ABSTRACT

IL-1R activation is required for neutrophil recruitment in an effective innate immune response against Staphylococcus aureus infection. In this study, we investigated the mechanism of IL-1R activation in vivo in a model of S. aureus infection. In response to a S. aureus cutaneous challenge, mice deficient in IL-1β, IL-1α/IL-1β, but not IL-1α, developed larger lesions with higher bacterial counts and had decreased neutrophil recruitment compared with wild-type mice. Neutrophil recruitment and bacterial clearance required IL-1β expression by bone marrow (BM)-derived cells and not by non-BM-derived resident cells. In addition, mice deficient in the inflammasome component apoptosis-associated speck-like protein containing a caspase recruitment domain (ASC) had the same defects in neutrophil recruitment and host defense as IL-1β-deficient mice, demonstrating an essential role for the inflammasome in mediating the production of active IL-1β to promote neutrophil recruitment in host defense against S. aureus. This finding was further supported by the ability of recombinant active IL-1β to control the infection and promote bacterial clearance in IL-1β-deficient mice. These studies define a key host defense circuit where inflammasome-mediated IL-1β production by BM-derived cells signals IL-1R on non-BM-derived resident cells to activate neutrophil recruitment in the innate immune response against S. aureus in vivo.

Purpose Treatment of oropharyngeal squamous cell carcinoma (OPSCC) is evolving toward risk-based modification of therapeutic intensity, which requires patient-specific estimates of overall survival (OS) and progression-free survival (PFS). Methods To develop and validate nomograms for OS and PFS, we used a derivation cohort of 493 patients with OPSCC with known p16 tumor status (surrogate of human papillomavirus) and cigarette smoking history (pack-years) randomly assigned to clinical trials using platinum-based chemoradiotherapy (NRG Oncology Radiation Therapy Oncology Group [RTOG] 0129 and 0522). Nomograms were created from Cox models and internally validated by use of bootstrap and cross-validation. Model discrimination was measured by calibration plots and the concordance index. Nomograms were externally validated in a cohort of 153 patients with OPSCC randomly assigned to a third trial, NRG Oncology RTOG 9003. Results Both models included age, Zubrod performance status, pack-years, education, p16 status, and T and N stage; the OS model also included anemia and age × pack-years interaction; and the PFS model also included marital status, weight loss, and p16 × Zubrod interaction. Predictions correlated well with observed 2-year and 5-year outcomes. The uncorrected concordance index was 0.76 (95% CI, 0.72 to 0.80) for OS and 0.70 (95% CI, 0.66 to 0.74) for PFS, and bias-corrected indices were similar. In the validation set, OS and PFS models were well calibrated, and OS and PFS were significantly different across tertiles of nomogram scores (log-rank P = .003;< .001). Conclusion The validated nomograms provided useful prediction of OS and PFS for patients with OPSCC treated with primary radiation-based therapy.

IMPORTANCE Cutaneous squamous cell carcinoma (CSCC) is one of the most common malignant tumors worldwide. There is conflicting evidence regarding the indications for and benefits of adjuvant radiation therapy for advanced CSCC tumors of the head and neck. OBJECTIVE To assess indications for adjuvant radiation therapy in patients with CSCC. DESIGN, SETTING, AND PARTICIPANTS Retrospective analysis of 349 patients with head and neck CSCC treated with primary resection with or without adjuvant radiation therapy at 2 tertiary referral centers from January 1, 2008, to June 30, 2016. MAIN OUTCOMES AND MEASURES Data were compared between treatment groups with a χ 2 analysis. Disease-free survival (DFS) and overall survival (OS) were analyzed using a Kaplan-Meier survival analysis with log-rank test and a Cox proportional hazards multivariate regression. RESULTS A total of 349 patients had tumors that met the inclusion criteria (mean [SD] age, 70 [12] years; age range, 32-94 years; 302 [86.5%] male), and 191 (54.7%) received adjuvant radiation therapy. The 5-year Kaplan-Meier estimates were 59.4% for DFS and 47.4% for OS. Patients with larger, regionally metastatic, poorly differentiated tumors with perineural invasion (PNI) and younger immunosuppressed patients were more likely to receive adjuvant radiation therapy. On Cox proportional hazards multivariate regression, patients with periorbital tumors (hazard ratio [HR], 2.48; 95% CI, 1.00-6.16), PNI (HR, 1.90; 95% CI, 1.12-3.19), or N2 or greater nodal disease (HR, 2.16; 95% CI, 1.13-4.16) had lower DFS. Immunosuppressed patients (HR, 2.17; 95% CI, 1.12-4.17) and those with N2 or greater nodal disease (HR, 2.43; 95% CI, 1.42-4.17) had lower OS. Adjuvant radiation therapy was associated with improved OS for the entire cohort (HR, 0.59; 95% CI, 0.38-0.90). In a subset analysis of tumors with PNI, adjuvant radiation therapy was associated with improved DFS (HR, 0.47; 95% CI, 0.23-0.93) and OS (HR, 0.44; 95% CI, 0.24-0.86). Adjuvant radiation therapy was also associated with improved DFS (HR, 0.36; 95% CI, 0.15-0.84) and OS (HR, 0.30; 95% CI, 0.15-0.61) in patients with regional disease. CONCLUSIONS AND RELEVANCE Among patients with advanced CSCC, receipt of adjuvant radiation therapy was associated with improved survival in those with PNI and regional disease.

To improve image quality and CT number accuracy of fast-scan low-dose cone-beam computed tomography (CBCT) through a deep-learning convolutional neural network (CNN) methodology for head-and-neck (HN) radiotherapy. Fifty-five paired CBCT and CT images from HN patients were retrospectively analysed. Among them, 15 patients underwent adaptive replanning during treatment, thus had same-day CT/CBCT pairs. The remaining 40 patients (post-operative) had paired planning CT and 1st fraction CBCT images with minimal anatomic changes. A 2D U-Net architecture with 27-layers in 5 depths was built for the CNN. CNN training was performed using data from 40 post-operative HN patients with 2080 paired CT/CBCT slices. Validation and test datasets include 5 same-day datasets with 260 slice pairs and 10 same-day datasets with 520 slice pairs, respectively. To examine the impact of differences in training dataset selection and network performance as a function of training data size, additional networks were trained using 30, 40 and 50 datasets. Image quality of enhanced CBCT images were quantitatively compared against the CT image using mean absolute error (MAE) of Hounsfield units (HU), signal-to-noise ratio (SNR) and structural similarity (SSIM). Enhanced CBCT images reduced artifact distortion and improved soft tissue contrast. Networks trained with 40 datasets had imaging performance comparable to those trained with 50 datasets and outperformed those trained with 30 datasets. Comparison of CBCT and enhanced CBCT images demonstrated improvement in average MAE from 172.73 to 49.28 HU, SNR from 8.27 to 14.25 dB, and SSIM from 0.42 to 0.85. The image processing time is 2 s per patient using a NVIDIA GeForce GTX 1080 Ti GPU. The proposed deep-leaning methodology was fast and effective for image quality enhancement of fast-scan low-dose CBCT. This method has potential to support fast online-adaptive re-planning for HN cancer patients.