The prognostic effect of mechanical, ultrastructural, and ECM signatures in glioblastoma core and rim.

Glioblastoma (GBM) is a highly invasive, aggressive brain cancer that carries a median survival of 15 months and is resistant to standard therapeutics. Recent studies have demonstrated that intratumoral heterogeneity plays a critical role in promoting resistance by mediating tumor adaptation through microenvironmental cues. GBM can be separated into two distinct regions-a core and a rim, which are thought to drive specific aspects of tumor evolution. These differences in tumor progression are regulated by the diverse biomolecular and biophysical signals in these regions, but the acellular biophysical characteristics remain poorly described. This study investigates the mechanical and ultrastructural characteristics of the tumor extracellular matrix (ECM) in patient-matched GBM core and rim tissues. Seven patient-matched tumor core and rim samples and one non-neoplastic control were analyzed using atomic force microscopy, scanning electron microscopy, and immunofluorescence imaging to quantify mechanical, ultrastructural, and ECM composition changes. The results reveal significant differences in biophysical parameters between GBM core, rim, and non-neoplastic tissues. The GBM core is stiffer, denser, and is rich in ECM proteins hyaluronic acid and tenascin-C when compared to tumor rim and non-neoplastic tissues. These alterations are intimately related and have prognostic effect with stiff, dense tissue correlating with longer progression-free survival. These findings reveal new insights into the spatial heterogeneity of biophysical parameters in the GBM tumor microenvironment and identify a set of characteristics that may correlate with patient prognosis. In the long term, these characteristics may aid in the development of strategies to combat therapeutic resistance.

Spatially Resolved Microglia/Macrophages in Recurrent Glioblastomas Overexpress Fatty Acid Metabolism and Phagocytic Genes.

BACKGROUND: Glioblastoma (GBM) tumors are rich in tumor-associated microglia/macrophages. Changes associated with treatment in this specific cell population are poorly understood. Therefore, we studied changes in gene expression of tumor-associated microglia/macrophages (Iba1+) cells in de novo versus recurrent GBMs. METHODS: NanoString GeoMx® Digital Spatial Transcriptomic Profiling of microglia/macrophages (Iba1+) and glial cells (Gfap+) cells identified on tumor sections was performed on paired de novo and recurrent samples obtained from three IDH-wildtype GBM patients. The impact of differentially expressed genes on patient survival was evaluated using publicly available data. RESULTS: Unsupervised analyses of the NanoString GeoMx® Digital Spatial Profiling data revealed clustering based on the transcriptomic data from Iba1+ and Gfap+ cells. As expected, conventional differential gene expression and enrichment analyses revealed upregulation of immune-function-related genes in Iba1+ cells compared to Gfap+ cells. A focused differential gene expression analysis revealed upregulation of phagocytosis and fatty acid/lipid metabolism genes in Iba1+ cells in recurrent GBM samples compared to de novo GBM samples. Importantly, of these genes, the lipid metabolism gene PLD3 consistently correlated with survival in multiple different publicly available datasets. CONCLUSION: Tumor-associated microglia/macrophages in recurrent GBM overexpress genes involved in fatty acid/lipid metabolism. Further investigation is needed to fully delineate the role of PLD phospholipases in GBM progression.

Computational Modeling of Probiotic Recovery from 3D-Bioprinted Scaffolds for Localized Vaginal Application.

Lactobacilli, play a beneficial role in the female reproductive tract (FRT), regulating pH via lactic acid metabolism to help maintain a healthy environment. Bacterial vaginosis (BV) is characterized by a dysregulated flora in which anaerobes such as Gardnerella vaginalis (Gardnerella) create a less acidic environment. Current treatment focuses on antibiotic administration, including metronidazole, clindamycin, or tinidazole; however, lack of patient compliance as well as antibiotic resistance may contribute to 50% recurrence within a year. Recently, locally administered probiotics such as Lactobacillus crispatus (L. crispatus) have been evaluated as a prophylactic against recurrence. To mitigate the lack of patient compliance, sustained probiotic delivery has been proposed via 3D-bioprinted delivery vehicles. Successful delivery depends on a variety of vehicle fabrication parameters influencing timing and rate of probiotic recovery; detailed evaluation of these parameters would benefit from computational modeling complementary to experimental evaluation. This study implements a novel simulation platform to evaluate sustained delivery of probiotics from 3D-bioprinted scaffolds, taking into consideration bacterial lactic acid production and associated pH changes. The results show that the timing and rate of probiotic recovery can be realistically simulated based on fabrication parameters that affect scaffold degradation and probiotic survival. Longer term, the proposed approach could help personalize localized probiotic delivery to the FRT to advance women's health.

Evaluation of innate and adaptive immune system interactions in the tumor microenvironment via a 3D continuum model.

Immune cells in the tumor microenvironment (TME) are known to affect tumor growth, vascularization, and extracellular matrix (ECM) deposition. Marked interest in system-scale analysis of immune species interactions within the TME has encouraged progress in modeling tumor-immune interactions in silico. Due to the computational cost of simulating these intricate interactions, models have typically been constrained to representing a limited number of immune species. To expand the capability for system-scale analysis, this study develops a three-dimensional continuum mixture model of tumor-immune interactions to simulate multiple immune species in the TME. Building upon a recent distributed computing implementation that enables efficient solution of such mixture models, major immune species including monocytes, macrophages, natural killer cells, dendritic cells, neutrophils, myeloid-derived suppressor cells (MDSC), cytotoxic, helper, regulatory T-cells, and effector and regulatory B-cells and their interactions are represented in this novel implementation. Immune species extravasate from blood vasculature, undergo chemotaxis toward regions of high chemokine concentration, and influence the TME in proportion to locally defined levels of stimulation. The immune species contribute to the production of angiogenic and tumor growth factors, promotion of myofibroblast deposition of ECM, upregulation of angiogenesis, and elimination of living and dead tumor species. The results show that this modeling approach offers the capability for quantitative insight into the modulation of tumor growth by diverse immune-tumor interactions and immune-driven TME effects. In particular, MDSC-mediated effects on tumor-associated immune species' activation levels, volume fraction, and influence on the TME are explored. Longer term, linking of the model parameters to particular patient tumor information could simulate cancer-specific immune responses and move toward a more comprehensive evaluation of immunotherapeutic strategies.

Multiple breast cancer liver metastases response to macrophage-delivered nanotherapy evaluated via a 3D continuum model.

Breast cancer liver metastases (BCLM) are usually unresectable and difficult to treat with systemic chemotherapy. A major reason for chemotherapy failure is that BCLM are typically small, avascular nodules, with poor transport and fast washout of therapeutics from surrounding capillaries. We have previously shown that nanoalbumin-bound paclitaxel (nab-PTX) encapsulated in porous silicon multistage nanovectors (MSV) is preferentially taken up by tumour-associated macrophages (TAM) in the BCLM microenvironment. The TAM alter therapeutic transport characteristics and retain it in the tumour vicinity, increasing cytotoxicity. Computational modeling has shown that therapeutic regimens could be designed to eliminate single lesions. To evaluate clinically-relevant scenarios, this study develops a modeling framework to evaluate MSV-nab-PTX therapy targeting multiple BCLM. An experimental model of BCLM, splenic injection of breast cancer 4 T1 cells was established in BALB/C mice. Livers were analyzed histologically to determine size and density of BCLM. The data were used to calibrate a 3D continuum mixture model solved via distributed computing to enable simulation of multiple BCLM. Overall tumour burden was analyzed as a function of metastases number and potential therapeutic regimens. The computational model enables realistic 3D representation of metastatic tumour burden in the liver, with the capability to evaluate BCLM growth and therapy response for hundreds of lesions. With the given parameter set, the model projects that repeated MSV-nab-PTX treatment in intervals <7 days would control the tumour burden. We conclude that nanotherapy targeting TAM associated with BCLM may be evaluated and fine-tuned via 3D computational modeling that realistically simulates multiple metastases.

Simulation of 3D centimeter-scale continuum tumor growth at sub-millimeter resolution via distributed computing.

Simulation of cm-scale tumor growth has generally been constrained by the computational cost to numerically solve the associated equations, with models limited to representing mm-scale or smaller tumors. While the work has proven useful to the study of small tumors and micro-metastases, a biologically-relevant simulation of cm-scale masses as would be typically detected and treated in patients has remained an elusive goal. This study presents a distributed computing (parallelized) implementation of a mixture model of tumor growth to simulate 3D cm-scale vascularized tissue at sub-mm resolution. The numerical solving scheme utilizes a two-stage parallelization framework. The solution is written for GPU computation using the CUDA framework, which handles all Multigrid-related computations. Message Passing Interface (MPI) handles distribution of information across multiple processes, freeing the program from RAM and the processing limitations found on single systems. On each system, Nvidia's CUDA library allows for fast processing of model data using GPU-bound computing on fewer systems. The results show that a combined MPI-CUDA implementation enables the continuum modeling of cm-scale tumors at reasonable computational cost. Further work to calibrate model parameters to particular tumor conditions could enable simulation of patient-specific tumors for clinical application.