Survival data for pancreatic cancer are usually based on actuarial calculations and actual long-term survival rates are rarely reported. Here we use population-level data from the Surveillance, Epidemiology, and End Results program for patients with microscopically confirmed pancreatic ductal adenocarcinoma diagnosed from 1975 to 2011. A total of 84,275 patients with at least 5 years of follow-up were evaluated (follow-up cutoff date: December 31, 2016). Actual 5-year survival for pancreatic cancer increased from 0.9% in 1975 to 4.2% in 2011 in patients of all stages (p < 0.001), while in surgically resected patients, it rose from 1.5% to 17.4% (p < 0.001). In non-resected patients, the actual 5-year survival remained unchanged over the same time period (0.8% vs 0.9%; p = 0.121). Multivariable analysis of surgically resected patients diagnosed in the recent time era (2004–2011) showed that age, gender, grade, tumour size, TNM-stage and chemotherapy were significant independent predictors of actual 5-year survival, while age, grade and TNM-stage were significant independent predictors in non-resected patients. However, unfavourable clinicopathological factors did not preclude long-term survival. Collectively, our findings indicate that actual 5-year survival for pancreatic cancer is still below 5% despite improvement of survival for the subset of patients undergoing surgical resection.

Background Pancreatic ductal adenocarcinoma has the lowest survival rate among all major cancers and is the third leading cause of cancer-related mortality. The stagnant survival statistics and dismal response rates to current therapeutics highlight the need for more efficient preclinical models. Patient-derived organoids (PDOs) offer new possibilities as powerful preclinical models able to account for interpatient variability. Organoid development can be divided into four different key phases: establishment, propagation, drug screening and response prediction. Establishment entails tailored tissue extraction and growth protocols, propagation requires consistent multiplication and passaging, while drug screening and response prediction will benefit from shorter and more precise assays, and clear decision-making tools. Conclusions This review attempts to outline the most important challenges that remain in exploiting organoid platforms for drug discovery and clinical applications. Some of these challenges may be overcome by novel methods that are under investigation, such as 3D bioprinting systems, microfluidic systems, optical metabolic imaging and liquid handling robotics. We also propose an optimized organoid workflow inspired by all technical solutions we have presented.

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