In the ever-evolving landscape of cancer therapy, while cancer treatments such as chemotherapy, radiotherapy, and targeted therapy aim to eradicate malignant cells, they also inadvertently trigger cellular senescence in both cancerous and microenvironmental tissues. Therapy-induced senescence (TIS) can act as a barrier against tumor growth by halting cell proliferation in the short term, but the long-term persistence of therapy-induced senescent (TISnt) cells may pose a significant challenge in cancer management. Their distinct characteristics, like senescence-associated secretory phenotype (SASP), metabolic dysregulation, and immune evasion, make them exhibit remarkable heterogeneity to orchestrate the tumor microenvironment (TME), resulting in therapy resistance. However, how these TISnt cells functioning differently in cancer progression, and the intricate mechanisms by which they remodel the senescence-associated immunosuppressive microenvironment present challenges for improving anticancer therapy. Therefore, this review summarizes the heterogeneous TISnt cell phenotypes contributing to an accumulated senescent state, outlines their multidimensional interactions in the senescent microenvironment, and discusses current senescence-targeting strategies. Building on the current understanding of TIS, we propose potential avenues for improving TIS-targeting methodologies in the context of head and neck cancer, a representative heterogeneous malignancy, which can substantially enhance the efficacy of the "one-two punch" sequential treatment approach for head and neck cancer.
Humans ; Cellular Senescence/drug effects* ; Tumor Microenvironment ; Neoplasms/pathology* ; Senescence-Associated Secretory Phenotype

Objective: The presence of coagulative necrosis (CN) in clear cell renal cell carcinoma (ccRCC) indicates a poor prognosis, while the absence of CN indicates a good prognosis. The purpose of this study was to build and validate a radiomics signature based on preoperative CT imaging data to estimate CN status in ccRCC. Materials and Methods: Altogether, 105 patients with pathologically confirmed ccRCC were retrospectively enrolled in this study and then divided into training (n = 72) and validation (n = 33) sets. Thereafter, 385 radiomics features were extracted from the three-dimensional volumes of interest of each tumor, and 10 traditional features were assessed by two experienced radiologists using triple-phase CT-enhanced images. A multivariate logistic regression algorithm was used to build the radiomics score and traditional predictors in the training set, and their performance was assessed and then tested in the validation set. The radiomics signature to distinguish CN status was then developed by incorporating the radiomics score and the selected traditional predictors. The receiver operating characteristic (ROC) curve was plotted to evaluate the predictive performance. Results: The area under the ROC curve (AUC) of the radiomics score, which consisted of 7 radiomics features, was 0.855 in the training set and 0.885 in the validation set. The AUC of the traditional predictor, which consisted of 2 traditional features, was 0.843 in the training set and 0.858 in the validation set. The radiomics signature showed the best performance with an AUC of 0.942 in the training set, which was then confirmed with an AUC of 0.969 in the validation set. Conclusion The CT-based radiomics signature that incorporated radiomics and traditional features has the potential to be used as a non-invasive tool for preoperative prediction of CN in ccRCC.