Glioblastoma multiforme (GBM) is a primary malignant brain tumor characterized by a high grade of malignancy and an extremely unfavorable prognosis. The current efficacy of established treatments for GBM is insufficient, necessitating the prompt development of novel therapeutic approaches. The progress made in the fundamental scientific understanding of GBM is swiftly translated into more advanced stages of therapeutic studies. Despite extensive efforts to identify new therapeutic approaches, GBM exhibits a high mortality rate. The current efficacy of treatments for GBM patients is insufficient due to factors such as tumor heterogeneity, the blood–brain barrier, glioma stem cells, drug efflux pumps, and DNA damage repair mechanisms. Considering this, pharmacological cocktail therapy has demonstrated a growing efficacy in addressing these challenges. Towards this, various forms of immunotherapy, including the immune checkpoint blockade, chimeric antigen receptor T (CAR T) cell therapy, oncolytic virotherapy, and vaccine therapy have emerged as potential strategies for enhancing the prognosis of GBM. Current investigations are focused on exploring combination therapies to mitigate undesirable side effects and enhance immune responses against tumors. Furthermore, clinical trials are underway to evaluate the efficacy of several strategies to circumvent the blood–brain barrier (BBB) to achieve targeted delivery in patients suffering from recurrent GBM. In this review, we have described the biological and molecular targets for GBM therapy, pharmacologic therapy status, prominent resistance mechanisms, and new treatment approaches. We also discuss these promising therapeutic approaches to assess prospective innovative therapeutic agents and evaluated the present state of preclinical and clinical studies in GBM treatment. Overall, this review attempts to provide comprehensive information on the current status of GBM therapy.

Advances in Glioblastoma Therapy: An Update on Current Approaches

The most prevalent and aggressive type of brain cancer, namely, glioblastoma (GBM), is characterized by intra- and inter-tumor heterogeneity and strong spreading capacity, which makes treatment ineffective. A true therapeutic answer is still in its infancy despite various studies that have made significant progress toward understanding the mechanisms behind GBM recurrence and its resistance. The primary causes of GBM recurrence are attributed to the heterogeneity and diffusive nature; therefore, monitoring the tumor’s heterogeneity and spreading may offer a set of therapeutic targets that could improve the clinical management of GBM and prevent tumor relapse. Additionally, the blood–brain barrier (BBB)-related poor drug delivery that prevents effective drug concentrations within the tumor is discussed. With a primary emphasis on signaling heterogeneity, tumor infiltration, and computational modeling of GBM, this review covers typical therapeutic difficulties and factors contributing to drug resistance development and discusses potential therapeutic approaches.

Understanding Glioblastoma Signaling, Heterogeneity, Invasiveness, and Drug Delivery Barriers

Drug resistance continues to be a major limiting factor across diverse anti-cancer therapies. Contributing to the complexity of this challenge is cancer plasticity, in which one cancer subtype switches to another in response to treatment, for example, triple-negative breast cancer (TNBC) to Her2-positive breast cancer. For optimal treatment outcomes, accurate tumor diagnosis and subsequent therapeutic decisions are vital. This study assessed a novel approach to characterize treatment-induced evolutionary changes of distinct tumor cell subpopulations to identify and therapeutically exploit anticancer drug resistance.In this research, an information-theoretic single-cell quantification strategy was developed to provide a high-resolution and individualized assessment of tumor composition for a customized treatment approach. Briefly, this single-cell quantification strategy computes cell barcodes based on at least 100,000 tumor cells from each experiment and reveals a cell-specific signaling signature (CSSS) composed of a set of ongoing processes in each cell.Using these CSSS-based barcodes, distinct subpopulations evolving within the tumor in response to an outside influence, like anticancer treatments, were revealed and mapped. Barcodes were further applied to assign targeted drug combinations to each individual tumor to optimize tumor response to therapy. The strategy was validated using TNBC models and patient-derived tumors known to switch phenotypes in response to radiotherapy (RT).We show that a barcode-guided targeted drug cocktail significantly enhances tumor response to RT and prevents regrowth of once-resistant tumors. The strategy presented herein shows promise in preventing cancer treatment resistance, with significant applicability in clinical use. 

/pdf/computational-quantification-and-characterization-of-3urgkkkk.pdf

Computational quantification and characterization of independently evolving cellular subpopulations within tumors is critical to inhibit anti-cancer therapy resistance

Nanozymes are nanomaterials with mimicked enzymatic activity, whose catalytic activity can be designed by changing their physical parameters and chemical composition. With the development of biomedical and material science, artificially created nanozymes have high biocompatibility and can catalyze specific biochemical reactions under biological conditions, thus playing a vital role in regulating physiological activities. Under pathological conditions, natural enzymes are limited in their catalytic capacity by the varying reaction conditions. In contrast, compared to natural enzymes, nanozymes have advantages such as high stability, simplicity of modification, targeting ability, and versatility. As a result, the novel role of nanozymes in medicine, especially in tumor therapy, is gaining increasing attention. In this review, function and application of various nanozymes in the treatment of cancer are summarized. Future exploration paths of nanozymes in cancer therapies based on new insights arising from recent research are outlined.

Progress and prospects of nanozymes for enhanced antitumor therapy

This review explores the hallmarks of cancer resistance, including drug efflux mediated by ATP-binding cassette (ABC) transporters, metabolic reprogramming characterized by the Warburg effect, and the dynamic interplay between cancer cells and mitochondria. The role of cancer stem cells (CSCs) in treatment resistance and the regulatory influence of non-coding RNAs, such as long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and circular RNAs (circRNAs), are studied. The chapter emphasizes future directions, encompassing advancements in immunotherapy, strategies to counter adaptive resistance, integration of artificial intelligence for predictive modeling, and the identification of biomarkers for personalized treatment. The comprehensive exploration of these hallmarks provides a foundation for innovative therapeutic approaches, aiming to navigate the complex landscape of cancer resistance and enhance patient outcomes. 

Hallmarks of Cancer Resistance

The following paper proposes a combination of a supervised encoder-decoder neural network with coded apertures. Coded apertures provide improved sensitivity and signal-to-noise ratio (SNR) in planar images. The unique array design of this method overcomes the spatial frequency cutoff found in standard multi-pinhole arrays. In this design, the pinholes were positioned to minimize loss in spatial frequencies. The large number of pinholes results in significant overlapping on the detector. To overcome the overlapping issue, reconstruction of the object from the obtained image is done using inverse filtering methods. However, traces of duplications remain leading to a decline in SNR, contrast, and resolution. The proposed technique addresses the challenge of image distortion caused by the lack of accuracy in the inverse filter methods, by using a deep neural network. In this work, the coded aperture is combined with a deep convolutional neural network (CNN) to remove noise caused by pinhole imaging and inverse filter limitations. Compared to only using Wiener filtering, the proposed method delivers higher SNR, contrast, and resolution. The imaging system is presented in detail with experimental results that illustrate its efficiency.

Deep learning method for pinhole array color image reconstruction.

Abstract Bacillus subtilis biofilms are well known for their complex and highly adaptive morphology. Indeed, their phenotypical diversity and intra-biofilm heterogeneity make this gram-positive bacterium the subject of many scientific papers on the structure of biofilms. The “robustness” of biofilms is a term often used to describe their level of susceptibility to antimicrobial agents and various mechanical and molecular inhibition/eradication methods. In this paper, we use computational analytics to quantify Bacillus subtilis morphological response to proximity to an antimicrobial source, in the form of the antiseptic chlorhexidine. Chlorhexidine droplets, placed in proximity to Bacillus subtilis macrocolonies at different distances result in morphological changes, quantified using Python-based code, which we have made publicly available. Our results quantify peripheral and inner core deformation as well as differences in cellular viability of the two regions. The results reveal that the inner core, which is often characterized by the presence of wrinkled formations in the macrocolony, is more preserved than the periphery. Furthermore, the paper describes a crescent-shaped colony morphology which occurs when the distance from the chlorhexidine source is 0.5 cm, as well as changes observed in the growth substrate of macrocolonies exposed to chlorhexidine. 

/pdf/an-open-source-computational-tool-for-measuring-bacterial-12zgksbx.pdf

An open-source computational tool for measuring bacterial biofilm morphology and growth kinetics upon one-sided exposure to an antimicrobial source

Bacillus subtilis biofilms are well known for their complex and highly adaptive morphology. Indeed, their phenotypical diversity and intra-biofilm heterogeneity make this gram-positive bacterium the subject of many scientific papers on the structure of biofilms. The ‘robustness’ of biofilms is a term often used to describe their level of susceptibility to anti-microbial agents and various mechanical and molecular inhibition/eradication methods. In this paper, we use computational analytics to quantify Bacillus subtilis morphological response to proximity to an anti-microbial source, in the form of the antiseptic chlorhexidine. Chlorhexidine droplets, placed in proximity to Bacillus subtilis macrocolonies at different distances result in morphological changes, quantified using Python-based code, which we have made publicly available. Our results quantify peripheral and inner core deformation as well as differences in cellular viability of the two regions. The results reveal that the inner core, which is often characterized by the presence of wrinkled formations in the macrocolony, is more preserved than the periphery. Furthermore, the paper describes a modal shift in colony morphology which occurs when the distance from the chlorhexidine source is 0.5 cm, as well as changes observed in the growth substrate of macrocolonies exposed to chlorhexidine.

Bacillus subtilis Biofilms in the Presence of Chlorhexidine – a Computational Approach to Morphology and Growth Kinetics Analysis

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Computational quantification and characterization of independently evolving cellular subpopulations within tumors is critical to inhibit anti-cancer therapy resistance

Deep learning method for pinhole array color image reconstruction.

An open-source computational tool for measuring bacterial biofilm morphology and growth kinetics upon one-sided exposure to an antimicrobial source

Bacillus subtilis Biofilms in the Presence of Chlorhexidine – a Computational Approach to Morphology and Growth Kinetics Analysis