Histology-guided mathematical model of tumor oxygenation: sensitivity analysis of physical and computational parameters.

A hybrid off-lattice agent-based model has been developed to reconstruct the tumor tissue oxygenation landscape based on histology images and simulated interactions between vasculature and cells with microenvironment metabolites. Here, we performed a robustness sensitivity analysis of that model's physical and computational parameters. We found that changes in the domain boundary conditions, the initial conditions, and the Michaelis constant are negligible and, thus, do not affect the model outputs. The model is also not sensitive to small perturbations of the vascular influx or the maximum consumption rate of oxygen. However, the model is sensitive to large perturbations of these parameters and changes in the tissue boundary condition, emphasizing an imperative aim to measure these parameters experimentally.

Targeting myeloid-derived suppressor cells with gemcitabine to enhance efficacy of adoptive cell therapy in bladder cancer.

Background: New therapeutics in development for bladder cancer need to address the recalcitrant nature of the disease. Intravesical adoptive cell therapy (ACT) with tumor infiltrating lymphocytes (TIL) can potentially induce durable responses in bladder cancer while maximizing T cells at the tumor site. T cells infused into the bladder directly encounter immunosuppressive populations, such as myeloid derived suppressor cells (MDSCs), that can attenuate T cell responses. Intravesical instillation of gemcitabine can be used as a lymphodepleting agent to precondition the bladder microenvironment for infused T cell products. Methods: Urine samples from bladder cancer patients and healthy donors were analyzed by flow cytometry and cytometric bead array for immune profiling and cytokine quantification. MDSCs were isolated from the urine and cocultured with stimulated T cells to assess effects on proliferation. An orthotopic murine model of bladder cancer was established using the MB49-OVA cell line and immune profiling was performed. MDSCs from tumor-bearing mice were cocultured with OT-I splenocytes to assess T cell proliferation. Mice received intravesical instillation of gemcitabine and depletion of immune cells was measured via flow cytometry. Bladder tumor growth of mice treated with intravesical gemcitabine, OT-I transgenic T cells, or combination was monitored via ultrasound measurement. Results: In comparison to healthy donors, urine specimen from bladder cancer patients show high levels of MDSCs and cytokines associated with myeloid chemotaxis, T cell chemotaxis, and inflammation. T cells isolated from healthy donors were less proliferative when cocultured with MDSCs from the urine. Orthotopic murine bladder tumors also presented with high levels of MDSCs along with enrichment of cytokines found in the patient urine samples. MDSCs isolated from spleens of tumor-bearing mice exerted suppressive effects on the proliferation of OT-I T cells. Intravesical instillation of gemcitabine reduced overall immune cells, MDSCs, and T cells in orthotopic bladder tumors. Combination treatment with gemcitabine and OT-I T cells resulted in sustained anti-tumor responses in comparison to monotherapy treatments. Conclusion: MDSCs are enriched within the microenvironment of bladder tumors and are suppressive to T cells. Gemcitabine can be used to lymphodeplete bladder tumors and precondition the microenvironment for intravesical ACT.

Current State of Cell Therapies for Genitourinary Malignancies.

ABSTRACT: Genitourinary (GU) cancers have greatly benefited from immunotherapy treatments, such as immune checkpoint inhibitors. However, the durable clinical response rate for these agents remains relatively low, calling for more innovative immunotherapy approaches. Adoptive cell therapy has shown a significant advancement in the treatment of cancer in recent years and represents a great potential for the treatment of GU cancers. This review summarizes the current advancements in cellular therapy strategies for the treatment of renal cell carcinoma, bladder cancer, and prostate and penile cancers. Further, current and past clinical trials of adoptive cell therapy in GU tumors are reviewed. Finally, a perspective on the future of cell therapy in GU tumors is discussed.

TIM-3 blockade enhances IL-12-dependent antitumor immunity by promoting CD8+ T cell and XCR1+ dendritic cell spatial co-localization.

BACKGROUND: T cell immunoglobulin and mucin domain containing-3 (TIM-3) blocking antibodies are currently being evaluated in clinical trials for solid and hematological malignancies. Despite its identification on T cells, TIM-3 is predominantly expressed by myeloid cells, including XCR1+ type I conventional dendritic cells (cDC1s). We have recently shown that TIM-3 blockade promotes expression of CXCR3 chemokine ligands by tumor cDCs, but how this drives a CD8+ T cell-dependent response to therapy is unclear. METHODS: T cell infiltration, effector function, and spatial localization in relation to XCR1+ cDC1s were evaluated in a murine orthotopic mammary carcinoma model during response to TIM-3 blockade and paclitaxel chemotherapy. Mixed bone marrow chimeras and diphtheria toxin depletion were used to determine the role of specific genes in cDC1s during therapeutic responses. RESULTS: TIM-3 blockade increased interferon-γ expression by CD8+ T cells without altering immune infiltration. cDC1 expression of CXCL9, but not CXCL10, was required for response to TIM-3 blockade. CXCL9 was also necessary for the increased proximity observed between CD8+ T cells and XCR1+ cDC1s during therapy. Tumor responses were dependent on cDC1 expression of interleukin-12, but not MHCI. CONCLUSIONS: TIM-3 blockade increases exposure of intratumoral CD8+ T cells to cDC1-derived cytokines, with implications for the design of therapeutic strategies using antibodies against TIM-3.

Contact lens-based lysozyme detection in tear using a mobile sensor.

We report a method for sensing analytes in tear-fluid using commercial contact lenses (CLs) as sample collectors for subsequent analysis with a cost-effective and field-portable reader. In this study we quantify lysozyme, the most prevalent protein in tear fluid, non-specifically bound to CLs worn by human participants. Our mobile reader uses time-lapse imaging to capture an increasing fluorescent signal in a standard well-plate, the rate-of-change of which is used to indirectly infer lysozyme concentration through the use of a standard curve. We empirically determined the best-suited CL material for our sampling procedure and assay, and subsequently monitored the lysozyme levels of nine healthy human participants over a two-week period. Of these participants who were regular CL wearers (6 out of 9), we observed an increase in lysozyme levels from 6.89 ± 2.02 µg mL-1 to 10.72 ± 3.22 µg mL-1 (mean ± SD) when inducing an instance of digital eye-strain by asking them to play a game on their mobile-phones during the CL wear-duration. We also observed a lower mean lysozyme concentration (2.43 ± 1.66 µg mL-1) in a patient cohort with dry eye disease (DED) as compared to the average monitoring level of healthy (no DED) human participants (6.89 ± 2.02 µg mL-1). Taken together, this study demonstrates tear-fluid analysis with simple and non-invasive sampling steps along with a rapid, easy-to-use, and cost-effective measurement system, ultimately indicating physiological differences in human participants. We believe this method could be used in future tear-fluid studies, even supporting multiplexed detection of a panel of tear biomarkers toward improved diagnostics and prognostics as well as personalized mobile-health applications.

Computational Sensing Using Low-Cost and Mobile Plasmonic Readers Designed by Machine Learning.

Plasmonic sensors have been used for a wide range of biological and chemical sensing applications. Emerging nanofabrication techniques have enabled these sensors to be cost-effectively mass manufactured onto various types of substrates. To accompany these advances, major improvements in sensor read-out devices must also be achieved to fully realize the broad impact of plasmonic nanosensors. Here, we propose a machine learning framework which can be used to design low-cost and mobile multispectral plasmonic readers that do not use traditionally employed bulky and expensive stabilized light sources or high-resolution spectrometers. By training a feature selection model over a large set of fabricated plasmonic nanosensors, we select the optimal set of illumination light-emitting diodes needed to create a minimum-error refractive index prediction model, which statistically takes into account the varied spectral responses and fabrication-induced variability of a given sensor design. This computational sensing approach was experimentally validated using a modular mobile plasmonic reader. We tested different plasmonic sensors with hexagonal and square periodicity nanohole arrays and revealed that the optimal illumination bands differ from those that are "intuitively" selected based on the spectral features of the sensor, e.g., transmission peaks or valleys. This framework provides a universal tool for the plasmonics community to design low-cost and mobile multispectral readers, helping the translation of nanosensing technologies to various emerging applications such as wearable sensing, personalized medicine, and point-of-care diagnostics. Beyond plasmonics, other types of sensors that operate based on spectral changes can broadly benefit from this approach, including e.g., aptamer-enabled nanoparticle assays and graphene-based sensors, among others.