Cancer stem cells (CSCs) are a major contributor to therapeutic resistance, tumour recurrence, and disease progression because of their self-renewal capacity, phenotypic plasticity, immune evasion, and ability to persist after treatment. Chimeric antigen receptor (CAR)-based immunotherapy has transformed the treatment of several hematological malignancies, but effective targeting of CSC-driven disease, especially solid tumour, remains challenging. Antigen heterogeneity and loss, CSC plasticity, CAR-cell dysfunction, metabolic constraints, and an immunosuppressive tumour microenvironment (TME) can limit long-term therapeutic responses. CSC-associated antigens such as CD133, CD44, EpCAM, HER2, CSPG4, ALDH, and EGFRvIII have been investigated as CAR targets, although their heterogeneous and context-dependent expression raises important concerns regarding CSC specificity and on-target/off-tumour toxicity. Artificial intelligence (AI) and computational approaches provide opportunities to integrate genomic, transcriptomic, proteomic, spatial, and clinical data for CSC-associated antigen discovery, biomarker identification, CAR design, patient stratification, and toxicity prediction. However, the evidence supporting these applications varies considerably, ranging from computational and preclinical studies to retrospective clinical modelling, while prospective clinical validation of AI-guided CSC-targeted CAR therapy remains limited. Similarly, next-generation platforms, including CAR-natural killer (CAR-NK) cells, CAR-macrophages (CAR-M), gamma-delta CAR-T cells, and CAR-invariant natural killer T (CAR-iNKT) cells, remain at different stages of translational development. This review critically examines CSC biology, mechanisms of resistance to CAR-based therapy, emerging CAR platforms, and the potential role of AI in precision immunogenetics. Particular emphasis is placed on differentiating CSC-intrinsic, CAR-cell-intrinsic, and TME-mediated resistance and on defining the evidence maturity of AI applications. Finally, current challenges in data availability, tumour heterogeneity, model bias, external validation, interpretability, safety, and prospective clinical translation are discussed, together with priorities for developing clinically validated AI-enabled CSC-targeted CAR therapies.
Abstract Background Prostate cancer is major cause of cancer-related morbidity and mortality in men worldwide. Emerging evidence highlights the dragon scale fern ( Pyrrosia piloselloides or P. piloselloides ) has demonstrated anticancer potential, but its molecular mechanisms against prostate cancer remain unclear. The study aimed to investigate its potential anticancer compounds and underlying molecular mechanisms using an integrated in silico approach. Methods A structured computational workflow integrating network pharmacology, molecular simulations, and MM/GBSA analysis was employed to explore the potential mechanism of bioactive compounds of P. piloselloides against prostate cancer-related targets. Additionally, the pharmacokinetic and toxicity profiles were evaluated using ADMET prediction. Results Eight bioactive compounds of the plants were identified as potentially interacting with 32 prostate cancer-related proteins, including nine key targets: ESR1, MYC, CTNNB1, PPARG, AKT1, STAT3, BCL2, AR, and ERBB2. Clustering analysis identified 20 proteins within the major cluster, that subsequently dianalisis enrichment idapati bahwa mereka were associated with 235 molecular functions, 142 cellular components, and more than 1,000 biological processes, including epithelial cell proliferation dan identified terlibat dalam 161 enriched pathways, including the PI3K–Akt and ERBB signaling pathways. Docking analysis revealed variable predicted binding affinities, particularly for 7-dehydrodiosgenin 3-acetate against ERBB2 (− 11.85 kcal/mol), AKT1 (− 9.49 kcal/mol), and BCL2 (− 9.38 kcal/mol), stigmasterol against PPARG (− 9.63 kcal/mol), ERBB2 (− 9.94 kcal/mol), and AR (− 9.31 kcal/mol), and 6-hydroxymethaqualone against ESR1 (− 9.34 kcal/mol). Molecular dynamics simulations indicated overall structural stability with localized flexibility, while MM/GBSA analysis yielded favorable predicted binding free energies for ERBB2 with 6-hydroxymethaqualone (− 45.57 kcal/mol), PPARG with stigmasterol (− 41.52 kcal/mol), and BCL2 with 7-dehydrodiosgenin 3-acetate (− 23.35 kcal/mol). The compounds also showed generally favorable predicted ADMET profiles, providing basis for further experimental evaluation. Conclusion The integrated computational analysis identified 6-hydroxymethaqualone, stigmasterol, and 7-dehydrodiosgenin 3-acetate as compounds with promising predicted interactions across prostate cancer-related targets. These findings provide a basis for further investigation and experimental validation.
Abstract Background This study aimed to identify and optimize a multi-epitope vaccine construct against Epstein–Barr virus (EBV)-associated Burkitt lymphoma, with particular emphasis on adjuvant selection, using an immunoinformatics approach. Methods CTL, HTL, and B-cell epitopes derived from gp350, EBNA1, LMP2A, and BZLF1 were screened based on their predicted immunological properties. We assembled candidate epitopes into multi-epitope vaccine constructs with different configurations and evaluated them using immunoinformatics analyses. Results A total of 39 CTL, 21 HTL, and 10 B-cell epitopes met the predefined selection criteria. Five multi-epitope vaccine constructs (R1–R5) were generated and compared. The 50 S ribosomal protein L7/L12 was selected as the adjuvant, and R5 was selected as the final construct based on its comparatively favorable predicted tertiary structure and molecular interactions with TLR2 and TLR4. R5 consisted of 790 amino acids and showed favorable predicted antigenicity (0.6479), solubility, non-allergenicity, and non-toxicity. The predicted population coverage was 98.46% worldwide. Molecular docking demonstrated favorable interactions with TLR2 and TLR4, while iMODS analysis indicated structural stability and favorable dynamic properties.Immune simulation predicted both cellular and humoral immune responses following three administrations at 28-day intervals. Conclusion The L7/L12-adjuvanted R5 construct demonstrated favorable predicted immunological, structural, and molecular characteristics in silico. These findings support its consideration as a candidate for further experimental investigation against EBV-associated Burkitt lymphoma. However, experimental validation is required to determine its actual immunogenicity, safety, and protective efficacy.
Abstract Background HER-2 positive breast cancer accounts for 15–20% of cases and is associated with aggressive behavior and poor prognosis. Anti-HER-2 therapy, including trastuzumab and pertuzumab, has improved outcomes in Western populations; however, real-world data in Egyptian patients remain limited. This study evaluated pathological response, disease-free survival (DFS), and overall survival (OS) among Egyptian patients with non-metastatic HER-2 positive breast cancer treated before and after the introduction of anti-HER-2 therapy. Methods We retrospectively analyzed 200 female patients with non-metastatic HER-2 positive breast cancer treated in two treatment eras: before the introduction of anti-HER-2 therapy (2014–2016, n = 100) and after its introduction (2020–2022, n = 100). Patients in the later era received trastuzumab alone or trastuzumab plus pertuzumab in the neoadjuvant or adjuvant setting. Outcomes included pathological complete response (pCR), DFS, OS. Survival outcomes were assessed using Kaplan–Meier analysis and compared using the log-rank test. Univariable and multivariable Cox proportional hazards regression analyses were performed for DFS and OS. Results Anti-HER-2 therapy was associated with a higher pCR rate among patients who received neoadjuvant treatment (40.0% vs. 10.3%, p < 0.001) and more favorable postoperative tumor and nodal staging. The median follow-up duration was 51.2 months in the non-anti-HER-2 group and 40.0 months in the anti-HER-2 group. During follow-up, disease recurrence occurred in 60% of patients in the non-anti-HER-2 group compared with 17% in the anti-HER-2 group ( p < 0.001), while overall mortality was 13% in the anti-HER-2 group compared with 59% in the non-anti-HER-2 group ( p < 0.001). Multivariable Cox regression analysis showed that anti-HER-2 therapy was associated with improved DFS (HR 0.274, 95% CI 0.108–0.697; P = 0.007) and OS (HR 0.260, 95% CI 0.093–0.728; P = 0.010). Conclusion In this retrospective study of Egyptian patients with non-metastatic HER-2 positive breast cancer, treatment in the later era, when anti-HER-2 therapy was available, was associated with higher pathological response rates and improved DFS and OS. However, given the historical-cohort design and differences in baseline characteristics and treatment strategies between the two eras, these findings cannot establish a causal effect of anti-HER-2 therapy alone. Larger multicenter studies are warranted to further evaluate outcomes and address treatment-era and selection-related confounding in the Egyptian setting.
Ferroptosis is a distinct form of regulated cell death driven by iron-dependent lipid peroxidation and oxidative membrane damage, and it differs fundamentally from apoptosis, necroptosis, and pyroptosis in its molecular mechanisms and execution pathways. Increasing evidence indicates that ferroptosis plays a pivotal role in cancer biology by regulating tumour initiation, progression, metastasis, therapeutic resistance, and interactions within the tumour microenvironment (TME). Dysregulation of iron metabolism, lipid peroxidation, amino acid metabolism, and antioxidant defence systems, including the System Xc⁻–GSH–GPX4, FSP1–CoQ₁₀, GCH1–BH4, and DHODH pathways, collectively determines cellular susceptibility to ferroptosis. Recent studies have further highlighted the contribution of organelle crosstalk, immune cell regulation, and stromal interactions in shaping ferroptotic responses and tumour evolution. In this review, we comprehensively summarise the molecular mechanisms underlying ferroptosis, its regulatory signalling networks, and the major milestones that have advanced the field. We further discuss the multifaceted role of ferroptosis across diverse malignancies, its influence on epithelial–mesenchymal transition, tumour invasion, metastasis, and immune modulation, and its emerging significance in overcoming resistance to chemotherapy, radiotherapy, targeted therapy, and immunotherapy. In addition, we critically evaluate recent progress in ferroptosis-based therapeutic strategies, including nanomedicine, combination therapies, and translational advances, while highlighting current clinical challenges such as tumour heterogeneity, limited pharmacodynamic biomarkers, and safety concerns. Collectively, ferroptosis represents a promising therapeutic vulnerability that bridges fundamental cancer biology with precision oncology. Continued development of biomarker-guided patient stratification, targeted drug delivery systems, and rational combination strategies is expected to accelerate the clinical translation of ferroptosis-based therapies and improve outcomes for patients with refractory and treatment-resistant cancers.
