Overcoming chemotherapy resistance in low-grade gliomas: A computational approach

Artificial intelligence in neuro-oncology: methodological bases, practical applications and ethical and regulatory issues

Artificial Intelligence (AI) is transforming neuro-oncology by enhancing diagnosis, treatment planning, and prognosis prediction. AI-driven approaches-such as CNNs and deep learning-have improved the detection and classification of brain tumors through advanced imaging techniques and genomic analysis. Explainable AI methods mitigate the "black box" problem, promoting model transparency and clinical trust. Mechanistic models complement AI by integrating biological principles, enabling precise tumor growth predictions and treatment response assessments. AI applications also include the creation of digital twins for personalized therapy optimization, virtual clinical trials, and predictive modeling for estimation of tumor resection and pattern of recurrence. However, challenges such as data bias, ethical concerns, and regulatory compliance persist. The European Artificial Intelligence Act and the Health Data Space Regulation impose strict data protection and transparency requirements. This review explores AI's methodological foundations, clinical applications, and ethical challenges in neuro-oncology, emphasizing the need for interdisciplinary collaboration and regulatory adaptation.

Understanding therapeutic tolerance through a mathematical model of drug-induced resistance

There is growing recognition that phenotypic plasticity enables cancer cells to adapt to various environmental conditions. An example of this adaptability is the ability of an initially sensitive population of cancer cells to acquire resistance and persist in the presence of therapeutic agents. Understanding the implications of this drug-induced resistance is essential for predicting transient and long-term tumor dynamics subject to treatment. This paper introduces a mathematical model of drug-induced resistance which provides excellent fits to time-resolved in vitro experimental data. From observational data of total numbers of cells, the model unravels the relative proportions of sensitive and resistance subpopulations and quantifies their dynamics as a function of drug dose. The predictions are then validated using data on drug doses that were not used when fitting parameters. Optimal control techniques are then utilized to discover dosing strategies that could lead to better outcomes as quantified by lower total cell volume.

The impact of cognitive behavioral therapy on disease uncertainty, stressful life events, quality of life, anxiety, and depression in glioma patients undergoing chemotherapy: a quasi-experimental study

OBJECTIVE

To investigate the effect of cognitive behavioral therapy on disease uncertainty and stressful life events in glioma patients undergoing chemotherapy.

METHODS

This quasi-experimental study enrolled 90 glioma patients from Sanmenxia Central Hospital between January and December 2021. Patients were divided into an intervention group (n = 45) or a control group (n = 45). The intervention group received cognitive behavioral therapy provided by nurses, while the control group received routine nursing care. Pre- and post-intervention assessments were conducted using the Mishel uncertainty in illness scale (MUIS), life events scale (LES), self-rating anxiety scale (SAS), self-rating depression scale (SDS), and quality of life scale (WHOQOL-BREF).

RESULTS

After four cycles of chemotherapy, the study group demonstrated a statistically significant decrease in MUIS and LES scores compared to the control group (p < 0.05). The study group showed significantly lower SAS and SDS scores than the control group (p < 0.05). Finally, the study group reported significantly higher WHOQOL-BREF scores than the control group (p < 0.05).

CONCLUSION

The study revealed that the group that received CBT showed significant improvements in the psychological well-being of glioma patients undergoing chemotherapy. These findings suggest that incorporating CBT into standard nursing care can effectively improve the psychological well-being and quality of life of glioma patients during chemotherapy.

Analysis of ABCC3 in glioma progression: implications for prognosis, immunotherapy, and drug resistance

As a primary brain cancer, glioma presents significant challenges in treatment and prognosis. Identifying reliable biomarkers is crucial for improving patient outcomes. This study focuses on the ABCC3 gene, exploring its function as a standalone predictive indictor and its correlation with immune infiltration and resistance to chemotherapy in glioma. A multi-faceted approach was adopted for this analysis. We scrutinized the RNA expression patterns of the ABCC3 gene across a spectrum of cancer types, with a concentrated focus on glioma. Our methodological arsenal included bioinformatics analysis, immunohistochemistry (ICH), western blot (WB), and cell counting Kit-8 (CCK8) assays. These techniques were instrumental in gauging the prognostic impact of ABCC3 and elucidating its associations with immune cell infiltration and chemotherapy resistance. The investigation revealed a significant elevated levels of ABCC3 in high grade glioma (HGG) tissues compared to lower grade glioma (LGG) tissues. Notably, upregulation of ABCC3 were associated with a shorter overall survival in patients with glioma. Furthermore, ABCC3 emerged as an independent factor in prognostication, with predictive capability for 1-, 3-, and 5-year survival rates. As far as immune response is concerned, ABCC3's expression correlates positively with the expression of several immune cells and checkpoint genes. The study also uncovered the role of ABCC3 in drug resistance, particularly regarding temozolomide (TMZ), a primary therapeutic agent in glioma treatment. The study reveals ABCC3 as a significant biomarker in glioma, associated with lower survival, enhanced immune infiltration, and increased resistance to chemotherapy. These findings emphasize its promise as a novel target for glioma therapies.

AMBER: A Modular Model for Tumor Growth, Vasculature and Radiation Response

Computational models of tumor growth are valuable for simulating the dynamics of cancer progression and treatment responses. In particular, agent-based models (ABMs) tracking individual agents and their interactions are useful for their flexibility and ability to model complex behaviors. However, ABMs have often been confined to small domains or, when scaled up, have neglected crucial aspects like vasculature. Additionally, the integration into tumor ABMs of precise radiation dose calculations using gold-standard Monte Carlo (MC) methods, crucial in contemporary radiotherapy, has been lacking. Here, we introduce AMBER, an Agent-based fraMework for radioBiological Effects in Radiotherapy that computationally models tumor growth and radiation responses. AMBER is based on a voxelized geometry, enabling realistic simulations at relevant pre-clinical scales by tracking temporally discrete states stepwise. Its hybrid approach, combining traditional ABM techniques with continuous spatiotemporal fields of key microenvironmental factors such as oxygen and vascular endothelial growth factor, facilitates the generation of realistic tortuous vascular trees. Moreover, AMBER is integrated with TOPAS, an MC-based particle transport algorithm that simulates heterogeneous radiation doses. The impact of radiation on tumor dynamics considers the microenvironmental factors that alter radiosensitivity, such as oxygen availability, providing a full coupling between the biological and physical aspects. Our results show that simulations with AMBER yield accurate tumor evolution and radiation treatment outcomes, consistent with established volumetric growth laws and radiobiological understanding. Thus, AMBER emerges as a promising tool for replicating essential features of tumor growth and radiation response, offering a modular design for future expansions to incorporate specific biological traits.

Transmembrane Protein TMEM230, Regulator of Glial Cell Vascular Mimicry and Endothelial Cell Angiogenesis in High-Grade Heterogeneous Infiltrating Gliomas and Glioblastoma

High-grade gliomas (HGGs) and glioblastoma multiforme (GBM) are characterized by a heterogeneous and aggressive population of tissue-infiltrating cells that promote both destructive tissue remodeling and aberrant vascularization of the brain. The formation of defective and permeable blood vessels and microchannels and destructive tissue remodeling prevent efficient vascular delivery of pharmacological agents to tumor cells and are the significant reason why therapeutic chemotherapy and immunotherapy intervention are primarily ineffective. Vessel-forming endothelial cells and microchannel-forming glial cells that recapitulate vascular mimicry have both infiltration and destructive remodeling tissue capacities. The transmembrane protein TMEM230 (C20orf30) is a master regulator of infiltration, sprouting of endothelial cells, and microchannel formation of glial and phagocytic cells. A high level of TMEM230 expression was identified in patients with HGG, GBM, and U87-MG cells. In this study, we identified candidate genes and molecular pathways that support that aberrantly elevated levels of TMEM230 play an important role in regulating genes associated with the initial stages of cell infiltration and blood vessel and microchannel (also referred to as tumor microtubule) formation in the progression from low-grade to high-grade gliomas. As TMEM230 regulates infiltration, vascularization, and tissue destruction capacities of diverse cell types in the brain, TMEM230 is a promising cancer target for heterogeneous HGG tumors.