Packaging of the eukaryotic genome with histone and other proteins forms a chromatin structure that regulates the outcome of all DNA mediated processes. The cellular pathways that ensure genomic stability detect and repair DNA damage through mechanisms that are critically dependent upon chromatin structures established by histones and, particularly upon transient histone post-translational modifications. Though subjected to a range of modifications, histone methylation is especially crucial for DNA damage repair, as the methylated histones often form platforms for subsequent repair protein binding at damaged sites. In this review, we highlight and discuss how histone methylation impacts the maintenance of genome integrity through effects related to DNA repair and repair pathway choice.

Therapy resistance is imposing a daunting challenge on effective clinical management of breast cancer. Although the development of resistance to drugs is multifaceted, reprogramming of energy metabolism pathways is emerging as a central but heterogenous regulator of this therapeutic challenge. Metabolic heterogeneity in cancer cells is intricately associated with alterations of different signaling networks and activation of DNA damage response pathways. Here we consider how the dynamic metabolic milieu of cancer cells regulates their DNA damage repair ability to ultimately contribute to development of therapy resistance. Diverse epigenetic regulators are crucial in remodeling the metabolic landscape of cancer. This epigenetic–metabolic interplay profoundly affects genomic stability of the cancer cells as well as their resistance to genotoxic therapies. These observations identify defining mechanisms of cancer epigenetics–metabolism–DNA repair axis that can be critical for devising novel, targeted therapeutic approaches that could sensitize cancer cells to conventional treatment strategies.

Glioblastoma is an aggressive central nervous system tumor characterized by diffuse infiltration. Despite substantial advances in oncology, survival outcomes have shown little improvement over the past three decades. Radiotherapy remains a cornerstone of treatment; however, it faces several challenges, including considerable inter-observer variability in clinical target volume delineation, dose constraints associated with adjacent organs at risk, and the persistently poor prognosis of affected patients. Recent advances in artificial intelligence, particularly deep learning, have shown promise in automating radiation therapy mapping to improve consistency, accuracy, and efficiency. This narrative review explores current auto segmentation frameworks, dose mapping, and biologically informed radiotherapy planning guided by multimodal imaging and mathematical modeling. Studies have demonstrated reproducible tumor segmentations with DSCs exceeding 0.90, reduced planning within minutes, and emerging predictive capabilities for treatment response. Radiogenomic integration has enabled imaging-based classification of critical biomarkers with high accuracy, reinforcing the potential of deep learning models in personalized radiotherapy. Despite these innovations, deployment into clinical practice remains limited, primarily due to insufficient external validation and single-institution training datasets. This review emphasizes the importance of large, annotated imaging datasets, multi-institutional collaboration, and biologically explainable modeling to successfully translate deep learning into glioblastoma radiation planning and longitudinal monitoring.

Glioblastoma is an aggressive central nervous system tumor characterized by diffuse infiltration. Despite substantial advances in oncology, survival outcomes have shown little improvement over the past three decades. Radiotherapy remains a cornerstone of treatment; however, it faces several challenges, including considerable inter-observer variability in clinical target volume delineation, dose constraints associated with adjacent organs at risk, and the persistently poor prognosis of affected patients. Recent advances in artificial intelligence, particularly deep learning, have shown promise in automating radiation therapy mapping to improve consistency, accuracy, and efficiency. This narrative review explores current auto segmentation frameworks, dose mapping, and biologically informed radiotherapy planning guided by multimodal imaging and mathematical modeling. Studies have demonstrated reproducible tumor segmentations with DSCs exceeding 0.90, reduced planning within minutes, and emerging predictive capabilities for treatment response. Radiogenomic integration has enabled imaging-based classification of critical biomarkers with high accuracy, reinforcing the potential of deep learning models in personalized radiotherapy. Despite these innovations, deployment into clinical practice remains limited, primarily due to insufficient external validation and single-institution training datasets. This review emphasizes the importance of large, annotated imaging datasets, multi-institutional collaboration, and biologically explainable modeling to successfully translate deep learning into glioblastoma radiation planning and longitudinal monitoring.

Hepatocellular carcinoma (HCC) is a major cause of cancer-related deaths, and cases are on the rise due to increased prevalence of metabolic syndromes and alcohol consumption. HCC has a poor prognosis and is challenging to treat due to the immune-tolerant nature of the liver and resulting immunosuppressive tumor microenvironment (TME). Radiotherapy has emerged as an effective HCC treatment that is able to remodel the TME of HCC tumors by inducing dsDNA breaks that activate the cGAS-STING pathway and downstream anti-tumor immune response. However, numerous mechanisms of radioresistance can limit radiotherapy efficacy, including suppression of radiation-induced cGAS-STING activation and, paradoxically, the immunosuppressive effects that can arise from sustained or context-dependent cGAS-STING signaling. In this review, we will first examine how underlying conditions such as liver matrix stiffness, gut bacteria dysbiosis, and tumor mutations can impair STING activation and how novel STING agonists and DNA damage response (DDR) inhibitors can promote radiosensitivity by enhancing STING activation. We will further discuss how immunotherapy can synergize with radiotherapy by mitigating the immunosuppressive effects of STING activation as well as how artificial intelligence can be integrated to improve HCC patient outcomes.