Back

Adaptive Plasticity Tumor Cells Modulate MAPK-Targeting Therapy Response in Colorectal Cancer

Villarreal, O. E.; Xu, Y.; Tran, H.; Machado, A.; Prescod, D.; Anderson, A.; Minelli, R.; Peoples, M.; Lee, H. M.; Wong, C. W.; Fowlkes, N.; Kanikarla, P.; Sorokin, A.; Alshenaifi, J. Y.; Coker, O.; Lin, K.; Bristow, C.; Viale, A.; Shen, J. P.; Parseghian, C.; Marszalek, J. R.; Corcoran, R.; Kopetz, S.

2025-01-24 cancer biology
10.1101/2025.01.22.634215 bioRxiv
Show abstract

MAPK pathway inhibitors (MAPKi) are increasingly used in the treatment of advanced colorectal cancer, but often produce short-lived responses in patients. Although acquired resistance by de novo mutations in tumors have been found to reduce response in some patients, additional mechanisms underlying the limited response durability of MAPK targeting therapy remain unknown. Here, we denote new contributory tumor biology and provide insight on the impact of tumor plasticity on therapy response. Analysis of MAPKi treated patients revealed activation of stemness programs and increased ASCL2 expression, which are associated with poor outcomes. Greater ASCL2 with MAPKi treatment was also seen in patient-derived CRC models, independent of driver mutations. We find ASCL2 denotes a distinct cell population, arising from phenotypic plasticity, with a proliferative, stem-like phenotype, and decreased sensitivity to MAPKi therapy, which were named adaptive plasticity tumor (APT) cells. MAPK pathway suppression induces the APT phenotype in cells, resulting in APT cell enrichment in tumors and limiting therapy response in preclinical and clinical data. APT cell depletion improved MAPKi treatment efficacy and extended MAPKi response durability in mice. These findings uncover a cellular program that mitigates the impact of MAPKi therapies and highlights the importance of addressing tumor plasticity to improve clinical outcomes.

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.