Despite its promise as a highly useful therapy for pancreatic cancer (PC), the addition of external beam radiation therapy to PC treatment has shown varying success in clinical trials. Understanding PC radioresistance and discovery of methods to sensitise PC to radiation will increase patient survival and improve quality of life. In this study, we identified PC radioresistance-associated pathways using global, unbiased techniques. Radioresistant cells were generated by sequential irradiation and recovery, and global genome cDNA microarray analysis was performed to identify differentially expressed genes in radiosensitive and radioresistant cells. Ingenuity pathway analysis was performed to discover cellular pathways and functions associated with differential radioresponse and identify potential small-molecule inhibitors for radiosensitisation. The expression of FDPS, one of the most differentially expressed genes, was determined in human PC tissues by IHC and the impact of its pharmacological inhibition with zoledronic acid (ZOL, Zometa) on radiosensitivity was determined by colony-forming assays. The radiosensitising effect of Zol in vivo was determined using allograft transplantation mouse model. Microarray analysis indicated that 11 genes (FDPS, ACAT2, AG2, CLDN7, DHCR7, ELFN2, FASN, SC4MOL, SIX6, SLC12A2, and SQLE) were consistently associated with radioresistance in the cell lines, a majority of which are involved in cholesterol biosynthesis. We demonstrated that knockdown of farnesyl diphosphate synthase (FDPS), a branchpoint enzyme of the cholesterol synthesis pathway, radiosensitised PC cells. FDPS was significantly overexpressed in human PC tumour tissues compared with healthy pancreas samples. Also, pharmacologic inhibition of FDPS by ZOL radiosensitised PC cell lines, with a radiation enhancement ratio between 1.26 and 1.5. Further, ZOL treatment resulted in radiosensitisation of PC tumours in an allograft mouse model. Unbiased pathway analysis of radioresistance allowed for the discovery of novel pathways associated with resistance to ionising radiation in PC. Specifically, our analysis indicates the importance of the cholesterol synthesis pathway in PC radioresistance. Further, a novel radiosensitiser, ZOL, showed promising results and warrants further study into the universality of these findings in PC, as well as the true potential of this drug as a clinical radiosensitiser.

As the most lethal major cancer, pancreatic cancer is a global healthcare challenge. Personalized medicine utilizing cutting-edge multi-omics data holds potential for major breakthroughs in tackling this critical problem. Radiomics and deep learning, two trendy quantitative imaging methods that take advantage of data science and modern medical imaging, have shown increasing promise in advancing the precision management of pancreatic cancer via diagnosing of precursor diseases, early detection, accurate diagnosis, and treatment personalization and optimization. Radiomics employs manually-crafted features, while deep learning applies computer-generated automatic features. These two methods aim to mine hidden information in medical images that is missed by conventional radiology and gain insights by systematically comparing the quantitative image information across different patients in order to characterize unique imaging phenotypes. Both methods have been studied and applied in various pancreatic cancer clinical applications. In this review, we begin with an introduction to the clinical problems and the technology. After providing technical overviews of the two methods, this review focuses on the current progress of clinical applications in precancerous lesion diagnosis, pancreatic cancer detection and diagnosis, prognosis prediction, treatment stratification, and radiogenomics. The limitations of current studies and methods are discussed, along with future directions. With better standardization and optimization of the workflow from image acquisition to analysis and with larger and especially prospective high-quality datasets, radiomics and deep learning methods could show real hope in the battle against pancreatic cancer through big data-based high-precision personalization.

(1) Background: Radiomics use high-throughput mining of medical imaging data to extract unique information and predict tumor behavior. Currently available clinical prediction models poorly predict treatment outcomes in pancreatic adenocarcinoma. Therefore, we used radiomic features of primary pancreatic tumors to develop outcome prediction models and compared them to traditional clinical models. (2) Methods: We extracted and analyzed radiomic data from pre-radiation contrast-enhanced CTs of 74 pancreatic cancer patients undergoing stereotactic body radiotherapy. A panel of over 800 radiomic features was screened to create overall survival and local-regional recurrence prediction models, which were compared to clinical prediction models and models combining radiomic and clinical information. (3) Results: A 6-feature radiomic signature was identified that achieved better overall survival prediction performance than the clinical model (mean concordance index: 0.66 vs. 0.54 on resampled cross-validation test sets), and the combined model improved the performance slightly further to 0.68. Similarly, a 7-feature radiomic signature better predicted recurrence than the clinical model (mean AUC of 0.78 vs. 0.66). (4) Conclusion: Overall survival and recurrence can be better predicted with models based on radiomic features than with those based on clinical features for pancreatic cancer.

PURPOSE: This study sought to identify differences in clinical characteristics, outcomes, and treatments between adult and pediatric patients with Ewing sarcoma family of tumors (ESFT). METHODS: Using the Surveillance, Epidemiology, and End Results Database from 1983-2013, 1,870 patients were analyzed (n=976 pediatric, n=894 adult). Between the two groups, demographic, tumor, and treatment characteristics were collated and compared. The chi-squared test determined differences in proportions of the variables between groups. Survival analysis was done by the Kaplan-Meier method; distributions were compared using the log-rank test. Univariate and multivariate analyses were performed in order to examine variables correlating with overall survival (OS), the primary endpoint. RESULTS: Adult patients had a poorer prognosis and were more likely to present with PNET histology, along with distant metastasis and soft tissue primary site. In patients undergoing surgery, RT was not associated with higher OS in either children or adults. If no surgery was performed, receipt of RT was associated with higher OS in adults but not children. Adulthood negatively correlated with OS on multivariate analysis when adjusting for potential confounding factors. Other salient factors associated with OS were male gender, metastatic disease, non-extremity bone location, treatment era, and PNET histology. However, when examining the most recent subset (patients treated 2004-2013), RT was associated with improved OS in both pediatrics and adults, which was an independent predictor on multivariate analysis. CONCLUSIONS: Adult patients with ESFT have inferior survival as compared to pediatric patients, likely related to earlier clinical detection in the latter.