Back

Ablative radiotherapy improves survival in autochthonous cancer mouse models

Schmidt, D. R.; Gramatikov, I. M. T.; Sheen, A.; Williams, C. L.; Hurwitz, M.; Dodge, L. E.; Holupka, E.; Kiger, W. S.; Cornwall-Brady, M. R.; Huang, W.; Mak, H. H.; Cormier, K.; Condon, C.; Wittrup, D.; Yilmaz, O. H.; Stevenson, M. A.; Down, J. D.; Floyd, S. R.; Roper, J.; Vander Heiden, M. G.

2022-07-04 cancer biology
10.1101/2022.07.01.498353 bioRxiv
Show abstract

Genetically engineered mouse models (GEMMs) of cancer are powerful tools to study mechanisms of disease progression and therapy response, yet little is known about how these models respond to multimodality therapy used for curative intent in patients. Radiation therapy (RT) is frequently used to treat localized cancers with curative intent, delay progression of oligometastases, and palliate symptoms of metastatic disease. RT can be combined with surgery, systemic therapy, and/or immunotherapy for definitive treatment of solid tumors. Here we report the development, testing, and validation of a murine platform that faithfully emulates human stereotactic ablative radiotherapy (SABR). We demonstrate that SABR regimens used in clinical practice can be effectively delivered in mouse models and establish the intestinal tract as the dose limiting organ for abdominopelvic radiation with notable, but transient, lymphodepletion. SABR alters tumor stroma and immune environment, improves survival in GEMMs of primary prostate and colorectal cancer, and synergizes with androgen deprivation in prostate cancer. While SABR is capable of fully ablating xenografts it is unable to completely eradicate disease in GEMMs. These data show that some GEMMs are resistant to curative therapy suggesting the existence of treatment-resistant persister cells in these models.

Matching journals

The top 4 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.