DNA damage threatens genome integrity and contributes to cancer, ageing, and other diseases. Conventional assays have established the foundations of DNA damage research, but many provide low-throughput, qualitative, or indirect measurements and cannot localize lesions across the genome. DNA damage omics addresses these limitations by coupling lesion-specific enrichment or enzymatic labeling with next-generation sequencing to profile damage at genome-wide, and in some cases single-base or single-cell, resolution. This review first summarizes the major sources and types of DNA damage and the capabilities of classical detection methods. It then organizes sequencing strategies for double-strand breaks, single-strand breaks, DNA cross-links, and base lesions according to their capture principles, resolution, and biological readouts. For each lesion class, we relate the molecular event captured by an assay to its experimental requirements, principal sources of bias, and most appropriate applications, thereby distinguishing direct lesion mapping from measurements of repair intermediates or downstream genomic consequences. Across these approaches, integration with chromatin, epigenomic, transcriptomic, single-cell, and spatial data indicates that DNA damage is distributed non-randomly and is shaped by sequence context, chromatin state, and three-dimensional genome organization. The review also compares major technical constraints, including enrichment bias, limited sensitivity in low-input samples, incomplete coverage of repetitive or GC-extreme regions, and the lack of standardized analytical pipelines. Emerging long-read, spatiotemporal, and single-cell approaches may improve lesion-to-mutation mapping and support biomarker discovery and therapeutic stratification. By linking detection principles to appropriate biological questions and explicitly defining current limitations, this review provides a practical framework for selecting DNA damage sequencing methods and for advancing their mechanistic and translational use.
Cancer stem cells (CSCs) represent a dynamic tumor cell state associated with self-renewal, phenotypic plasticity, disease recurrence, and therapeutic resistance. CSC-targeted cellular immunotherapy, including chimeric antigen receptor (CAR)-based approaches, is therefore constrained by heterogeneous antigen expression, phenotypic switching, tumor microenvironmental suppression, and the risk of on-target, off-tumor toxicity. These challenges are particularly pronounced in solid malignancies, where limited immune-cell trafficking, hypoxia, metabolic competition, and stromal barriers complicate treatment compared with hematological cancers. Artificial intelligence (AI) gives a framework for integrating high-dimensional molecular, cellular, spatial, and clinical data to address these limitations. Single-cell and multi-omics machine learning can identify stemness-associated states and resistance programs that may be missed by conventional single-marker approaches, while predictive models can prioritize tumor-selective antigens and anticipate treatment resistance or toxicity. Computational protein-design approaches further enable optimization of CAR antigen-binding domains, with emerging experimental studies demonstrating improved receptor performance using AI-designed binders. AI may also support multi-antigen and logic-gated CAR selection and longitudinal monitoring of CSC-state transitions during treatment. However, most current applications remain computational or preclinical, and clinical utility has not yet been established prospectively. This review examines AI strategies for overcoming CSC-associated resistance and improving therapeutic specificity and safety, while distinguishing computational, preclinical, retrospective clinical and prospective clinical evidence. Integrating AI with molecular engineering, experimental validation, and prospective clinical evaluation may enable more adaptive, selective and safer CSC-targeted cellular immunotherapies. Particular emphasis is placed on disease-specific modeling, tumor-versus-normal discrimination, resistance-network integration, external validation and reproducibility as prerequisites for responsible clinical translation across diverse patient populations.
Pancreatic ductal adenocarcinoma (PDAC) remains largely refractory to immunotherapy. Stereotactic body radiotherapy (SBRT) delivers ablative, immunomodulatory doses that may render this immunologically inert tumor susceptible to immune control, although the relevant effects are hard to attribute to radiation alone. Here we synthesize how SBRT reshapes the PDAC microenvironment, applying throughout an evidence hierarchy that separates changes seen after SBRT given without concurrent immunotherapy from those arising in combination therapy. We propose, as a testable hypothesis rather than as an established conclusion, that SBRT produces a dose-tunable balance between activation and suppression rather than either alone. The activating arm is a real but spatially constrained immunostimulatory response, comprising antigen release, dendritic-cell cross-presentation, and cytotoxic T cell recruitment. The suppressive arm is a reversible, radiation-induced program on four coordinated axes: myeloid recruitment, regulatory T cell and transforming growth factor-β activation, desmoplastic stromal exclusion, and adaptive checkpoint induction. Dose and fractionation determine the balance point, but the two arms rest on unequal evidence: activation on resected human specimens, suppression on murine causality outside a single profiling study. The practical implication is that the most promising agent to add to irradiation is the one releasing whichever brake dominates in that tumor. Trial data are consistent with this. SBRT with pembrolizumab and trametinib prolonged survival in locally recurrent resected disease, whereas SBRT with nivolumab, with or without ipilimumab, did not in refractory metastatic disease. We close by outlining a three-layer biomarker strategy for patient selection—spanning spatial tissue architecture, tumor antigenicity, and circulating dynamics, and with the paired-sampling, stratified, and sequence-testing designs needed to adjudicate it.
Hepatocellular carcinoma (HCC) accounts for a substantial proportion of cancer mortality worldwide, and clinical outcomes remain poor for patients with advanced disease. T cell receptor-engineered T cells (TCR-T) represent an attractive immunotherapeutic modality because they can recognize peptide epitopes derived from intracellular tumor proteins and displayed on the cell surface by major histocompatibility complex molecules. This review critically summarizes recent progress in TCR-T therapy for HCC, with particular emphasis on innovations in TCR engineering. We survey the current landscape of targetable antigens in HCC and current approaches for identifying therapeutic TCR candidates, followed by a discussion of TCR-T and review their major safety and toxicity concerns. Importantly, we discuss the key challenges that limit the clinical efficacy, particularly the immunosuppressive tumor microenvironment, antigen heterogeneity, HLA restriction, and limited T cell persistence. We further highlight emerging strategies to enhance therapeutic efficacy and safety, including genetic engineering, metabolic and functional optimization, combinational approaches, and controllable safety mechanisms. Special considerations regarding TCR-T therapy for HCC recurrence after liver transplantation are also discussed. Finally, we outline future directions centered on refined target selection, artificial intelligence-assisted TCR discovery and design, scalable manufacturing, biomarker-driven patient selection, and rigorous safety evaluation. These insights provide a structured perspective on the current status of TCR-T therapy and the key steps required to facilitate its clinical translation in HCC.
Persistent infection with carcinogenic human papillomavirus (HPV) types causes the great majority of cervical cancers. For more than a decade, randomized trials, surveillance studies, and national registry analyses have demonstrated reductions in vaccine-type HPV infection, high-grade cervical precancer, and invasive cervical cancer following HPV vaccination. Direct evidence regarding cervical cancer mortality has emerged more recently because cohorts vaccinated in early adolescence first had to reach ages at which cervical cancer deaths became observable. A 2026 population-based analysis from England reported no cervical cancer deaths among the most highly vaccinated women aged 20 to 24 years during 2020 to 2024, with substantial mortality reductions also observed in older vaccinated cohorts, although uncertainty increased with age. This narrative review synthesizes the biological and epidemiologic evidence linking HPV vaccination to mortality prevention, critically appraises the England mortality study and its age-stratified findings, considers limitations related to cervical screening and the modeled counterfactual, and discusses implications for cervical cancer elimination, including relevance to Qatar and the wider Eastern Mediterranean region. Current evidence supports early adolescent HPV vaccination as a central cancer-prevention intervention, while continued cervical screening, timely treatment, high and equitable vaccine coverage, and long-term surveillance remain essential. The available mortality evidence is compelling at the population-program level but should not be interpreted as an individual-level randomized estimate of vaccine effectiveness against cervical cancer death.
