Is bactibilia a predictor of poor outcome of pancreaticoduodenectomy?

BACKGROUND: Although bile infection has been proposed to increase infective complications following pancreaticoduodenectomy, its association with infective complications and non-infective complications like pancreatic fistula is still controversial. METHODS: Seventy-six patients who had undergone pancreaticoduodenectomy between July 2007 and December 2008 were included in a prospective database and their data analyzed. In all patients intraoperative bile from the bile duct was cultured. Preoperative, intra-operative, and post-operative variables were recorded and analyzed. RESULTS: Bile culture showed positive growth in 35 patients and negative growth in 41. Twenty patients in the positive group underwent ERCP and stenting. The patients with a positive bile culture had a higher incidence of infective complications including intra-abdominal abscess (n=8), wound infection (n=27), bacteremia (n=10), and renal insufficiency (n=9). There was no increase in the rate of non-infective complications of pancreaticoduodenectomy including pancreatic fistula (n=7), delayed gastric emptying (n=9), and post-operative hemorrhage (n=3). The hospital stay was significantly prolonged in the patients with a positive bile culture (P=0.0002). CONCLUSIONS: Pre-operative biliary drainage is significantly associated with bile infection, and bile infection increases the overall rates of infective complications and renal insufficiency. Because of the high incidence of complications is associated with infected bile, routine intra-operative bile culture is recommended in patients undergoing pancreaticoduodenectomy. Pre-operative prophylaxis is dependent on sensitivity of cases to perioperative antibiotics and intra-operative bile culture report. Because of its significant association with infected bile, biliary stenting should be used in strictly selected cases.

Predicting clinical response to anticancer drugs using an ex vivo platform that captures tumour heterogeneity.

Predicting clinical response to anticancer drugs remains a major challenge in cancer treatment. Emerging reports indicate that the tumour microenvironment and heterogeneity can limit the predictive power of current biomarker-guided strategies for chemotherapy. Here we report the engineering of personalized tumour ecosystems that contextually conserve the tumour heterogeneity, and phenocopy the tumour microenvironment using tumour explants maintained in defined tumour grade-matched matrix support and autologous patient serum. The functional response of tumour ecosystems, engineered from 109 patients, to anticancer drugs, together with the corresponding clinical outcomes, is used to train a machine learning algorithm; the learned model is then applied to predict the clinical response in an independent validation group of 55 patients, where we achieve 100% sensitivity in predictions while keeping specificity in a desired high range. The tumour ecosystem and algorithm, together termed the CANScript technology, can emerge as a powerful platform for enabling personalized medicine.