Preoperative biomarkers related to inflammation may identify high-risk anastomoses in colorectal cancer surgery : explorative study

Anastomotic leakage is a major complication after colorectal surgery, presumed to correspond with a process of failed wound healing, involving inflammation. Circulating levels of inflammation-related biomarkers were investigated in preoperative samples from 41 patients with leakage, who had elective treatment with a primary anastomosis for non-disseminated colorectal cancer, matched to 41 complication-free controls. A total of 15 inflammation-related proteins were elevated before surgery in patients with rectal cancer with leakage, of which C-X-C motif chemokine 6 and C-C motif chemokine 11 remained significantly increased after controlling for multiplicity. As a corresponding expression pattern difference did not emerge when tissue adjacent to the anastomosis was evaluated with immunohistochemistry, findings may reflect a systemic rather than a local host response. While these findings require validation before implementation into surgical practice, they highlight the need for further translational investigations as a promising research area to help decrease leakage rates. Background Colorectal anastomotic leakage can be considered a process of failed wound healing, for which related biomarkers might be a promising research area to decrease leak rates. Methods Patients who had elective surgery with a primary anastomosis for non-metastatic colorectal cancer, at two university hospitals between 1 January 2010 and 31 December 2015 were included. Patients with an anastomotic leak were identified and matched (1:1) to complication-free controls on the basis of sex, age, tumour stage, tumour location, and operating hospital. Preoperative blood samples were analysed by use of protein panels associated with systemic or enteric inflammation by proteomics, and enzyme-linked immunosorbent assays. Multivariable projection methods were used in the statistical analyses and adjusted for multiple comparisons to reduce false positivity. Rectal cancer tissue samples were evaluated with immunohistochemistry to determine local expression of biomarkers that differed significantly between cases and controls. Results Out of 726 patients undergoing resection, 41 patients with anastomotic leakage were matched to 41 controls. Patients with rectal cancer with leakage displayed significantly elevated serum levels of 15 proteins related to inflammation. After controlling for a false discovery rate, levels of C-X-C motif chemokine 6 (CXCL6) and C-C motif chemokine 11 (CCL11) remained significant. In patients with colonic cancer with leakage, levels of high-sensitivity C-reactive protein (hs-CRP) were increased before surgery. Local expression of CXCL6 and CCL11, and their receptors, were similar in rectal tissues between cases and controls. Conclusion Patients with anastomotic leakage could have an upregulated inflammatory response before surgery, as expressed by elevated serological levels of CXCL6 and CCL11 for rectal cancer and hs-CRP levels in patients with colonic cancer respectively. Preoperative inflammation-related serum proteins were evaluated in a case-control study of 41 patients with anastomotic leakage matched 1:1 with 41 complication-free controls. The chemokines C-X-C motif chemokine 6 and C-C motif chemokine 11 were significantly increased before surgery in patients with rectal cancer and leakage, a finding requiring further validation.Peer reviewe

Circulating Tissue Polypeptide-Specific Antigen in Pre-Diagnostic Pancreatic Cancer Samples

Simple Summary:& nbsp;Detecting cancer early significantly increases the chances of successful (surgical) treatment. Pancreatic cancer is one of the deadliest cancer forms, since it is usually discovered at a late and already spread stage. Finding biomarkers showing pancreatic cancer at an early stage is a possible approach to early detection and improved treatment. The aim of our study was to assess the potential of tissue polypeptide specific antigen (TPS) as a biomarker for early pancreatic cancer detection. We studied TPS levels in blood plasma samples from a population-based biobank in Vasterbotten, Sweden that were collected before individuals were diagnosed with pancreatic cancer. Although TPS levels are raised at diagnosis, this occurs late, and thus TPS does not seem to hold promise as an early detection marker for pancreatic cancer.& nbsp;Early detection of pancreatic ductal adenocarcinoma (PDAC) is challenging, and late diagnosis partly explains the low 5-year survival. Novel and sensitive biomarkers are needed to enable early PDAC detection and improve patient outcomes. Tissue polypeptide specific antigen (TPS) has been studied as a biomarker in PDAC diagnostics, and it has previously been shown to reflect clinical status better than the 'golden standard' biomarker carbohydrate antigen 19-9 (CA 19-9) that is most widely used in the clinical setting. In this cross-sectional case-control study using pre-diagnostic plasma samples, we aim to evaluate the potential of TPS as a biomarker for early PDAC detection. Furthermore, in a subset of individuals with multiple samples available at different time points before diagnosis, a longitudinal analysis was used. We assessed plasma TPS levels using enzyme-linked immunosorbent assay (ELISA) in 267 pre-diagnostic PDAC plasma samples taken up to 18.8 years before clinical PDAC diagnosis and in 320 matched healthy controls. TPS levels were also assessed in 25 samples at PDAC diagnosis. Circulating TPS levels were low both in pre-diagnostic samples of future PDAC patients and in healthy controls, whereas TPS levels at PDAC diagnosis were significantly increased (odds ratio 1.03; 95% confidence interval: 1.01-1.05) in a logistic regression model adjusted for age. In conclusion, TPS levels increase late in PDAC progression and hold no potential as a biomarker for early detection.Peer reviewe

Risk prediction for estrogen receptor-specific breast cancers in two large prospective cohorts

Background: Few published breast cancer (BC) risk prediction models consider the heterogeneity of predictor variables between estrogen-receptor positive (ER+) and negative (ER-) tumors. Using data from two large cohorts, we examined whether modeling this heterogeneity could improve prediction. Methods: We built two models, for ER+ (ModelER+) and ER- tumors (ModelER-), respectively, in 281,330 women (51% postmenopausal at recruitment) from the European Prospective Investigation into Cancer and Nutrition cohort. Discrimination (C-statistic) and calibration (the agreement between predicted and observed tumor risks) were assessed both internally and externally in 82,319 postmenopausal women from the Women’s Health Initiative study. We performed decision curve analysis to compare ModelER+ and the Gail model (ModelGail) regarding their applicability in risk assessment for chemoprevention. Results: Parity, number of full-term pregnancies, age at first full-term pregnancy and body height were only associated with ER+ tumors. Menopausal status, age at menarche and at menopause, hormone replacement therapy, postmenopausal body mass index, and alcohol intake were homogeneously associated with ER+ and ER- tumors. Internal validation yielded a C-statistic of 0.64 for ModelER+ and 0.59 for ModelER-. External validation reduced the C-statistic of ModelER+ (0.59) and ModelGail (0.57). In external evaluation of calibration, ModelER+ outperformed the ModelGail: the former led to a 9% overestimation of the risk of ER+ tumors, while the latter yielded a 22% underestimation of the overall BC risk. Compared with the treat-all strategy, ModelER+ produced equal or higher net benefits irrespective of the benefit-to-harm ratio of chemoprevention, while ModelGail did not produce higher net benefits unless the benefit-to-harm ratio was below 50. The clinical applicability, i.e. the area defined by the net benefit curve and the treat-all and treat-none strategies, was 12.7 × 10− 6 for ModelER+ and 3.0 × 10− 6 for ModelGail. Conclusions: Modeling heterogeneous epidemiological risk factors might yield little improvement in BC risk prediction. Nevertheless, a model specifically predictive of ER+ tumor risk could be more applicable than an omnibus model in risk assessment for chemoprevention