Mechanistic model for booster doses effectiveness in healthy, cancer, and immunosuppressed patients infected with SARS-CoV-2.

SARS-CoV-2 vaccines are effective at limiting disease severity, but effectiveness is lower among patients with cancer or immunosuppression. Effectiveness wanes with time and varies by vaccine type. Moreover, previously prescribed vaccines were based on the ancestral SARS-CoV-2 spike-protein that emerging variants may evade. Here, we describe a mechanistic mathematical model for vaccination-induced immunity. We validate it with available clinical data and use it to simulate the effectiveness of vaccines against viral variants with lower antigenicity, increased virulence, or enhanced cell binding for various vaccine platforms. The analysis includes the omicron variant as well as hypothetical future variants with even greater immune evasion of vaccine-induced antibodies and addresses the potential benefits of the new bivalent vaccines. We further account for concurrent cancer or underlying immunosuppression. The model confirms enhanced immunogenicity following booster vaccination in immunosuppressed patients but predicts ongoing booster requirements for these individuals to maintain protection. We further studied the impact of variants on immunosuppressed individuals as a function of the interval between multiple booster doses. Our model suggests possible strategies for future vaccinations and suggests tailored strategies for high-risk groups.

In silico dynamics of COVID-19 phenotypes for optimizing clinical management.

Understanding the underlying mechanisms of COVID-19 progression and the impact of various pharmaceutical interventions is crucial for the clinical management of the disease. We developed a comprehensive mathematical framework based on the known mechanisms of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, incorporating the renin-angiotensin system and ACE2, which the virus exploits for cellular entry, key elements of the innate and adaptive immune responses, the role of inflammatory cytokines, and the coagulation cascade for thrombus formation. The model predicts the evolution of viral load, immune cells, cytokines, thrombosis, and oxygen saturation based on patient baseline condition and the presence of comorbidities. Model predictions were validated with clinical data from healthy people and COVID-19 patients, and the results were used to gain insight into identified risk factors of disease progression including older age; comorbidities such as obesity, diabetes, and hypertension; and dysregulated immune response. We then simulated treatment with various drug classes to identify optimal therapeutic protocols. We found that the outcome of any treatment depends on the sustained response rate of activated CD8+ T cells and sufficient control of the innate immune response. Furthermore, the best treatment-or combination of treatments-depends on the preinfection health status of the patient. Our mathematical framework provides important insight into SARS-CoV-2 pathogenesis and could be used as the basis for personalized, optimal management of COVID-19.

Combining microenvironment normalization strategies to improve cancer immunotherapy.

Advances in immunotherapy have revolutionized the treatment of multiple cancers. Unfortunately, tumors usually have impaired blood perfusion, which limits the delivery of therapeutics and cytotoxic immune cells to tumors and also results in hypoxia-a hallmark of the abnormal tumor microenvironment (TME)-that causes immunosuppression. We proposed that normalization of TME using antiangiogenic drugs and/or mechanotherapeutics can overcome these challenges. Recently, immunotherapy with checkpoint blockers was shown to effectively induce vascular normalization in some types of cancer. Although these therapeutic approaches have been used in combination in preclinical and clinical studies, their combined effects on TME are not fully understood. To identify strategies for improved immunotherapy, we have developed a mathematical framework that incorporates complex interactions among various types of cancer cells, immune cells, stroma, angiogenic molecules, and the vasculature. Model predictions were compared with the data from five previously reported experimental studies. We found that low doses of antiangiogenic treatment improve immunotherapy when the two treatments are administered sequentially, but that high doses are less efficacious because of excessive vessel pruning and hypoxia. Stroma normalization can further increase the efficacy of immunotherapy, and the benefit is additive when combined with vascular normalization. We conclude that vessel functionality dictates the efficacy of immunotherapy, and thus increased tumor perfusion should be investigated as a predictive biomarker of response to immunotherapy.

Experimental and computational analyses reveal dynamics of tumor vessel cooption and optimal treatment strategies.

Cooption of the host vasculature is a strategy that some cancers use to sustain tumor progression without-or before-angiogenesis or in response to antiangiogenic therapy. Facilitated by certain growth factors, cooption can mediate tumor infiltration and confer resistance to antiangiogenic drugs. Unfortunately, this mode of tumor progression is difficult to target because the underlying mechanisms are not fully understood. Here, we analyzed the dynamics of vessel cooption during tumor progression and in response to antiangiogenic treatment in gliomas and brain metastases. We followed tumor evolution during escape from antiangiogenic treatment as cancer cells coopted, and apparently mechanically compressed, host vessels. To gain deeper understanding, we developed a mathematical model, which incorporated compression of coopted vessels, resulting in hypoxia and formation of new vessels by angiogenesis. Even if antiangiogenic therapy can block such secondary angiogenesis, the tumor can sustain itself by coopting existing vessels. Hence, tumor progression can only be stopped by combination therapies that judiciously block both angiogenesis and cooption. Furthermore, the model suggests that sequential blockade is likely to be more beneficial than simultaneous blockade.

Remodeling of extracellular matrix due to solid stress accumulation during tumor growth.

Solid stresses emerge as the expanding tumor displaces and deforms the surrounding normal tissue, and also as a result of intratumoral component interplay. Among other things, solid stresses are known to induce extensive extracellular matrix synthesis and reorganization. In this study, we developed a mathematical model of tumor growth that distinguishes the contribution to stress generation by collagenous and non-collagenous tumor structural components, and also investigates collagen fiber remodeling exclusively due to solid stress. To this end, we initially conducted in vivo experiments using an orthotopic mouse model for breast cancer to monitor primary tumor growth and derive the mechanical properties of the tumor. Subsequently, we fitted the mathematical model to experimental data to determine values of the model parameters. According to the model, intratumoral solid stress is compressive, whereas extratumoral stress in the tumor vicinity is compressive in the radial direction and tensile in the periphery. Furthermore, collagen fibers engaged in stress generation only in the peritumoral region, and not in the interior where they were slackened due to the compressive stress state. Peritumoral fibers were driven away from the radial direction, tended to realign tangent to the tumor-host interface, and were also significantly stretched by tensile circumferential stresses. By means of this remodeling, the model predicts that the tumor is enveloped by a progressively thickening capsule of collagen fibers. This prediction is consistent with long-standing observations of tumor encapsulation and histologic sections that we performed, and it further corroborates the expansive growth hypothesis for the capsule formation.

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.

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.

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.

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.

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.

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.

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.

Pancreatic Cancer Presents Distinct Nanomechanical Properties During Progression.

Cancer progression is closely related to changes in the structure and mechanical properties of the tumor microenvironment (TME). In many solid tumors, including pancreatic cancer, the interplay among the different components of the TME leads to a desmoplastic reaction mainly due to collagen overproduction. Desmoplasia is responsible for the stiffening of the tumor, poses a major barrier to effective drug delivery and has been associated with poor prognosis. The understanding of the involved mechanisms in desmoplasia and the identification of nanomechanical and collagen-based properties that characterize the state of a particular tumor can lead to the development of novel diagnostic and prognostic biomarkers. In this study, in vitro experiments were conducted using two human pancreatic cell lines. Morphological and cytoskeleton characteristics, cells' stiffness and invasive properties were assessed using optical and atomic force microscopy techniques and cell spheroid invasion assay. Subsequently, the two cell lines were used to develop orthotopic pancreatic tumor models. Tissue biopsies were collected at different times of tumor growth for the study of the nanomechanical and collagen-based optical properties of the tissue using Atomic Force Microscopy (AFM) and picrosirius red polarization microscopy, respectively. The results from the in vitro experiments demonstrated that the more invasive cells are softer and present a more elongated shape with more oriented F-actin stress fibers. Furthermore, ex vivo studies of orthotopic tumor biopsies on MIAPaCa-2 and BxPC-3 murine tumor models highlighted that pancreatic cancer presents distinct nanomechanical and collagen-based optical properties relevant to cancer progression. The stiffness spectrums (in terms of Young's modulus values) showed that the higher elasticity distributions were increasing during cancer progression mainly due desmoplasia (collagen overproduction), while a lower elasticity peak was evident - due to cancer cells softening - on both tumor models. Optical microscopy studies highlighted that collagen content increases while collagen fibers tend to form align patterns. Consequently, during cancer progression nanomechanical and collagen-based optical properties alter in relation to changes in collagen content. Therefore, they have the potential to be used as novel biomarkers for assessing and monitoring tumor progression and treatment outcomes.

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.

Translational nanomedicine potentiates immunotherapy in sarcoma by normalizing the microenvironment.

Nanocarrier-based chemo-immunotherapy has succeeded in clinical trials and understanding its effect on the tumor microenvironment could facilitate development of strategies to increase efficacy of these regimens further. NC-6300 (epirubicin micelle) demonstrates anti-tumor activity in sarcoma patients, but whether it is combinable with immune checkpoint inhibition is unclear. Here, we tested NC-6300 combined with anti-PD-L1 antibody in mouse models of osteosarcoma and fibrosarcoma. We found that sarcoma responds to NC-6300 in a dose-dependent manner, while anti-PD-L1 efficacy is potentiated even at a dose of NC-6300 less than 10% of the maximum tolerated dose. Furthermore, NC-6300 is more effective than the maximum tolerated dose of doxorubicin in increasing the tumor growth delay induced by anti-PD-L1 antibody. We investigated the mechanism of action of this combination. NC-6300 induces immunogenic cell death and its effect on the efficacy of anti-PD-L1 antibody is dependent on T cells. Also, NC-6300 normalized the tumor microenvironment (i.e., ameliorated pathophysiology towards normal phenotype) as evidenced through increased blood vessel maturity and reduced fibrosis. As a result, the combination with anti-PD-L1 antibody increased the intratumor density and proliferation of T cells. In conclusion, NC-6300 potentiates immune checkpoint inhibition in sarcoma, and normalization of the tumor microenvironment should be investigated when developing nanocarrier-based chemo-immunotherapy regimens.

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.

Normalizing tumor microenvironment with nanomedicine and metronomic therapy to improve immunotherapy.

Nanomedicine offered hope for improving the treatment of cancer but the survival benefits of the clinically approved nanomedicines are modest in many cases when compared to conventional chemotherapy. Metronomic therapy, defined as the frequent, low dose administration of chemotherapeutics - is being tested in clinical trials as an alternative to the conventional maximum tolerated dose (MTD) chemotherapy schedule. Although metronomic chemotherapy has not been clinically approved yet, it has shown better survival than MTD in many preclinical studies. When beneficial, metronomic therapy seems to be associated with normalization of the tumor microenvironment including improvements in tumor perfusion, tissue oxygenation and drug delivery as well as activation of the immune system. Recent preclinical studies suggest that nanomedicines can cause similar changes in the tumor microenvironment. Here, by employing a mathematical framework, we show that both approaches can serve as normalization strategies to enhance treatment. Furthermore, employing murine breast and fibrosarcoma tumor models as well as ultrasound shear wave elastography and contrast-enhanced ultrasound, we provide evidence that the approved nanomedicine Doxil can induce normalization in a dose-dependent manner by improving tumor perfusion as a result of tissue softening. Finally, we show that pretreatment with a normalizing dose of Doxil can improve the efficacy of immune checkpoint inhibition.

Nanomechanical properties of solid tumors as treatment monitoring biomarkers.

Many tumors, such as types of sarcoma and breast cancer, stiffen as they grow in a host healthy tissue, while individual cancer cells are becoming softer. Tumor stiffening poses major pathophysiological barriers to the effective delivery of drugs and compromises treatment efficacy. It has been established that normalization of the mechanical properties of a tumor by targeting components of the tumor microenvironment (TME) enhances the delivery of anti-cancer agents and consequently the therapeutic outcome. Consequently, there is an urgent need for the development of biomarkers, which characterize the mechanical state of a particular tumor for the development of personalized treatments or for monitoring therapeutic strategies that target the TME. In this work, Atomic Force Microscopy (AFM) was used to assess human and murine nanomechanical properties from tumor biopsies. In the case of murine tumor models, the nanomechanical properties during tumor progression were measured and a TME normalization drug (tranilast) along with chemotherapy doxorubicin were employed in order to investigate whether AFM has the ability to capture changes in the nanomechanical properties of a tumor during treatment. The nanomechanical data were further correlated with ex vivo characterization of structural components of the TME. The results highlighted that nanomechanical properties alter during cancer progression and AFM measurements are sensitive enough to capture even small alterations during different types of treatments, namely normalization and chemotherapy. The identification of unique AFM-based nanomechanical properties can lead to the development of biomarkers for treatment prediction and monitoring. STATEMENT OF SIGNIFICANCE: Cancer progression is associated with vast remodeling of the tumor microenvironment resulting in changes in the mechanical properties of the tissue. Indeed, many tumors stiffen as they grow and this stiffening compromises treatment efficacy. As a result, a number of treatments target tumor microenvironment in order to normalize its mechanical properties. Consequently, there is an urgent need for the development of innovative tools that can assess the mechanical properties of a particular tumor and monitor tumor progression and treatment outcomes. This work highlights the use of atomic force microscopy (AFM) for assessing the elasticity spectrum of solid tumors at different stages and during treatment. This knowledge is essential for the development of AFM-based nanomechanical biomarkers for treatment prediction and monitoring.

Strategies to minimize heterogeneity and optimize clinical trials in Acute Respiratory Distress Syndrome (ARDS): Insights from mathematical modelling.

BACKGROUND: Mathematical modelling may aid in understanding the complex interactions between injury and immune response in critical illness. METHODS: We utilize a system biology model of COVID-19 to analyze the effect of altering baseline patient characteristics on the outcome of immunomodulatory therapies. We create example parameter sets meant to mimic diverse patient types. For each patient type, we define the optimal treatment, identify biologic programs responsible for clinical responses, and predict biomarkers of those programs. FINDINGS: Model states representing older and hyperinflamed patients respond better to immunomodulation than those representing obese and diabetic patients. The disparate clinical responses are driven by distinct biologic programs. Optimal treatment initiation time is determined by neutrophil recruitment, systemic cytokine expression, systemic microthrombosis and the renin-angiotensin system (RAS) in older patients, and by RAS, systemic microthrombosis and trans IL6 signalling for hyperinflamed patients. For older and hyperinflamed patients, IL6 modulating therapy is predicted to be optimal when initiated very early (<4th day of infection) and broad immunosuppression therapy (corticosteroids) is predicted to be optimally initiated later in the disease (7th - 9th day of infection). We show that markers of biologic programs identified by the model correspond to clinically identified markers of disease severity. INTERPRETATION: We demonstrate that modelling of COVID-19 pathobiology can suggest biomarkers that predict optimal response to a given immunomodulatory treatment. Mathematical modelling thus constitutes a novel adjunct to predictive enrichment and may aid in the reduction of heterogeneity in critical care trials. FUNDING: C.V. received a Marie Sklodowska Curie Actions Individual Fellowship (MSCA-IF-GF-2020-101028945). R.K.J.'s research is supported by R01-CA208205, and U01-CA 224348, R35-CA197743 and grants from the National Foundation for Cancer Research, Jane's Trust Foundation, Advanced Medical Research Foundation and Harvard Ludwig Cancer Center. No funder had a role in production or approval of this manuscript.