Next-Generation in vivo Modeling of Human Cancers

Do We Need to Add the Type of Treatment Planning System, Dose Calculation Grid Size, and CT Density Curve to Predictive Models?

Background: Generalizability and domain dependency are critical challenges in developing predictive models for healthcare, particularly in medical diagnostics and radiation oncology. Predictive models designed to assess tumor recurrence rely on comprehensive and high-quality datasets, encompassing treatment planning parameters, imaging protocols, and patient-specific data. However, domain dependency, arising from variations in dose calculation algorithms, computed tomography (CT) density conversion curves, imaging modalities, and institutional protocols, can significantly undermine model reliability and clinical utility. Methods: This study evaluated dose calculation differences in the head and neck cancer treatment plans of 19 patients using two treatment planning systems, Pinnacle 9.10 and RayStation 11, with similar dose calculation algorithms. Variations in the dose grid size and CT density conversion curves were assessed for their impact on domain dependency. Results: Results showed that dose grid size differences had a more significant influence within RayStation than Pinnacle, while CT curve variations introduced potential domain discrepancies. The findings underscore the critical role of precise and standardized treatment planning in enhancing the reliability of predictive modeling for tumor recurrence assessment. Conclusions: Incorporating treatment planning parameters, such as dose distribution and target volumes, as explicit features in model training can mitigate the impact of domain dependency and enhance prediction accuracy. Solutions such as multi-institutional data harmonization and domain adaptation techniques are essential to improve model generalizability and robustness. These strategies support the better integration of predictive modeling into clinical workflows, ultimately optimizing patient outcomes and personalized treatment strategies.

Insights into gemcitabine resistance in pancreatic cancer: association with metabolic reprogramming and TP53 pathogenicity in patient derived xenografts

BACKGROUND

With poor prognosis and high mortality, pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies. Standard of care therapies for PDAC have included gemcitabine for the past three decades, although resistance often develops within weeks of chemotherapy initiation through an array of possible mechanisms.

METHODS

We reanalyzed publicly available RNA-seq gene expression profiles of 28 PDAC patient-derived xenograft (PDX) models before and after a 21-day gemcitabine treatment using our validated analysis pipeline to identify molecular markers of intrinsic and acquired resistance.

RESULTS

Using normalized RNA-seq quantification measurements, we first identified oxidative phosphorylation and interferon alpha pathways as the two most enriched cancer hallmark gene sets in the baseline gene expression profile associated with intrinsic gemcitabine resistance and sensitivity, respectively. Furthermore, we discovered strong correlations between drug-induced expression changes in glycolysis and oxidative phosphorylation genes and response to gemcitabine, which suggests that these pathways may be associated with acquired gemcitabine resistance mechanisms. Thus, we developed prediction models using baseline gene expression profiles in those pathways and validated them in another dataset of 12 PDAC models from Novartis. We also developed prediction models based on drug-induced expression changes in genes from the Molecular Signatures Database (MSigDB)'s curated 50 cancer hallmark gene sets. Finally, pathogenic TP53 mutations correlated with treatment resistance.

CONCLUSION

Our results demonstrate that concurrent upregulation of both glycolysis and oxidative phosphorylation pathways occurs in vivo in PDAC PDXs following gemcitabine treatment and that pathogenic TP53 status had association with gemcitabine resistance in these models. Our findings may elucidate the molecular basis for gemcitabine resistance and provide insights for effective drug combination in PDAC chemotherapy.

Recent Advances in Graphene Oxide-Based on Organoid Culture as Disease Model and Cell Behavior - A Systematic Literature Review

Due to their ability to replicate the in vivo microenvironment through cell interaction and induce cells to stimulate cell function, three-dimensional cell culture models can overcome the limitations of two-dimensional models. Organoids are 3D models that demonstrate the ability to replicate the natural structure of an organ. In most organoid tissue cultures, matrigel made of a mouse tumor extracellular matrix protein mixture is an essential ingredient. However, its tumor-derived origin, batch-to-batch variation, high cost, and safety concerns have limited the usefulness of organoid drug development and regenerative medicine. Its clinical application has also been hindered by the fact that organoid generation is dependent on the use of poorly defined matrices. Therefore, matrix optimization is a crucial step in developing organoid culture that introduces alternatives as different materials. Recently, a variety of substitute materials has reportedly replaced matrigel. The purpose of this study is to review the significance of the latest advances in materials for cell culture applications and how they enhance build network systems by generating proper cell behavior. Excellence in cell behavior is evaluated from their cell characteristics, cell proliferation, cell differentiation, and even gene expression. As a result, graphene oxide as a matrix optimization demonstrated high potency in developing organoid models. Graphene oxide can promote good cell behavior and is well known for having good biocompatibility. Hence, advances in matrix optimization of graphene oxide provide opportunities for the future development of advanced organoid models.

Efficacy of combined tumor irradiation and KCa3.1-targeting with TRAM-34 in a syngeneic glioma mouse model

The intermediate-conductance calcium-activated potassium channel KCa3.1 has been proposed to be a new potential target for glioblastoma treatment. This study analyzed the effect of combined irradiation and KCa3.1-targeting with TRAM-34 in the syngeneic, immune-competent orthotopic SMA-560/VM/Dk glioma mouse model. Whereas neither irradiation nor TRAM-34 treatment alone meaningfully prolonged the survival of the animals, the combination significantly prolonged the survival of the mice. We found an irradiation-induced hyperinvasion of glioma cells into the brain, which was inhibited by concomitant TRAM-34 treatment. Interestingly, TRAM-34 did neither radiosensitize nor impair SMA-560's intrinsic migratory capacities in vitro. Exploratory findings hint at increased TGF-β1 signaling after irradiation. On top, we found a marginal upregulation of MMP9 mRNA, which was inhibited by TRAM-34. Last, infiltration of CD3+, CD8+ or FoxP3+ T cells was not impacted by either irradiation or KCa3.1 targeting and we found no evidence of adverse events of the combined treatment. We conclude that concomitant irradiation and TRAM-34 treatment is efficacious in this preclinical glioma model.

Tumor heterogeneity: preclinical models, emerging technologies, and future applications

Heterogeneity describes the differences among cancer cells within and between tumors. It refers to cancer cells describing variations in morphology, transcriptional profiles, metabolism, and metastatic potential. More recently, the field has included the characterization of the tumor immune microenvironment and the depiction of the dynamics underlying the cellular interactions promoting the tumor ecosystem evolution. Heterogeneity has been found in most tumors representing one of the most challenging behaviors in cancer ecosystems. As one of the critical factors impairing the long-term efficacy of solid tumor therapy, heterogeneity leads to tumor resistance, more aggressive metastasizing, and recurrence. We review the role of the main models and the emerging single-cell and spatial genomic technologies in our understanding of tumor heterogeneity, its contribution to lethal cancer outcomes, and the physiological challenges to consider in designing cancer therapies. We highlight how tumor cells dynamically evolve because of the interactions within the tumor immune microenvironment and how to leverage this to unleash immune recognition through immunotherapy. A multidisciplinary approach grounded in novel bioinformatic and computational tools will allow reaching the integrated, multilayered knowledge of tumor heterogeneity required to implement personalized, more efficient therapies urgently required for cancer patients.

Expert opinion on translational research for advanced glioblastoma treatment

Malignant gliomas are known to be one of the most difficult diseases to diagnose and treat because of the infiltrative growth pattern, rapid progression, and poor prognosis. Many antitumor drugs are not ideal for the treatment of gliomas due to the blood-brain barrier. Temozolomide (TMZ) is a DNA alkylating agent that can cross the blood-brain barrier. As the only first-line chemotherapeutic drug for malignant gliomas at present, TMZ is widely utilized to provide a survival benefit; however, some patients are inherently insensitive to TMZ. In addition, patients could develop acquired resistance during TMZ treatment, which limits antitumor efficacy. To clarify the mechanism underlying TMZ resistance, numerous studies have provided multilevel solutions, such as improving the effective concentration of TMZ in tumors and developing novel small molecule drugs. This review discusses the in-depth mechanisms underlying TMZ drug resistance, thus aiming to provide possibilities for the establishment of personalized therapeutic strategies against malignant gliomas and the accelerated development and transformation of new targeted drugs.

Application status and future prospects of the PDX model in lung cancer

Lung cancer is one of the most prevalent, fatal, and highly heterogeneous diseases that, seriously threaten human health. Lung cancer is primarily caused by the aberrant expression of multiple genes in the cells. Lung cancer treatment options include surgery, radiation, chemotherapy, targeted therapy, and immunotherapy. In recent decades, significant progress has been made in developing therapeutic agents for lung cancer as well as a biomarker for its early diagnosis. Nonetheless, the alternative applications of traditional pre-clinical models (cell line models) for diagnosis and prognosis prediction are constrained by several factors, including the lack of microenvironment components necessary to affect cancer biology and drug response, and the differences between laboratory and clinical results. The leading reason is that substantial shifts accrued to cell biological behaviors, such as cell proliferative, metastatic, invasive, and gene expression capabilities of different cancer cells after decades of growing indefinitely in vitro. Moreover, the introduction of individualized treatment has prompted the development of appropriate experimental models. In recent years, preclinical research on lung cancer has primarily relied on the patient-derived tumor xenograft (PDX) model. The PDX provides stable models with recapitulate characteristics of the parental tumor such as the histopathology and genetic blueprint. Additionally, PDXs offer valuable models for efficacy screening of new cancer drugs, thus, advancing the understanding of tumor biology. Concurrently, with the heightened interest in the PDX models, potential shortcomings have gradually emerged. This review summarizes the significant advantages of PDXs over the previous models, their benefits, potential future uses and interrogating open issues.

Assessment of antitumor activity of BP-C1, a platinum-based anticancer agent with a lignin-derived polymeric ligand, in autochthonous induced and spontaneous carcinogenesis rodent models

BACKGROUND

A standard approach to study the anticancer activity of novel drugs is their testing in animals with inoculated tumors, which has some limitations. An alternative is the use of spontaneous or carcinogen-induced tumor models as they have better translation potential. The carcinogen-induced and transgenic tumor models were used to assess the antitumor activity of BP-C1, a platinum-containing drug with lignin-derived polymeric ligand.

METHODS

We used female Swiss-H-derived mice and Wistar female rats to induce autochthonous tumors via exposure to benzo[a]pyrene and 1,2-dimethylhydrazine, respectively. Additionally, transgenic HER-2/neu FVB/N female mice, prone to the development of spontaneous mammary carcinomas, were used.

RESULTS

Antitumor activity of BP-C1 was observed in soft tissue sarcomas, induced by benzo[a]pyrene. The animals treated with BP-C1 exhibited more stabilizations and therapy responses compared to placebo controls. The efficacy of BP-C1 was somewhat reduced compared to cyclophosphamide; however, their combination resulted in an enhanced antitumor effect. For the 1,2-dimethylhydrazine-induced rat colon cancer model, BP-C1 reduced tumor multiplicity by 21-41 %. For mammary adenocarcinomas in HER-2/neu FVB/N mice, short-termed complete responses were observed in the BP-C1 groups with a frequency of 12-13 %, while complete responses were absent in the placebo group.

CONCLUSION

The results acquired indicated a wide spectrum of antitumor activity of BP-C1.

Patient-derived cancer models: Valuable platforms for anticancer drug testing

Molecularly targeted treatments and immunotherapy are cornerstones in oncology, with demonstrated efficacy across different tumor types. Nevertheless, the overwhelming majority metastatic disease is incurable due to the onset of drug resistance. Preclinical models including genetically engineered mouse models, patient-derived xenografts and two- and three-dimensional cell cultures have emerged as a useful resource to study mechanisms of cancer progression and predict efficacy of anticancer drugs. However, variables including tumor heterogeneity and the complexities of the microenvironment can impair the faithfulness of these platforms. Here, we will discuss advantages and limitations of these preclinical models, their applicability for drug testing and in co-clinical trials and potential strategies to increase their reliability in predicting responsiveness to anticancer medications.

Linear Tumor Regression of Rectal Cancer in Daily MRI during Preoperative Chemoradiotherapy: An Insight of Tumor Regression Velocity for Personalized Cancer Therapy

Objective: Neoadjuvant chemoradiotherapy (CCRT) is current standards of care for locally advanced rectal cancer. The precise and thorough investigation of a tumor during the full course of CCRT by means of daily MRI can provide an idea on real-time treatment sensitivity in addition to tumor biology. Tumor volumetry from daily MRI during CCRT may allow patient-driven treatment decisions. Material and Methods: Patients diagnosed with cT3-4 and/or cN+ rectal adenocarcinoma undergoing preoperative CCRT with capecitabine on the pelvis up to 50 Gy in 25 daily fractions from November 2018 to June 2019 were consecutively included. Rectal tumor volume was uniformly measured by a single physician (YKK) in daily 0.35T MRI obtained with MR-guided linear accelerator. Primary endpoint was to assess the pattern of tumor volume regression throughout the full course of CCRT using daily registration MRI. Secondary endpoint was to assess the effect of tumor regression velocity on disease-free survival (DFS). Tumor regression velocity (cc) per fraction of each patient was calculated using the simple regression analysis of tumor volumes from fraction 1 to fraction 25. Results: Twenty patients were included. Daily tumor volumetry demonstrated linear tumor regression during CCRT. The tumor regression velocity of all 20 patients was 2.40 cc per fraction (R2 = 0.93; p < 0.001). The median tumor regression velocity was 1.52 cc per fraction. Patients with tumor regression velocity ≥ 1.52 cc per fraction were grouped as rapid regressors (N = 9), and those with tumor regression velocity < 1.52 cc per fraction were grouped as slow regressors (N = 11). Rapid regressors had greater tumor regression velocity (4.58 cc per fraction) compared to that of slow regressors (0.78 cc per fraction) with statistical significance (p < 0.001). The mean DFS of rapid regressors was 36.8 months, numerically longer than the 31.9 months of slow regressors (p = 0.400) without statistical significance. Rapid regressors had numerically superior DFS rate compared to slow regressors without statistical significance. The 2-year DFS was 88.9% for rapid regressors and 72.7% for slow regressors, respectively (p = 0.400). Conclusion: This study is the first observation of linear tumor regression in daily MRI during the preoperative CCRT of locally advanced rectal cancer. Daily tumor regression velocity discriminated DFS, although without statistical significance. This study with a phenomenal approach is hypothesis-generating. Nevertheless, the potential of CCRT from therapeutics to a newer level, the “theranostics”, has been inceptively suggested. Further validation studies for the value of daily tumor volumetry on treatment decisions are warranted.

Recapitulating Tumor Hypoxia in a Cleanroom-Free, Liquid-Pinning-Based Microfluidic Tumor Model

In tumors, the metabolic demand of cancer cells often outpaces oxygen supply, resulting in a gradient of tumor hypoxia accompanied with heterogeneous resistance to cancer therapeutics. Models recapitulating tumor hypoxia are therefore essential for developing more effective cancer therapeutics. Existing in vitro models often fail to capture the spatial heterogeneity of tumor hypoxia or involve high-cost, complex fabrication/handling techniques. Here, we designed a highly tunable microfluidic device that induces hypoxia through natural cell metabolism and oxygen diffusion barriers. We adopted a cleanroom-free, micromilling-replica-molding strategy and a microfluidic liquid-pinning approach to streamline the fabrication and tumor model establishment. We also implemented a thin-film oxygen diffusion barrier design, which was optimized through COMSOL simulation, to support both two-dimensional (2-D) and three-dimensional (3-D) hypoxic models. We demonstrated that liquid-pinning enables an easy, injection-based micropatterning of cancer cells of a wide range of parameters, showing the high tunability of our design. Human breast cancer and prostate cancer cells were seeded and stained after 24 h of 2-D and 3-D culture to validate the natural induction of hypoxia. We further demonstrated the feasibility of the parallel microfluidic channel design to evaluate dual therapeutic conditions in the same device. Overall, our new microfluidic tumor model serves as a user-friendly, cost-effective, and highly scalable platform that provides spatiotemporal analysis of the hypoxic tumor microenvironments suitable for high-content biological studies and therapeutic discoveries.

The Antioxidant Properties of Mushroom Polysaccharides can Potentially Mitigate Oxidative Stress, Beta-Cell Dysfunction and Insulin Resistance

Diabetes mellitus is a prevalent metabolic and endocrine illness affecting people all over the world and is of serious health and financial concern. Antidiabetic medicine delivered through pharmacotherapy, including synthetic antidiabetic drugs, are known to have several negative effects. Fortunately, several natural polysaccharides have antidiabetic properties, and the use of these polysaccharides as adjuncts to conventional therapy is becoming more common, particularly in underdeveloped nations. Oxidative stress has a critical role in the development of diabetes mellitus (DM). The review of current literature presented here focusses, therefore, on the antioxidant properties of mushroom polysaccharides used in the management of diabetic complications, and discusses whether these antioxidant properties contribute to the deactivation of the oxidative stress-related signalling pathways, and to the amelioration of β-cell dysfunction and insulin resistance. In this study, we conducted a systematic review of the relevant information concerning the antioxidant and antidiabetic effects of mushrooms from electronic databases, such as PubMed, Scopus or Google Scholar, for the period 1994 to 2021. In total, 104 different polysaccharides from mushrooms have been found to have antidiabetic effects. Most of the literature on mushroom polysaccharides has demonstrated the beneficial effects of these polysaccharides on reactive oxygen and nitrogen species (RONS) levels. This review discuss the effects of these polysaccharides on hyperglycemia and other alternative antioxidant therapies for diabetic complications through their applications and limits, in order to gain a better understanding of how they can be used to treat DM. Preclinical and phytochemical investigations have found that most of the active polysaccharides extracted from mushrooms have antioxidant activity, reducing oxidative stress and preventing the development of DM. Further research is necessary to confirm whether mushroom polysaccharides can effectively alleviate hyperglycemia, and the mechanisms by which they do this, and to investigate whether these polysaccharides might be utilized as a complementary therapy for the prevention and management of DM in the future.

Organ-on-Chip platforms to study tumor evolution and chemosensitivity

Despite tremendous advancements in oncology research and therapeutics, cancer remains a primary cause of death worldwide. One of the significant factors in this critical challenge is a precise diagnosis and limited knowledge on how the tumor microenvironment (TME) behaves to the treatment and its role in chemo-resistance. Therefore, it is critical to understand the contribution of a heterogeneous TME in cancer drug response in individual patients for effective therapy management. Micro-physiological systems along with tissue engineering have facilitated the development of more physiologically relevant platforms, known as Organ-on-Chips (OoC). OoC platforms recapitulate the critical hallmarks of the TME in vitro and subsequently abet in sensitivity and efficacy testing of anti-cancer drugs before clinical trials. The OoC platforms incorporating conventional in vitro models enable researchers to control the cellular, molecular, chemical, and biophysical parameters of the TME in precise combinations while analyzing how they contribute to tumor progression and therapy response. This review discusses the application of OoC platforms integrated with conventional 2D cell lines, 3D organoids and spheroid models, and the organotypic tissue slices, including patient-derived and xenograft tumor slice cultures in cancer treatment responses. We summarize the relevance and drawbacks of conventional in vitro models in assessing cancer treatment response, challenges and limitations associated with OoC models, and future opportunities enabled by the OoC technologies towards developing personalized cancer diagnostics and therapeutics.

Tumour neoantigen mimicry by microbial species in cancer immunotherapy

Tumour neoantigens arising from cancer-specific mutations generate a molecular fingerprint that has a definite specificity for cancer. Although this fingerprint perfectly discriminates cancer from healthy somatic and germline cells, and is therefore therapeutically exploitable using immune checkpoint blockade, gut and extra-gut microbial species can independently produce epitopes that resemble tumour neoantigens as part of their natural gene expression programmes. Such tumour molecular mimicry is likely not only to influence the quality and strength of the body's anti-cancer immune response, but could also explain why certain patients show favourable long-term responses to immune checkpoint blockade while others do not benefit at all from this treatment. This article outlines the requirement for tumour neoantigens in successful cancer immunotherapy and draws attention to the emerging role of microbiome-mediated tumour neoantigen mimicry in determining checkpoint immunotherapy outcome, with far-reaching implications for the future of cancer immunotherapy.

Experimental Models of Hepatocellular Carcinoma-A Preclinical Perspective

Hepatocellular carcinoma (HCC), the most frequent form of primary liver carcinoma, is a heterogenous and complex tumor type with increased incidence, poor prognosis, and high mortality. The actual therapeutic arsenal is narrow and poorly effective, rendering this disease a global health concern. Although considerable progress has been made in terms of understanding the pathogenesis, molecular mechanisms, genetics, and therapeutical approaches, several facets of human HCC remain undiscovered. A valuable and prompt approach to acquire further knowledge about the unrevealed aspects of HCC and novel therapeutic candidates is represented by the application of experimental models. Experimental models (in vivo and in vitro 2D and 3D models) are considered reliable tools to gather data for clinical usability. This review offers an overview of the currently available preclinical models frequently applied for the study of hepatocellular carcinoma in terms of initiation, development, and progression, as well as for the discovery of efficient treatments, highlighting the advantages and the limitations of each model. Furthermore, we also focus on the role played by computational studies (in silico models and artificial intelligence-based prediction models) as promising novel tools in liver cancer research.

Evaluating the transcriptional fidelity of cancer models

BACKGROUND

Cancer researchers use cell lines, patient-derived xenografts, engineered mice, and tumoroids as models to investigate tumor biology and to identify therapies. The generalizability and power of a model derive from the fidelity with which it represents the tumor type under investigation; however, the extent to which this is true is often unclear. The preponderance of models and the ability to readily generate new ones has created a demand for tools that can measure the extent and ways in which cancer models resemble or diverge from native tumors.

METHODS

We developed a machine learning-based computational tool, CancerCellNet, that measures the similarity of cancer models to 22 naturally occurring tumor types and 36 subtypes, in a platform and species agnostic manner. We applied this tool to 657 cancer cell lines, 415 patient-derived xenografts, 26 distinct genetically engineered mouse models, and 131 tumoroids. We validated CancerCellNet by application to independent data, and we tested several predictions with immunofluorescence.

RESULTS

We have documented the cancer models with the greatest transcriptional fidelity to natural tumors, we have identified cancers underserved by adequate models, and we have found models with annotations that do not match their classification. By comparing models across modalities, we report that, on average, genetically engineered mice and tumoroids have higher transcriptional fidelity than patient-derived xenografts and cell lines in four out of five tumor types. However, several patient-derived xenografts and tumoroids have classification scores that are on par with native tumors, highlighting both their potential as faithful model classes and their heterogeneity.

CONCLUSIONS

CancerCellNet enables the rapid assessment of transcriptional fidelity of tumor models. We have made CancerCellNet available as a freely downloadable R package ( https://github.com/pcahan1/cancerCellNet ) and as a web application ( http://www.cahanlab.org/resources/cancerCellNet_web ) that can be applied to new cancer models that allows for direct comparison to the cancer models evaluated here.

Translational Research in the Era of Precision Medicine: Where We Are and Where We Will Go

The advent of Precision Medicine has globally revolutionized the approach of translational research suggesting a patient-centric vision with therapeutic choices driven by the identification of specific predictive biomarkers of response to avoid ineffective therapies and reduce adverse effects. The spread of "multi-omics" analysis and the use of sensors, together with the ability to acquire clinical, behavioral, and environmental information on a large scale, will allow the digitization of the state of health or disease of each person, and the creation of a global health management system capable of generating real-time knowledge and new opportunities for prevention and therapy in the individual person (high-definition medicine). Real world data-based translational applications represent a promising alternative to the traditional evidence-based medicine (EBM) approaches that are based on the use of randomized clinical trials to test the selected hypothesis. Multi-modality data integration is necessary for example in precision oncology where an Avatar interface allows several simulations in order to define the best therapeutic scheme for each cancer patient.

Vitamin C deficient reduces proliferation in a human periventricular tumor stem cell-derived glioblastoma model

Glioblastoma multiforme (GBM) is the most common and aggressive brain tumor with a median survival of 14.6 months. GBM is highly resistant to radio- and chemotherapy, and remains without a cure; hence, new treatment strategies are constantly sought. Vitamin C, an essential micronutrient and antioxidant, was initially described as an antitumor molecule; however, several studies have shown that it can promote tumor progression and angiogenesis. Thus, considering the high concentrations of vitamin C present in the brain, our aim was to study the effect of vitamin C deficiency on the progression of GBM using a GBM model generated by the stereotactic injection of human GBM cells (U87-MG or HSVT-C3 cells) in the subventricular zone of guinea pig brain. Initial characterization of U87-MG and HSVT-C3 cells showed that HSVT-C3 are highly proliferative, overexpress p53, and are resistant to ferroptosis. To induce intraperiventricular tumors, animals received control or a vitamin C-deficient diet for 3 weeks, after which histopathological and confocal microscopy analyses were performed. We demonstrated that the vitamin C-deficient condition reduced the glomeruloid vasculature and microglia/macrophage infiltration in U87-MG tumors. Furthermore, tumor size, proliferation, glomeruloid vasculature, microglia/macrophage infiltration, and invasion were reduced in C3 tumors carried by vitamin C-deficient guinea pigs. In conclusion, the effect of the vitamin C deficiency was dependent on the tumor cell used for GBM induction. HSVT-C3 cells, a cell line with stem cell features isolated from a human subventricular GBM, showed higher sensitivity to the deficient condition; however, vitamin C deficiency displayed an antitumor effect in both GBM models analyzed.

Considering the Experimental use of Temozolomide in Glioblastoma Research

Temozolomide (TMZ) currently remains the only chemotherapeutic component in the approved treatment scheme for Glioblastoma (GB), the most common primary brain tumour with a dismal patient's survival prognosis of only ~15 months. While frequently described as an alkylating agent that causes DNA damage and thus-ultimately-cell death, a recent debate has been initiated to re-evaluate the therapeutic role of TMZ in GB. Here, we discuss the experimental use of TMZ and highlight how it differs from its clinical role. Four areas could be identified in which the experimental data is particularly limited in its translational potential: 1. transferring clinical dosing and scheduling to an experimental system and vice versa; 2. the different use of (non-inert) solvent in clinic and laboratory; 3. the limitations of established GB cell lines which only poorly mimic GB tumours; and 4. the limitations of animal models lacking an immune response. Discussing these limitations in a broader biomedical context, we offer suggestions as to how to improve transferability of data. Finally, we highlight an underexplored function of TMZ in modulating the immune system, as an example of where the aforementioned limitations impede the progression of our knowledge.

Microglia/Brain Macrophages as Central Drivers of Brain Tumor Pathobiology

One of the most common brain tumors in children and adults is glioma or astrocytoma. There are few effective therapies for these cancers, and patients with malignant glioma fare poorly, even after aggressive surgery, chemotherapy, and radiation. Over the past decade, it is now appreciated that these tumors are composed of numerous distinct neoplastic and non-neoplastic cell populations, which could each influence overall tumor biology and response to therapy. Among these noncancerous cell types, monocytes (microglia and macrophages) predominate. In this Review, we discuss the complex interactions involving microglia and macrophages relevant to glioma formation, progression, and response to therapy.

Patient‑derived orthotopic xenograft glioma models fail to replicate the magnetic resonance imaging features of the original patient tumor

Patient-derived orthotopic glioma xenograft models are important platforms used for pre-clinical research of glioma. In the present study, the diagnostic ability of magnetic resonance imaging (MRI) was examined with regard to the identification of biomarkers obtained from patient-derived glioma xenografts and human tumors. Conventional MRI, diffusion weighted imaging and dynamic contrast-enhanced (DCE)-MRI were used to analyze seven pairs of high grade gliomas with their corresponding xenografts obtained from non-obese diabetic-severe-combined immunodeficiency nude mice. Tumor samples were collected for transcriptome sequencing and histopathological staining, and differentially expressed genes were screened between the original tumors and the corresponding xenografts. Gene Ontology (GO) analysis was performed to predict the functions of these genes. In 6 cases of xenografts with diffuse growth, the degree of enhancement was significantly lower compared with the original tumors. Histopathological staining indicated that the microvascular area and microvascular diameter of the xenografts were significantly lower compared with the original tumors (P=0.009 and P=0.007, respectively). In one case, there was evidence of nodular tumor growth in the mouse. Both MRI and histopathological staining showed a clear demarcation between the transplanted tumors and the normal brain tissues. The relative apparent diffusion coefficient values of the 7 cases examined were significantly higher compared with the corresponding original tumors (P=0.001) and transfer coefficient values derived from DCE-MRI of the tumor area was significantly lower compared with the original tumors (P=0.016). GO analysis indicated that the expression levels of extracellular matrix-associated genes, angiogenesis-associated genes and immune function-associated genes in the original tumors were higher compared with the corresponding xenografts. In conclusion, the data demonstrated that the MRI features of patient-derived xenograft glioma models in mice were different compared with those of the original patient tumors. Differential gene expression may underlie the differences noted in the MRI features between original tumors and corresponding xenografts. The results of the present study highlight the precautions that should be taken when extrapolating data from patient-derived xenograft studies, and their applicability to humans.

Application of Highly Immunocompromised Mice for the Establishment of Patient-Derived Xenograft (PDX) Models

Patient-derived xenograft (PDX) models are created by engraftment of patient tumor tissues into immunocompetent mice. Since a PDX model retains the characteristics of the primary patient tumor including gene expression profiles and drug responses, it has become the most reliable in vivo human cancer model. The engraftment rate increases with the introduction of Non-obese diabetic Severe combined immunodeficiency (NOD/SCID)-based immunocompromised mice, especially the NK-deficient NOD strains NOD/SCID/interleukin-2 receptor gamma chain(IL2Rγ)null (NOG/NSG) and NOD/SCID/Jak3(Janus kinase 3)null (NOJ). Success rates differ with tumor origin: gastrointestinal tumors acquire a higher engraftment rate, while the rate is lower for breast cancers. Subcutaneous transplantation is the most popular method to establish PDX, but some tumors require specific environments, e.g., orthotropic or renal capsule transplantation. Human hormone treatment is necessary to establish hormone-dependent cancers such as prostate and breast cancers. PDX mice with human hematopoietic and immune systems (humanized PDX) are powerful tools for the analysis of tumor-immune system interaction and evaluation of immunotherapy response. A PDX biobank equipped with patients' clinical data, gene-expression patterns, mutational statuses, tumor tissue architects, and drug responsiveness will be an authoritative resource for developing specific tumor biomarkers for chemotherapeutic predictions, creating individualized therapy, and establishing precise cancer medicine.

Challenges and Chances in the Preclinical to Clinical Translation of Anticancer Metallodrugs

Despite being “sentenced to death” for quite some time, anticancer platinum compounds are still the most frequently prescribed cancer therapies in the oncological routine and recent exciting news from late-stage clinical studies on combinations of metallodrugs with immunotherapies suggest that this situation will not change soon. It is perhaps surprising that relatively simple molecules like cisplatin, discovered over 50 years ago, are still widely used clinically, while none of the highly sophisticated metal compounds developed over the last decade, including complexes with targeting ligands and multifunctional (nano)formulations, have managed to obtain clinical approval. In this book chapter, we summarize the current status of ongoing clinical trials for anticancer metal compounds and discuss the reasons for previous failures, as well as new opportunities for the clinical translation of metal complexes.

Ezh2 inhibition in Kras-driven lung cancer amplifies inflammation and associated vulnerabilities

Kras-driven non–small-cell-lung cancers (NSCLCs) are a leading cause of death with limited therapeutic options. Serresi et al. show that inhibiting Ezh2 in orthotopic KrasG12D-driven NSCLC unleashes an inflammatory response rewiring tumor progression and amplifying associated vulnerabilities that could be therapeutically exploited.

Kras-driven non–small-cell lung cancers (NSCLCs) are a leading cause of death with limited therapeutic options. Many NSCLCs exhibit high levels of Ezh2, the enzymatic subunit of polycomb repressive complex 2 (PRC2). We tested Ezh2 inhibitors as single agents or before chemotherapy in mice with orthotopic Kras-driven NSCLC grafts, which homogeneously express Ezh2. These tumors display sensitivity to EZH2 inhibition by GSK126 but also amplify an inflammatory program involving signaling through NF-κB and genes residing in PRC2-regulated chromatin. During this process, tumor cells overcome GSK126 antiproliferative effects. We identified oncogenes that may mediate progression through an in vivo RNAi screen aimed at targets of PRC2/NF-κB. An in vitro compound screening linked GSK126-driven inflammation and therapeutic vulnerability in human cells to regulation of RNA synthesis and proteostasis. Interestingly, GSK126-treated NSCLCs in vivo also showed an enhanced response to a combination of nimesulide and bortezomib. Thus, Ezh2 inhibition may restrict cell proliferation and promote defined adaptive responses. Targeting these responses potentially improves outcomes in Kras-driven NSCLCs.