Immunotherapy fails to cure most cancer patients. Preclinical studies indicate that radiotherapy synergizes with immunotherapy, promoting radiation-induced antitumor immunity. Most preclinical immunotherapy studies utilize transplant tumor models, which overestimate patient responses. Here, we show that transplant sarcomas are cured by PD-1 blockade and radiotherapy, but identical treatment fails in autochthonous sarcomas, which demonstrate immunoediting, decreased neoantigen expression, and tumor-specific immune tolerance. We characterize tumor-infiltrating immune cells from transplant and primary tumors, revealing striking differences in their immune landscapes. Although radiotherapy remodels myeloid cells in both models, only transplant tumors are enriched for activated CD8+ T cells. The immune microenvironment of primary murine sarcomas resembles most human sarcomas, while transplant sarcomas resemble the most inflamed human sarcomas. These results identify distinct microenvironments in murine sarcomas that coevolve with the immune system and suggest that patients with a sarcoma immune phenotype similar to transplant tumors may benefit most from PD-1 blockade and radiotherapy.

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We are developing imaging methods for a co-clinical trial investigating synergy between immunotherapy and radiotherapy. We perform longitudinal micro-computed tomography (micro-CT) of mice to detect lung metastasis after treatment. This work explores deep learning (DL) as a fast approach for automated lung nodule detection. We used data from control mice both with and without primary lung tumors. To augment the number of training sets, we have simulated data using real augmented tumors inserted into micro-CT scans. We employed a convolutional neural network (CNN), trained with four competing types of training data: (1) simulated only, (2) real only, (3) simulated and real, and (4) pretraining on simulated followed with real data. We evaluated our model performance using precision and recall curves, as well as receiver operating curves (ROC) and their area under the curve (AUC). The AUC appears to be almost identical (0.76–0.77) for all four cases. However, the combination of real and synthetic data was shown to improve precision by 8%. Smaller tumors have lower rates of detection than larger ones, with networks trained on real data showing better performance. Our work suggests that DL is a promising approach for fast and relatively accurate detection of lung tumors in mice.

Objective: To develop and characterize novel methods of extreme spatially fractionated kV radiation therapy (including mini-GRID therapy) and to evaluate efficacy in the context of a pre-clinical mouse study. Approach: Spatially fractionated GRIDs were precision-milled from 3 mm thick lead sheets compatible with mounting on a 225 kVp small animal irradiator (X-Rad). Three pencil-beam GRIDs created arrays of 1 mm diameter beams, and three ‘bar’ GRIDs created 1x20 mm rectangular fields. GRIDs projected 20x20 mm2 fields at isocenter, and beamlets were spaced at 1, 1.25, and 1.5 mm, respectively. Peak-to-valley ratios and dose distributions were evaluated with Gafchromic film. Syngeneic transplant tumors were induced by intramuscular injection of a soft tissue sarcoma cell line into the gastrocnemius muscle of C57BL/6 mice. Tumor-bearing mice were randomized to four groups: unirradiated control, conventional irradiation of entire tumor, GRID therapy, and hemi-irradiation (half-beam block, 50% tumor volume treated). All irradiated mice received a single fraction of 15 Gy. Results: High peak-to-valley ratios were achieved (bar GRIDs: 11.9±0.9, 13.6±0.4, 13.8±0.5; pencil-beam GRIDs: 18.7±0.6, 26.3±1.5, 31.0±3.3). Pencil-beam GRIDs could theoretically spare more intra-tumor immune cells than bar GRIDs, but they treat less tumor tissue (3-4% vs 19-23% area receiving 90% prescription, respectively). Bar GRID and hemi-irradiation treatments significantly delayed tumor growth (P<0.05), but not as much as a conventional treatment (P<0.001). No significant difference was found in tumor growth delay between GRID and hemi-irradiation. Significance: High peak-to-valley ratios were achieved with kV grids: two-to-five times higher than MV grids in literature. GRID irradiation and hemi-irradiation delayed tumor growth, but neither was as effective as conventional whole tumor uniform dose treatment. Single fraction GRID therapy could not initiate an anti-cancer immune response strong enough to match conventional RT outcomes, but follow-up studies will evaluate the combination of mini-GRID with immune checkpoint blockade.

The purpose of this study was to investigate if radiomic analysis based on spectral micro-CT with nanoparticle contrast-enhancement can differentiate tumors based on lymphocyte burden. High mutational load transplant soft tissue sarcomas were initiated in Rag2+/− and Rag2−/− mice to model varying lymphocyte burden. Mice received radiation therapy (20 Gy) to the tumor-bearing hind limb and were injected with a liposomal iodinated contrast agent. Five days later, animals underwent conventional micro-CT imaging using an energy integrating detector (EID) and spectral micro-CT imaging using a photon-counting detector (PCD). Tumor volumes and iodine uptakes were measured. The radiomic features (RF) were grouped into feature-spaces corresponding to EID, PCD, and spectral decomposition images. The RFs were ranked to reduce redundancy and increase relevance based on TL burden. A stratified repeated cross validation strategy was used to assess separation using a logistic regression classifier. Tumor iodine concentration was the only significantly different conventional tumor metric between Rag2+/− (TLs present) and Rag2−/− (TL-deficient) tumors. The RFs further enabled differentiation between Rag2+/− and Rag2−/− tumors. The PCD-derived RFs provided the highest accuracy (0.68) followed by decomposition-derived RFs (0.60) and the EID-derived RFs (0.58). Such non-invasive approaches could aid in tumor stratification for cancer therapy studies.