Search Results (1,705)

Breast cancer stem-like cells (bCSCs) fundamentally represent a highly dynamic “immune-adaptive functional state” rather than a fixed cellular lineage, serving as the core engine driving tumor recurrence, metastasis, and therapeutic resistance. Despite rapid advances, the heterogeneity of bCSC states and their intricate interactions with the immune microenvironment lack systematic integration. This review centers on the dynamic evolution and niche adaptation of bCSCs. First, we systematically dissect the multilayered regulatory network maintaining stemness, encompassing core transcription factors, epigenetic–metabolic coupling, and the synergistic mechanisms of critical signaling pathways such as Wnt and Notch. Second, we propose a trinary “stemness–immune–spatial” feedback model, elucidating how bCSCs achieve active immune evasion by downregulating antigen presentation, secreting immunosuppressive factors, and embedding within perivascular “immune-cold niches.” Finally, leveraging a multi-omics integration perspective, we reconstruct precision intervention strategies, exploring the synergistic potential of targeting stemness pathways in conjunction with immunotherapies like PD-1/PD-L1 blockade and STING agonists. Furthermore, we highlight the pivotal role of integrating organoids, PDX models, and AI-assisted decision systems in overcoming heterogeneity and enabling personalized treatment. By establishing a closed-loop framework spanning mechanistic insight to spatially precise intervention, this review aims to provide novel theoretical foundations and translational pathways to surmount the bottleneck of therapeutic resistance in breast cancer. Full article

Background/Objectives: Financial toxicity (FT) is increasingly recognised as a critical dimension of the cancer care continuum, reflecting both objective financial burden and subjective financial distress arising from cancer-related care. Head and neck cancers (HNC) may be particularly vulnerable to FT because treatment often involves multimodal care, functional morbidity, prolonged rehabilitation, and disruption to employment. This scoping review mapped and synthesised the literature on FT in a focused subset of head and neck cancers (HNC), namely malignancies of the oral cavity, oropharynx, nasopharynx, sinonasal tract, and major and minor salivary glands. Methods: A scoping review was conducted in accordance with the methodological guidance of the Joanna Briggs Institute for scoping reviews to identify and synthesise studies addressing FT in the selected HNC subsites. Searches were undertaken in MEDLINE, Embase, Scopus, Web of Science, CINAHL, EconLit, and Global Index Medicus for English-language studies published between 1 January 2015 and 1 January 2025. The search window was restricted to this period to capture the more contemporary evolution of FT as a distinct research construct in oncology. Eligible studies included adult patients and reported patient-level FT outcomes, including direct costs, indirect costs, out-of-pocket expenditure, financial hardship, financial distress, employment disruption, or related economic strain. Findings were synthesised narratively and organised thematically. Results: Twenty-five studies published between 2015 and 2025 were included. The evidence base was dominated by cross-sectional and retrospective designs, with limited prospective follow-up and very little intervention-focused research. FT was conceptualised heterogeneously across studies, spanning direct expenditure, indirect and non-medical costs, subjective financial distress, and coping-related consequences. Questionnaire-based approaches were used in 13 studies, but only a smaller subset employed FT-specific instruments such as COST. Across the literature, FT was most commonly associated with lower income, weaker financial protection, employment disruption, rural residence in some settings, and more intensive treatment. Reported downstream associations included poorer quality of life, psychological distress, care alteration, and work-related burden, although evidence for treatment delay or survival effects was more limited and should be interpreted cautiously. Conclusions: In this focused HNC subset, FT appears multidimensional, socially patterned, and clinically relevant. However, the literature remains methodologically fragmented, with inconsistent measurement and sparse longitudinal evidence. Future work should prioritise validated and tumour-specific assessment strategies, prospective study designs, and evaluation of mitigation interventions that address both direct and indirect burden across the cancer continuum. Full article

Background: Targeted therapies directed against oncogenic drivers have substantially improved outcomes for patients with epidermal growth factor receptor (EGFR)-mutant and anaplastic lymphoma kinase (ALK)-rearranged non-small cell lung cancer (NSCLC). Despite high initial response rates, most patients ultimately develop acquired resistance to tyrosine kinase inhibitors (TKIs), reflecting complex biological adaptations under therapeutic pressure. Methods: This narrative review synthesizes experimental, translational, and clinical studies examining how environmental and occupational respiratory exposures may influence resistance mechanisms in EGFR- and ALK-driven NSCLC. The review emphasizes exposure-associated signaling plasticity, inflammatory microenvironmental modulation, metabolic reprogramming, and pharmacokinetic alterations. Results: Recent evidence suggests that respiratory exposures, including cigarette smoke, air pollution, diesel exhaust, and occupational inhalational toxicants, can modulate oncogenic signaling networks relevant to resistance to targeted therapies. These mechanisms include aberrant EGFR activation, bypass signaling through the mesenchymal–epithelial transition receptor (MET) and SRC pathways, epithelial–mesenchymal transition (EMT), adaptive kinome remodeling, and exposure-associated inflammatory signaling, all of which may influence tumor evolution and therapeutic response. Conclusions: This review introduces a novel exposome-driven conceptual framework integrating environmental exposures with signaling plasticity and resistance evolution in oncogene-driven NSCLC. These findings support the concept that the respiratory exposome may represent an underrecognized modifier of targeted therapy response. Incorporating structured exposure assessment into precision oncology approaches may refine risk stratification and inform exposure-aware therapeutic strategies. Full article

We performed a protein-docking study for eight DNA aptamers (SEQ1–SEQ8) against chicken Cluster of Differentiation 40 (chCD40), which were experimentally identified via SELEX in our previous study. In silico and molecular docking analyses were performed to predict and obtain the secondary and tertiary structures of the aptamers. Aptamers SEQ3 and SEQ4, which showed the best inhibitory effects, were selected and utilized to produce a DNA-based vaccine adjuvant using rolling circle amplification (RCA). These aptamers had been previously characterized via mass spectroscopy to determine their molecular weight and regions that could potentially interact with chCD40. In the present study, these results were corroborated and expanded. A series of free software methods, including Mfold v.1.0, 3dADN v.2.0, ClusPro v.2.0, Hdock v.1.0, and PLIP v.1.0, were used to determine the aptamers’ secondary and tertiary structures and docking interactions, as well as the specific residues involved in the interactions and their distances. The structures were used to explain and thus understand their effect on the binding, selectivity, and stability of the aptamers. The main objective of the study was to determine whether these aptamers could be used as vaccine adjuvants against viral and bacterial pathogens, specifically chicken avian influenza. The docking results were in good agreement with the experimental and biological results. The procedure employed in this study could be an easy and effective tool for exploring the potential of the new technology of systematic evolution of ligands by exponential enrichment (SELEX) in the preparation of aptamers to control viral and bacterial infections as well as diseases, such as cancer and Alzheimer’s. Full article

Glycolysis is a defining feature of cancer metabolism, originally described by the Warburg effect. Increasing evidence indicates that cancer-associated glycolysis is not uniformly upregulated but dynamically rewired in response to oncogenic signaling, cellular demands, and microenvironmental cues. However, a framework integrating its temporal evolution and functional roles across tumorigenesis remains limited. In particular, how glycolytic rewiring drives malignant transformation, adapts during tumor progression, and generates context-dependent vulnerabilities has not been systematically synthesized. In this review, we examine glycolysis as a dynamic metabolic network evolving throughout tumor development. We discuss how early glycolytic rewiring, driven by oncogenic signaling and metabolic–epigenetic coupling, supports cell fate transitions and establishes redox and biosynthetic capacity during tumorigenesis. We then outline how glycolysis is remodeled during tumor progression through coordinated transcriptional, epigenetic, and post-translational regulation, as well as microenvironmental interactions and metabolic heterogeneity. Furthermore, we highlight glycolysis as an integrative hub linking immune evasion, cell death regulation, and metabolic plasticity, and discuss how glycolytic rewiring creates context-dependent metabolic dependencies that may be therapeutically exploited, along with emerging technologies that enable high-resolution characterization of tumor metabolism. Together, this review provides a conceptual framework for understanding glycolytic rewiring in cancer and outlines potential avenues for targeting metabolic vulnerabilities. Full article

Clinical prediction from electronic health records (EHRs) is complicated by pervasive missingness and label scarcity, which make performance sensitive to the match between data conditions and pipeline choice. Choosing the best pipeline for a new incomplete dataset still requires costly trial-and-error. We cast this as an algorithm selection problem and address two bottlenecks—instance scarcity and distance quality—that have so far prevented meta-learning from reaching clinical settings. Graph neural networks offer diverse strategies (patient similarity networks, bipartite imputation graphs, attention-driven feature interaction), yet no single architecture dominates across missingness patterns, and selecting the best pipeline for a new dataset remains a trial-and-error approach. Formal algorithm selection could automate this choice but requires many characterized meta-instances—more than clinical settings typically provide. We propose two solutions: (1) constructive instance augmentation, applying controlled quality perturbations (MCAR and MNAR missingness injection, label trimming) to 20 base EHR datasets to expand the meta-knowledge base to 83 characterized meta-instances, each described by a 10-dimensional missingness fingerprint, without additional model training; and (2) dynamic-supervised metric learning, using differential evolution to optimize fingerprint feature weights so that static distances preserve method-performance similarity captured by dynamic fingerprints, which require model sweeps and are unavailable at deployment. Under base-dataset-level leave-one-dataset-out cross-validation over 21 pipelines, the resulting metric-learned kNN recommender attains the highest win rate (20.5%) among non-oracle strategies on the augmented store, selecting the correct pipeline more often than any fixed default. At deployment, the recommender needs only the 10-dimensional static fingerprint with pre-learned weights; no sweep data is required for new datasets. Cross-domain evaluation on 25 external subsets (colorectal cancer, kidney disease, MIMIC-IV) demonstrates framework modularity: when the fingerprint module is adapted (standard meta-features in place of the missingness-specific set), the recommender achieves regret of 0.025 (55% below random selection). Full article

Cancer exhibits pronounced heterogeneity at both spatial and cellular levels, contributing to variability in therapeutic responses and the emergence of treatment resistance. This heterogeneity is underscored by the diverse genetic, epigenetic, and phenotypic variations found within tumor cell populations. Cancer stem cells (CSCs), although representing a minor fraction of tumor cells, possess the capacity to self-renew and differentiate, thereby driving the dynamic evolution of tumor heterogeneity. CSCs interact intricately with various elements of the tumor microenvironment (TME), further amplifying this heterogeneity. Recent advancements in organoid technology have facilitated the development of CSC-derived organoid models that more faithfully recapitulate the TME and intratumoral heterogeneity, which conventional 2D culture systems fail to replicate. These CSC-derived organoid systems not only preserve the structural and genomic characteristics of tumors, but they also enable the exploration and evaluation of therapeutic strategies that reflect tumor complexity. However, CSC-derived organoid systems face several challenges, such as the rarity of CSCs, lack of standardized culture conditions, absence of TME components, limited predictive accuracy, and insufficient modeling of tumor heterogeneity. This review discusses these limitations and explores potential solutions, including the use of artificial intelligence (AI) to enhance treatment predictability. These innovations may improve the utility of organoid models for therapeutic evaluation and for targeting tumor heterogeneity. Ultimately, CSC-derived organoids may serve as a valuable platform for advancing precision medicine and cancer research. Full article

Breast reconstruction represents an integral component of contemporary breast cancer management, with substantial impact on patients’ psychological well-being, body image, and overall quality of life. Given the profound symbolic and personal significance of the breast, mastectomy—whether total or partial—extends beyond oncologic resection and may result in considerable aesthetic, functional, and psychosocial consequences. For this reason, reconstructive planning should be incorporated into the initial multidisciplinary treatment strategy while ensuring that oncologic safety and adjuvant therapies are never compromised. Breast reconstruction may be achieved using autologous tissue, implant-based techniques, or a combination of both approaches. Each method carries specific advantages, limitations, and potential complications and must be tailored to the individual patient’s oncologic status, anatomy, and expectations. This article provides a historical overview of the evolution of breast cancer treatment and reconstructive techniques. It further examines the principles, benefits, and challenges associated with different reconstructive modalities, highlighting key considerations in clinical decision-making and long-term outcomes. Full article

Gene amplification resulting from double strand breaks (DSBs) is a typical genetic alteration in tumorigenesis and drug-resistant progression. Amplified oncogenes and drug-resistant genes are present on extrachromosomal DNAs (ecDNAs), or chromosomal homogeneously staining regions (HSRs). Considering the role of mismatch repair (MMR) as a sensor of DSBs, we hypothesized that MMR may be involved in gene amplification. We used two MTX-resistant HT-29 colorectal cancer cell lines, which served as models with amplified genes mainly in HSRs or ecDNAs. Expression of MSH2, a key protein in MMR, was increased following the acquisition of MTX-resistant. MMR inhibition was achieved by depleting MSH2. Suppression of MMR led to decreased copy numbers of amplified genes as well as the quantity of ecDNAs and HSR. This was caused by the decreased efficiency of DSBs repair, which resulted from the reduced ability of MMR to recruit DSBs repair proteins. Additionally, it accelerated the formation of micronuclei (MN)/nuclear buds (NBUDs), which functioned to eliminate the amplified genes. Furthermore, the suppression of MMR was capable of inhibiting cell proliferation and enhancing MTX-sensitivity in ecDNA-containing cells. Conversely, suppression of MMR had no effect on gene amplification in HSR-containing cells. Our findings demonstrate that MMR plays a pivotal role in gene amplification through mediating DSBs repair pathways and facilitating the formation of MN/NBUDs in ecDNA-containing cells. MMR is likely to emerge as a prime therapeutic target worthy of in-depth exploration in future clinical investigations. Full article

Drug resistance is a major cause of treatment failure in breast cancer, yet mutation-centered models do not fully explain delayed resistance, reversible tolerance, or re-sensitization after treatment interruption. Here, we synthesize recent findings in drug-tolerant persister (DTP) biology, clonal evolution, and tumor ecosystem dynamics to propose a breast cancer-focused Resistance Continuum as a conceptual framework for organizing the transition from initial therapy to stable resistance across ER-positive, HER2-positive, and triple-negative disease. Here, we synthesize recent findings in drug-tolerant persister (DTP) biology, clonal evolution, and tumor ecosystem dynamics to propose a breast cancer-focused Resistance Continuum as a conceptual framework for organizing the transition from initial therapy to stable resistance across ER-positive, HER2-positive, and triple-negative disease. This framework describes a canonical, but not universal, trajectory spanning treatment-naïve heterogeneity, pre-adaptive priming, reversible DTP states, cycling persisters, and genetically stabilized resistant clones. We discuss how epigenetic and metabolic plasticity may sustain persistence, and we present epigenetic memory as an emerging hypothesis linking repeated non-genetic persistence to facilitated resistance in selected contexts. We also compare subtype-specific features of DTP biology, outline a multi-omics roadmap for interrogating the continuum, and highlight therapeutic opportunities for resistance interception. Overall, the Resistance Continuum is intended as a working scaffold to integrate current evidence and guide future mechanistic and translational studies. Full article

Background/Objectives: Cancer remains one of the leading causes of mortality worldwide, with lung and colon cancers among the most prevalent. Conventional histopathological diagnosis is time-consuming, requires expert pathologists, and is susceptible to human error. Methods: To address these limitations, this study proposes an automated classification framework for lung and colon cancer using histopathological images. The proposed method employs a lightweight pretrained deep learning model, MobileNetV3-Small, through transfer learning. Training is performed on an enhanced version of the LC25000 dataset, in which redundant image patches are removed to improve robustness and clinical generalizability. The images were initially available in multiple resolutions, which are resized to 224 × 224 × 3 to match the canonical input size of MobileNetV3-Small. Deep features are extracted from the dropout layer as it provides regularized representation of high-level features by reducing the overfitting (dimension N × 1024), which are optimized using a differential evolution algorithm, reducing the feature space to N × 60. These optimized features are evaluated using multiple classifiers. Results: Experimental results demonstrate a maximum classification accuracy of 98.14% using a Quadratic Support Vector Machine (SVM) and a 21.3× speed-up achieved with bagged trees, outperforming several state-of-the-art approaches representing a 3.34% improvement over the baseline study on the enhanced dataset. Conclusions: The results confirm that the proposed framework effectively balances high accuracy with computational efficiency. The use of a lightweight deep model combined with feature optimization makes the approach well-suited for practical clinical environments. Full article

Breast-conserving therapy, consisting of lumpectomy followed by adjuvant radiotherapy, is the standard of care for early-stage breast cancer, providing oncologic outcomes equivalent to mastectomy while preserving breast anatomy and quality of life. Radiotherapy remains a cornerstone of treatment across disease stages, significantly reducing local recurrence rates and improving long-term survival. Advances in radiotherapy techniques—including conventional fractionation, hypofractionation, tumor-bed boost delivery, and regional nodal irradiation—have optimized oncologic efficacy while inducing a broad spectrum of time-dependent morphological changes in breast tissue. Accurate imaging surveillance is therefore essential to distinguish expected post-radiotherapy changes from tumor recurrence and to avoid unnecessary diagnostic or therapeutic interventions. This review provides a comprehensive overview of contemporary breast radiotherapy protocols, their impact on post-treatment imaging appearances, and current recommendations for imaging surveillance. Characteristic findings across mammography, ultrasound, magnetic resonance imaging, and nuclear medicine modalities are discussed, with emphasis on their temporal evolution from acute inflammatory changes to chronic fibrosis, fat necrosis, and architectural distortion. Recognition of these imaging patterns, together with integration of radiotherapy-related parameters into image interpretation, is crucial for accurate diagnosis, early detection of recurrence, and informed clinical management of breast cancer survivors. Full article

Background: Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer and is generally associated with an excellent prognosis. Historically, treatment strategies were uniform and frequently aggressive, including total thyroidectomy and routine radioiodine ablation, even in low-risk cases. Current Perspective: Over the past decade, the management of PTC has shifted toward a de-escalation paradigm. This transition is driven by high evidence showing that the majority of PTCs follow an indolent course, with low recurrence and mortality rates. As a result, there is increasing emphasis on tailoring the extent of surgery and adjuvant therapy to individual patient risk profiles. Active surveillance, hemithyroidectomy, and selective use of radioiodine now represent valid alternatives to traditional radical approaches, particularly for low-risk tumors. Clinical Implications: The goal of this evolution is to balance oncologic safety with quality of life, reducing overtreatment and minimizing long-term complications such as hypoparathyroidism or recurrent laryngeal nerve injury. Personalized treatment decisions are now guided by tumor biology, molecular markers, and refined risk stratification systems. Conclusions: This article will review the current evidence supporting this shift, highlight the challenges of implementation in clinical practice, and discuss future trends in the management of papillary thyroid carcinoma. Full article

Background: Esophagectomy remains the definitive curative treatment for esophageal cancer but is historically burdened by significant procedure-related morbidity. Anastomotic leakage (AL) is still the “Achilles’ heel” of esophageal surgery, serving as a primary benchmark for surgical quality due to its profound impact on patient recovery, healthcare costs, and long-term oncological outcomes. While surgical expertise and perioperative care have matured, reported AL rates remain persistently high. This necessitates a shift in focus from purely technical modifications toward integrated, data-driven preventive strategies. Purpose: Five years after our initial review, this update synthesizes the rapid evolution in AL prevention. We evaluate the transition from empirical surgical pragmatism to evidence-based protocols, integrating recent breakthroughs in real-time perfusion monitoring, prophylactic endoluminal technologies, and multidisciplinary patient optimization. This work provides a contemporary “roadmap” for navigating the complexities of esophageal reconstruction. Conclusions: The prevention of AL has evolved into a multimodal “bundle” that begins well before the index operation. This review highlights the critical shift toward quantitative perfusion assessment via indocyanine green fluorescence angiography, which is increasingly replacing subjective visual inspection as the standard for anastomotic site selection. We discuss the emerging role of gastric ischemic preconditioning as a biological strategy to enhance conduit vascularity, alongside the paradigm of proactive management using preemptive endoluminal vacuum therapy to mitigate septic sequelae in high-risk cases. Furthermore, we examine technical refinements in conduit construction and conditioning—focusing on the ‘tension-perfusion’ relationship—and the essential role of structured prehabilitation within enhanced recovery after surgery frameworks. While the quality of evidence remains heterogeneous, the move toward standardized reporting and objective monitoring marks a new era of precision in esophageal surgery. Full article

Breast cancer remains the most frequently diagnosed malignancy in women worldwide, accounting for approximately 2.3 million new cases and 670,000 deaths annually. The diagnostic landscape has undergone a paradigm shift over the past two decades, evolving from morphology-based classification toward molecularly informed, precision-guided strategies. Early and accurate diagnosis is fundamental to improving outcomes; advances in imaging technology, including digital breast tomosynthesis (DBT), contrast-enhanced mammography (CEM), and abbreviated magnetic resonance imaging (MRI), have improved sensitivity and specificity in diverse patient populations. Simultaneously, the integration of artificial intelligence (AI) and radiomics into screening workflows offers unprecedented potential for risk stratification and a reduction in false-positives. At the pathological level, multi-gene expression profiling assays such as Oncotype DX, MammaPrint, Prosigna, and EndoPredict have refined prognostic classification and guide adjuvant chemotherapy decisions in early-stage hormone receptor-positive disease. The emergence of liquid biopsy, circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and exosomal biomarkers provides minimally invasive tools for real-time monitoring of response, residual disease, and the evolution of resistance mechanisms. Precision diagnostics now encompass next-generation sequencing (NGS)-based comprehensive genomic profiling, enabling identification of actionable alterations such as PIK3CA mutations, HER2 amplification, BRCA1/2 pathogenic variants, and NTRK fusions, each linked to approved therapeutic agents. The purpose of this review is to provide a comprehensive synthesis of current and emerging diagnostic modalities in breast cancer—from population-level screening to individualized molecular profiling—and to examine how integrative, multimodal diagnostic platforms are reshaping clinical decision-making in the era of precision medicine. Full article