Aerosolized immunotherapeutic nanoparticle inhalation potentiates PD-L1 blockade for locally advanced lung cancer.

Despite therapeutic advancements, the prognosis of locally advanced non-small cell lung cancer (LANSCLC), which has invaded multiple lobes or the other lung and intrapulmonary lymph nodes, remains poor. The emergence of immunotherapy with immune checkpoint blockade (ICB) is transforming cancer treatment. However, only a fraction of lung cancer patients benefit from ICB. Significant clinical evidence suggests that the proinflammatory tumor microenvironment (TME) and programmed death-ligand 1 (PD-L1) expression correlate positively with response to the PD-1/PD-L1 blockade. We report here a liposomal nanoparticle loaded with cyclic dinucleotide and aerosolized (AeroNP-CDN) for inhalation delivery to deep-seated lung tumors and target CDN to activate stimulators of interferon (IFN) genes in macrophages and dendritic cells (DCs). Using a mouse model that recapitulates the clinical LANSCLC, we show that AeroNP-CDN efficiently mitigates the immunosuppressive TME by reprogramming tumor-associated macrophage from the M2 to M1 phenotype, activating DCs for effective tumor antigen presentation and increasing tumor-infiltrating CD8+ T cells for adaptive anticancer immunity. Intriguingly, activation of interferons by AeroNP-CDN also led to increased PD-L1 expression in lung tumors, which, however, set a stage for response to anti-PD-L1 treatment. Indeed, anti-PD-L1 antibody-mediated blockade of IFNs-induced immune inhibitory PD-1/PD-L1 signaling further prolonged the survival of the LANSCLC-bearing mice. Importantly, AeroNP-CDN alone or combination immunotherapy was safe without local or systemic immunotoxicity. In conclusion, this study demonstrates a potential nano-immunotherapy strategy for LANSCLC, and mechanistic insights into the evolution of adaptive immune resistance provide a rational combination immunotherapy to overcome it.

Transfer Learning Approach to Vascular Permeability Changes in Brain Metastasis Post-Whole-Brain Radiotherapy.

The purpose of this study is to further validate the utility of our previously developed CNN in an alternative small animal model of BM through transfer learning. Unlike the glioma model, the BM mouse model develops multifocal intracranial metastases, including both contrast enhancing and non-enhancing lesions on DCE MRI, thus serving as an excellent brain tumor model to study tumor vascular permeability. Here, we conducted transfer learning by transferring the previously trained GBM CNN to DCE MRI datasets of BM mice. The CNN was re-trained to learn about the relationship between BM DCE images and target permeability maps extracted from the Extended Tofts Model (ETM). The transferred network was found to accurately predict BM permeability and presented with excellent spatial correlation with the target ETM PK maps. The CNN model was further tested in another cohort of BM mice treated with WBRT to assess vascular permeability changes induced via radiotherapy. The CNN detected significantly increased permeability parameter Ktrans in WBRT-treated tumors (p < 0.01), which was in good agreement with the target ETM PK maps. In conclusion, the proposed CNN can serve as an efficient and accurate tool for characterizing vascular permeability and treatment responses in small animal brain tumor models.

Deep learning quantification of vascular pharmacokinetic parameters in mouse brain tumor models.

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 T1-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.

Intrapleural nano-immunotherapy promotes innate and adaptive immune responses to enhance anti-PD-L1 therapy for malignant pleural effusion.

Malignant pleural effusion (MPE) is indicative of terminal malignancy with a uniformly fatal prognosis. Often, two distinct compartments of tumour microenvironment, the effusion and disseminated pleural tumours, co-exist in the pleural cavity, presenting a major challenge for therapeutic interventions and drug delivery. Clinical evidence suggests that MPE comprises abundant tumour-associated myeloid cells with the tumour-promoting phenotype, impairing antitumour immunity. Here we developed a liposomal nanoparticle loaded with cyclic dinucleotide (LNP-CDN) for targeted activation of stimulators of interferon genes signalling in macrophages and dendritic cells and showed that, on intrapleural administration, they induce drastic changes in the transcriptional landscape in MPE, mitigating the immune cold MPE in both effusion and pleural tumours. Moreover, combination immunotherapy with blockade of programmed death ligand 1 potently reduced MPE volume and inhibited tumour growth not only in the pleural cavity but also in the lung parenchyma, conferring significantly prolonged survival of MPE-bearing mice. Furthermore, the LNP-CDN-induced immunological effects were also observed with clinical MPE samples, suggesting the potential of intrapleural LNP-CDN for clinical MPE immunotherapy.

An inhalable nanoparticulate STING agonist synergizes with radiotherapy to confer long-term control of lung metastases.

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.