Background: To evaluate the role of γ-H2AX in peripheral blood lymphocytes (PBLs) as a predictive biomarker of the severity of oral mucositis (OM) in head and neck cancer (HNC) patients with receiving radiotherapy. Methods: In vitro assays for evaluating DNA damage and repair kinetics were performed on blood samples withdrawn from 25 HNC patients undergoing radiotherapy or chemoradiotherapy before radiotherapy. As for the in vivo study, blood samples were also withdrawn before radiotherapy, and 1 hour after radiotherapy on the fourth and last days. Flow cytometry was used to assess the expression of γ-H2AX in PBLs. OM was assessed using the World Health Organization (WHO) scores twice a week and correlated with the expression of γ-H2AX. Results: The in vitro assay results showed that patients with severe OM had higher γ-H2AX-specific relative fluorescence at various irradiation doses in the damage kinetics assay, with significantly higher γ-H2AX expression at 8 Gy (p = 0.039), and also at 24 hours after irradiation at a dose of 2 Gy in the repair kinetics assay, compared to the patients with mild OM (p = 0.008). The optimal cutoff value for relative fluorescence of γ-H2AX was 0.960, 24 hours post-irradiation. However, there were no significant differences in γ-H2AX expression at different times between the two groups, as assessed with the in vivo assay. Conclusions: These results suggest that the damage and repair kinetics of γ-H2AX from PBLs in the in vitro study may have predictive value for identifying the grades of OM among HNC patients prior to radiotherapy.

Over-expression of epidermal growth factor receptor (EGFR) or insulin-like growth factor-1 receptor (IGF-1R) have been shown to closely correlate with radioresistance of breast cancer cells. This study aimed to investigate the impact of co-inhibition of EGFR and IGF-1R on the radiosensitivity of two breast cancer cells with different profiles of EGFR and IGF-1R expression. The MCF-7 (EGFR +/−, IGF-1R +++) and MDA-MB-468 (EGFR +++, IGF-1R +++) breast cancer cell lines were used. Radiosensitizing effects were determined by colony formation assay. Apoptosis and cell cycle distribution were measured by flow cytometry. Phospho-Akt and phospho-Erk1/2 were quantified by western blot. In vivo studies were conducted using MDA-MB-468 cells xenografted in nu/nu mice. In MDA-MB-468 cells, the inhibition of IGF-1R upregulated the p-EGFR expression. Either EGFR (AG1478) or IGF-1R inhibitor (AG1024) radiosensitized MDA-MB-468 cells. In MCF-7 cells, radiosensitivity was enhanced by AG1024, but not by AG1478. Synergistical radiosensitizing effect was observed by co-inhibition of EGFR and IGF-1R only in MDA-MB-468 cells with a DMF10% of 1.90. The co-inhibition plus irradiation significantly induced more apoptosis and arrested the cells at G0/G1 phase in MDA-MB-468 cells. Only co-inhibition of EGFR and IGF-1R synergistically diminished the expression of p-Akt and p-Erk1/2 in MDA-MB-468 cells. In vivo studies further verified the radiosensitizing effects by co-inhibition of both pathways in a MDA-MB-468 xenograft model. Our data suggested that co-inhibition of EGFR and IGF-1R synergistically radiosensitized breast cancer cells with both EGFR and IGF-1R high expression. The approach may have an important therapeutic implication in the treatment of breast cancer patients with high expression of EGFR and IGF-1R.

Background: Radiotherapy (RT) is a core modality for cancer treatment, yet it is plagued by inter-observer variability in target delineation, inefficient manual workflows, and challenges in fusing multi-type clinical data. Large language models (LLMs), with their superior semantic understanding and cross-modal fusion capabilities present novel solutions to these challenges. Scope: This narrative review provided a comprehensive overview of the current landscape and emerging trends of LLM applications across the entire RT workflow. Findings: LLMs demonstrated substantial clinical utility in key RT domains, including automated target volume delineation (e.g., Medformer, Radformer), dose prediction (e.g., DoseGNN), treatment planning automation (e.g., GPT-Plan), patient education, clinical decision support, medical information extraction, and prognosis assessment. These applications not only have the potential to enhance the accuracy and efficiency of RT but also facilitate the standardization of clinical pathways. However, widespread clinical adoption was impeded by critical limitations, including model hallucinations, insufficient generalizability, and unresolved issues regarding data privacy and ethical governance. Conclusions: LLMs possessed transformative potential to revolutionize radiation oncology. Future endeavors should prioritize technical refinements to mitigate model deficiencies, establish standardized evaluation benchmarks, and develop robust ethical frameworks. These concerted efforts are crucial for translating LLM research into clinical practice and advancing the era of intelligent, precision RT.