Ovarian cancer is the deadliest gynecological malignancy. Though most patients enter remission following initial interventions, relapse is common and often fatal. Accordingly, there is a substantial need for ovarian cancer therapies that prevent relapse. Following remission generated by surgical debulking and chemotherapy, but prior to relapse, resected and inactivated tumor tissue could be used as a personalized vaccine antigen source. The patient’s own tumor contains relevant antigens and, when combined with the appropriate adjuvant, could generate systemic antitumor immunity to prevent relapse. Here, we model this process in mice to investigate the optimal tumor preparation and vaccine adjuvant. Cowpea mosaic virus (CPMV) has shown remarkable efficacy as an immunostimulatory cancer therapy in ovarian cancer mouse models, so we use CPMV as an adjuvant in a prophylactic vaccine against a murine ovarian cancer model. Compared to its codelivery with tumor antigens prepared in three other ways, we show that CPMV co-delivered with irradiated ovarian cancer cells constitutes an effective prophylactic vaccine against a syngeneic model of ovarian cancer in C57BL/6J mice. Following two vaccinations, 72% of vaccinated mice reject tumor challenges, and all those mice survived subsequent rechallenges, demonstrating immunologic memory formation. This study supports remission-stage vaccines using irradiated patient tumor tissue as a promising option for treating ovarian cancer, and validates CPMV as an antitumor vaccine adjuvant for that purpose.

Neuroblastoma (NBL) cells highly express disialoganglioside GD2, which is restricted and weakly expressed in selected healthy cells, making it a desirable target of immunotherapy. Over the past two decades, application of dinutuximab, an anti-GD2 monoclonal antibody (mAb), has been one of the few new therapies to substantially improve outcomes to current levels. Given the persistent challenge of relapse and therapeutic resistance, there is an urgent need for new effective and tolerable treatment options for high-risk NBL. Recent breakthroughs in immune checkpoint inhibitor (ICI) therapeutics have not translated into high-risk NBL, like many other major pediatric solid tumors. Given the suppressed tumor microenvironment (TME), single ICIs like anti-CTLA4 and anti-PD1 have not demonstrated significant antitumor response rates. Meanwhile, emerging studies are reporting novel advancements in GD2-based therapies, targeted therapies, nanomedicines, and other immunotherapies such as adoptive transfer of natural killer (NK) cells and chimeric antigen receptors (CARs), and these hold interesting promise for the future of high-risk NBL patient care. Herein, we summarize the current state of the art in NBL therapeutic options and highlight the unique challenges posed by NBL that have limited the successful adoption of immune-modifying therapies. Through this review, we aim to direct the field’s attention to opportunities that may benefit from a combination immunotherapy strategy.

The calculation of electromagnetic field distributions within structured media is central to the optimization and validation of photonic devices. We introduce WaveY-Net, a hybrid data- and physics-augmented convolutional neural network that can predict electromagnetic field distributions with ultra fast speeds and high accuracy for entire classes of dielectric photonic structures. This accuracy is achieved by training the neural network to learn only the magnetic near-field distributions of a system and to use a discrete formalism of Maxwell's equations in two ways: as physical constraints in the loss function and as a means to calculate the electric fields from the magnetic fields. As a model system, we construct a surrogate simulator for periodic silicon nanostructure arrays and show that the high speed simulator can be directly and effectively used in the local and global freeform optimization of metagratings. We anticipate that physics-augmented networks will serve as a viable Maxwell simulator replacement for many classes of photonic systems, transforming the way they are designed.

Surgery for local control is a cornerstone of anticancer therapy with demonstrated survival benefit. However, surgery-induced modulation of antitumor immunity may also contribute to cancer progression and relapse. Despite evidence for a pro-tumor surgery effect in adult cancers, there remain significant knowledge gaps as to the influence surgery has on recurrence or metastatic outgrowth in pediatric cancers. High-risk neuroblastoma (HR-NB) is the most common extracranial solid tumor of childhood. While almost all children with HR-NB undergo surgery, nearly 50% suffer metastatic relapse and succumb to their disease. To ascertain if surgery may contribute to recurrence in HR-NB, we developed a mouse model to comprehensively interrogate the systemic effect of surgery on distant tumor growth and immune modulation. This model demonstrated that MYCN-amplified HR-NB tumor growth was accelerated by surgery compared to tumor-bearing mice without surgical stress. Accelerated tumor growth was absent in HR-NB cells engrafted to immune deficient mice, suggesting that an intact immune system may be needed for surgery to exert its pro-growth effect on distant tumor cells. Consistent with that genetic ablation model, flow cytometry measured a decrease in splenic macrophages (Mϕ) and dendritic cells (DC) and an increase in myeloid-derived suppressor cells (MDSC) after surgery. Perioperative treatment with polyinosinic-polycytidylic acid [poly(I:C)] ameliorated surgery-induced tumor growth. These findings provide direct insight into the systemic surgical effect on pediatric solid tumor growth and identify innate immune adjuvants as a potential perioperative treatment to mitigate this effect.

We propose SNAP-DDM, a general DDM electromagnetic solver where an ensemble of specialized neural operators is trained to accelerate the solving of subdomain problems containing arbitrary boundary conditions and geometric parameters.