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Simple Summary: This paper presents a novel approach to produce virtual contrast enhanced (VCE) images for nasopharyngeal cancer (NPC) without the use of contrast agents, which carry certain risks. This model uses pixelwise gradient term to capture the shape and a GAN terms to capture the texture of the real contrast enhanced T1C images With similar accuracy to existing models, our method shows an advantage in reproducing texture closer to the realistic contrast-enhanced images. This results are tested by various measures, including mean absolute error (MAE), mean square error (MSE) and structural similarity index (SSIM) for similarity accuracy; total mean square variation per mean intensity (TMSVPMI), the total absolute vari-ation per mean intensity (TAVPMI), Tenengrad function per mean intensity (TFPMI) and variance function per mean intensity (VFPMI) Various variations of the model, including fine-tuning of the hyperparameters, normalization methods on the images and using single modality, have also been investigated to test the optimal performance. Background: The development of advanced computational models for medical imaging is crucial for improving diagnostic accuracy in healthcare. This paper introduces a novel approach for virtual contrast enhancement (VCE) in magnetic resonance imaging (MRI), particularly focusing on nasopharyngeal cancer (NPC). Methods: The proposed model, Pixelwise Gradient Model with GAN for Virtual Contrast Enhancement (PGMGVCE), makes use of pixelwise gradient methods with Generative Adversarial Networks (GANs) to enhance T1-weighted (T1-w) and T2-weighted (T2-w) MRI images. This approach combines the benefits of both modalities to simulate the effects of gadolinium-based contrast agents, thereby reducing associated risks. Various modifications of PGMGVCE, including changing hyperparameters, using normalization methods (z-score, Sigmoid and Tanh) and training the model with T1-w or T2-w images only, were tested to optimize the model's performance. Results: PGMGVCE demonstrated a similar accuracy to the existing model in terms of mean absolute error (MAE) (8.56 ± 0.45 for Li's model; 8.72 ± 0.48 for PGMGVCE), mean square error (MSE) (12.43 ± 0.67 for Li's model; 12.81 ± 0.73 for PGMGVCE) and structural similarity index (SSIM) (0.71 ± 0.08 for Li's model; 0.73 ± 0.12 for PGMGVCE). However, it showed improvements in texture representation, as indicated by total mean square variation per mean intensity (TMSVPMI) (0.124 ± 0.022 for ground truth; 0.079 ± 0.024 for Li's model; 0.120 ± 0.027 for PGMGVCE), total absolute variation per mean intensity (TAVPMI) (0.159 ± 0.031 for ground truth; 0.100 ± 0.032 for Li's model; 0.153 ± 0.029 for PGMGVCE), Tenengrad function per mean intensity (TFPMI) (1.222 ± 0.241 for ground truth; 0.981 ± 0.213 for Li's model; 1.194 ± 0.223 for PGMGVCE) and variance function per mean intensity (VFPMI) (0.0811 ± 0.005 for ground truth; 0.0667   ± 0.006 for Li's model; 0.0761   ± 0.006 for PGMGVCE). Conclusions: PGMGVCE presents an innovative and safe approach to VCE in MRI, demonstrating the power of deep learning in enhancing medical imaging. This model paves the way for more accurate and risk-free diagnostic tools in medical imaging. [ABSTRACT FROM AUTHOR]

Simple Summary: In this paper, we introduce a new technique for enhancing medical image diagnosis through transfer learning (TL). The approach addresses the issue of limited labelled images by pre-training deep learning models on similar medical images and then refining them with a small set of annotated medical images. Our method demonstrated excellent results in classifying the humerus and wrist, surpassing previous methods, and showing greater robustness in various experiments. Furthermore, we demonstrate the adaptability of the approach with a CT case, which showed improvements in the results. Medical image classification poses significant challenges in real-world scenarios. One major obstacle is the scarcity of labelled training data, which hampers the performance of image-classification algorithms and generalisation. Gathering sufficient labelled data is often difficult and time-consuming in the medical domain, but deep learning (DL) has shown remarkable performance, although it typically requires a large amount of labelled data to achieve optimal results. Transfer learning (TL) has played a pivotal role in reducing the time, cost, and need for a large number of labelled images. This paper presents a novel TL approach that aims to overcome the limitations and disadvantages of TL that are characteristic of an ImageNet dataset, which belongs to a different domain. Our proposed TL approach involves training DL models on numerous medical images that are similar to the target dataset. These models were then fine-tuned using a small set of annotated medical images to leverage the knowledge gained from the pre-training phase. We specifically focused on medical X-ray imaging scenarios that involve the humerus and wrist from the musculoskeletal radiographs (MURA) dataset. Both of these tasks face significant challenges regarding accurate classification. The models trained with the proposed TL were used to extract features and were subsequently fused to train several machine learning (ML) classifiers. We combined these diverse features to represent various relevant characteristics in a comprehensive way. Through extensive evaluation, our proposed TL and feature-fusion approach using ML classifiers achieved remarkable results. For the classification of the humerus, we achieved an accuracy of 87.85%, an F1-score of 87.63%, and a Cohen's Kappa coefficient of 75.69%. For wrist classification, our approach achieved an accuracy of 85.58%, an F1-score of 82.70%, and a Cohen's Kappa coefficient of 70.46%. The results demonstrated that the models trained using our proposed TL approach outperformed those trained with ImageNet TL. We employed visualisation techniques to further validate these findings, including a gradient-based class activation heat map (Grad-CAM) and locally interpretable model-independent explanations (LIME). These visualisation tools provided additional evidence to support the superior accuracy of models trained with our proposed TL approach compared to those trained with ImageNet TL. Furthermore, our proposed TL approach exhibited greater robustness in various experiments compared to ImageNet TL. Importantly, the proposed TL approach and the feature-fusion technique are not limited to specific tasks. They can be applied to various medical image applications, thus extending their utility and potential impact. To demonstrate the concept of reusability, a computed tomography (CT) case was adopted. The results obtained from the proposed method showed improvements. [ABSTRACT FROM AUTHOR]

Simple Summary: In this paper, we investigate the application of deep learning for classifying whole-slide images of cutaneous histopathological specimens into melanoma and non-melanoma. To do so, we used a total of 66 images (33 melanomas and 33 non-melanomas) to train models and evaluated them on 90 whole-slide images (40 melanomas and 50 non-melanomas). The best model achieved ROC–AUC at 0.821 for the whole-slide image level and 0.936 for the tile level. Although the histopathological diagnosis of cutaneous melanocytic lesions is fairly accurate and reliable among experienced surgical pathologists, it is not perfect in every case (especially melanoma). Microscopic examination–clinicopathological correlation is the gold standard for the definitive diagnosis of melanoma. Pathologists may encounter diagnostic controversies when melanoma closely mimics Spitz's nevus or blue nevus, exhibits amelanotic histopathology, or is in situ. It would be beneficial if diagnosing cutaneous melanocytic lesions can be automated by using deep learning, particularly when assisting surgical pathologists with their workloads. In this preliminary study, we investigated the application of deep learning for classifying cutaneous melanoma in whole-slide images (WSIs). We trained models via weakly supervised learning using a dataset of 66 WSIs (33 melanomas and 33 non-melanomas). We evaluated the models on a test set of 90 WSIs (40 melanomas and 50 non-melanomas), achieving ROC–AUC at 0.821 for the WSI level and 0.936 for the tile level by the best model. [ABSTRACT FROM AUTHOR]

Simple Summary: The glaziovianin A derivative gatastatin, presented as a γ-tubulin-specific inhibitor, could represent a viable chemotherapeutic strategy to solve the specificity issues associated with targeting α and β tubulin. Since gatastatin's specificity for γ tubulin has not been confirmed by an in silico analysis or verified experimentally by other groups, we undertook finding a molecular-level elucidation of the binding mode of gatastatin and comparing its predicted binding affinity values for both α-β and γ tubulin. We believe that our paper opens the possibility for the rational design of a long-sought candidate drug with desired specificity and selectivity for γ tubulin. Given its critical role in cell mitosis, the tubulin γ chain represents a viable chemotherapeutic target to solve the specificity issues associated with targeting α and β tubulin. Since γ tubulin is overexpressed in glioblastoma multiforme (GBM) and some breast lesions, the glaziovianin A derivative gatastatin, presented as a γ-tubulin-specific inhibitor, could yield a successful therapeutic strategy. The present work aims to identify the binding sites and modes of gatastatin and its derivatives through molecular-docking simulations. Computational binding free energy predictions were compared to experimental microscale thermophoresis assay results. The computational simulations did not reveal a strong preference toward γ tubulin, suggesting that further derivatization may be needed to increase its specificity. [ABSTRACT FROM AUTHOR]

Simple Summary: Anti-cancer drug response prediction models aim to reduce the time necessary for developing a treatment for patients affected by this complex disease. Their goal is to decrease the number of required biological experiments by computationally weeding out unpromising compounds. In this work, we explore the potential gains of incorporating large-scale applications of classical virtual screening techniques like molecular docking into cutting-edge deep learning models. We demonstrate improvement in performance as well as limitations of our approach. Cancer is a heterogeneous disease in that tumors of the same histology type can respond differently to a treatment. Anti-cancer drug response prediction is of paramount importance for both drug development and patient treatment design. Although various computational methods and data have been used to develop drug response prediction models, it remains a challenging problem due to the complexities of cancer mechanisms and cancer-drug interactions. To better characterize the interaction between cancer and drugs, we investigate the feasibility of integrating computationally derived features of molecular mechanisms of action into prediction models. Specifically, we add docking scores of drug molecules and target proteins in combination with cancer gene expressions and molecular drug descriptors for building response models. The results demonstrate a marginal improvement in drug response prediction performance when adding docking scores as additional features, through tests on large drug screening data. We discuss the limitations of the current approach and provide the research community with a baseline dataset of the large-scale computational docking for anti-cancer drugs. [ABSTRACT FROM AUTHOR]

Simple Summary: In recent years, mathematical models have revolutionized cancer research, illuminating the complex dynamics of tumor growth and aiding drug development. These models, reflecting biological and physical processes, are increasingly used in clinical practice, offering precise patient-specific predictions. Our work introduces an innovative in silico model to simulate tumor growth and invasiveness. The automated hybrid cell, replicating key tumor cell features, enables exploration of 3D tumor spheroid evolution. Sensitivity analyses reveal that tumor growth is primarily influenced by cell replication speed and adhesion, while invasiveness relies on chemotaxis. These insights shed light on tumor development mechanisms, guiding effective strategies against tumor progression. Our model serves as a valuable tool for advancing cancer biology research and potential therapeutic interventions. Purpose: In recent years, mathematical models have become instrumental in cancer research, offering insights into tumor growth dynamics, and guiding the development of pharmacological strategies. These models, encompassing diverse biological and physical processes, are increasingly used in clinical settings, showing remarkable predictive precision for individual patient outcomes and therapeutic responses. Methods: Motivated by these advancements, our study introduces an innovative in silico model for simulating tumor growth and invasiveness. The automated hybrid cell emulates critical tumor cell characteristics, including rapid proliferation, heightened motility, reduced cell adhesion, and increased responsiveness to chemotactic signals. This model explores the potential evolution of 3D tumor spheroids by manipulating biological parameters and microenvironment factors, focusing on nutrient availability. Results: Our comprehensive global and local sensitivity analysis reveals that tumor growth primarily depends on cell duplication speed and cell-to-cell adhesion, rather than external chemical gradients. Conversely, tumor invasiveness is predominantly driven by chemotaxis. These insights illuminate tumor development mechanisms, providing vital guidance for effective strategies against tumor progression. Our proposed model is a valuable tool for advancing cancer biology research and exploring potential therapeutic interventions. [ABSTRACT FROM AUTHOR]

Simple Summary: Cell migration is a key factor in the spread of metastatic tumors and a major contributor to cancer-related mortality. However, our comprehension of the underlying mechanisms remains incomplete. In this study, we utilized a wound healing assay to explore the migration and invasion of cancer cells in the context of metastasis. We developed a computational model using cellular automata, rigorously calibrated and validated with in vitro data from both tumor and non-tumor cell lines, offering a potent resource. This novel approach is of immense value to the pharmaceutical sector for discovering compounds that can impede cell migration, evaluating the efficacy of potential drugs to hinder cancer invasion, and assessing immune system responses. It stands as a breakthrough in the quest for more effective cancer therapies. Purpose: Cell migration is a critical driver of metastatic tumor spread, contributing significantly to cancer-related mortality. Yet, our understanding of the underlying mechanisms remains incomplete. Methods: In this study, a wound healing assay was employed to investigate cancer cell migratory behavior, with the aim of utilizing migration as a biomarker for invasiveness. To gain a comprehensive understanding of this complex system, we developed a computational model based on cellular automata (CA) and rigorously calibrated and validated it using in vitro data, including both tumoral and non-tumoral cell lines. Harnessing this CA-based framework, extensive numerical experiments were conducted and supported by local and global sensitivity analyses in order to identify the key biological parameters governing this process. Results: Our analyses led to the formulation of a power law equation derived from just a few input parameters that accurately describes the governing mechanism of wound healing. This groundbreaking research provides a powerful tool for the pharmaceutical industry. In fact, this approach proves invaluable for the discovery of novel compounds aimed at disrupting cell migration, assessing the efficacy of prospective drugs designed to impede cancer invasion, and evaluating the immune system's responses. [ABSTRACT FROM AUTHOR]

Simple Summary: ASCT2 is an attractive tumour metabolism target based on its critical role in cancer cell growth. The potential mechanisms of microtubule protein inhibitor C118P in breast cancer remain unknown. Identification of the potential target of C118P is essential. We evaluated the inhibitory effects of C118P on breast cancer. C118P restrained the tumour growth of MDA-MB-231 cells by inducing apoptosis, G2/M phase arrest, and autophagy. Furthermore, ASCT2 was confirmed to be a target of C118P. This is the first report to provide evidence that ASCT2 might be a candidate target of C118P in breast cancer treatment. Remarkably, this study will provide an opportunity for ASCT2 inhibition as a therapeutic strategy. Background: The microtubule protein inhibitor C118P shows excellent anti-breast cancer effects. However, the potential targets and mechanisms of C118P in breast cancer remain unknown. Methods: Real-time cellular analysis (RTCA) was used to detect cell viability. Apoptosis and the cell cycle were detected by flow cytometry. Computer docking simulations, surface plasmon resonance (SPR) technology, and microscale thermophoresis (MST) were conducted to study the interaction between C118P and alanine-serine-cysteine transporter 2 (ASCT2). Seahorse XF technology was used to measure the basal oxygen consumption rate (OCR). The effect of C118P in the adipose microenvironment was explored using a co-culture model of adipocytes and breast cancer cells and mouse cytokine chip. Results: C118P inhibited proliferation, potentiated apoptosis, and induced G2/M cell cycle arrest in breast cancer cells. Notably, ASCT2 was validated as a C118P target through reverse docking, SPR, and MST. C118P suppressed glutamine metabolism and mediated autophagy via ASCT2. Similar results were obtained in the adipocyte–breast cancer microenvironment. Adipose-derived interleukin-6 (IL-6) promoted the proliferation of breast cancer cells by enhancing glutamine metabolism via ASCT2. C118P inhibited the upregulation of ASCT2 by inhibiting the effect of IL-6 in co-cultures. Conclusion: C118P exerts an antitumour effect against breast cancer via the glutamine transporter ASCT2. [ABSTRACT FROM AUTHOR]

Simple Summary: Cancer cells are characterized by the accumulation of genetic mutations due to failures of the repair machinery. In some cases, excessive "repair" may cause DNA damage if the machinery runs amok and acts on DNA sequences that have not been damaged. In this report, we interrogate mutations in the FBH1 gene, which restrains homologous recombination, one mechanism of DNA double-strand break repair in eukaryotes. We find that mutations in FBH1 co-occur with mutations in the breast cancer susceptibility gene BRCA2 and other DNA damage repair genes. These findings suggest that FBH1 contributes to the general destabilization of the repair machinery in cancer cells. Homologous recombination (HR) is the major mechanism of rescue of stalled replication forks or repair of DNA double-strand breaks (DSBs) during S phase or mitosis. In human cells, HR is facilitated by the BRCA2-BRCA1-PALB2 module, which loads the RAD51 recombinase onto a resected single-stranded DNA end to initiate repair. Although the process is essential for error-free repair, unrestrained HR can cause chromosomal rearrangements and genome instability. F-box DNA Helicase 1 (FBH1) antagonizes the role of BRCA2-BRCA1-PALB2 to restrict hyper-recombination and prevent genome instability. Here, we analyzed reported FBH1 mutations in cancer cells using the Catalogue of Somatic Mutations in Cancers (COSMIC) to understand how they interact with the BRCA2-BRCA1-PALB2. Consistent with previous results from yeast, we find that FBH1 mutations co-occur with BRCA2 mutations and to some degree BRCA1 and PALB2. We also describe some co-occurring mutations with RAD52, the accessory RAD51 loader and facilitator of single-strand annealing, which is independent of RAD51. In silico modeling was used to investigate the role of key FBH1 mutations on protein function, and a Q650K mutation was found to destabilize the protein structure. Taken together, this work highlights how mutations in several DNA damage repair genes contribute to cellular transformation and immortalization. [ABSTRACT FROM AUTHOR]

Simple Summary: The tumor microenvironment, especially tumor-associated fibroblasts (TAFs), has been extensively studied in cancer, but not as much in pituitary adenoma (PA). Studies of TAFs will provide a better understanding of the mechanisms by which PA exhibits aggressive behavior. Our study proved that TAFs promote PA progression through exosomal circDennd1b. circDennd1b, as a ceRNA, upregulated the expression of the target gene ONECUT2 by sponging miR-145-5p, thereby transcriptionally regulating FGFR3 and activating the downstream MAPK pathway and finally promoting the PA progression. Moreover, the suppression of ONECUT2 and TAFs can improve the efficacy of clinical drugs for PA. TAF participated in the progression of various cancers, including PA via the release of soluble factors. Exosomes belonged to extracellular vesicles, which were revealed as a crucial participator in intercellular communication. However, the expression pattern and effect of TAF-derived exosomes remained largely unknown in PA. In the present study, we performed in silico analysis based on public RNA-seq datasets to generate the circRNA/miRNA regulatory network. The qRT-PCR, Western blotting, RNA pull-down, and luciferase assay were performed to investigate the effect of TAF-derived exosomes. TAF-derived exosomal circDennd1b was significantly upregulated in PA and promoted the proliferation, migration, and invasion of PA cells via sponging miR-145-5p in PA cells. In addition, miR-145-5p directly regulated One Cut homeobox 2 (ONECUT2/OC2) expression and inhibited the promoting effect of ONECUT2 on PA. We further demonstrated that ONECUT2 transcriptionally increased fibroblast growth factor receptor 3 (FGFR3) expression, which further activates the mitogen-activated protein kinases (MAPK) pathway, thus promoting PA progression. Moreover, the suppression of TAFs by ABT-263 and ONECUT2 by CSRM617 inhibited the growth of PA. In conclusion, our study illustrated that TAF-derived exosomal circDennd1b affected PA progression via regulating ONECUT2 expression, which provides a potential therapeutic strategy against aggressive PA. [ABSTRACT FROM AUTHOR]

Simple Summary: High-grade serous ovarian cancer (HGSOC) is a highly lethal form of ovarian carcinoma characterized by molecular heterogeneity. This heterogeneity is a proposed causal factor in treatment response, development of resistance mechanisms, and increased relapse rates. HGSOC exhibits four molecular subtypes with distinct characteristics. The identification of treatment targets with high efficacy in patient subpopulation such as these molecular subtypes is an intriguing challenge across tumour entities. To exemplarily characterize the mesenchymal subtype of HGSOC and elucidate targeted treatment opportunities, we integrated bulk and single-cell RNA data analysis, and investigated the composition of the TME, its cell–cell communication, and transcription factor regulation. Together with causal inference analysis, we identified several treatment opportunities specific to the mesenchymal subtype. Such targeted treatment based on stratified patient groups may benefit patient survival and highlight the advantages of personalized treatment. Background: Tumour heterogeneity in high-grade serous ovarian cancer (HGSOC) is a proposed cause of acquired resistance to treatment and high rates of relapse. Among the four distinct molecular subtypes of HGSOC, the mesenchymal subtype (MES) has been observed with high frequency in several study cohorts. Moreover, it exhibits aggressive characteristics with poor prognosis. The failure to adequately exploit such subtypes for treatment results in high mortality rates, highlighting the need for effective targeted therapeutic strategies that follow the idea of personalized medicine (PM). Methods: As a proof-of-concept, bulk and single-cell RNA data were used to characterize the distinct composition of the tumour microenvironment (TME), as well as the cell–cell communication and its effects on downstream transcription of MES. Moreover, transcription factor activity contextualized with causal inference analysis identified novel therapeutic targets with potential causal impact on transcription factor dysregulation promoting the malignant phenotype. Findings: Fibroblast and macrophage phenotypes are of utmost importance for the complex intercellular crosstalk of MES. Specifically, tumour-associated macrophages were identified as the source of interleukin 1 beta (IL1B), a signalling molecule with significant impact on downstream transcription in tumour cells. Likewise, signalling molecules tumour necrosis factor (TNF), transforming growth factor beta (TGFB1), and C-X-C motif chemokine 12 (CXCL12) were prominent drivers of downstream gene expression associated with multiple cancer hallmarks. Furthermore, several consistently hyperactivated transcription factors were identified as potential sources for treatment opportunities. Finally, causal inference analysis identified Yes-associated protein 1 (YAP1) and Nuclear Receptor Subfamily 2 Group F Member 6 (NR2F6) as novel therapeutic targets in MES, verified in an independent dataset. Interpretation: By utilizing a sophisticated bioinformatics approach, several candidates for treatment opportunities, including YAP1 and NR2F6 were identified. These candidates represent signalling regulators within the cellular network of the MES. Hence, further studies to confirm these candidates as potential targeted therapies in PM are warranted. [ABSTRACT FROM AUTHOR]

Simple Summary: As computer performance continues to grow at more affordable costs, mathematical modelling and in silico experimentation begin to play a larger role in understanding cancer evolution. The aim of our work is to formulate a control strategy for the Adaptive Cellular Therapy (ACT) that can fully eradicates the Chronic Myelogenous Leukemia (CML) cells population in a mathematical model describing interactions between naive T cells, effector T cells and CML cancer cells in the circulatory blood system. Mathematical analysis and numerical simulations allow us to conclude that it is possible to design a personalized administration protocol for the ACT in the form of a pulse train with asymmetrical waves and a fixed amplitude to achieve complete CML cancer cells eradication. The amplitude of the impulse on which the treatment is applied is given by an arithmetical combination of the parameters of the system with at least a duty cycle of 45 min/day. This paper is devoted to exploring personalized applications of cellular immunotherapy as a control strategy for the treatment of chronic myelogenous leukemia described by a dynamical system of three first-order ordinary differential equations. The latter was achieved by applying both the Localization of Compact Invariant Sets and Lyapunov's stability theory. Combination of these two approaches allows us to establish sufficient conditions on the immunotherapy treatment parameter to ensure the complete eradication of the leukemia cancer cells. These conditions are given in terms of the system parameters and by performing several in silico experimentations, we formulated a protocol for the therapy application that completely eradicates the leukemia cancer cells population for different initial tumour concentrations. The formulated protocol does not dangerously increase the effector T cells population. Further, complete eradication is considered when solutions go below a finite critical value below which cancer cells cannot longer persist; i.e., one cancer cell. Numerical simulations are consistent with our analytical results. [ABSTRACT FROM AUTHOR]

Simple Summary: We introduced a novel approach to quantitatively validate the performance of a hybrid spatio-temporal method called spatial quantitative systems pharmacology (spQSP). This platform is composed of a compartmental QSP model describing tumor growth dynamics, anti-tumor immune response and immune checkpoint therapy in a whole-patient and a spatial agent-based model, describing the tumor to simulate the effect of anti-PD-1 therapy (an immune checkpoint inhibitor) on simulated intratumoral heterogeneity. Four spatial metrics adopted from computational digital pathology, along with the ratio of cancer cells to immune cells, were used to categorize the tumor microenvironment as "cold", "mixed" and "compartmentalized" patterns, which were related to the efficacy of the treatment. This study compared the intratumoral heterogeneity description capability of the metrics to facilitate future comprehensive and tangible research on specific cancer types, different therapeutics as single agents or combination therapies, and immunopathological multiplexed samples. Having a better quantitative understanding of intratumoral heterogeneity using numerical simulations can help design more effective treatments. Spatial heterogeneity is a hallmark of cancer. Tumor heterogeneity can vary with time and location. The tumor microenvironment (TME) encompasses various cell types and their interactions that impart response to therapies. Therefore, a quantitative evaluation of tumor heterogeneity is crucial for the development of effective treatments. Different approaches, such as multiregional sequencing, spatial transcriptomics, analysis of autopsy samples, and longitudinal analysis of biopsy samples, can be used to analyze the intratumoral heterogeneity (ITH) and temporal evolution and to reveal the mechanisms of therapeutic response. However, because of the limitations of these data and the uncertainty associated with the time points of sample collection, having a complete understanding of intratumoral heterogeneity role is challenging. Here, we used a hybrid model that integrates a whole-patient compartmental quantitative-systems-pharmacology (QSP) model with a spatial agent-based model (ABM) describing the TME; we applied four spatial metrics to quantify model-simulated intratumoral heterogeneity and classified the TME immunoarchitecture for representative cases of effective and ineffective anti-PD-1 therapy. The four metrics, adopted from computational digital pathology, included mixing score, average neighbor frequency, Shannon's entropy and area under the curve (AUC) of the G-cross function. A fifth non-spatial metric was used to supplement the analysis, which was the ratio of the number of cancer cells to immune cells. These metrics were utilized to classify the TME as "cold", "compartmentalized" and "mixed", which were related to treatment efficacy. The trends in these metrics for effective and ineffective treatments are in qualitative agreement with the clinical literature, indicating that compartmentalized immunoarchitecture is likely to result in more efficacious treatment outcomes. [ABSTRACT FROM AUTHOR]

Simple Summary: To date, breast cancer remains a leading cause of cancer-related deaths among women. The triple-negative breast cancer (TNBC) subtype is the most aggressive and invasive form, and it frequently develops resistance to chemotherapy. Hormonal therapy can also be ineffective against TNBC. Herein, we synthesized two new chromene compounds and tested their activity against hormone-responsive and TNBC cells. We found that both compounds specifically inhibited the proliferation of TNBC cells with little or no effect on hormone-responsive cells. We found that our compounds disrupted the polymerization of microtubules within the cytoskeleton, which are required for proper cell division. Consequently, the TNBC cells underwent permanent growth arrest and cell death. Moreover, the chromene compounds efficiently blocked TNBC migration and inhibited the formation of blood vessels; these are two events crucial for cancer metastasis. Our findings highlight the therapeutic potential of these compounds against TNBC. Breast cancer continues to be the leading cause of cancer-related deaths among women worldwide. The most aggressive type of breast cancer is triple-negative breast cancer (TNBC). Indeed, not only does TNBC not respond well to several chemotherapeutic agents, but it also frequently develops resistance to various anti-cancer drugs, including taxane mitotic inhibitors. This necessitates the search for newer, more efficacious drugs. In this study, we synthesized two novel chromene derivatives (C1 and C2) and tested their efficacy against a battery of luminal type A and TNBC cell lines. Our results show that C1 and C2 significantly and specifically inhibited TNBC cell viability but had no effect on the luminal A cell type. In addition, these novel compounds induced mitotic arrest, cell multinucleation leading to senescence, and apoptotic cell death through the activation of the extrinsic pathway. We also showed that the underlying mechanisms for these actions of C1 and C2 involved inhibition of microtubule polymerization and disruption of the F-actin cytoskeleton. Furthermore, both compounds significantly attenuated migration of TNBC cells and inhibited angiogenesis in vitro. Finally, we performed an in silico analysis, which revealed that these novel variants bind to the colchicine binding site in β-tubulin. Taken together, our data highlight the potential chemotherapeutic properties of two novel chromene compounds against TNBC. [ABSTRACT FROM AUTHOR]

Simple Summary: The safety of minimally invasive anatomical liver resection is a major concern for hepatobiliary surgeons. The Precision Anatomy for Minimally Invasive Hepato-Biliary-Pancreatic Surgery Expert Consensus Meeting was held in 2021. In this meeting, the importance of intraoperative staining of the dominant portal venous region was confirmed, with indocyanine green playing a central role. This article describes the latest findings on minimally invasive laparoscopic anatomical liver resection using the indocyanine green negative staining technique. Minimally invasive liver resection (MILR) is being widely utilized owing to recent advancements in laparoscopic and robot-assisted surgery. There are two main types of liver resection: anatomical (minimally invasive anatomical liver resection (MIALR)) and nonanatomical. MIALR is defined as a minimally invasive liver resection along the respective portal territory. Optimization of the safety and precision of MIALR is the next challenge for hepatobiliary surgeons, and intraoperative indocyanine green (ICG) staining is considered to be of considerable importance in this field. In this article, we present the latest findings on MIALR and laparoscopic anatomical liver resection using ICG at our hospital. [ABSTRACT FROM AUTHOR]

Simple Summary: Cancer is a complex disease that develops over time through accumulated mutations in DNA that transform normal cells into a cancerous state. To fully capture the complexity of the cancer genome, computational methods have been developed to summarize the mutational patterns of cancer, distinguish causal oncogenic mutations, and determine clinically useful mutational patterns. In this review, we survey different computational approaches with an emphasis on important clinical roles and provide insights into better integration of computational methods for clinical use. Since the rise of next-generation sequencing technologies, the catalogue of mutations in cancer has been continuously expanding. To address the complexity of the cancer-genomic landscape and extract meaningful insights, numerous computational approaches have been developed over the last two decades. In this review, we survey the current leading computational methods to derive intricate mutational patterns in the context of clinical relevance. We begin with mutation signatures, explaining first how mutation signatures were developed and then examining the utility of studies using mutation signatures to correlate environmental effects on the cancer genome. Next, we examine current clinical research that employs mutation signatures and discuss the potential use cases and challenges of mutation signatures in clinical decision-making. We then examine computational studies developing tools to investigate complex patterns of mutations beyond the context of mutational signatures. We survey methods to identify cancer-driver genes, from single-driver studies to pathway and network analyses. In addition, we review methods inferring complex combinations of mutations for clinical tasks and using mutations integrated with multi-omics data to better predict cancer phenotypes. We examine the use of these tools for either discovery or prediction, including prediction of tumor origin, treatment outcomes, prognosis, and cancer typing. We further discuss the main limitations preventing widespread clinical integration of computational tools for the diagnosis and treatment of cancer. We end by proposing solutions to address these challenges using recent advances in machine learning. [ABSTRACT FROM AUTHOR]

Simple Summary: Cholangiocarcinoma is a rare, aggressive, and heterogeneous malignancy of the bile duct. A typical feature of cholangiocarcinoma is that the cancer cells are embedded in a dense stroma. However, the functional role of the reactive tumor stroma has not been fully elucidated, which is certainly due to the lack of suitable in vitro models. Tumor stromal cells or tumor-associated fibroblasts play a central role in angiogenesis, metastasis, and the development of resistance to therapy in cholangiocarcinoma. We successfully isolated tumor cells and tumor-associated fibroblasts from a patient, generated cell lines, characterized the cells in detail, and established an innovative autologous tumor model. By detailed characterization of the tumor/tumor-associated fibroblast model we demonstrated that tumor cells interact with tumor-associated fibroblasts. This model can be used to study tumor stromal crosstalk, tumor angiogenesis and invasion, and the development of drug resistance. Cholangiocarcinoma (CCA) are characterized by their desmoplastic and hypervascularized tumor microenvironment (TME), which is mainly composed of tumor cells and cancer-associated fibroblasts (CAFs). CAFs play a pivotal role in general and CCA tumor progression, angiogenesis, metastasis, and the development of treatment resistance. To our knowledge, no continuous human in vivo-like co-culture model is available for research. Therefore, we aimed to establish a new model system (called MUG CCArly) that mimics the desmoplastic microenvironment typically seen in CCA. Proteomic data comparing the new CCA tumor cell line with our co-culture tumor model (CCTM) indicated a higher gene expression correlation of the CCTM with physiological CCA characteristics. A pro-angiogenic TME that is typically observed in CCA could also be better simulated in the CCTM group. Further analysis of secreted proteins revealed CAFs to be the main source of these angiogenic factors. Our CCTM MUG CCArly represents a new, reproducible, and easy-to-handle 3D CCA model for preclinical studies focusing on CCA-stromal crosstalk, tumor angiogenesis, and invasion, as well as the immunosuppressive microenvironment and the involvement of CAFs in the way that drug resistance develops. [ABSTRACT FROM AUTHOR]

Simple Summary: The clinical development of new cancer drugs is based on clinical trials that compare the new drug with the one representing the standard of care. However, it takes some time for the new drug to become available for patient use. In the meantime, the standard of care may have been updated and therefore the efficacy of the new drug needs to be tested against the new standard of care. This objective is usually pursued by performing indirect comparisons: since no "real" trial is available comparing the new drug with the new standard of care, this comparison is carried out by means of a simulated trial wherein patients of real trials are reconstructed individually, pooled together according to an original design of comparison, and finally subjected to standard statistics. Previously published trials are used to perform this comparison. We describe a new method to perform these indirect comparisons, called the IPDfromKM-Shiny method, that lies midway between simple interpolation and advanced statistics. More than 10 investigations have already been conducted in which this method has been used to perform indirect comparisons, especially in the area of haemato-oncology. In the light of these preliminary experiences, the IPDfromKM-Shiny method is proving to be a valid advancement for conducting comparative research in the area of evidence-based medicine. In the area of evidence-based medicine, the IPDfromKM-Shiny method is an innovative method of survival analysis, midway between artificial intelligence and advanced statistics. Its main characteristic is that an original software investigates the Kaplan-Meier graphs of trials so that individual-patient data are reconstructed. These reconstructed patients represent a new form of original clinical material. The typical objective of investigations based on this method is to analyze the available evidence, especially in oncology, to perform indirect comparisons, and determine the place in therapy of individual agents. This review examined the most recent applications of the IPDfromKM-Shiny method, in which a new web-based software—published in 2021—was used. Reported here are 14 analyses, mostly focused on oncological treatments. Indirect comparisons were based on overall survival or progression free survival. Each of these analyses provided original information to compare treatments with one another and select the most appropriate depending on patient characteristics. These analyses can also be useful to assess equivalence from a regulatory viewpoint. All investigations stressed the importance of heterogeneity to better interpret the evidence generated by IPDfromKM-Shiny investigations. In conclusion, these investigations showed that the reconstruction of individual patient data through this online tool is a promising new method for analyzing trials based on survival endpoints. This new approach deserves further investigation, particularly in the area of indirect comparisons. [ABSTRACT FROM AUTHOR]

Simple Summary: Obtaining the PD-1/PD-L1 status is conducive to observing the patient's response rate and constructing individualized immunotherapy strategies. However, biopsies are invasive in assessing the PD-1 status, entail sampling bias due to tumor heterogeneity, are expensive and a slow process, and introduce increased risks of complications. Our research explored a new model based on transformer and CT images to predict PD-1 use. We confirmed that our model can accurately predict the expression of PD-1 via the study of a cohort of 93 patients collected in West China Hospital. The promising diagnostic performance shows that our model is an effective and noninvasive classification method, providing a practical tool for predicting various receptors. The expression status of programmed cell death protein 1 (PD-1) in patients with hepatocellular carcinoma (HCC) is associated with the checkpoint blockade treatment responses of PD-1/PD-L1. Thus, accurately and preoperatively identifying the status of PD-1 has great clinical implications for constructing personalized treatment strategies. To investigate the preoperative predictive value of the transformer-based model for identifying the status of PD-1 expression, 93 HCC patients with 75 training cohorts (2859 images) and 18 testing cohorts (670 images) were included. We propose a transformer-based network architecture, ResTransNet, that efficiently employs convolutional neural networks (CNNs) and self-attention mechanisms to automatically acquire a persuasive feature to obtain a prediction score using a nonlinear classifier. The area under the curve, receiver operating characteristic curve, and decision curves were applied to evaluate the prediction model's performance. Then, Kaplan–Meier survival analyses were applied to evaluate the overall survival (OS) and recurrence-free survival (RFS) in PD-1-positive and PD-1-negative patients. The proposed transformer-based model obtained an accuracy of 88.2% with a sensitivity of 88.5%, a specificity of 88.9%, and an area under the curve of 91.1% in the testing cohort. [ABSTRACT FROM AUTHOR]

This paper describes a method to reconstruct bendable superficial hyperthermia applicators for routine clinical patient-specific treatment planning. The reconstruction uses a CT scan with a flexible silicone dummy applicator positioned on the patient. The curvature was approximated by two second-degree polynomial functions. A realistic treatment series was mimicked using a standard Alderson radiation therapy phantom and a treatment planning model was reconstructed from a CT scan. The variation among treatment curvatures was compared to the modelled curvature. The mathematical approximation of the applicator curvature was validated for this phantom experiment, as well as for clinical treatments. The average maximum variation among the successive mimicked sessions was 3.67 ± 0.69 mm (range 2.98–4.60 mm). The maximum deviation between the treatment curvature and the modelled curvature was 4.35 mm. Comparing the treatment and approximated curvature yielded a maximum deviation between 2.98 mm and 4.12 mm. For clinical treatments the maximum deviation of the treatment and approximated curvature varied between 0.48 mm and 1.98 mm. These results allow adequate reconstruction of bendable hyperthermia applicators for treatment planning, which can further improve treatment quality, for example by optimizing the water bolus temperature for patient-specific tumor depths. Predictive parameters for hyperthermia treatment outcome can easily be evaluated and compared for various input parameters. [ABSTRACT FROM AUTHOR]

Simple Summary: The need for predictive and prognostic biomarkers in colorectal carcinoma (CRC) brought us to an era where the use of artificial intelligence (AI) models is increasing. We investigated the expression of Claudin-7, a tight junction component, which plays a crucial role in maintaining the integrity of normal epithelial mucosa, and its potential prognostic role in advanced CRCs by drawing a parallel between statistical and AI algorithms. Claudin-7 immunohistochemical expression was evaluated in the tumor core and invasion front of CRCs and correlated with clinicopathological parameters and survival using statistical and AI algorithms. The Kaplan–Meier univariate survival analysis showed that the immunohistochemical overexpression of Claudin-7 in the tumor invasive front may represent a poor prognostic factor in advanced stages of CRCs. On the contrary, AI models could not predict the same outcome, probably because of the small number of patients included in our cohort. Aim: The need for predictive and prognostic biomarkers in colorectal carcinoma (CRC) brought us to an era where the use of artificial intelligence (AI) models is increasing. We investigated the expression of Claudin-7, a tight junction component, which plays a crucial role in maintaining the integrity of normal epithelial mucosa, and its potential prognostic role in advanced CRCs, by drawing a parallel between statistical and AI algorithms. Methods: Claudin-7 immunohistochemical expression was evaluated in the tumor core and invasion front of CRCs from 84 patients and correlated with clinicopathological parameters and survival. The results were compared with those obtained by using various AI algorithms. Results: the Kaplan–Meier univariate survival analysis showed a significant correlation between survival and Claudin-7 intensity in the invasive front (p = 0.00), a higher expression being associated with a worse prognosis, while Claudin-7 intensity in the tumor core had no impact on survival. In contrast, AI models could not predict the same outcome on survival. Conclusion: The study showed through statistical means that the immunohistochemical overexpression of Claudin-7 in the tumor invasive front may represent a poor prognostic factor in advanced stages of CRCs, contrary to AI models which could not predict the same outcome, probably because of the small number of patients included in our cohort. [ABSTRACT FROM AUTHOR]

Simple Summary: Human papillomavirus (HPV)-related cancers continue to be a major medical concern, and there exists an urgent need to improve the current therapeutic approaches by combining strategies or proposing new compounds to offer more specific and less invasive treatments. The aim of this work was to discover potential inhibitors of the E6/E6AP/p53 complex formation. We started this work with an initial in silico approach including molecular docking and molecular dynamics simulations, and these tools allowed us to select potential inhibitors, using E6 protein as a target. In addition, we found that lucidin and taxifolin were able to selectively decrease the viability of HPV-positive cells to re-establish p53 protein levels and to induce apoptosis. These findings represent a promising starting point for the development of anti-HPV drugs. Cervical cancer is the fourth leading cause of death in women worldwide, with 99% of cases associated with a human papillomavirus (HPV) infection. Given that HPV prophylactic vaccines do not exert a therapeutic effect in individuals previously infected, have low coverage of all HPV types, and have poor accessibility in developing countries, it is unlikely that HPV-associated cancers will be eradicated in the coming years. Therefore, there is an emerging need for the development of anti-HPV drugs. Considering HPV E6's oncogenic role, this protein has been proposed as a relevant target for cancer treatment. In the present work, we employed in silico tools to discover potential E6 inhibitors, as well as biochemical and cellular assays to understand the action of selected compounds in HPV-positive cells (Caski and HeLa) vs. HPV-negative (C33A) and non-carcinogenic (NHEK) cell lines. In fact, by molecular docking and molecular dynamics simulations, we found three phenolic compounds able to dock in the E6AP binding pocket of the E6 protein. In particular, lucidin and taxifolin were able to inhibit E6-mediated p53 degradation, selectively reduce the viability, and induce apoptosis in HPV-positive cells. Altogether, our data can be relevant for discovering promising leads for the development of specific anti-HPV drugs. [ABSTRACT FROM AUTHOR]

Much has occurred since our 2010 report in Cancers. In the past few years we published several extensive reviews of our research so a brief review is all that will be provided here. We proposed in the earlier reports that most relapses in breast cancer occur within 5 years of surgery and seem to be associated with some unspecified manner of surgery-induced metastatic initiation. These events can be identified in relapse data and are correlated with clinical data. In the last few years an unexpected mechanism has become apparent. Retrospective analysis of relapse events by a Brussels anesthesiology group reported that a perioperative NSAID analgesic seems to reduce early relapses five-fold. We then proposed that primary surgery produces a transient period of systemic inflammation. This has now been identified by inflammatory markers in serum post mastectomy. That could explain the early relapses. It is possible that an inexpensive and non-toxic NSAID can reduce breast cancer relapses significantly. We want to take this opportunity to discuss database quality issues and our relapse hazard data in some detail. We also present a demonstration that the computer simulation can be calibrated with Adjuvant-on-line, an often used clinical tool for prognosis in breast cancer. [ABSTRACT FROM AUTHOR]

Simple Summary: The study contributes to our understanding of the role of lncRNAs in the regulation of Notch signaling and their target genes in ovarian cancer. These lncRNAs were identified with in silico analyses using The Cancer Genome Atlas and the results were validated using our transcriptome data from the in vitro NOTCH1/3 silencing experiments and the qRT-PCR analysis, using tissue samples from patients with ovarian tumors. Finally, to improve our understanding of the complexity of Notch signaling, we identified master transcriptional factors which might orchestrate the upregulation of Notch-related lncRNAs and coding-genes in ovarian cancer. Notch signaling is a druggable target in high-grade serous ovarian cancers; however, its complexity is not clearly understood. Recent revelations of the biological roles of lncRNAs have led to an increased interest in the oncogenic action of lncRNAs in various cancers. In this study, we performed in silico analyses using The Cancer Genome Atlas data to discover novel Notch-related lncRNAs and validated our transcriptome data via NOTCH1/3 silencing in serous ovarian cancer cells. The expression of novel Notch-related lncRNAs was down-regulated by a Notch inhibitor and was upregulated in high-grade serous ovarian cancers, compared to benign or borderline ovarian tumors. Functionally, Notch-related lncRNAs were tightly linked to Notch-related changes in diverse gene expressions. Notably, genes related to DNA repair and spermatogenesis showed specific correlations with Notch-related lncRNAs. Master transcription factors, including EGR1, CTCF, GABPα, and E2F4 might orchestrate the upregulation of Notch-related lncRNAs, along with the associated genes. The discovery of Notch-related lncRNAs significantly contributes to our understanding of the complex crosstalk of Notch signaling with other oncogenic pathways at the transcriptional level. [ABSTRACT FROM AUTHOR]

Simple Summary: Lymphangiogenesis is the formation of new lymphatic vessels in physiological conditions but has also been found to be associated with pathologies. For example, it has been proven to be involved in cancer progression and metastatic dissemination through the body. Thus, it became a key element to study in the management of this widespread disease. To date, the study of lymphangiogenesis takes place at the biological (in vitro and in vivo) and computational (in silico) levels. The association of these complementary fields combined with imaging techniques constitutes a real toolbox in pathological lymphangiogenesis understanding. Lymphangiogenesis (LA) is the formation of new lymphatic vessels by lymphatic endothelial cells (LECs) sprouting from pre-existing lymphatic vessels. It is increasingly recognized as being involved in many diseases, such as in cancer and secondary lymphedema, which most often results from cancer treatments. For some cancers, excessive LA is associated with cancer progression and metastatic dissemination to the lymph nodes (LNs) through lymphatic vessels. The study of LA through in vitro, in vivo, and, more recently, in silico models is of paramount importance in providing novel insights and identifying the key molecular actors in the biological dysregulation of this process under pathological conditions. In this review, the different biological (in vitro and in vivo) models of LA, especially in a cancer context, are explained and discussed, highlighting their principal modeled features as well as their advantages and drawbacks. Imaging techniques of the lymphatics, complementary or even essential to in vivo models, are also clarified and allow the establishment of the link with computational approaches. In silico models are introduced, theoretically described, and illustrated with examples specific to the lymphatic system and the LA. Together, these models constitute a toolbox allowing the LA research to be brought to the next level. [ABSTRACT FROM AUTHOR]

Simple Summary: Hepatocellular carcinoma accounts for around 75% of all liver cancers, and represents the fourth most common cause of cancer-related deaths worldwide. Microwave ablation is a worldwide-diffused treatment of hepatocellular carcinoma. According to the literature, the success rate for completely eliminating small liver tumors in patients treated with microwave ablation is greater than 85%. Microwave ablation is also highly recommended for COVID-19 patients with liver tumors as a fast treatment with a short recovery time. The involvement of the temperature dependence of the heat capacity, the thermal conductivity, and blood perfusion, is pivotal for establishing the correct ablation process and preserving the healthy tissue. The obtained simulation results clearly show that precisely localized heating distributions and heating efficiency can be achieved by using a multislot antenna probe. Deeper knowledge in this area would aid in the prediction and planning of patient-individual procedures. Microwave ablation at 2.45 GHz is gaining popularity as an alternative therapy to hepatic resection with a higher overall survival rate than external beam radiation therapy and proton beam therapy. It also offers better long-term recurrence-free overall survival when compared with radiofrequency ablation. To improve the design and optimization of microwave ablation procedures, numerical models can provide crucial information. A three-dimensional model of the antenna and targeted tissue without homogeneity assumptions are the most realistic representation of the physical problem. Due to complexity and computational resources consumption, most of the existing numerical studies are based on using two-dimensional axisymmetric models to emulate actual three-dimensional cancers and surrounding tissue, which is often far from reality. The main goal of this study is to develop a fully three-dimensional model of a multislot microwave antenna immersed into liver tissue affected by early-stage hepatocellular carcinoma. The geometry of the tumor is taken from the 3D-IRCADb-01 liver tumors database. Simulations were performed involving the temperature dependence of the blood perfusion, dielectric and thermal properties of both healthy and tumoral liver tissues. The water content changes during the ablation process are also included. The optimal values of the input power and the ablation time are determined to ensure complete treatment of the tumor with minimal damage to the healthy tissue. It was found that a multislot antenna is designed to create predictable, large, spherical zones of the ablation that are not influenced by varying tissue environments. The obtained results may be useful for determining optimal conditions necessary for microwave ablation to be as effective as possible for treating early-stage hepatocellular carcinoma, with minimized invasiveness and collateral damages. [ABSTRACT FROM AUTHOR]

Simple Summary: BRF2, a subunit of the RNA polymerase III transcription complex, is upregulated in a wide variety of cancers and is a potential therapeutic target; however, no effective drugs are available to target BRF2. The upregulation of BRF2 in cancer cells confers survival via the prevention of oxidative stress-induced apoptosis. In this manuscript, we report the identification of potential BRF2 inhibitors through in silico drug repurposing screening. We further characterized bexarotene as a hit compound for the development of selective BRF2 inhibitors and provide experimental validation to support the repurposing of this FDA-approved drug as an agent to reduce the cellular levels of ROS and consequent BRF2 expression in cancers with elevated levels of oxidative stress. The overexpression of BRF2, a selective subunit of RNA polymerase III, has been shown to be crucial in the development of several types of cancers, including breast cancer and lung squamous cell carcinoma. Predominantly, BRF2 acts as a central redox-sensing transcription factor (TF) and is involved in rescuing oxidative stress (OS)-induced apoptosis. Here, we showed a novel link between BRF2 and the DNA damage response. Due to the lack of BRF2-specific inhibitors, through virtual screening and molecular dynamics simulation, we identified potential drug candidates that interfere with BRF2-TATA-binding Protein (TBP)-DNA complex interactions based on binding energy, intermolecular, and torsional energy parameters. We experimentally tested bexarotene as a potential BRF2 inhibitor. We found that bexarotene (Bex) treatment resulted in a dramatic decline in oxidative stress and Tert-butylhydroquinone (tBHQ)-induced levels of BRF2 and consequently led to a decrease in the cellular proliferation of cancer cells which may in part be due to the drug pretreatment-induced reduction of ROS generated by the oxidizing agent. Our data thus provide the first experimental evidence that BRF2 is a novel player in the DNA damage response pathway and that bexarotene can be used as a potential inhibitor to treat cancers with the specific elevation of oxidative stress. [ABSTRACT FROM AUTHOR]

Simple Summary: Mathematical and computational models, such as quantitative systems pharmacology (QSP) models, are becoming a popular tool in drug discovery. The advancement of imaging techniques creates a unique opportunity to further expand these models and enhance their predictive power by incorporating characteristics of individual patients embedded in the image data obtained from tissue samples. The aim of this study is to develop a platform which combines the strength of QSP models and spatially resolved agent-based models (ABM), and create a model of cancer development in the context of anti-cancer immunity and immune checkpoint inhibition therapy. The model can be applied in virtual clinical trials and biomarker discovery to help refine trial design. Quantitative systems pharmacology (QSP) models have become increasingly common in fundamental mechanistic studies and drug discovery in both academic and industrial environments. With imaging techniques widely adopted and other spatial quantification of tumor such as spatial transcriptomics gaining traction, it is crucial that these data reflecting tumor spatial heterogeneity be utilized to inform the QSP models to enhance their predictive power. We developed a hybrid computational model platform, spQSP-IO, to extend QSP models of immuno-oncology with spatially resolved agent-based models (ABM), combining their powers to track whole patient-scale dynamics and recapitulate the emergent spatial heterogeneity in the tumor. Using a model of non-small-cell lung cancer developed based on this platform, we studied the role of the tumor microenvironment and cancer–immune cell interactions in tumor development and applied anti-PD-1 treatment to virtual patients and studied how the spatial distribution of cells changes during tumor growth in response to the immune checkpoint inhibition treatment. Using parameter sensitivity analysis and biomarker analysis, we are able to identify mechanisms and pretreatment measurements correlated with treatment efficacy. By incorporating spatial data that highlight both heterogeneity in tumors and variability among individual patients, spQSP-IO models can extend the QSP framework and further advance virtual clinical trials. [ABSTRACT FROM AUTHOR]

Simple Summary: Online treatment monitoring is an important tool to ensure the safety and effectiveness of hyperthermia cancer therapy. However, current solutions provide only sparse/inaccurate data, demand extensive access to complex and expensive infrastructure, or are associated with increased toxicity. In this study, we present a simulation-based evaluation of the feasibility of electrical impedance tomography (EIT) for hyperthermia treatment monitoring. EIT is a low cost, information-rich, non-invasive technique that could potentially be adapted and employed to reconstruct conductivity changes and translate them to temperature- and perfusion-change maps. Using an innovative reconstruction methodology that leverages (ideally personalized) treatment simulations, physics-motivated constraints, multiple frequencies, measurement-derived compensation, and novel numerical approaches, we investigated the impact of factors such as noise and reference model accuracy on the temperature- and perfusion-reconstruction accuracy. Results suggest that EIT can provide valuable real-time monitoring capabilities. As a next step, experimental confirmation under real-world conditions is needed to validate our results. We present a simulation study investigating the feasibility of electrical impedance tomography (EIT) as a low cost, noninvasive technique for hyperthermia (HT) treatment monitoring and adaptation. Temperature rise in tissues leads to perfusion and tissue conductivity changes that can be reconstructed in 3D by EIT to noninvasively map temperature and perfusion. In this study, we developed reconstruction methods and investigated the achievable accuracy of EIT by simulating HT treatmentlike scenarios, using detailed anatomical models with heterogeneous conductivity distributions. The impact of the size and location of the heated region, the voltage measurement signal-to-noise ratio, and the reference model personalization and accuracy were studied. Results showed that by introducing an iterative reconstruction approach, combined with adaptive prior regions and tissue-dependent penalties, planning-based reference models, measurement-based reweighting, and physics-based constraints, it is possible to map conductivity-changes throughout the heated domain, with an accuracy of around 5% and cm-scale spatial resolution. An initial exploration of the use of multifrequency EIT to separate temperature and perfusion effects yielded promising results, indicating that temperature reconstruction accuracy can be in the order of 1 °C. Our results suggest that EIT can provide valuable real-time HT monitoring capabilities. Experimental confirmation in real-world conditions is the next step. [ABSTRACT FROM AUTHOR]

Simple Summary: Radiomics aims to extract high-dimensional features from clinical images and associate them to clinical outcomes. These associations may be further investigated with machine learning models; however, guidelines on the most suitable method to support clinical decisions are still missing. To improve the reliability and the accuracy of radiomic features in the prediction of a binary variable in a lung cancer setting, we compared several machine learning classifiers and feature selection methods on simulated data. These account for important characteristics that may vary in real clinical datasets: sample size, outcome balancing and association strength between radiomic features and outcome variables. We were able to suggest the most suitable classifiers for each studied case and to evaluate the impact of method choices. Our work highlights the importance of these choices in radiomic analyses and provides guidelines on how to select the best models for the data at hand. Radiomics uses high-dimensional sets of imaging features to predict biological characteristics of tumors and clinical outcomes. The choice of the algorithm used to analyze radiomic features and perform predictions has a high impact on the results, thus the identification of adequate machine learning methods for radiomic applications is crucial. In this study we aim to identify suitable approaches of analysis for radiomic-based binary predictions, according to sample size, outcome balancing and the features–outcome association strength. Simulated data were obtained reproducing the correlation structure among 168 radiomic features extracted from Computed Tomography images of 270 Non-Small-Cell Lung Cancer (NSCLC) patients and the associated to lymph node status. Performances of six classifiers combined with six feature selection (FS) methods were assessed on the simulated data using AUC (Area Under the Receiver Operating Characteristics Curves), sensitivity, and specificity. For all the FS methods and regardless of the association strength, the tree-based classifiers Random Forest and Extreme Gradient Boosting obtained good performances (AUC ≥ 0.73), showing the best trade-off between sensitivity and specificity. On small samples, performances were generally lower than in large–medium samples and with larger variations. FS methods generally did not improve performances. Thus, in radiomic studies, we suggest evaluating the choice of FS and classifiers, considering specific sample size, balancing, and association strength. [ABSTRACT FROM AUTHOR]

Simple Summary: Recent works on dynamic amino acid positron emission tomography (PET) imaging of gliomas have highlighted characteristic behaviors of time-activity curves (TACs) extracted from the whole tumor w.r.t. the grade, genotype, and outcome. However, gliomas are known to be highly heterogeneous tumors. Here, we aim at highlighting similar dynamic behaviors at the voxel level within the tumor volume in [S-methyl-11C]methionine PET data of 33 glioma patients using principal component analysis (PCA). The PCA model was derived from TACs of 20 patients and subsequently applied to 13 other patients in whom our approach was shown to outperform classical pharmacokinetic modeling to this end. Our parameter-free approach provides additional parametric maps from dynamic methionine PET scans with little modification of the routine protocol and no arterial sampling. This early methodological work paves the way for various clinical studies on glioma heterogeneity with applications for treatment planning and response evaluation. Recent works have demonstrated the added value of dynamic amino acid positron emission tomography (PET) for glioma grading and genotyping, biopsy targeting, and recurrence diagnosis. However, most of these studies are based on hand-crafted qualitative or semi-quantitative features extracted from the mean time activity curve within predefined volumes. Voxelwise dynamic PET data analysis could instead provide a better insight into intra-tumor heterogeneity of gliomas. In this work, we investigate the ability of principal component analysis (PCA) to extract relevant quantitative features from a large number of motion-corrected [S-methyl-11C]methionine ([11C]MET) PET frames. We first demonstrate the robustness of our methodology to noise by means of numerical simulations. We then build a PCA model from dynamic [11C]MET acquisitions of 20 glioma patients. In a distinct cohort of 13 glioma patients, we compare the parametric maps derived from our PCA model to these provided by the classical one-compartment pharmacokinetic model (1TCM). We show that our PCA model outperforms the 1TCM to distinguish characteristic dynamic uptake behaviors within the tumor while being less computationally expensive and not requiring arterial sampling. Such methodology could be valuable to assess the tumor aggressiveness locally with applications for treatment planning and response evaluation. This work further supports the added value of dynamic over static [11C]MET PET in gliomas. [ABSTRACT FROM AUTHOR]

Simple Summary: The PARP family consists of 17 proteins, and some of them are responsible for cancer cells' viability. Much attention is therefore given to the search for chemical compounds with the ability to suppress distinct PARP family members (for example, PARP-5a and 5b). Here, we present the results of a family-wide bioinformatic analysis of an important functional region in the PARP structure and describe factors that can guide the design of highly selective compounds. The PARP family consists of 17 members with diverse functions, including those related to cancer cells' viability. Several PARP inhibitors are of great interest as innovative anticancer drugs, but they have low selectivity towards distinct PARP family members and exert serious adverse effects. We describe a family-wide study of the nicotinamide (NA) binding site, an important functional region in the PARP structure, using comparative bioinformatic analysis and molecular modeling. Mutations in the NA site and D-loop mobility around the NA site were identified as factors that can guide the design of selective PARP inhibitors. Our findings are of particular importance for the development of novel tankyrase (PARPs 5a and 5b) inhibitors for cancer therapy. [ABSTRACT FROM AUTHOR]

Simple Summary: We developed and validated a preclinical in vivo hyperthermic intraperitoneal chemotherapy (HIPEC) setup using state-of-the-art techniques in rats, including dedicated treatment planning tools and a computer-aided design to achieve well-controlled homogeneous peritoneal flow. Our four-inflow construction resulted in more stable and homogeneous thermal distributions than using a one-inflow construction, with lower standard deviations and less thermal losses. Core temperatures were kept stable using occasional tail cooling, and rarely exceeded 39 °C. This validated design can improve accuracy in future in vivo experiments investigating the impact of relevant treatment parameters on the efficacy of different HIPEC protocols such as: drug type, temperature and duration. Background: Hyperthermic intraperitoneal chemotherapy (HIPEC) after cytoreductive surgery (CRS) is used for treating peritoneal metastases of various origins. Present HIPEC protocols have rarely been validated for relevant parameters such as optimal agent, duration and perfusate temperature. In vitro experiments are not completely representative of clinical circumstances. Therefore, a good preclinical in vivo HIPEC model is needed in which temperature distributions can be well-controlled and are stable throughout treatments. Methods: We designed a setup able to generate and maintain a homogeneous flow during a 90-min HIPEC procedure using our in-house developed treatment planning tools and computer aided design (CAD) techniques. Twelve rats were treated with heated phosphate-buffered saline (PBS) using two catheter setups (one vs. four- inflows) and extensive thermometry. Simulated and measured thermal distribution and core temperatures were evaluated for the different setups. Results: Overall, the four-inflow resulted in more stable and more homogeneous thermal distributions than the one-inflow, with lower standard deviations (0.79 °C vs. 1.41 °C at the outflow, respectively) and less thermal losses. The average thermal loss was 0.4 °C lower for rats treated with the four-inflow setup. Rat core temperatures were kept stable using occasional tail cooling, and rarely exceeded 39 °C. Conclusion: Increasing the number of inflow catheters from one to four resulted in increased flow and temperature homogeneity and stability. Tail cooling is an adequate technique to prevent rats from overheating during 90-min treatments. This validated design can improve accuracy in future in vivo experiments investigating the impact of relevant parameters on the efficacy of different HIPEC protocols. [ABSTRACT FROM AUTHOR]

Pancreatic adenocarcinoma (PDAC) is one of the major causes of cancer-associated deaths worldwide, with a dismal prognosis that has not significantly changed over the last decades. Transcriptional analysis has provided valuable insights into pancreatic tumorigenesis. Specifically, pancreatic cancer subtypes were identified, characterized by specific mutations and gene expression changes associated with differences in patient survival. In addition to differentially regulated mRNAs, non-coding RNAs, including long non-coding RNAs (lncRNAs), were shown to have subtype-specific expression patterns. Hence, we aimed to characterize prognostic lncRNAs with deregulated expression in the squamous subtype of PDAC, which has the worst prognosis. Extensive in silico analyses followed by in vitro experiments identified long intergenic non-coding RNA 261 (LINC00261) as a downregulated lncRNA in the squamous subtype of PDAC, which is generally associated with transforming growth factor β (TGFβ) signaling in human cancer cells. Its genomic neighbor, the transcription factor forkhead box protein A2 (FOXA2), regulated LINC00261 expression by direct binding of the LINC00261 promoter. CRISPR-mediated knockdown and promoter knockout validated the importance of LINC00261 in TGFβ-mediated epithelial–mesenchymal transition (EMT) and established the epithelial marker E-cadherin, an important cell adhesion protein, as a downstream target of LINC00261. Consequently, depletion of LINC00261 enhanced motility and invasiveness of PANC-1 cells in vitro. Altogether, our data suggest that LINC00261 is an important tumor-suppressive lncRNA in PDAC that is involved in maintaining a pro-epithelial state associated with favorable disease outcome. [ABSTRACT FROM AUTHOR]

Elevated levels of nuclear Y-box binding protein 1 (YB-1) are linked to poor prognosis in cancer. It has been proposed that entry into the nucleus requires specific proteasomal cleavage. However, evidence for cleavage is contradictory and high YB-1 levels are prognostic regardless of cellular location. Here, using confocal microscopy and mass spectrometry, we find no evidence of specific proteolytic cleavage. Doxorubicin treatment, and the resultant G2 arrest, leads to a significant increase in the number of cells where YB-1 is not found in the cytoplasm, suggesting that its cellular localisation is variable during the cell cycle. Live cell imaging reveals that the location of YB-1 is linked to progression through the cell cycle. Primarily perinuclear during G1 and S phases, YB-1 enters the nucleus as cells transition through late G2/M and exits at the completion of mitosis. Atomistic modelling and molecular dynamics simulations show that dephosphorylation of YB-1 at serine residues 102, 165 and 176 increases the accessibility of the nuclear localisation signal (NLS). We propose that this conformational change facilitates nuclear entry during late G2/M. Thus, the phosphorylation status of YB-1 determines its cellular location. [ABSTRACT FROM AUTHOR]

Glioblastoma multiforme (GBM) is the most aggressive and deadly brain tumor, portending a median 13-month survival even following gross total resection with adjuvant chemotherapy and radiotherapy. This prognosis necessitates improved therapies for the disease. A target of interest for novel chemotherapies is the Warburg Effect, which describes the tumor's shift away from oxidative phosphorylation towards glycolysis. Here, we elucidate GLUT1 (Glucose transporter 1) and one of its associated binding partners, TUBB4 (Tubulin 4), as potentially druggable targets in GBM. Using data mining approach, we demonstrate that GLUT1 is overexpressed as a function of tumor grade in astrocytoma's and that its overexpression is associated with poorer prognosis. Using both mass spectrometry performed on hGBM (human glioblastoma patient specimen) and in silico modeling, we show that GLUT1 interacts with TUBB4, and more accurately demonstrates GLUT1's binding with fasentin. Proximity ligation assay (PLA) and immunoprecipitation studies confirm GLUT1 interaction with TUBB4. Treatment of GSC33 and GSC28 cells with TUBB4 inhibitor, CR-42-24, reduces the expression of GLUT1 however, TUBB4 expression is unaltered upon fasentin treatment. Using human pluripotent stem cell antibody array, we demonstrate reduced levels of Oct3/4, Nanog, Sox2, Sox17, Snail and VEGFR2 (Vascular endothelial growth factor receptor 2) upon CR-42-24 treatment. Overall, our data confirm that silencing TUBB4 or GLUT1 reduce GSC tumorsphere formation, self-renewal and proliferation in vitro. These findings suggest GLUT1 and its binding partner TUBB4 as druggable targets that warrant further investigation in GBM. [ABSTRACT FROM AUTHOR]

Hyperthermia therapy (40–44 °C) is a promising option to increase efficacy of radiotherapy/chemotherapy for brain tumours, in particular paediatric brain tumours. The Chalmers Hyperthermia Helmet is developed for this purpose. Hyperthermia treatment planning is required for treatment optimisation, but current planning systems do not involve a physically correct model of cerebrospinal fluid (CSF). This study investigates the necessity of fluid modelling for treatment planning. We made treatments plans using the Helmet for both pre-operative and post-operative cases, comparing temperature distributions predicted with three CSF models: a convective "fluid" model, a non-convective "solid" CSF model, and CSF models with increased effective thermal conductivity ("high-k"). Treatment plans were evaluated by T90, T50 and T10 target temperatures and treatment-limiting hot spots. Adequate heating is possible with the helmet. In the pre-operative case, treatment plan quality was comparable for all three models. In the post-operative case, the high-k models were more accurate than the solid model. Predictions to within ±1 °C were obtained by a 10–20-fold increased effective thermal conductivity. Accurate modelling of the temperature in CSF requires fluid dynamics, but modelling CSF as a solid with enhanced effective thermal conductivity might be a practical alternative for a convective fluid model for many applications. [ABSTRACT FROM AUTHOR]

Flavonoids have emerged as promising compounds capable of preventing colorectal cancer (CRC) due to their anti-oxidant and anti-inflammatory properties. It is hypothesized that the metabolites of flavonoids are primarily responsible for the observed anti-cancer effects owing to the unstable nature of the parent compounds and their degradation by colonic microflora. In this study, we investigated the ability of one metabolite, 2,4,6-trihydroxybenzoic acid (2,4,6-THBA) to inhibit Cyclin Dependent Kinase (CDK) activity and cancer cell proliferation. Using in vitro kinase assays, we demonstrated that 2,4,6-THBA dose-dependently inhibited CDKs 1, 2 and 4 and in silico studies identified key amino acids involved in these interactions. Interestingly, no significant CDK inhibition was observed with the structurally related compounds 3,4,5-trihydroxybenzoic acid (3,4,5-THBA) and phloroglucinol, suggesting that orientation of the functional groups and specific amino acid interactions may play a role in inhibition. We showed that cellular uptake of 2,4,6-THBA required the expression of functional SLC5A8, a monocarboxylic acid transporter. Consistent with this, in cells expressing functional SLC5A8, 2,4,6-THBA induced CDK inhibitory proteins p21Cip1 and p27Kip1 and inhibited cell proliferation. These findings, for the first time, suggest that the flavonoid metabolite 2,4,6-THBA may mediate its effects through a CDK- and SLC5A8-dependent pathway contributing to the prevention of CRC. [ABSTRACT FROM AUTHOR]

Prostate cancer (PrCa) metastasis is the major cause of mortality and morbidity among men. Metastatic PrCa cells are typically adopted for aberrant glucose metabolism. Thus, chemophores that reprogram altered glucose metabolic machinery in cancer cells can be useful agent for the repression of PrCa metastasis. Herein, we report that cucurbitacin D (Cuc D) effectively inhibits glucose uptake and lactate production in metastatic PrCa cells via modulating glucose metabolism. This metabolic shift by Cuc D was correlated with decreased expression of GLUT1 by its direct binding as suggested by its proficient molecular docking (binding energy −8.5 kcal/mol). Cuc D treatment also altered the expression of key oncogenic proteins and miR-132 that are known to be involved in glucose metabolism. Cuc D (0.1 to 1 µM) treatment inhibited tumorigenic and metastatic potential of human PrCa cells via inducing apoptosis and cell cycle arrest in G2/M phase. Cuc D treatment also showed inhibition of tumor growth in PrCa xenograft mouse model with concomitant decrease in the expression of GLUT1, PCNA and restoration of miR-132. These results suggest that Cuc D is a novel modulator of glucose metabolism and could be a promising therapeutic modality for the attenuation of PrCa metastasis. [ABSTRACT FROM AUTHOR]

Peritoneal metastasis (PM) originating from gastrointestinal and gynecological malignancies are associated with a poor prognosis and rapid disease progression. Cytoreductive surgery (CRS) with hyperthermic intraperitoneal chemotherapy (HIPEC) is an effective treatment option with curative intent. Hyperthermia enhances the cytotoxicity of chemotherapeutic drugs, thereby killing microscopic tumors and reducing the risk of tumor recurrence. Eight parameters potentially have an impact on the efficacy of HIPEC: the type of drug, drug concentrations, carrier solution, volume of the perfusate, temperature of the perfusate, duration of the treatment, the technique of delivery, and patient selection. In this review, a literature search was performed on PubMed, and a total of 564 articles were screened of which 168 articles were included. Although HIPEC is a successful treatment, there is no standardized method for delivering HIPEC: the choice of parameters is presently largely determined by institutional preferences. We discuss the current choice of the parameters and hypothesize about improvements toward uniform standardization. Quantifying the effect of each parameter separately is necessary to determine the optimal way to perform HIPEC procedures. In vivo, in vitro, in silico, and other experimental studies should shed light on the role of each of the eight parameters. [ABSTRACT FROM AUTHOR]

It is well established that cancer cells exhibit alterations in chromatin structure and accessibility. Indeed, the dysregulation of many protein-coding players with enzymatic activity (DNA and histone-modifying enzymes) and chromatin remodelers have been depicted in various tumor models in recent years. Still, little attention has been directed towards testicular germ cell tumors (TGCTs)—representing the most common neoplasm among young adult Caucasian men—with most studies focusing on exploring the role of DNA methyltransferases (DNMTs) and DNA demethylases (TETs). TGCTs represent a complex tumor model, associated with developmental and embryogenesis-related phenomena, and display seldom (cyto)genetic aberrations, leaving room for Epigenetics to explain such morphological and clinical diversity. Herein, we have summarized the major findings that were reported in literature regarding the dysregulation of DNA/histone-modifying enzymes and chromatin remodelers in TGCTs. Additionally, we performed in silico analysis of The Cancer Genome Atlas database to find the most relevant of those players in TGCTs. We concluded that several DNA/histone-modifying enzymes and chromatin remodelers may serve as biomarkers for subtyping, dictating prognosis and survival, and, possibly, for serving as targets of directed, less toxic therapies. [ABSTRACT FROM AUTHOR]

Breast cancer is the most frequently diagnosed cancer in women and the second most common cancer overall, with nearly 1.7 million new cases worldwide every year. Breast cancer patients need accurate tools for early diagnosis and to improve treatment. Biomarkers are increasingly used to describe and evaluate tumours for prognosis, to facilitate and predict response to therapy and to evaluate residual tumor, post-treatment. Here, we evaluate different methods to separate Diaminobenzidine (DAB) from Hematoxylin and Eosin (H&E) staining for Wnt-1, a potential cytoplasmic breast cancer biomarker. A method comprising clustering and Color deconvolution allowed us to recognize and quantify Wnt-1 levels accurately at pixel levels. Experimental validation was conducted using a set of 12,288 blocks of m × n pixels without overlap, extracted from a Tissue Microarray (TMA) composed of 192 tissue cores. Intraclass Correlations (ICC) among evaluators of the data of 0.634 , 0.791 , 0.551 and 0.63 for each Allred class and an average ICC of 0.752 among evaluators and automatic classification were obtained. Furthermore, this method received an average rating of 4.26 out of 5 in the Wnt-1 segmentation process from the evaluators. [ABSTRACT FROM AUTHOR]

Intrinsically disordered protein YAP (yes-associated protein) interacts with TEADs transcriptional factors family (transcriptional enhancer associated domain) creating three interfaces. Interface 3, between the Ω-loop of YAP and a shallow pocket of TEAD was identified as the most important TEAD zone for YAP-TEAD interaction. Using the first X-ray structure of the hYAP50–71-hTEAD1209–426 complex (PDB 3KYS) published in 2010, a protein-protein interaction inhibitors-enriched library (175,000 chemical compounds) was screened against this hydrophobic pocket of TEAD. Four different chemical families have been identified and evaluated using biophysical techniques (thermal shift assay, microscale thermophoresis) and in cellulo assays (luciferase activity in transfected HEK293 cells, RTqPCR in MDA-MB231 cells). A first promising hit with micromolar inhibition in the luciferase gene reporter assay was discovered. This hit also decreased mRNA levels of TEAD target genes. [ABSTRACT FROM AUTHOR]

Proton cancer therapy (PCT) utilizes high-energy proton projectiles to obliterate cancerous tumors with low damage to healthy tissues and without the side effects of X-ray therapy. The healing action of the protons results from their damage on cancerous cell DNA. Despite established clinical use, the chemical mechanisms of PCT reactions at the molecular level remain elusive. This situation prevents a rational design of PCT that can maximize its therapeutic power and minimize its side effects. The incomplete characterization of PCT reactions is partially due to the health risks associated with experimental/clinical techniques applied to human subjects. To overcome this situation, we are conducting time-dependent and non-adiabatic computer simulations of PCT reactions with the electron nuclear dynamics (END) method. Herein, we present a review of our previous and new END research on three fundamental types of PCT reactions: water radiolysis reactions, proton-induced DNA damage and electron-induced DNA damage. These studies are performed on the computational prototypes: proton + H2O clusters, proton + DNA/RNA bases and + cytosine nucleotide, and electron + cytosine nucleotide + H2O. These simulations provide chemical mechanisms and dynamical properties of the selected PCT reactions in comparison with available experimental and alternative computational results. [ABSTRACT FROM AUTHOR]