Do pancreatic cancer and chronic pancreatitis share the same genetic risk factors? A PANcreatic Disease ReseArch (PANDoRA) consortium investigation.

Pancreatic ductal adenocarcinoma (PDAC) is a very aggressive tumor with a five-year survival of less than 6%. Chronic pancreatitis (CP), an inflammatory process in of the pancreas, is a strong risk factor for PDAC. Several genetic polymorphisms have been discovered as susceptibility loci for both CP and PDAC. Since CP and PDAC share a consistent number of epidemiologic risk factors, the aim of this study was to investigate whether specific CP risk loci also contribute to PDAC susceptibility. We selected five common SNPs (rs11988997, rs379742, rs10273639, rs2995271 and rs12688220) that were identified as susceptibility markers for CP and analyzed them in 2,914 PDAC cases, 356 CP cases and 5,596 controls retrospectively collected in the context of the international PANDoRA consortium. We found a weak association between the minor allele of the PRSS1-PRSS2-rs10273639 and an increased risk of developing PDAC (ORhomozygous = 1.19, 95% CI 1.02-1.38, p = 0.023). Additionally all the SNPs confirmed statistically significant associations with risk of developing CP, the strongest being PRSS1-PRSS2-rs10273639 (ORheterozygous = 0.51, 95% CI 0.39-0.67, p = 1.10 × 10-6 ) and MORC4-rs 12837024 (ORhomozygous = 2.07 (1.55-2.77, ptrend = 0.7 × 10-11 ). Taken together, the results from our study do not support variants rs11988997, rs379742, rs10273639, rs2995271 and rs12688220 as strong predictors of PDAC risk, but further support the role of these SNPs in CP susceptibility. Our study suggests that CP and PDAC probably do not share genetic susceptibility, at least in terms of high frequency variants.

Prognostic value of automated KI67 scoring in breast cancer: a centralised evaluation of 8088 patients from 10 study groups.

BACKGROUND: The value of KI67 in breast cancer prognostication has been questioned due to concerns on the analytical validity of visual KI67 assessment and methodological limitations of published studies. Here, we investigate the prognostic value of automated KI67 scoring in a large, multicentre study, and compare this with pathologists' visual scores available in a subset of patients. METHODS: We utilised 143 tissue microarrays containing 15,313 tumour tissue cores from 8088 breast cancer patients in 10 collaborating studies. A total of 1401 deaths occurred during a median follow-up of 7.5 years. Centralised KI67 assessment was performed using an automated scoring protocol. The relationship of KI67 levels with 10-year breast cancer specific survival (BCSS) was investigated using Kaplan-Meier survival curves and Cox proportional hazard regression models adjusted for known prognostic factors. RESULTS: Patients in the highest quartile of KI67 (>12 % positive KI67 cells) had a worse 10-year BCSS than patients in the lower three quartiles. This association was statistically significant for ER-positive patients (hazard ratio (HR) (95 % CI) at baseline = 1.96 (1.31-2.93); P = 0.001) but not for ER-negative patients (1.23 (0.86-1.77); P = 0.248) (P-heterogeneity = 0.064). In spite of differences in characteristics of the study populations, the estimates of HR were consistent across all studies (P-heterogeneity = 0.941 for ER-positive and P-heterogeneity = 0.866 for ER-negative). Among ER-positive cancers, KI67 was associated with worse prognosis in both node-negative (2.47 (1.16-5.27)) and node-positive (1.74 (1.05-2.86)) tumours (P-heterogeneity = 0.671). Further classification according to ER, PR and HER2 showed statistically significant associations with prognosis among hormone receptor-positive patients regardless of HER2 status (P-heterogeneity = 0.270) and among triple-negative patients (1.70 (1.02-2.84)). Model fit parameters were similar for visual and automated measures of KI67 in a subset of 2440 patients with information from both sources. CONCLUSIONS: Findings from this large-scale multicentre analysis with centrally generated automated KI67 scores show strong evidence in support of a prognostic value for automated KI67 scoring in breast cancer. Given the advantages of automated scoring in terms of its potential for standardisation, reproducibility and throughput, automated methods appear to be promising alternatives to visual scoring for KI67 assessment.

Novel associations between inflammation-related proteins and adiposity: A targeted proteomics approach across four population-based studies.

Chronic low-grade inflammation has been proposed as a linking mechanism between obesity and the development of inflammation-related conditions such as insulin resistance and cardiovascular disease. Despite major advances in the last 2 decades, the complex relationship between inflammation and obesity remains poorly understood. Therefore, we aimed to identify novel inflammation-related proteins associated with adiposity. We investigated the association between BMI and waist circumference and 72 circulating inflammation-related proteins, measured using the Proximity Extension Assay (Olink Proteomics), in 3,308 participants of four independent European population-based studies (KORA-Fit, BVSII, ESTHER, and Bialystok PLUS). In addition, we used body fat mass measurements obtained by Dual-energy X-ray absorptiometry (DXA) in the Bialystok PLUS study to further validate our results and to explore the relationship between inflammation-related proteins and body fat distribution. We found 14 proteins associated with at least one measure of adiposity across all four studies, including four proteins for which the association is novel: DNER, SLAMF1, RANKL, and CSF-1. We confirmed previously reported associations with CCL19, CCL28, FGF-21, HGF, IL-10RB, IL-18, IL-18R1, IL-6, SCF, and VEGF-A. The majority of the identified inflammation-related proteins were associated with visceral fat as well as with the accumulation of adipose tissue in the abdomen and the trunk. In conclusion, our study provides new insights into the immune dysregulation observed in obesity that might help uncover pathophysiological mechanisms of disease development.

Systematic review with meta-analysis: volatile organic compound analysis to improve faecal immunochemical testing in the detection of colorectal cancer.

BACKGROUND: Faecal immunochemical test (FIT) is emerging as a valid test to rule-out the presence of colorectal cancer (CRC). However, the accuracy of FIT is dependent on the cut-off applied. An additional low-cost test could improve further detection of CRC. AIMS: To evaluate the efficacy of combined FIT and volatile organic compounds (VOC) in the detection of CRC within symptomatic populations. METHODS: Systematic reviews on the diagnostic accuracy of FIT and VOC, for the detection of CRC, were updated. Meta-analyses were performed adopting a bivariate model for sensitivity and specificity. Clinical utility of combined FIT and VOC was estimated using Fagan's nomogram. Post-test probability of FIT negatives was used as a pre-test probability for VOC. RESULTS: The pooled sensitivity and specificity of FIT at 10 µg/g faeces, for the detection of CRC, were 0.914 (95% confidence interval [CI] = 0.894-0.936) and 0.783 (CI = 0.850-0.696), respectively. For VOC, the sensitivity was 0.837 (CI = 0.781-0.881) and the specificity was 0.803 (CI = 0.870-0.712). The area under the curve for FIT and VOC were 0.926 and 0.885, respectively. In a population with 5% CRC prevalence, the estimated probability of having CRC following a negative FIT was 0.5% and following both negative FIT and VOC was 0.1%. CONCLUSIONS: In a FIT-negative symptomatic population, VOC can be a good test to rule-out the presence of CRC. The estimated probability reduction by 0.4% when both tests being negative offers adequate safety netting in primary care for the exclusion of CRC. The number needed to colonoscope to identify one CRC is eight if either FIT or VOC positive. Cost-effectiveness and clinical accuracy of this approach will need further evaluation.

High-throughput automated scoring of Ki67 in breast cancer tissue microarrays from the Breast Cancer Association Consortium.

Automated methods are needed to facilitate high-throughput and reproducible scoring of Ki67 and other markers in breast cancer tissue microarrays (TMAs) in large-scale studies. To address this need, we developed an automated protocol for Ki67 scoring and evaluated its performance in studies from the Breast Cancer Association Consortium. We utilized 166 TMAs containing 16,953 tumour cores representing 9,059 breast cancer cases, from 13 studies, with information on other clinical and pathological characteristics. TMAs were stained for Ki67 using standard immunohistochemical procedures, and scanned and digitized using the Ariol system. An automated algorithm was developed for the scoring of Ki67, and scores were compared to computer assisted visual (CAV) scores in a subset of 15 TMAs in a training set. We also assessed the correlation between automated Ki67 scores and other clinical and pathological characteristics. Overall, we observed good discriminatory accuracy (AUC = 85%) and good agreement (kappa = 0.64) between the automated and CAV scoring methods in the training set. The performance of the automated method varied by TMA (kappa range= 0.37-0.87) and study (kappa range = 0.39-0.69). The automated method performed better in satisfactory cores (kappa = 0.68) than suboptimal (kappa = 0.51) cores (p-value for comparison = 0.005); and among cores with higher total nuclei counted by the machine (4,000-4,500 cells: kappa = 0.78) than those with lower counts (50-500 cells: kappa = 0.41; p-value = 0.010). Among the 9,059 cases in this study, the correlations between automated Ki67 and clinical and pathological characteristics were found to be in the expected directions. Our findings indicate that automated scoring of Ki67 can be an efficient method to obtain good quality data across large numbers of TMAs from multicentre studies. However, robust algorithm development and rigorous pre- and post-analytical quality control procedures are necessary in order to ensure satisfactory performance.