Back

Intratumoral dose heterogeneity promotes adaptive anti-tumor immunity and predicts clinical response to radiopharmaceutical therapy

Takashima, M. E.; Kwon, O.; Ells, Z.; Li, V. R.; Sawicki, C.; Welch Schwartz, R.; Ahn, S. H.; Hyun, M.; Idrissou, M. B.; Berg, T. J.; Clark, P. A.; Lawless, M.; Besemer, A.; Bradshaw, T.; Perlman, S.; Jin, W.; Antonelli, M.; Adeniyi, A. O.; Donnelly Haasch, C.; Chen, T.; Wang, Y.; Kumari, R.; Hernandez, R. T.; Weichert, J.; Belanger, A. P.; Ong, I.; Floberg, J.; Meyer, C.; Kishan, A. U.; Calais, J.; Bednarz, B.; Morris, Z. S.

2026-07-24 cancer biology
10.64898/2026.07.23.740178 bioRxiv
Show abstract

Radiopharmaceutical therapies (RPT) deliver non-uniform radiation dose in tumors and the impact of this on response is poorly understood. Dose heterogeneity could engender treatment resistance in low dose regions, yet we hypothesize that a broader array of dose-dependent immuno-radiobiological mechanisms in tumor microenvironments (TME) and preservation of immune function in low-dose regions could promote adaptive anti-tumor immunity and response. In murine models, non-uniform lutetium-177 delivering <2.5 Gy to >20 Gy in a TME induced broader immunomodulatory effects and T cell-dependent survival improvement compared to more uniform distributions. Preserving low-dose regions promoted dendritic cell activation and TME infiltration of clonally expanded CD8+ T cells. In three independent cohorts of patients with prostate cancer, heterogeneous tumor dose distribution strongly correlated with improved clinical outcomes. These findings defy expected radiobiological dose-response and define a novel mechanism of action for RPT, supporting clinical investigation of dose distribution for optimizing patient selection and personalized dosing.

Matching journals

The top 5 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.