FAT1 as a tumor mutation burden specific gene affects the immunotherapy effect in head and neck squamous cell cancer.

BACKGROUND: Response to immunotherapy is the main challenge of head and neck squamous cancer (HNSCC) treatment. Previous studies have indicated that tumor mutational burden (TMB) is associated with prognosis, but it is not always a precise index. Hence, investigating specific genetic mutations and tumor microenvironment (TME) changes in TMB-high patients is essential for precision therapy of HNSCC. METHODS: A total of 33 HNSCC patients were enrolled in this study. We calculated the TMB score based on next-generation sequencing (NGS) sequencing and grouped these patients based on TMB score. Then, we examined the immune microenvironment of HNSCC using assessments of the bulk transcriptome and the single-cell RNA sequence (scRNA-seq) focusing on the molecular nature of TMB and mutations in HNSCC from our cohort. The association of the mutation pattern and TMB was analyzed in The Cancer Genome Atlas (TCGA) and validated by our cohort. RESULTS: 33 HNSCC patients were divided into three groups (TMB-low, -medium, and -high) based on TMB score. In the result of 520-gene panel sequencing data, we found that FAT1 and LRP1B mutations were highly prevalent in TMB-high patients. FAT1 mutations are associated with resistance to immunotherapy in HNSCC patients. This involves many metabolism-related pathways like RERE, AIRE, HOMER1, etc. In the scRNA-seq data, regulatory T cells (Tregs), monocytes, and DCs were found mainly enriched in TMB-high samples. CONCLUSION: Our analysis unraveled the FAT1 gene as an assistant predictor when we use TMB as a biomarker of drug resistance in HNSCC. Tregs, monocytes, and dendritic cells (DCs) were found mainly enriched in TMB-high samples.

Predicting survival in patients with buccal cancer: A study based on SEER database and external validation in China.

OBJECTIVE: Buccal mucosa cancer (BMC) is one of the most common oral cancers and has poor prognosis. The study aimed to develop and validate nomograms for predicting the 1-, 3-, and 5-year overall survival (OS) and cancer-specific survival (CSS) of BMC patients. METHODS: We collected and reviewed information on BMC patients diagnosed between 2004 and 2019 from the Surveillance Epidemiology and End Results database. Two nomograms were developed and validated to predict the OS and CSS based on predictors identified by univariate and multivariate Cox regression. An extra external validation was further performed using data from Sun Yat-sen Memorial Hospital (SYSMH). RESULTS: A total of 3154 BMC patients included in this study were randomly assigned to training and validation groups in a 2:1 ratio. Independent prognostic predictors were identified, confirmed, and fitted into nomograms for OS and CSS, respectively. The C-indices are 0.767 (Training group OS), 0.801 (Training group CSS), 0.763 (Validation group OS), and 0.781 (Validation group OS), respectively. Moreover, the nomograms exhibited remarkable precision in forecasting and significant clinical significance, as evidenced by receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses (DCA). The final validation using our data from SYSMH also showed high accuracy and substantial clinical benefits within the nomograms. The C-indices are 0.849 (SYSMH group OS) and 0.916 (SYSMH group CSS). These indexes are better than tumor, node, and metastasis stage based on prediction results. CONCLUSIONS: The nomograms developed with great performance predicted 1-, 3-, and 5-year OS and CSS of BMC patients. Use of the nomograms in clinical practices shall bring significant benefits to BMC patients.

Regulating Lipid Metabolism via Mitochondrial Dynamics in Tongue Squamous Cell Carcinoma Cancer Stem Cells.

BACKGROUND: Cancer stem cells (CSCs) are a sub-population of cancer cells present in many kinds of malignant tumors that have the potential for self-proliferation and differentiation. These cells have been demonstrated as the main cause of tumor recurrence and metastasis. Strong evidence indicates that CSCs prefer reprogrammed fatty acid ß-oxidation over oxidative phosphorylation for sustaining energy supply. Although mitochondrial dynamics participate in the regulation of cancer stemness, the correlation between the inhibition of mitochondrial fission and the regulation of lipid metabolism in CSCs remains poorly understood. METHODS: The human tongue squamous cell carcinoma (TSCC) cell lines CAL27 and SAS were used to obtain the CSCs by 3D Spheroid Culture. Then,western blot methods, RT-PCR and flow cytometry analysis were used to identify the TSCC CSCs. Next, Immunofluorescence method, transmission electron microscopy detection and western blot methods were used to evaluate the mitochondrial morphology and the quantity of lipid droplets (LDs). Lastly, lipidomic analysis was applied to explored the lipidomic alterations of TSCC CSCs with different mitochondrial morphology. RESULTS: Here, we show that the quantity of lipid droplets containing intracellular triglyceride (TG) can be decreased by regulating mitochondrial morphology. Lipidomic analysis using ultraperformance liquid chromatography-mass spectrometry (UPLC-MS) also compared alterations in lipid metabolites in tongue squamous cell carcinoma (TSCC) CSCs, TSCC cells (non-CSCs), and CSCs with different mitochondrial morphology. Discriminant lipids of statistical significance were successfully annotated, including phosphatidylcholines (PCs), phosphatidylethanolamines (PEs), sphingomyelins (SMs), triacylglycerols (TGs), phosphatidylglycerols (PGs), phosphatidylserines (PSs), lysophosphatidylcholines (LPCs), and lysophosphatidylethanolamines (LPEs). CONCLUSION: This study provides a deeper insight into the alterations of lipid metabolism associated with TSCC CSCs, non-CSCs and CSCs regulated by mitochondrial dynamics and thus serves as a guide toward novel targeted therapies.

Neoadjuvant chemoimmunotherapy in resectable locally advanced oral squamous cell carcinoma: a single-center retrospective cohort study.

BACKGROUND: Surgery and postoperative adjuvant therapy is the standard treatment for locally advanced resectable oral squamous cell carcinoma (OSCC), while neoadjuvant chemoimmunotherapy (NACI) is believed to lead better outcomes. This study aims to investigate the effectiveness of NACI regimens in treating locally advanced resectable OSCC. MATERIALS AND METHODS: Patients diagnosed with locally advanced resectable OSCC who received NACI and non-NACI were reviewed between December 2020 and June 2022 in our single center. The pathologic response was evaluated to the efficacy of NACI treatment. Adverse events apparently related to NACI treatment were graded by Common Terminology Criteria for Adverse Events, version 5.0. Disease-free survival (DFS) and overall survival (OS) rate were assessed. RESULTS: Our analysis involved 104 patients who received NACI. Notably, the pathological complete response (PCR) rate was 47.1%, and the major pathological response (MPR) rate was 65.4%. The top three grade 1-2 treatment-related adverse events (TRAEs) were alopecia (104; 100%), anemia (81; 77.9%) and pruritus (62; 59.6%). Importantly, patients achieving MPR exhibited higher programmed cell death-ligand 1 (PD-L1) combined positive score (CPS). The diagnostic value of CPS as a biomarker for NACI efficacy was enhanced when combined total cholesterol level. The 3-year estimated DFS rates were 89.0% in the NACI cohort compared to 60.8% in the non-NACI cohort, while the 3-year estimated OS rates were 91.3% versus 64.0%, respectively. CONCLUSIONS: The NACI treatment showed safe and encouragingly efficacious for locally advanced resectable OSCC patients. The high response rates and favorable prognosis suggest this approach as a potential treatment option. Prospective randomized controlled trials are needed to further validate these findings.

Integrated Single-cell and Bulk RNA Sequencing Analysis Cross Talk between Ferroptosis-related Genes and Prognosis in Oral Cavity Squamous Cell Carcinoma.

BACKGROUND: Ferroptosis is a new type of programmed apoptosis and plays an important role in tumour inhibition and immunotherapy. OBJECTIVE: In this study, we aimed to explore the potential role of ferroptosis-related genes (FRGs) and the potential therapeutic targets in oral cavity squamous cell carcinoma (OCSCC). METHODS: The transcription data of OCSCC samples were obtained from the Cancer Genome Atlas (TCGA) database as a training dataset. The prognostic FRGs were extracted by univariate Cox regression analysis. Then, we constructed a prognostic model using the least absolute shrinkage and selection operator (LASSO) and Cox analysis to determine the independent prognosis FRGs. Based on this model, risk scores were calculated for the OCSCC samples. The model's capability was further evaluated by the receiver operating characteristic curve (ROC). Then, we used the GSE41613 dataset as an external validation cohort to confirm the model's predictive capability. Next, the immune infiltration and somatic mutation analysis were applied. Lastly, single-cell transcriptomic analysis was used to identify the key cells. RESULTS: A total of 12 prognostic FRGs were identified. Eventually, 6 FRGs were screened as independent predictors and a prognostic model was constructed in the training dataset, which significantly stratified OCSCC samples into high-risk and low-risk groups based on overall survival. The external validation of the model using the GSE41613 dataset demonstrated a satisfactory predictive capability for the prognosis of OCSCC. Further analysis revealed that patients in the highrisk group had distinct immune infiltration and somatic mutation patterns from low-risk patients. Mast cell infiltrations were identified as prognostic immune cells and played a role in OCSCC partly through ferroptosis. CONCLUSION: We successfully constructed a novel 6 FRGs model and identified a prognostic immune cell, which can serve to predict clinical prognoses for OCSCC. Ferroptosis may be a new direction for immunotherapy of OCSCC.

MRI-based deep learning and radiomics for prediction of occult cervical lymph node metastasis and prognosis in early-stage oral and oropharyngeal squamous cell carcinoma: a diagnostic study.

INTRODUCTION: The incidence of occult cervical lymph node metastases (OCLNM) is reported to be 20-30% in early-stage oral cancer and oropharyngeal cancer. There is a lack of an accurate diagnostic method to predict occult lymph node metastasis and to help surgeons make precise treatment decisions. AIM: To construct and evaluate a preoperative diagnostic method to predict OCLNM in early-stage oral and oropharyngeal squamous cell carcinoma (OC and OP SCC) based on deep learning features (DLFs) and radiomics features. METHODS: A total of 319 patients diagnosed with early-stage OC or OP SCC were retrospectively enrolled and divided into training, test and external validation sets. Traditional radiomics features and DLFs were extracted from their MRI images. The least absolute shrinkage and selection operator (LASSO) analysis was employed to identify the most valuable features. Prediction models for OCLNM were developed using radiomics features and DLFs. The effectiveness of the models and their clinical applicability were evaluated using the area under the curve (AUC), decision curve analysis (DCA), and survival analysis. RESULTS: Seventeen prediction models were constructed. The Resnet50 deep learning (DL) model based on the combination of radiomics and DL features achieves the optimal performance, with AUC values of 0.928 (95% CI: 0.881-0.975), 0.878 (95% CI: 0.766-0.990), 0.796 (95% CI: 0.666-0.927), and 0.834 (95% CI: 0.721-0.947) in the training, test, external validation set1, and external validation set2, respectively. Moreover, the Resnet50 model has great prediction value of prognosis in patients with early-stage OC and OP SCC. CONCLUSION: The proposed MRI-based Resnet50 DL model demonstrated high capability in diagnosis of OCLNM and prognosis prediction in the early-stage OC and OP SCC. The Resnet50 model could help refine the clinical diagnosis and treatment of the early-stage OC and OP SCC.

A comprehensive AI model development framework for consistent Gleason grading.

BACKGROUND: Artificial Intelligence(AI)-based solutions for Gleason grading hold promise for pathologists, while image quality inconsistency, continuous data integration needs, and limited generalizability hinder their adoption and scalability. METHODS: We present a comprehensive digital pathology workflow for AI-assisted Gleason grading. It incorporates A!MagQC (image quality control), A!HistoClouds (cloud-based annotation), Pathologist-AI Interaction (PAI) for continuous model improvement, Trained on Akoya-scanned images only, the model utilizes color augmentation and image appearance migration to address scanner variations. We evaluate it on Whole Slide Images (WSI) from another five scanners and conduct validations with pathologists to assess AI efficacy and PAI. RESULTS: Our model achieves an average F1 score of 0.80 on annotations and 0.71 Quadratic Weighted Kappa on WSIs for Akoya-scanned images. Applying our generalization solution increases the average F1 score for Gleason pattern detection from 0.73 to 0.88 on images from other scanners. The model accelerates Gleason scoring time by 43% while maintaining accuracy. Additionally, PAI improve annotation efficiency by 2.5 times and led to further improvements in model performance. CONCLUSIONS: This pipeline represents a notable advancement in AI-assisted Gleason grading for improved consistency, accuracy, and efficiency. Unlike previous methods limited by scanner specificity, our model achieves outstanding performance across diverse scanners. This improvement paves the way for its seamless integration into clinical workflows.

Gleason grading is a well-accepted diagnostic standard to assess the severity of prostate cancer in patients' tissue samples, based on how abnormal the cells in their prostate tumor look under a microscope. This process can be complex and time-consuming. We explore how artificial intelligence (AI) can help pathologists perform Gleason grading more efficiently and consistently. We build an AI-based system which automatically checks image quality, standardizes the appearance of images from different equipment, learns from pathologists' feedback, and constantly improves model performance. Testing shows that our approach achieves consistent results across different equipment and improves efficiency of the grading process. With further testing and implementation in the clinic, our approach could potentially improve prostate cancer diagnosis and management.