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

Sonopermeation combined with stroma normalization enables complete cure using nano-immunotherapy in murine breast tumors

Nano-immunotherapy shows great promise in improving patient outcomes, as seen in advanced triple-negative breast cancer, but it does not cure the disease, with median survival under two years. Therefore, understanding resistance mechanisms and developing strategies to enhance its effectiveness in breast cancer is crucial. A key resistance mechanism is the pronounced desmoplasia in the tumor microenvironment, which leads to dysfunction of tumor blood vessels and thus, to hypoperfusion, limited drug delivery and hypoxia. Ultrasound sonopermeation and agents that normalize the tumor stroma have been employed separately to restore vascular abnormalities in tumors with some success. Here, we performed in vivo studies in two murine, orthotopic breast tumor models to explore if combination of ultrasound sonopermeation with a stroma normalization drug can synergistically improve tumor perfusion and enhance the efficacy of nano-immunotherapy. We found that the proposed combinatorial treatment can drastically reduce primary tumor growth and in many cases tumors were no longer measurable. Overall survival studies showed that all mice that received the combination treatment survived and rechallenge experiments revealed that the survivors obtained immunological memory. Employing ultrasound elastography and contrast enhanced ultrasound along with proteomics analysis, flow cytometry and immunofluorescene staining, we found the combinatorial treatment reduced tumor stiffness to normal levels, restoring tumor perfusion and oxygenation. Furthermore, it increased infiltration and activity of immune cells and altered the levels of immunosupportive chemokines. Finally, using machine learning analysis, we identified that tumor stiffness, CD8+ T cells and M2-type macrophages were strong predictors of treatment response.

Using mathematical modelling and AI to improve delivery and efficacy of therapies in cancer

Mathematical modelling has proven to be a valuable tool in predicting the delivery and efficacy of molecular, antibody-based, nano and cellular therapy in solid tumours. Mathematical models based on our understanding of the biological processes at subcellular, cellular and tissue level are known as mechanistic models that, in turn, are divided into continuous and discrete models. Continuous models are further divided into lumped parameter models - for describing the temporal distribution of medicine in tumours and normal organs - and distributed parameter models - for studying the spatiotemporal distribution of therapy in tumours. Discrete models capture interactions at the cellular and subcellular levels. Collectively, these models are useful for optimizing the delivery and efficacy of molecular, nanoscale and cellular therapy in tumours by incorporating the biological characteristics of tumours, the physicochemical properties of drugs, the interactions among drugs, cancer cells and various components of the tumour microenvironment, and for enabling patient-specific predictions when combined with medical imaging. Artificial intelligence-based methods, such as machine learning, have ushered in a new era in oncology. These data-driven approaches complement mechanistic models and have immense potential for improving cancer detection, treatment and drug discovery. Here we review these diverse approaches and suggest ways to combine mechanistic and artificial intelligence-based models to further improve patient treatment outcomes.

Using Cancer-Associated Fibroblasts as a Shear-Wave Elastography Imaging Biomarker to Predict Anti-PD-1 Efficacy of Triple-Negative Breast Cancer

In the clinical setting, the efficacy of single-agent immune checkpoint inhibitors (ICIs) in triple-negative breast cancer (TNBC) remains suboptimal. Therefore, there is a pressing need to develop predictive biomarkers to identify non-responders. Considering that cancer-associated fibroblasts (CAFs) represent an integral component of the tumor microenvironment that affects the stiffness of solid tumors on shear-wave elastography (SWE) imaging, wound healing CAFs (WH CAFs) were identified in highly heterogeneous TNBC. This subtype highly expressed vitronectin (VTN) and constituted the majority of CAFs. Moreover, WH CAFs were negatively correlated with CD8+ T cell infiltration levels and influenced tumor proliferation in the Eo771 mouse model. Furthermore, multi-omics analysis validated its role in immunosuppression. In order to non-invasively classify patients as responders or non-responders to ICI monotherapy, a deep learning model was constructed to classify the level of WH CAFs based on SWE imaging. As anticipated, this model effectively distinguished the level of WH CAFs in tumors. Based on the classification of the level of WH CAFs, while tumors with a high level of WH CAFs were found to exhibit a poor response to anti programmed cell death protein 1 (PD-1) monotherapy, they were responsive to the combination of anti-PD-1 and erdafitinib, a selective fibroblast growth factor receptor (FGFR) inhibitor. Overall, these findings establish a reference for a novel non-invasive method for predicting ICI efficacy to guide the selection of TNBC patients for precision treatment in clinical settings.

Single-cell RNA sequencing reveals ECM remodeling-tumor stiffness-FAK as a key driver of vestibular schwannoma progression

Vestibular schwannoma (VS), characterized by the absence of merlin expression, is the most prevalent benign tumor located at the cerebellopontine angle, lacking approved pharmaceutical interventions except for off-label utilization of bevacizumab. The role of Tumor stiffness-Focal adhesion kinase (FAK) activation in fueling tumor progression is well-established, with merlin deficiency serving as a biomarker for tumor sensitivity to FAK inhibitors. In this context, we investigated whether Tumor stiffness-FAK contributes to VS progression. Single-cell RNA sequencing revealed associations between VS progression and gene sets related to "Response to mechanical stimulus" and "Neurotrophin signaling pathway". Histological studies indicated a potential involvement of neurotrophins in early stages of VS tumorigenesis, while enhanced Extracellular matrix (ECM) remodeling-Tumor stiffness-FAK signaling accompanies later stages of VS progression. In vitro experiments demonstrated that elevated matrix stiffness induces cytoskeletal remodeling, cell proliferation, and metalloproteinase expression in VS cells by activating FAK. Conversely, FAK inhibition diminishes these effects. Collectively, this study suggests that ECM remodeling-Tumor stiffness contributes to VS progression via FAK activation, positioning FAK as a promising therapeutic target in treating VS.

The mechanopathology of the tumor microenvironment: detection techniques, molecular mechanisms and therapeutic opportunities

The mechanical properties of the tumor microenvironment (TME) undergo significant changes during tumor growth, primarily driven by alterations in extracellular (ECM) stiffness and tumor viscoelasticity. These mechanical changes not only promote tumor progression but also hinder therapeutic efficacy by impairing drug delivery and activating mechanotransduction pathways that regulate crucial cellular processes such as migration, proliferation, and resistance to therapy. In this review, we examine the mechanisms through which tumor cells sense and transmit mechanical signals to maintain homeostasis in the biomechanically altered TME. We explore current computational modelling strategies for mechanotransduction pathways, highlighting the need for developing models that incorporate additional components of the mechanosignaling machinery. Furthermore, we review available methods for measuring the mechanical properties of tumors in clinical settings and strategies aiming at restoring the TME and blocking deregulated mechanotransduction pathways. Finally, we propose that proper characterization and a deeper understanding of the mechanical landscape of the TME, both at the tissue and cellular levels, are essential for developing therapeutic strategies that account for the influence of mechanical forces on treatment efficacy.

Impact of the tumor microenvironment on delivery of nanomedicine in tumors treated with ultrasound and microbubbles

The delivery of nanoparticles to tumors has been shown preclinically to be improved by microbubble-mediated ultrasound. However, the mechanisms and biological effects are not fully understood. In this study, we explored the influence of the tumor microenvironment on nanoparticle uptake and microdistribution both with and without ultrasound and microbubble treatment. Three murine tumor models, KPC (pancreatic ductal adenocarcinoma), 4T1 (triple negative mammary carcinoma) and CT26 (colon carcinoma), were characterized with respect to extracellular matrix composition, tumor stiffness and perfusion. KPC and 4T1 tumors presented higher levels of collagen and hyaluronic acid and were stiffer compared to CT26, whereas all three tumors had similar levels of sulfated glycosaminoglycans. Furthermore, the 4T1 tumors appeared poorly vascularized with a lower cell density compared to KPC and CT26. All three tumors presented similar nanoparticle uptake, but extravasated nanoparticles traveled significantly shorter in KPC tumors compared to 4T1 and CT26. The effect of ultrasound and microbubble treatment on the tumor uptake and penetration of polymer nanoparticles into the extracellular matrix were evaluated using a treatment protocol previously shown to increase nanoparticle delivery to tumors. Interestingly, we found a significant increase in nanoparticle uptake in the soft CT26 tumor, but no effect of the ultrasound treatment in the stiff KPC and 4T1 tumors, suggesting that tumor stiffness is an important parameter for treatment with ultrasound and microbubbles. Ultrasound treatment resulted in a modest but not statistically significant improvement in nanoparticle penetration through the extracellular matrix. In tumors demonstrating increased uptake of nanoparticles following ultrasound treatment, the uptake correlated positively with blood volume. These findings emphasize the importance of taking the tumor microenvironment into consideration when optimizing ultrasound parameters for delivery of nanomedicine.

Extracellular matrix stiffness regulates colorectal cancer progression via HSF4

BACKGROUND

Colorectal cancer (CRC) has high incidence and mortality rates, with severe prognoses during invasion and metastasis stages. Despite advancements in diagnostic and therapeutic technologies, the impact of the tumour microenvironment, particularly extracellular matrix (ECM) stiffness, on CRC progression and metastasis is not fully understood.

METHODS

This study included 107 CRC patients. Tumour stiffness was assessed using magnetic resonance elastography (MRE), and collagen ratio was analysed with Masson staining. CRC cell lines were cultured on matrices of varying stiffness, followed by transcriptome sequencing to identify stiffness-related genes. An HSF4 knockout CRC cell model was cultured in different ECM stiffness to evaluate the effects of HSF4 on cell proliferation, migration, and invasion in vitro and in vivo.

RESULTS

CRC tumour stiffness was significantly higher than normal tissue and positively correlated with collagen content and TNM staging. High-stiffness matrices significantly regulated cell functions and signalling pathways. High HSF4 (heat shock transcriptional factor 4) expression was strongly associated with tumour stiffness and poor prognosis. HSF4 expression increased with higher TNM stages, and its knockout significantly inhibited cell proliferation, migration, and invasion, especially on high-stiffness matrices. In vivo experiments confirmed that HSF4 promoted tumour growth and metastasis, independent of collagen protein increase.

CONCLUSIONS

This study reveals that tumour stiffness promotes the proliferation and metastasis of CRC by regulating EMT-related signalling pathways through HSF4. Tumour stiffness and HSF4 could be valuable targets for prognostic assessment and therapeutic intervention in CRC.

Deciphering mechanical cues in the microenvironment: from non-malignant settings to tumor progression

The tumor microenvironment functions as a dynamic and intricate ecosystem, comprising a diverse array of cellular and non-cellular components that precisely orchestrate pivotal tumor behaviors, including invasion, metastasis, and drug resistance. While unraveling the intricate interplay between the tumor microenvironment and tumor behaviors represents a tremendous challenge, recent research illuminates a crucial biological phenomenon known as cellular mechanotransduction. Within the microenvironment, mechanical cues like tensile stress, shear stress, and stiffness play a pivotal role by activating mechanosensitive effectors such as PIEZO proteins, integrins, and Yes-associated protein. This activation initiates cascades of intrinsic signaling pathways, effectively linking the physical properties of tissues to their physiological and pathophysiological processes like morphogenesis, regeneration, and immunity. This mechanistic insight offers a novel perspective on how the mechanical cues within the tumor microenvironment impact tumor behaviors. While the intricacies of the mechanical tumor microenvironment are yet to be fully elucidated, it exhibits distinct physical attributes from non-malignant tissues, including elevated solid stresses, interstitial hypertension, augmented matrix stiffness, and enhanced viscoelasticity. These traits exert notable influences on tumor progression and treatment responses, enriching our comprehension of the multifaceted nature of the microenvironment. Through this innovative review, we aim to provide a new lens to decipher the mechanical attributes within the tumor microenvironment from non-malignant contexts, broadening our knowledge on how these factors promote or inhibit tumor behaviors, and thus offering valuable insights to identify potential targets for anti-tumor strategies.

Ultrasound and Microbubble-Induced Reduction of Functional Vasculature Depends on the Microbubble, Tumor Type and Time After Treatment

OBJECTIVE

Ultrasound in combination with microbubbles can enhance accumulation and improve the distribution of various therapeutic agents in tumor tissue, leading to improved efficacy. Understanding the impact of treatment on the tumor microenvironment, concurrently with how microenvironment attributes affect treatment outcome, will be important for selecting appropriate patient cohorts in future clinical trials. The main aim of this work was to investigate the influence of ultrasound and microbubbles on the functional vasculature of cancer tissue.

METHODS

Four different tumor models in mice (bone, pancreatic, breast and colon cancer) were characterized with respect to vascular parameters using contrast-enhanced ultrasound imaging. The effect of treatment with microbubbles and ultrasound was then investigated using immunohistochemistry and confocal microscopy, quantifying the total amount of vasculature and fraction of functional vessels. Two different microbubbles were used, the clinical contrast agent SonoVue and the large bubbles generated by Acoustic Cluster Therapy (ACT), tailored for therapeutic purposes.

RESULTS

The colon cancer model displayed slower flow but a higher vascular volume than the other models. The pancreatic model showed the fastest flow but also the lowest vascular volume. Ultrasound and SonoVue transiently reduced the amount of functional vasculature in breast and colon tumors immediately after treatment. No reduction was observed for ACT, likely due to shorter ultrasound pulses and lower pressures applied.

CONCLUSION

Variation between tumor models due to tissue characteristics emphasizes the importance of evaluating treatment suitability in the specific tissue of interest, as altered perfusion could have a large impact on drug delivery and therapeutic outcome.

Developing a Prognostic Model for Intrahepatic Cholangiocarcinoma Patients With Elevated Preoperative Carbohydrate Antigen 19-9 Levels: Volume-Adjusted CA19-9 (VACA) as a Novel Biomarker

PURPOSE

The predictive sensitivity of carbohydrate antigen 19-9 (CA19-9) in assessing the prognosis of intrahepatic cholangiocarcinoma (ICC) remains inadequate. Integrating CA19-9 with tumor volume offers a potentially viable strategy for improving prognostic accuracy. This study aimed to develop a prognostic model utilizing volume-adjusted CA19-9 (VACA) for ICC patients.

PATIENTS AND METHODS

A retrospective analysis was conducted on data from 436 ICC patients. These patients from two centers were divided into the training (n = 291, Center 1) and validation (n = 145, Center 2) cohorts. Using the training cohort, univariate and multivariable Cox regression analyses were employed to identify clinicopathological characteristics significantly associated with overall survival (OS) and recurrence-free survival (RFS), which enabled the construction of prognostic nomograms both with and without VACA. The nomograms' discriminatory and calibration abilities were assessed using receiver operating characteristic (ROC) curves, decision curve analysis (DCA) curves, and calibration curves, applying both training and validation cohorts.

RESULTS

VACA emerged as an independent variable that significantly correlated with prognosis. The nomogram incorporating VACA demonstrated superior accuracy in predicting OS and RFS rates compared to the model without VACA. In the validation cohort, the nomogram with VACA yielded area under the ROC curve (AUC) values of 0.695 (95% CI = 0.597∼0.793) and 0.666 (95% CI = 0.559∼0.773) (1- year), 0.662 (95% CI = 0.518∼0.806) and 0.651 (95% CI = 0.446∼0.857) (3- years), and 0.701 (95% CI = 0.486∼0.916) and 0.703 (95% CI = 0.428∼0.978) (5- years) for OS and RFS, respectively, along with improved calibration and DCA curves.

CONCLUSIONS

VACA, formed by integrating tumor volume with CA19-9, exhibits promising prognostic capabilities. The nomogram incorporating data from two centers and utilizing VACA demonstrates robust prognostic performance and holds clinical utility.

CONDENSED ABSTRACT

Combining CA19-9 with tumor volume presents a potentially viable strategy for improving prognostic accuracy. The nomogram incorporating VACA demonstrates robust prognostic performance and holds clinical utility.

Mechanical forces inducing oxaliplatin resistance in pancreatic cancer can be targeted by autophagy inhibition

Pancreatic cancer remains one of the most lethal malignancies, with limited treatment options and poor prognosis. A common characteristic among pancreatic cancer patients is the biomechanically altered tumor microenvironment (TME), which among others is responsible for the elevated mechanical stresses in the tumor interior. Although significant research has elucidated the effect of mechanical stress on cancer cell proliferation and migration, it has not yet been investigated how it could affect cancer cell drug sensitivity. Here, we demonstrated that mechanical stress triggers autophagy activation, correlated with increased resistance to oxaliplatin treatment in pancreatic cancer cells. Our results demonstrate that inhibition of autophagy using hydroxychloroquine (HCQ) enhanced the oxaliplatin-induced apoptotic cell death in pancreatic cancer cells exposed to mechanical stress. The combined treatment of HCQ with losartan, a known modulator of mechanical abnormalities in tumors, synergistically enhanced the therapeutic efficacy of oxaliplatin in murine pancreatic tumor models. Furthermore, our study revealed that the use of HCQ enhanced the efficacy of losartan to alleviate mechanical stress levels and restore blood vessel integrity beyond its role in autophagy modulation. These findings underscore the potential of co-targeting mechanical stresses and autophagy as a promising therapeutic strategy to overcome drug resistance and increase chemotherapy efficacy.

Tumor Microenvironment Reprogramming Improves Nanomedicine-Based Chemo-Immunotherapy in Sarcomas

Sarcomas are a heterogeneous group of rare cancers that originate in soft tissues or bones. Their complexity and tendency for metastases make 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 leads 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. In this study, we tested the mechanotherapeutic ketotifen combined with either pegylated liposomal doxorubicin (PLD) or pegylated liposomal coencapsulated alendronate-doxorubicin (PLAD) plus anti-programmed cell death protein 1 antibody in mouse models of fibrosarcoma and osteosarcoma. We found that ketotifen successfully reprogrammed the TME by reducing tumor stiffness and increasing perfusion, proven by changes measured by shear-wave elastography and contrast-enhanced ultrasound, respectively, and enhanced the therapeutic efficacy of our nanomedicine-based chemo-immunotherapy protocols. Furthermore, we observed a trend toward improved antitumor responses when nano-chemotherapy is given alongside anti-programmed cell death protein 1 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 cells, and decreased the number of regulatory T cells. In addition, the combination also altered the polarization of tumor-associated macrophages, favoring the M1 immune-supportive phenotype over the M2 immunosuppressive phenotype. Collectively, our findings provide evidence that ketotifen-induced TME reprogramming can improve the efficacy of nanomedicine-based chemo-immunotherapy in sarcomas.

A convolutional attention model for predicting response to chemo-immunotherapy from ultrasound elastography in mouse tumor models

BACKGROUND

In the era of personalized cancer treatment, understanding the intrinsic heterogeneity of tumors is crucial. Despite some patients responding favorably to a particular treatment, others may not benefit, leading to the varied efficacy observed in standard therapies. This study focuses on the prediction of tumor response to chemo-immunotherapy, exploring the potential of tumor mechanics and medical imaging as predictive biomarkers. We have extensively studied "desmoplastic" tumors, characterized by a dense and very stiff stroma, which presents a substantial challenge for treatment. The increased stiffness of such tumors can be restored through pharmacological intervention with mechanotherapeutics.

METHODS

We developed a deep learning methodology based on shear wave elastography (SWE) images, which involved a convolutional neural network (CNN) model enhanced with attention modules. The model was developed and evaluated as a predictive biomarker in the setting of detecting responsive, stable, and non-responsive tumors to chemotherapy, immunotherapy, or the combination, following mechanotherapeutics administration. A dataset of 1365 SWE images was obtained from 630 tumors from our previous experiments and used to train and successfully evaluate our methodology. SWE in combination with deep learning models, has demonstrated promising results in disease diagnosis and tumor classification but their potential for predicting tumor response prior to therapy is not yet fully realized.

RESULTS

We present strong evidence that integrating SWE-derived biomarkers with automatic tumor segmentation algorithms enables accurate tumor detection and prediction of therapeutic outcomes.

CONCLUSIONS

This approach can enhance personalized cancer treatment by providing non-invasive, reliable predictions of therapeutic outcomes.

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.

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.

Biophysical and biochemical aspects of immune cell-tumor microenvironment interactions

The tumor microenvironment (TME), composed of and influenced by a heterogeneous set of cancer cells and an extracellular matrix, plays a crucial role in cancer progression. The biophysical aspects of the TME (namely, its architecture and mechanics) regulate interactions and spatial distributions of cancer cells and immune cells. In this review, we discuss the factors of the TME-notably, the extracellular matrix, as well as tumor and stromal cells-that contribute to a pro-tumor, immunosuppressive response. We then discuss the ways in which cells of the innate and adaptive immune systems respond to tumors from both biochemical and biophysical perspectives, with increased focus on CD8+ and CD4+ T cells. Building upon this information, we turn to immune-based antitumor interventions-specifically, recent biophysical breakthroughs aimed at improving CAR-T cell therapy.

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.

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.

Multiscale elasticity mapping of biological samples in 3D at optical resolution

The mechanical properties of biological tissues have emerged as an integral determinant of tissue function in health and disease. Nonetheless, characterizing the elasticity of biological samples in 3D and at high resolution remains challenging. Here, we present a µElastography platform: a scalable elastography system that maps the elastic properties of tissues from cellular to organ scales. The platform leverages the use of a biocompatible, thermo-responsive hydrogel to deliver compressive stress to a biological sample and track its resulting deformation. By surrounding the specimen with a reference hydrogel of known Young's modulus, we are able to map the absolute values of elastic properties in biological samples. We validate the experimental and computational components of the platform using a hydrogel phantom and verify the system's ability to detect internal mechanical heterogeneities. We then apply the platform to map the elasticity of multicellular spheroids and the murine lymph node. With these applications, we demonstrate the platform's ability to map tissue elasticity at internal planes of interest, as well as capture mechanical heterogeneities neglected by most macroscale characterization techniques. The µElastography platform, designed to be implementable in any biology lab with access to 3D microscopy (e.g., confocal, multiphoton, or optical coherence microscopy), will provide the capability to characterize the mechanical properties of biological samples to labs across the large community of biological sciences by eliminating the need of specialized instruments such as atomic force microscopy. STATEMENT OF SIGNIFICANCE: Understanding the elasticity of biological tissues is of great importance, but characterizing these properties typically requires highly specialized equipment. Utilizing stimulus-responsive hydrogels, we present a scalable, hydrogel-based elastography method that uses readily available reagents and imaging modalities to generate resolved maps of internal elasticity within biomaterials and biological samples at optical resolution. This new approach is capable of detecting internal stiffness heterogeneities within the 3D bulk of samples and is highly scalable across both imaging modalities and biological length scales. Thus, it will have significant impact on the measurement capabilities of labs studying engineered biomaterials, mechanobiology, disease progression, and tissue engineering and development.

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.