Back

YAP/TEAD drives treatment-induced adaptive immunosuppression in EGFR-mutant lung cancer

Honkanen, M.; Baumgarten, J.; Dibus, N.; Rannikko, J. H.; Gojkovic, M.; Cejas, P.; Xie, Y.; Ortutay, Z.; Merilahti, J.; Tienhaara, M.; Farahani, I.; Boettcher, S.; Jänne, P. A.; Kauko, O.; Nemenoff, R. A.; Heasley, L. E.; Haikala, H. M. E.; Hollmen, M.; Kurppa, K. J.

2026-01-24 cancer biology
10.64898/2026.01.22.701073 bioRxiv
Show abstract

Residual disease remains a major obstacle for achieving durable responses in patients treated with oncogene-targeted therapy. Drug-tolerant persister (DTP) cells emerging under treatment and persisting in residual tumors are considered to be the root of acquired resistance, yet their contribution to immune evasion in on-treatment tumors is poorly defined. Here, we show in the context of EGFR-mutant lung cancer that DTP cells actively contribute to the formation of an immunosuppressive tumor microenvironment during EGFR tyrosine kinase inhibitor (TKI) therapy. In syngeneic mouse models and in patients, EGFR TKI therapy leads to an accumulation of immunosuppressive macrophages, which is strictly treatment-dependent and fully reversible upon treatment cessation or progressive disease, respectively. Quiescent DTP cells directly drive the recruitment and immunosuppressive reprogramming of monocytes and macrophages through a YAP-driven secretome, and the DTP-reprogrammed monocytes suppress T cell proliferation and effector functions in vitro. Co-targeting YAP with a TEAD inhibitor ORM-47286 rewires the DTP secretome and inhibits macrophage reprogramming in vitro, and prevents immunosuppressive macrophage accumulation and improves the efficacy of EGFR TKI therapy in immunocompetent mouse models. Our findings highlight the previously unappreciated role of DTP cells in modulating the tumor microenvironment in on-treatment tumors, and position the treatment-induced YAP/TEAD activity in DTP cells as an important driver of adaptive immunosuppression during EGFR-targeted therapy.

Matching journals

The top 7 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.