It is widely known that tumor cells of basal and squamous cell carcinoma interact with the cellular and acellular components of the tumor microenvironment to promote tumor growth and progression. While this environment differs for basal and squamous cell carcinoma, the cellular players within both create an immunosuppressed environment by downregulating effector CD4+ and CD8+ T cells and promoting the release of pro-oncogenic Th2 cytokines. Understanding the crosstalk that occurs within the tumor microenvironment has led to the development of immunotherapeutic agents, including vismodegib and cemiplimab to treat BCC and SCC, respectively. However, further investigation of the TME will provide the opportunity to discover novel treatment options.

Skin cancer is the most common cancer diagnosis in the United States, with approximately one in five Americans expected to be diagnosed within their lifetime. Non-melanoma skin cancer is the most prevalent type of skin cancer, and as cases rise globally, physicians need reliable tools for early detection. Artificial intelligence has gained substantial interest as a decision support tool in medicine, particularly in image analysis, where deep learning has proven to be an effective tool. Because specialties such as dermatology rely primarily on visual diagnoses, deep learning could have many diagnostic applications, including the diagnosis of skin cancer. Furthermore, with the advancement of mobile smartphones and their increasingly powerful cameras, deep learning technology could also be utilized in remote skin cancer screening applications. Ultimately, the available data for the detection and diagnosis of skin cancer using deep learning technology are promising, revealing sensitivity and specificity that are not inferior to those of trained dermatologists. Work is still needed to increase the clinical use of AI-based tools, but based on the current data and the attitudes of patients and physicians, deep learning technology could be used effectively as a clinical decision-making tool in collaboration with physicians to improve diagnostic efficiency and accuracy.

PURPOSE Although beta-blockers (BBs) have been hypothesized to exert a beneficial effect on cancer survival through inhibition of beta-adrenergic signaling pathways, clinical data on this issue have been inconsistent. We investigated the impact of BBs on survival outcomes and efficacy of immunotherapy in patients with head and neck squamous cell carcinoma (HNSCC), non–small-cell lung cancer (NSCLC), melanoma, or squamous cell carcinoma of the skin (skin SCC), independent of comorbidity status or cancer treatment regimen. METHODS Patients (N = 4,192) younger than 65 years with HNSCC, NSCLC, melanoma, or skin SCC treated at MD Anderson Cancer Center from 2010 to 2021 were included. Overall survival (OS), disease-specific survival (DSS), and disease-free survival (DFS) were calculated. Kaplan-Meier and multivariate analyses adjusting for age, sex, TNM staging, comorbidities, and treatment modalities were performed to assess the effect of BBs on survival outcomes. RESULTS In patients with HNSCC (n = 682), BB use was associated with worse OS and DFS (OS: adjusted hazard ratio [aHR], 1.67; 95% CI, 1.06 to 2.62; P = .027; DFS: aHR, 1.67; 95% CI, 1.06 to 2.63; P = .027), with DSS trending to significance (DSS: aHR, 1.52; 95% CI, 0.96 to 2.41; P = .072). Negative effects of BBs were not observed in the patients with NSCLC (n = 2,037), melanoma (n = 1,331), or skin SCC (n = 123). Furthermore, decreased response to cancer treatment was observed in patients with HNSCC with BB use (aHR, 2.47; 95% CI, 1.14 to 5.38; P = .022). CONCLUSION The effect of BBs on cancer survival outcomes is heterogeneous and varies according to cancer type and immunotherapy status. In this study, BB intake was associated with worse DSS and DFS in patients with head and neck cancer not treated with immunotherapy, but not in patients with NSCLC or skin cancer.