To investigate the expression of programmed death-ligand 1 (PD-L1) and immune checkpoints and their prognostic value for resected head and neck squamous cell cancer (HNSCC). PD-L1 expression on tumor cells (TC) and tumor-infiltrating immune cells (IC), abundance of tumor-infiltrating lymphocytes (TILs), and expression of the immune checkpoints were investigated in 402 HNSCC patients. PD-L1 expression on TC and IC was categorized into four groups according to the percentage of PD-L1-positive cells. PD-L1 positivity was defined as ≥5% of cells based on immunohistochemistry. High PD-L1 expression on IC, but not TC, was an independent favorable prognostic factor for RFS and OS adjusted for age, gender, smoking, stage, and HPV. High frequencies of CD3+ or CD8+ TILs, Foxp3+ Tregs, and PD-1+ TILs were strongly associated with favorable prognosis. PD-L1 was exclusively expressed on either TC or IC. Transcriptome analysis demonstrated that IC3 expressed higher levels of the effector T cell markers than TC3, suggesting that PD-L1 expression is regulated via an adaptive IFNγ-mediated mechanism. High PD-L1 expression on IC, but not TC, and high abundance of PD-1+ T cells and Foxp3+ Tregs are favorable prognostic factors for resected HNSCC. This study highlights the importance of comprehensive assessment of both TC and IC.

OBJECTIVES To examine frailty transitions in Mexican American (MA) and European American (EA) older adults. DESIGN Longitudinal, observational cohort study. SETTING Socioeconomically diverse neighborhoods in San Antonio, Texas. PARTICIPANTS 312 MA and 285 EA community-dwelling older adults (65+) with frailty information at baseline (1992–96) and transition information at follow-up (2000–01) in the San Antonio Longitudinal Study of Aging (SALSA). MEASUREMENTS Five frailty characteristics (weight loss, exhaustion, weakness, slowness, and low physical activity), frailty score (0–5), and overall frailty state (non-frail = 0 characteristics, pre-frail = 1 or 2, frail = 3+) were assessed at baseline. Transitions (progressed, regressed, or no change) were assessed for frailty score and state. Odds ratios (OR) of progression and regression in individual characteristics were estimated using generalized estimating equations, adjusting for age, sex, ethnic group, socioeconomic status, comorbidity, diabetes, and follow-up interval. RESULTS Diabetes with macrovascular complications (OR=1.84, 95%CI: 1.02–3.33), fewer years of education (OR=0.96, 95%CI: 0.93–1.0) and follow-up interval (OR=1.3, 95%CI: 1.17–1.46) were significant predictors of progression in any frailty characteristic. Mortality increased by frailty state, and pre-frail individuals were more likely than frail to regress. CONCLUSION Diabetes with macrovascular complications and fewer years of education are important predictors of progression in any frailty characteristic. Because of increased risk of death compared with the non-frail state and the increased likelihood of regression compared with the frail state, the pre-frail state may be an optimal target for intervention.

ObjectiveThe objective of this retrospective study was to develop and validate a simple diagnostic prediction model by using ultrasound (US) features of thyroid nodules obtained from multicenter retrospective data.Materials and MethodsPatient data were collected from 20 different institutions and the data included 2000 thyroid nodules from 1796 patients. For developing a diagnostic prediction model to estimate the malignant risk of thyroid nodules using suspicious malignant US features, we developed a training model in a subset of 1402 nodules from 1260 patients. Several suspicious malignant US features were evaluated to create the prediction model using a scoring tool. The scores for such US features were estimated by calculating odds ratios, and the risk score of malignancy for each thyroid nodule was defined as the sum of these individual scores. Later, we verified the usefulness of developed scoring system by applying into the remaining 598 nodules from 536 patients.ResultsAmong 2000 tumors, 1268 were benign and 732 were malignant. In our multiple regression analysis models, the following US features were statistically significant for malignant nodules when using the training data set: hypoechogenicity, marked hypoechogenicity, non-parallel orientation, microlobulated or spiculated margin, ill-defined margins, and microcalcifications. The malignancy rate was 7.3% in thyroid nodules that did not have suspicious-malignant features on US. Area under the receiver operating characteristic (ROC) curve was 0.867, which shows that the US risk score help predict thyroid malignancy well. In the test data set, the malignancy rates were 6.2% in thyroid nodules without malignant features on US. Area under the ROC curve of the test set was 0.872 when using the prediction model.ConclusionThe predictor model using suspicious malignant US features may be helpful in risk stratification of thyroid nodules.

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