptosis repression is executed through regional binding specificity of G3BP1 to highly structured 3UTRs and cooperation with the RNA helicase UPF1 to mediate transcript decay in SGs. Altogether our findings advance novel fundamental principles of stress adaptation exploited in AML and LSCs that may extend to other cancers and uncover SGs as a novel axis for therapy development.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.