Mounting evidence suggests that the tumor microenvironment is profoundly immunosuppressive. Thus, mitigating tumor immunosuppression is crucial for inducing sustained antitumor immunity. Whereas previous studies involved intratumoral injection, we report here an inhalable nanoparticle-immunotherapy system targeting pulmonary antigen presenting cells (APCs) to enhance anticancer immunity against lung metastases. Inhalation of phosphatidylserine coated liposome loaded with STING agonist cyclic guanosine monophosphate–adenosine monophosphate (NP-cGAMP) in mouse models of lung metastases enables rapid distribution of NP-cGAMP to both lungs and subsequent uptake by APCs without causing immunopathology. NP-cGAMP designed for enhanced cytosolic release of cGAMP stimulates STING signaling and type I interferons production in APCs, resulting in the pro-inflammatory tumor microenvironment in multifocal lung metastases. Furthermore, fractionated radiation delivered to one tumor-bearing lung synergizes with inhaled NP-cGAMP, eliciting systemic anticancer immunity, controlling metastases in both lungs, and conferring long-term survival in mice with lung metastases and with repeated tumor challenge.

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Purpose: To assess early changes in brain metastasis in response to whole brain radiotherapy (WBRT) by longitudinal Magnetic Resonance Imaging (MRI). Materials and methods: Using a 7T system, MRI examinations of brain metastases in a breast cancer MDA-MD231-Br mouse model were conducted before and 24 hours after 3 daily fractionations of 4 Gy WBRT. Besides anatomic MRI, diffusion-weighted (DW) MRI and dynamic contrast-enhanced (DCE) MRI were applied to study cytotoxic effect and blood-tumor-barrier (BTB) permeability change, respectively. Results: Before treatment, high-resolution T2-weighted images revealed hyperintense multifocal lesions, many of which (~50%) were not enhanced on T1-weighted contrast images, indicating intact BTB in the brain metastases. While no difference in the number of new lesions was observed, WBRT-treated tumors were significantly smaller than sham controls (p < .05). DW MRI detected significant increase in apparent diffusion coefficient (ADC) in WBRT tumors (p < .05), which correlated with elevated caspase 3 staining of apoptotic cells. Many lesions remained non-enhanced post WBRT. However, quantitative DCE MRI analysis showed significantly higher permeability parameter, Ktrans, in WBRT than the sham group (p < .05), despite marked spatial heterogeneity. Conclusions: MRI allowed non-invasive assessments of WBRT induced changes in BTB permeability, which may provide useful information for potential combination treatment.

Background: Dynamic contrast-enhanced (DCE) MRI is widely used to assess vascular perfusion and permeability in cancer. In small animal applications, conventional modeling of pharmacokinetic (PK) parameters from DCE MRI images is complex and time consuming. This study is aimed at developing a deep learning approach to fully automate the generation of kinetic parameter maps, Ktrans (volume transfer coefficient) and Vp (blood plasma volume ratio), as a potential surrogate to conventional PK modeling in mouse brain tumor models based on DCE MRI. Methods: Using a 7T MRI, DCE MRI was conducted in U87 glioma xenografts growing orthotopically in nude mice. Vascular permeability Ktrans and Vp maps were generated using the classical Tofts model as well as the extended-Tofts model. These vascular permeability maps were then processed as target images to a twenty-four layer convolutional neural network (CNN). The CNN was trained on T 1 -weighted DCE images as source images and designed with parallel dual pathways to capture multiscale features. Furthermore, we performed a transfer study of this glioma trained CNN on a breast cancer brain metastasis (BCBM) mouse model to assess the potential of the network for alternative brain tumors. Results: Our data showed a good match for both Ktrans and Vp maps generated between the target PK parameter maps and the respective CNN maps for gliomas. Pixel-by-pixel analysis revealed intratumoral heterogeneous permeability, which was consistent between the CNN and PK models. The utility of the deep learning approach was further demonstrated in the transfer study of BCBM. Conclusions: Because of its rapid and accurate estimation of vascular PK parameters directly from the DCE dynamic images without complex mathematical modeling, the deep learning approach can serve as an efficient tool to assess tumor vascular permeability to facilitate small animal brain tumor research.

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