Primary tumors induce neutrophil extracellular traps with targetable metastasis promoting effects.

Targeting the dynamic tumor immune microenvironment (TIME) can provide effective therapeutic strategies for cancer. Neutrophils are the predominant leukocyte population in mice and humans, and mounting evidence implicates these cells during tumor growth and metastasis. Neutrophil extracellular traps (NETs) are networks of extracellular neutrophil DNA fibers that are capable of binding tumor cells to support metastatic progression. Here we demonstrate for the first time that circulating NET levels are elevated in advanced esophageal, gastric and lung cancer patients compared to healthy controls. Using pre-clinical murine models of lung and colon cancer in combination with intravital video microscopy, we show that NETs functionally regulate disease progression and that blocking NETosis through multiple strategies significantly inhibits spontaneous metastasis to the lung and liver. Further, we visualize how inhibiting tumor-induced NETs decreases cancer cell adhesion to liver sinusoids following intrasplenic injection - a mechanism previously thought to be driven primarily by exogenous stimuli. Thus, in addition to neutrophil abundance, the functional contribution of NETosis within the TIME has critical translational relevance and represents a promising target to impede metastatic dissemination.

Neural network and logistic regression diagnostic prediction models for giant cell arteritis: development and validation.

PURPOSE: To develop and validate neural network (NN) vs logistic regression (LR) diagnostic prediction models in patients with suspected giant cell arteritis (GCA). Design: Multicenter retrospective chart review. METHODS: An audit of consecutive patients undergoing temporal artery biopsy (TABx) for suspected GCA was conducted at 14 international medical centers. The outcome variable was biopsy-proven GCA. The predictor variables were age, gender, headache, clinical temporal artery abnormality, jaw claudication, vision loss, diplopia, erythrocyte sedimentation rate, C-reactive protein, and platelet level. The data were divided into three groups to train, validate, and test the models. The NN model with the lowest false-negative rate was chosen. Internal and external validations were performed. RESULTS: Of 1,833 patients who underwent TABx, there was complete information on 1,201 patients, 300 (25%) of whom had a positive TABx. On multivariable LR age, platelets, jaw claudication, vision loss, log C-reactive protein, log erythrocyte sedimentation rate, headache, and clinical temporal artery abnormality were statistically significant predictors of a positive TABx (P≤0.05). The area under the receiver operating characteristic curve/Hosmer-Lemeshow P for LR was 0.867 (95% CI, 0.794, 0.917)/0.119 vs NN 0.860 (95% CI, 0.786, 0.911)/0.805, with no statistically significant difference of the area under the curves (P=0.316). The misclassification rate/false-negative rate of LR was 20.6%/47.5% vs 18.1%/30.5% for NN. Missing data analysis did not change the results. CONCLUSION: Statistical models can aid in the triage of patients with suspected GCA. Misclassification remains a concern, but cutoff values for 95% and 99% sensitivities are provided (https://goo.gl/THCnuU).