Modulating cancer mechanopathology to restore vascular function and enhance immunotherapy.

Solid tumor pathology, characterized by abnormalities in the tumor microenvironment (TME), challenges therapeutic effectiveness. Mechanical factors, including increased tumor stiffness and accumulation of intratumoral forces, can determine the success of cancer treatments, defining the tumor's "mechanopathology" profile. These abnormalities cause extensive vascular compression, leading to hypoperfusion and hypoxia. Hypoperfusion hinders drug delivery, while hypoxia creates an unfavorable TME, promoting tumor progression through immunosuppression, heightened metastatic potential, drug resistance, and chaotic angiogenesis. Strategies targeting TME mechanopathology, such as vascular and stroma normalization, hold promise in enhancing cancer therapies with some already advancing to the clinic. Normalization can be achieved using anti-angiogenic agents, mechanotherapeutics, immune checkpoint inhibitors, engineered bacterial therapeutics, metronomic nanomedicine, and ultrasound sonopermeation. Here, we review the methods developed to rectify tumor mechanopathology, which have even led to cures in preclinical models, and discuss their bench-to-bedside translation, including the derivation of biomarkers from tumor mechanopathology for personalized therapy.

Tumor microenvironment reprogramming improves nanomedicine-based chemo-immunotherapy in sarcomas.

BACKGROUND/INTRODUCTION: Sarcomas are a heterogenous group of rare cancers that originate in soft tissues or bones. Their complexity and tendency for metastases makes treatment challenging, highlighting the need for new therapeutic approaches to improve patient survival. The difficulties in treating these cancers primarily stem from abnormalities within the tumor microenvironment (TME), which lead to reduced blood flow and oxygen levels in tumors. Consequently, this hampers the effective delivery of drugs to tumors and diminishes treatment efficacy despite higher, toxic doses of chemotherapy. Here, we tested the mechanotherapeutic ketotifen combined with either pegylated-liposomal doxorubicin (PLD) or pegylated-liposomal co-encapsulated alendronate-doxorubicin (PLAD) plus anti-PD-1 antibody in mouse models of fibrosarcoma and osteosarcoma. RESULTS: We found that ketotifen successfully reprogrammed the TME by reducing tumor stiffness and increasing perfusion, proven by changes measured by shear-wave-elastography (SWE) and contrast-enhanced-ultrasound (CEUS) respectively, and enhanced the therapeutic efficacy of our nanomedicine-based chemo-immunotherapy protocols. An additional observation was a trend to improved antitumor response when nano-chemotherapy is given alongside anti-PD1 and when the immunomodulator alendronate was present in the treatment. We next investigated the mechanisms of action of this combination. Ketotifen combined with nanomedicine-based chemo-immunotherapy, increased T-cell infiltration, specifically cytotoxic CD8+ T cells and CD4+ T helper-cell and decreased the number of regulatory-T-cells. In addition, the combination also altered the polarization of tumor associated macrophages, favouring the M1 immune-supportive phenotype over the M2 immuno-suppressive phenotype. CONCLUSION: Collectively, our findings provide evidence that ketotifen-induced TME reprograming can improve the efficacy of nanomedicine-based chemoimmunotherapy in sarcomas.

Dynamic heterogeneity in COVID-19: Insights from a mathematical model.

Critical illness, such as severe COVID-19, is heterogenous in presentation and treatment response. However, it remains possible that clinical course may be influenced by dynamic and/or random events such that similar patients subject to similar injuries may yet follow different trajectories. We deployed a mechanistic mathematical model of COVID-19 to determine the range of possible clinical courses after SARS-CoV-2 infection, which may follow from specific changes in viral properties, immune properties, treatment modality and random external factors such as initial viral load. We find that treatment efficacy and baseline patient or viral features are not the sole determinant of outcome. We found patients with enhanced innate or adaptive immune responses can experience poor viral control, resolution of infection or non-infectious inflammatory injury depending on treatment efficacy and initial viral load. Hypoxemia may result from poor viral control or ongoing inflammation despite effective viral control. Adaptive immune responses may be inhibited by very early effective therapy, resulting in viral load rebound after cessation of therapy. Our model suggests individual disease course may be influenced by the interaction between external and patient-intrinsic factors. These data have implications for the reproducibility of clinical trial cohorts and timing of optimal treatment.

Stabilizing Tumor-Resident Mast Cells Restores T-Cell Infiltration and Sensitizes Sarcomas to PD-L1 Inhibition.

PURPOSE: To explore the cellular cross-talk of tumor-resident mast cells (MC) in controlling the activity of cancer-associated fibroblasts (CAF) to overcome tumor microenvironment (TME) abnormalities, enhancing the efficacy of immune-checkpoint inhibitors in sarcoma. EXPERIMENTAL DESIGN: We used a coculture system followed by further validation in mouse models of fibrosarcoma and osteosarcoma with or without administration of the MC stabilizer and antihistamine ketotifen. To evaluate the contribution of ketotifen in sensitizing tumors to therapy, we performed combination studies with doxorubicin chemotherapy and anti-PD-L1 (B7-H1, clone 10F.9G2) treatment. We investigated the ability of ketotifen to modulate the TME in human sarcomas in the context of a repurposed phase II clinical trial. RESULTS: Inhibition of MC activation with ketotifen successfully suppressed CAF proliferation and stiffness of the extracellular matrix accompanied by an increase in vessel perfusion in fibrosarcoma and osteosarcoma as indicated by ultrasound shear wave elastography imaging. The improved tissue oxygenation increased the efficacy of chemoimmunotherapy, supported by enhanced T-cell infiltration and acquisition of tumor antigen-specific memory. Importantly, the effect of ketotifen in reducing tumor stiffness was further validated in sarcoma patients, highlighting its translational potential. CONCLUSIONS: Our study suggests the targeting of MCs with clinically administered drugs, such as antihistamines, as a promising approach to overcome resistance to immunotherapy in sarcomas.

In silico clinical studies for optimal COVID-19 vaccination schedules in patients with cancer.

This study introduces a tailored COVID-19 model for patients with cancer, incorporating viral variants and immune-response dynamics. The model aims to optimize vaccination strategies, contributing to personalized healthcare for vulnerable groups.

Machine learning analysis reveals tumor stiffness and hypoperfusion as biomarkers predictive of cancer treatment efficacy.

In the pursuit of advancing cancer therapy, this study explores the predictive power of machine learning in analyzing tumor characteristics, specifically focusing on the effects of tumor stiffness and perfusion (i.e., blood flow) on treatment efficacy. Recent advancements in oncology have highlighted the significance of these physiological properties of the tumor microenvironment in determining treatment outcomes. We delve into the relationship between these tumor attributes and the effectiveness of cancer therapies in preclinical tumor models. Utilizing robust statistical methods and machine learning algorithms, our research analyzes data from 1365 cases of various cancer types, assessing how tumor stiffness and perfusion influence the efficacy of treatment protocols. We also investigate the synergistic potential of combining drugs that modulate tumor stiffness and perfusion with standard cytotoxic treatments. By incorporating these predictors into treatment planning, our study aims to enhance the precision of cancer therapy, tailoring treatment to individual tumor profiles. Our findings demonstrate a significant correlation between stiffness/perfusion and treatment efficacy, highlighting a new way for personalized cancer treatment strategies.

A synergistic approach for modulating the tumor microenvironment to enhance nano-immunotherapy in sarcomas.

The lack of properly perfused blood vessels within tumors can significantly hinder the distribution of drugs, leading to reduced treatment effectiveness and having a negative impact on the quality of life of patients with cancer. This problem is particularly pronounced in desmoplastic cancers, where interactions between cancer cells, stromal cells, and the fibrotic matrix lead to tumor stiffness and the compression of most blood vessels within the tumor. To address this issue, two mechanotherapy approaches-mechanotherapeutics and ultrasound sonopermeation-have been employed separately to treat vascular abnormalities in tumors and have reached clinical trials. Here, we performed in vivo studies in sarcomas, to explore the conditions under which these two mechanotherapy strategies could be optimally combined to enhance perfusion and the efficacy of nano-immunotherapy. Our findings demonstrate that combination of the anti-histamine drug ketotifen, as a mechanotherapeutic, and sonopermeation effectively alleviates mechanical forces by decreasing 50 % collagen and hyaluronan levels and thus, reshaping the tumor microenvironment. Furthermore, the combined therapy normalizes the tumor vasculature by increasing two-fold the pericytes coverage. This combination not only improves six times tumor perfusion but also enhances drug delivery. As a result, blood vessel functionality is enhanced, leading to increased infiltration by 40 % of immune cells (CD4+ and CD8+ T-cells) and improving the antitumor efficacy of Doxil nanomedicine and anti-PD-1 immunotherapy. In conclusion, our research underscores the unique and synergistic potential of combining mechanotherapeutics and sonopermeation. Both approaches are undergoing clinical trials to enhance cancer therapy and have the potential to significantly improve nano-immunotherapy in sarcomas.

Toward innovative approaches for exploring the mechanically regulated tumor-immune microenvironment.

Within the complex tumor microenvironment, cells experience mechanical cues-such as extracellular matrix stiffening and elevation of solid stress, interstitial fluid pressure, and fluid shear stress-that significantly impact cancer cell behavior and immune responses. Recognizing the significance of these mechanical cues not only sheds light on cancer progression but also holds promise for identifying potential biomarkers that would predict therapeutic outcomes. However, standardizing methods for studying how mechanical cues affect tumor progression is challenging. This challenge stems from the limitations of traditional in vitro cell culture systems, which fail to encompass the critical contextual cues present in vivo. To address this, 3D tumor spheroids have been established as a preferred model, more closely mimicking cancer progression, but they usually lack reproduction of the mechanical microenvironment encountered in actual solid tumors. Here, we review the role of mechanical forces in modulating tumor- and immune-cell responses and discuss how grasping the importance of these mechanical cues could revolutionize in vitro tumor tissue engineering. The creation of more physiologically relevant environments that better replicate in vivo conditions will eventually increase the efficacy of currently available treatments, including immunotherapies.

Oct4 is a gatekeeper of epithelial identity by regulating cytoskeletal organization in skin keratinocytes.

Oct4 is a pioneer transcription factor regulating pluripotency. However, it is not well known whether Oct4 has an impact on epidermal cells. We generated OCT4 knockout clonal cell lines using immortalized human skin keratinocytes to identify a functional role for the protein. Here, we report that Oct4-deficient cells transitioned into a mesenchymal-like phenotype with enlarged size and shape, exhibited accelerated migratory behavior, decreased adhesion, and appeared arrested at the G2/M cell cycle checkpoint. Oct4 absence had a profound impact on cortical actin organization, with loss of microfilaments from the cell membrane, increased puncta deposition in the cytoplasm, and stress fiber formation. E-cadherin, ß-catenin, and ZO1 were almost absent from cell-cell contacts, while fibronectin deposition was markedly increased in the extracellular matrix (ECM). Mapping of the transcriptional and chromatin profiles of Oct4-deficient cells revealed that Oct4 controls the levels of cytoskeletal, ECM, and differentiation-related genes, whereas epithelial identity is preserved through transcriptional and non-transcriptional mechanisms.

Investigating the synergistic effects of immunotherapy and normalization treatment in modulating tumor microenvironment and enhancing treatment efficacy.

We developed a comprehensive mathematical model of cancer immunotherapy that takes into account: i) Immune checkpoint blockers (ICBs) and the interactions between cancer cells and the immune system, ii) characteristics of the tumor microenvironment, such as the tumor hydraulic conductivity, interstitial fluid pressure, and vascular permeability, iii) spatial and temporal variations of the modeled components within the tumor and the surrounding host tissue, iv) the transport of modeled components through the vasculature and between the tumor-host tissue with convection and diffusion, and v) modeling of the tumor draining lymph nodes were the antigen presentation and the development of cytotoxic immune cells take place. Our model successfully reproduced experimental data from various murine tumor types and predicted immune system profiling, which is challenging to achieve experimentally. It showed that combination of ICB therapy and normalization treatments, that aim to improve tumor perfusion, decreases interstitial fluid pressure and increases the concentration of both innate and adaptive immune cells at the tumor center rather than the periphery. Furthermore, using the model, we investigated the impact of modeled components on treatment outcomes. The analysis found that the number of functional vessels inside the tumor region and the ICB dose administered have the largest impact on treatment outcomes.

Pirfenidone-Loaded Polymeric Micelles as an Effective Mechanotherapeutic to Potentiate Immunotherapy in Mouse Tumor Models.

Ongoing research is actively exploring the use of immune checkpoint inhibitors to treat solid tumors by inhibiting the PD-1/PD-L1 axis and reactivating the function of cytotoxic T effector cells. Many types of solid tumors, however, are characterized by a dense and stiff stroma and are difficult to treat. Mechanotherapeutics have formed a recent class of drugs that aim to restore biomechanical abnormalities of the tumor microenvironment, related to increased stiffness and hypo-perfusion. Here, we have developed a polymeric formulation containing pirfenidone, which has been successful in restoring the tumor microenvironment in breast tumors and sarcomas. We found that the micellar formulation can induce similar mechanotherapeutic effects to mouse models of 4T1 and E0771 triple negative breast tumors and MCA205 fibrosarcoma tumors but with a dose 100-fold lower than that of the free pirfenidone. Importantly, a combination of pirfenidone-loaded micelles with immune checkpoint inhibition significantly delayed primary tumor growth, leading to a significant improvement in overall survival and in a complete cure for the E0771 tumor model. Furthermore, the combination treatment increased CD4+ and CD8+ T cell infiltration and suppressed myeloid-derived suppressor cells, creating favorable immunostimulatory conditions, which led to immunological memory. Ultrasound shear wave elastography (SWE) was able to monitor changes in tumor stiffness during treatment, suggesting optimal treatment conditions. Micellar encapsulation is a promising strategy for mechanotherapeutics, and imaging methods, such as SWE, can assist their clinical translation.

Dissecting the Impact of the Gut Microbiome on Cancer Immunotherapy.

The gut microbiome has emerged as a key regulator of response to cancer immunotherapy. However, there is a gap in our understanding of the underlying mechanisms by which the microbiome influences immunotherapy. To this end, we developed a mathematical model based on i) gut microbiome data derived from preclinical studies on melanomas after fecal microbiota transplant, ii) mechanistic modeling of antitumor immune response, and iii) robust association analysis of murine and human microbiome profiles with model-predicted immune profiles. Using our model, we could distill the complexity of these murine and human studies on microbiome modulation in terms of just two model parameters: the activation and killing rate constants of immune cells. We further investigated associations between specific bacterial taxonomies and antitumor immunity and immunotherapy efficacy. This model can guide the design of studies to refine and validate mechanistic links between the microbiome and immune system.

Mathematical modeling of intratumoral immunotherapy yields strategies to improve the treatment outcomes.

Intratumoral injection of immunotherapy aims to maximize its activity within the tumor. However, cytokines are cleared via tumor vessels and escape from the tumor periphery into the host-tissue, reducing efficacy and causing toxicity. Thus, understanding the determinants of the tumor and immune response to intratumoral immunotherapy should lead to better treatment outcomes. In this study, we developed a mechanistic mathematical model to determine the efficacy of intratumorally-injected conjugated-cytokines, accounting for properties of the tumor microenvironment and the conjugated-cytokines. The model explicitly incorporates i) the tumor vascular density and permeability and the tumor hydraulic conductivity, ii) conjugated-cytokines size and binding affinity as well as their clearance via the blood vessels and the surrounding tissue, and iii) immune cells-cancer cells interactions. Model simulations show how the properties of the tumor and of the conjugated-cytokines determine treatment outcomes and how selection of proper parameters can optimize therapy. A high tumor tissue hydraulic permeability allows for the uniform distribution of the cytokines into the tumor, whereas uniform tumor perfusion is required for sufficient access and activation of immune cells. The permeability of the tumor vessels affects the blood clearance of the cytokines and optimal values depend on the size of the conjugates. A size >5 nm in radius was found to be optimal, whereas the binding of conjugates should be high enough to prevent clearance from the tumor into the surrounding tissue. In conclusion, development of strategies to improve vessel perfusion and tissue hydraulic conductivity by reprogramming the microenvironment along with optimal design of conjugated-cytokines can enhance intratumoral immunotherapy.

Beyond matrix stiffness: targeting force-induced cancer drug resistance.

During tumor progression, mechanical abnormalities in the tumor microenvironment (TME) trigger signaling pathways in cells that activate cellular programs, resulting in tumor growth and drug resistance. In this review, we describe mechanisms of action for anti-cancer therapies and mechanotransduction programs that regulate cellular processes, including cell proliferation, apoptosis, survival and phenotype switching. We discuss how the therapeutic response is impacted by the three main mechanical TME abnormalities: high extracellular matrix (ECM) composition and stiffness; interstitial fluid pressure (IFP); and elevated mechanical forces. We also review drugs that normalize these abnormalities or block mechanosensors and mechanotransduction pathways. Finally, we discuss current challenges and perspectives for the development of new strategies targeting mechanically induced drug resistance in the clinic.

Gold Nanobipyramids for Near-Infrared Fluorescence-Enhanced Imaging and Treatment of Triple-Negative Breast Cancer.

There is an imminent need for novel strategies for the diagnosis and treatment of aggressive triple-negative breast cancer (TNBC). Cell-targeted multifunctional nanomaterials hold great potential, as they can combine precise early-stage diagnosis with local therapeutic delivery to specific cell types. In this study, we used mesoporous silica (MS)-coated gold nanobipyramids (MS-AuNBPs) for fluorescence imaging in the near-infrared (NIR) biological window, along with targeted TNBC treatment. Our MS-AuNBPs, acting partly as light amplification components, allow considerable metal-enhanced fluorescence for a NIR dye conjugated to their surfaces compared to the free dye. Fluorescence analysis confirms a significant increase in the dye's modified quantum yield, indicating that MS-AuNBPs can considerably increase the brightness of low-quantum-yield NIR dyes. Meanwhile, we tested the chemotherapeutic efficacy of MS-AuNBPs in TNBC following the loading of doxorubicin within the MS pores and functionalization to target folate receptor alpha (FRα)-positive cells. We show that functionalized particles target FRα-positive cells with significant specificity and have a higher potency than free doxorubicin. Finally, we demonstrate that FRα-targeted particles induce stronger antitumor effects and prolong overall survival compared to the clinically applied non-targeted nanotherapy, Doxil. Together with their excellent biocompatibility measured in vitro, this study shows that MS-AuNBPs are promising tools to detect and treat TNBCs.

Relationships between physical and immunological tumor microenvironment in pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is physically palpated as a hard tumor with an unfavorable prognosis. Assessing physical features and their association with pathological features could help to elucidate the mechanism of physical abnormalities in cancer tissues. A total of 93 patients who underwent radical surgery for pancreatic and bile duct cancers at a single center hospital during a 28-month period were recruited for this study that aimed to estimate the stiffness of PDAC tissues compared to the other neoplasms and assess relationships between tumor stiffness and pathological features. Physical alterations and pathological features of PDAC, with or without preoperative therapy, were analyzed. The immunological tumor microenvironment was evaluated using multiplexed fluorescent immunohistochemistry. The stiffness of PDAC correlated with the ratio of Azan-Mallory staining, α-smooth muscle actin, and collagen I-positive areas of the tumors. Densities of CD8+ T cells and CD204+ macrophages were associated with tumor stiffness in cases without preoperative therapy. Pancreatic ductal adenocarcinoma treated with preoperative therapy was softer than that without, and the association between tumor stiffness and immune cell infiltration was not shown after preoperative therapy. We observed the relationship between tumor stiffness and immunological features in human PDAC for the first time. Immune cell densities in the tumor center were smaller in hard tumors than in soft tumors without preoperative therapies. Preoperative therapy could alter physical and immunological aspects, warranting further study. Understanding of the correlations between physical and immunological aspects could lead to the development of new therapies.

Ultrasound stiffness and perfusion markers correlate with tumor volume responses to immunotherapy.

Immunotherapy has revolutionized the treatment of dozens of cancers and became a standard of care for some tumor types. However, the majority of patients do not benefit from current immunotherapeutics and many develop severe toxicities. Therefore, the identification of biomarkers to classify patients as likely responders or non-responders to immunotherapy is a timely task. Here, we test ultrasound imaging markers of tumor stiffness and perfusion. Ultrasound imaging is non-invasive and clinically available and can be used both for stiffness and perfusion evaluation. In this study, we employed syngeneic orthotopic models of two breast cancers, a fibrosarcoma and a melanoma, to demonstrate that ultrasound-derived measures of tumor stiffness and perfusion (i.e., blood volume) correlate with the efficacy of immune checkpoint inhibition (ICI) in terms of changes in primary tumor volume. To modulate tumor stiffness and perfusion and thus, get a range of therapeutic outcomes, we employed the mechanotherapeutic tranilast. Mechanotherapeutics combined with ICI are advancing through clinical trials, but biomarkers of response have not been tested until now. We found the existence of linear correlations between tumor stiffness and perfusion imaging biomarkers as well as strong linear correlations between the stiffness and perfusion markers with ICI efficacy on primary tumor growth rates. Our findings set the basis for ultrasound biomarkers predictive of ICI therapy in combination with mechanotherapeutics. STATEMENT OF SIGNIFICANCE: Hypothesis: Monitoring Tumor Microenvironment (TME) mechanical abnormalities can predict the efficacy of immune checkpoint inhibition and provide biomarkers predictive of response. Tumor stiffening and solid stress elevation are hallmarks of tumor patho-physiology in desmoplastic tumors. They induce hypo-perfusion and hypoxia by compressing tumor vessels, posing major barriers to immunotherapy. Mechanotherapeutics is a new class of drugs that target the TME to reduce stiffness and improve perfusion and oxygenation. In this study, we show that measures of stiffness and perfusion derived from ultrasound shear wave elastography and contrast enhanced ultrasound can provide biomarkers of tumor response.

Evaluation of growth-induced, mechanical stress in solid tumors and spatial association with extracellular matrix content.

Mechanical stresses in solid tumors play an important role in tumor progression and treatment efficacy but their quantification is under-investigated. Here, we developed an experimental and computational approach to calculate growth-induced, residual stresses and applied it to the breast (4T1), pancreatic (PAN02), and fibrosarcoma (MCA205) tumor models. Following resection, tumors are embedded in agarose gels and cuts are made in two perpendicular directions to release residual stress. With the use of image processing, the detailed bulging displacement profile is measured and finite elements models of the bulging geometry are developed for the quantification of the stress levels. The mechanical properties of the tumors are measured in vivo prior to resection with shear wave elastography. We find that the average magnitude of residual stresses ranges from 3.31 to 10.88 kPa, and they are non-uniformly distributed within the tissue due to the heterogeneity of the tumor microenvironment. Interestingly, we demonstrate that a second cut can still release a significant amount of stresses. We further find a strong association of spatial hyaluronan and collagen content with the spatial profile of stress for the MCA205 and PAN02 tumors and a partial association for the 4T1. Interestingly the colocalization of hyaluronan and collagen content had a stronger association with the spatial profile of stress for MCA205, PAN02, and 4T1. Finally, measurements of the elastic modulus with shear wave elastography show a nonlinear correlation with tumor volume for the more fibrotic MCA205 and 4T1 tumors. Overall, our results provide insights for a better understanding of the mechanical behavior of tumors.

An adaptive semi-implicit finite element solver for brain cancer progression modeling.

Glioblastoma is the most aggressive and infiltrative glioma, classified as Grade IV, with the poorest survival rate among patients. Accurate and rigorously tested mechanistic in silico modeling offers great value to understand and quantify the progression of primary brain tumors. This paper presents a continuum-based finite element framework that is built on high performance computing, open-source libraries to simulate glioblastoma progression. We adopt the established proliferation invasion hypoxia necrosis angiogenesis model in our framework to realize scalable simulations of cancer, and has demonstrated to produce accurate and efficient solutions in both two- and three-dimensional brain models. The in silico solver can successfully implement arbitrary order discretization schemes and adaptive remeshing algorithms. A model sensitivity analysis is conducted to test the impact of vascular density, cancer cell invasiveness and aggressiveness, the phenotypic transition potential, including that of necrosis, and the effect of tumor-induced angiogenesis in the evolution of glioblastoma. Additionally, individualized simulations of brain cancer progression are carried out using pertinent magnetic resonance imaging data, where the in silico model is used to investigate the complex dynamics of the disease. We conclude by arguing how the proposed framework can deliver patient-specific simulations of cancer prognosis and how it could bridge clinical imaging with modeling.