The HLA ligandome of oropharyngeal squamous cell carcinomas reveals shared tumour-exclusive peptides for semi-personalised vaccination

Background The immune peptidome of OPSCC has not previously been studied. Cancer-antigen specific vaccination may improve clinical outcome and efficacy of immune checkpoint inhibitors such as PD1/PD-L1 antibodies. Methods Mapping of the OPSCC HLA ligandome was performed by mass spectrometry (MS) based analysis of naturally presented HLA ligands isolated from tumour tissue samples (n = 40) using immunoaffinity purification. The cohort included 22 HPV-positive (primarily HPV-16) and 18 HPV-negative samples. A benign reference dataset comprised of the HLA ligandomes of benign haematological and tissue datasets was used to identify tumour-associated antigens. Results MS analysis led to the identification of naturally HLA-presented peptides in OPSCC tumour tissue. In total, 22,769 peptides from 9485 source proteins were detected on HLA class I. For HLA class II, 15,203 peptides from 4634 source proteins were discovered. By comparative profiling against the benign HLA ligandomic datasets, 29 OPSCC-associated HLA class I ligands covering 11 different HLA allotypes and nine HLA class II ligands were selected to create a peptide warehouse. Conclusion Tumour-associated peptides are HLA-presented on the cell surfaces of OPSCCs. The established warehouse of OPSCC-associated peptides can be used for downstream immunogenicity testing and peptide-based immunotherapy in (semi)personalised strategies

A T-cell antigen atlas for meningioma: novel options for immunotherapy

Meningiomas are the most common primary intracranial tumors. Although most symptomatic cases can be managed by surgery and/or radiotherapy, a relevant number of patients experience an unfavorable clinical course and additional treatment options are needed. As meningiomas are often perfused by dural branches of the external carotid artery, which is located outside the blood-brain barrier, they might be an accessible target for immunotherapy. However, the landscape of naturally presented tumor antigens in meningioma is unknown. We here provide a T-cell antigen atlas for meningioma by in-depth profiling of the naturally presented immunopeptidome using LC-MS/MS. Candidate target antigens were selected based on a comparative approach using an extensive immunopeptidome data set of normal tissues. Meningioma-exclusive antigens for HLA class I and II are described here for the first time. Top-ranking targets were further functionally characterized by showing their immunogenicity through in vitro T-cell priming assays. Thus, we provide an atlas of meningioma T-cell antigens which will be publicly available for further research. In addition, we have identified novel actionable targets that warrant further investigation as an immunotherapy option for meningioma

Estimating radiation effective doses from whole body computed tomography scans based on U.S. soldier patient height and weight

<p>Abstract</p> <p>Background</p> <p>The purpose of this study is to explore how a patient's height and weight can be used to predict the effective dose to a reference phantom with similar height and weight from a chest abdomen pelvis computed tomography scan when machine-based parameters are unknown. Since machine-based scanning parameters can be misplaced or lost, a predictive model will enable the medical professional to quantify a patient's cumulative radiation dose.</p> <p>Methods</p> <p>One hundred mathematical phantoms of varying heights and weights were defined within an x-ray Monte Carlo based software code in order to calculate organ absorbed doses and effective doses from a chest abdomen pelvis scan. Regression analysis was used to develop an effective dose predictive model. The regression model was experimentally verified using anthropomorphic phantoms and validated against a real patient population.</p> <p>Results</p> <p>Estimates of the effective doses as calculated by the predictive model were within 10% of the estimates of the effective doses using experimentally measured absorbed doses within the anthropomorphic phantoms. Comparisons of the patient population effective doses show that the predictive model is within 33% of current methods of estimating effective dose using machine-based parameters.</p> <p>Conclusions</p> <p>A patient's height and weight can be used to estimate the effective dose from a chest abdomen pelvis computed tomography scan. The presented predictive model can be used interchangeably with current effective dose estimating techniques that rely on computed tomography machine-based techniques.</p

Coronary revascularization treatment based on dual-source computed tomography

Therapy advice based on dual-source computed tomography (DSCT) in comparison with coronary angiography (CAG) was investigated and the results evaluated after 1-year follow-up. Thirty-three consecutive patients (mean age 61.9 years) underwent DSCT and CAG and were evaluated independently. In an expert reading (the “gold standard”), CAG and DSCT examinations were evaluated simultaneously by an experienced radiologist and cardiologist. Based on the presence of significant stenosis and current guidelines, therapy advice was given by all readers blinded from the results of other readings and clinical information. Patients were treated based on a multidisciplinary team evaluation including all clinical information. In comparison with the gold standard, CAG had a higher specificity (91%) and positive predictive value (PPV) (95%) compared with DSCT (82% and 91%, respectively). DSCT had a higher sensitivity (96%) and negative predictive value (NPV) (89%) compared with CAG (91% and 83%, respectively). The DSCT-based therapy advice did not lead to any patient being denied the revascularization they needed according to the multidisciplinary team evaluation. During follow-up, two patients needed additional revascularization. The high NPV for DSCT for revascularization assessment indicates that DSCT could be safely used to select patients benefiting from medical therapy only

Characterization of metabolically unhealthy normal-weight individuals: Risk factors and their associations with type 2 diabetes.

OBJECTIVE: A proportion of type 2 diabetes cases arise from normal-weight individuals who can therefore be considered to be &quot;metabolically unhealthy normal-weight&quot; (MUH-NW). It remains unclear which factors account for this access risk. Our aims were to identify risk factors for type 2 diabetes in normal-weight individuals and to compare the strengths of their associations with type 2 diabetes to that observed in overweight and obese participants. METHODS: A case-cohort, including 2027 sub-cohort participants and 706 incident type 2 cases, was designed within the population-based European Prospective Investigation into Cancer and Nutrition Potsdam study. Adjusted means and relative frequencies of anthropometric, lifestyle and biochemical risk factors were calculated in groups stratified by BMI and incident diabetes status. Cox regressions were applied to evaluate associations between these variables and diabetes risk stratified by BMI category. RESULTS: MUH-NW individuals were characterized by known diabetes risk factors, e.g. they were significantly more likely to be male, former smokers, hypertensive, and less physically active compared to normal-weight individuals without incident diabetes. Higher waist circumference (women: 75.5 vs. 73.1cm; men: 88.0 vs. 85.1cm), higher HbA1c (6.1 vs. 5.3%), higher triglycerides (1.47 vs. 1.11mmol/l), and higher levels of high sensitive C-reactive protein (0.81 vs. 0.51mg/l) as well as lower levels of HDL-cholesterol (1.28 vs. 1.49mmol/l) and adiponectin (6.32 vs. 8.25&mu;g/ml) characterized this phenotype. Stronger associations with diabetes among normal-weight participants compared to overweight and obese (p for interaction&lt;0.05) were observed for height, waist circumference, former smoking, and hypertension. CONCLUSIONS: Normal-weight individuals who develop diabetes have higher levels of diabetes risk factors, however, frequently still among the normal range. Still, hypertension, elevated HbA1c and lifestyle risk factors might be useful indicators of risk