Cancer science
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Immunotherapy against cancer, through immune checkpoint inhibitors targeting the programmed cell death-1/programmed cell death-ligand 1 axis, is particularly successful in tumors by relieving the immune escape. However, interindividual responses to immunotherapy are often heterogeneous. Therefore, it is essential to screen out predictive tumor biomarkers. ⋯ A significant positive correlation was observed between the abundance of Streptococcus and CD8+ T cells. These results highlighted the intimate relationship between commensal microbiota and the response to immunotherapy in NSCLC patients. Gut microbiota and respiratory microbiota are promising biomarkers to screen suitable candidates who are likely to benefit from immune checkpoint inhibitor-based immunotherapy.
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This study aims to build a radiological model based on standard MR sequences for detecting methylguanine methyltransferase (MGMT) methylation in gliomas using texture analysis. A retrospective cross-sectional study was undertaken in a cohort of 53 glioma patients who underwent standard preoperative magnetic resonance (MR) imaging. Conventional visual radiographic features and clinical factors were compared between MGMT promoter methylated and unmethylated groups. ⋯ The area under the ROC curve values for the combined model was 0.818, with 90.5% sensitivity and 72.7% specificity, in the GBM dataset, and 0.833, with 70.2% sensitivity and 90.6% specificity, in the overall gliomas dataset. Nomogram, calibration, and DCA also validated the performance of the combined model. The combined model based on texture features could be considered as a noninvasive imaging marker for detecting MGMT methylation status in glioma.
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Multicenter Study
An Individualized Model for Predicting COVID-19 Deterioration in Patients with Cancer: A Multicenter Retrospective Study.
The 2019 novel coronavirus has spread rapidly around the world. Cancer patients seem to be more susceptible to infection and disease deterioration, but the factors affecting the deterioration remain unclear. We aimed to develop an individualized model for prediction of coronavirus disease (COVID-19) deterioration in cancer patients. ⋯ The Kaplan-Meier deterioration-free survival of COVID-19 curves presented significant discrimination between the two risk groups in both training and validation cohorts. The model indicated good clinical applicability by DCA curves. This study presents an individualized nomogram model to individually predict the possibility of symptomatic deterioration of COVID-19 in patients with cancer.
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Cancer is characterized by an accumulation of somatic mutations that represent a source of neoantigens for targeting by antigen-specific T cells. Head and neck squamous cell carcinoma (HNSCC) has a relatively high mutation burden across all cancer types, and cellular immunity to neoantigens likely plays a key role in HNSCC clinical outcomes. Immune checkpoint inhibitors (CPIs) have brought new treatment options and hopes to patients with recurrent and/or metastatic HNSCC. ⋯ One such approach is personalized cancer vaccination targeting tumor-associated antigens and tumor-specific antigens, either as single agents or in combination with other therapies. Recent advances in next-generation genomic sequencing technologies and computational algorithms have enabled efficient identification of somatic mutation-derived neoantigens and are anticipated to facilitate the development of cancer vaccine strategies. Here, we review cancer vaccine approaches against HNSCC, including fundamental mechanisms of a cancer vaccine, considerations for selecting appropriate antigens, and combination therapies.