No abstract

Increasing evidence suggests a complex relationship between obesity, diabetes and cancer. Here we review the evidence for the association between obesity and diabetes and a wide range of cancer types. In many cases the evidence for a positive association is strong, but for other cancer types a more complex picture emerges with some site-specific cancers associated with obesity but not to diabetes, and some associated with type I but not type II diabetes. The evidence therefore suggests the existence of cumulative common and differential mechanisms influencing the relationship between these diseases. Importantly, we highlight the influence of antidiabetics on cancer and antineoplastic agents on diabetes and in particular that antineoplastic targeting of insulin/IGF-1 signalling induces hyperglycaemia that often evolves to overt diabetes. Overall, a coincidence of diabetes and cancer worsens outcome and increases mortality. Future epidemiology should consider dose and time of exposure to both disease and treatment, and should classify cancers by their molecular signatures. Well-controlled studies on the development of diabetes upon cancer treatment are necessary and should identify the underlying mechanisms responsible for these reciprocal interactions. Given the global epidemic of diabetes, preventing both cancer occurrence in diabetics and the onset of diabetes in cancer patients will translate into a substantial socioeconomic benefit.

PURPOSESomatostatin analogs (SSAs) are recommended for the first-line treatment of most patients with well-differentiated, gastroenteropancreatic (GEP) neuroendocrine tumors; however, benefit from treatment is heterogeneous. The aim of the current study was to develop and validate a progression-free survival (PFS) prediction model in SSA-treated patients.PATIENTS AND METHODSWe extracted data from the Spanish Group of Neuroendocrine and Endocrine Tumors Registry (R-GETNE). Patient eligibility criteria included GEP primary, Ki-67 of 20% or less, and first-line SSA monotherapy for advanced disease. An accelerated failure time model was developed to predict PFS, which was represented as a nomogram and an online calculator. The nomogram was externally validated in an independent series of consecutive eligible patients (The Christie NHS Foundation Trust, Manchester, United Kingdom).RESULTSWe recruited 535 patients (R-GETNE, n = 438; Manchester, n = 97). Median PFS and overall survival in the derivation cohort were 28.7 (95% CI, 23.8 to 31.1) and 85.9 months (95% CI, 71.5 to 96.7 months), respectively. Nine covariates significantly associated with PFS were primary tumor location, Ki-67 percentage, neutrophil-to-lymphocyte ratio, alkaline phosphatase, extent of liver involvement, presence of bone and peritoneal metastases, documented progression status, and the presence of symptoms when initiating SSA. The GETNE-TRASGU (Treated With Analog of Somatostatin in Gastroenteropancreatic and Unknown Primary NETs) model demonstrated suitable calibration, as well as fair discrimination ability with a C-index value of 0.714 (95% CI, 0.680 to 0.747) and 0.732 (95% CI, 0.658 to 0.806) in the derivation and validation series, respectively.CONCLUSIONThe GETNE-TRASGU evidence-based prognostic tool stratifies patients with GEP neuroendocrine tumors receiving SSA treatment according to their estimated PFS. This nomogram may be useful when stratifying patients with neuroendocrine tumors in future trials. Furthermore, it could be a valuable tool for making treatment decisions in daily clinical practice.

Patients with IBD have an increased risk for non-melanoma skin cancer and small bowel cancer. Immunosuppresive therapy is not related to a higher overall risk for cancer or worse tumor evolution in patients who maintain these drugs after cancer diagnosis.

Purpose: Despite major advances in the treatment of classic Hodgkin's lymphoma (cHL), f30% of patientsinadvancedstagesmayeventuallydie as resultof the disease,andcurrentmethods topredict prognosis are ratherunreliable.Thus, the applicationof robust techniques for theidentificationofbiomarkers associated with treatment response is essentialif new predictive tools are to be developed. Experimental Design: We used gene expression data from advanced cHL patients to identify transcriptional patterns from the tumoral cells and their nonneoplastic microenvironment, associated with lack of maintained treatment response. Gene-Set Enrichment Analysis was used to identify functionalpathways associated withunfavorable outcome that were significantly enrichedin either the Hodgkin's and Reed-Sternberg cells (regulation of the G 2 -M checkpoint, chaperones, histone modification, and signalingpathways) or the reactive cellmicroenvironment (mainly representedby specificT-cell populations and macrophage activation markers). Results:To explore the pathways identified previously, we used a series of 52 formalin-fixed paraffin-embedded advanced cHL samples and designed a real-time PCR-based low-density array that included the most relevant genes. A large majority of the samples (82.7%) and all selected genes were analyzed successfully with this approach. Conclusions:The results of this assay can be combined in a single risk score integrating these biologicalpathways associated with treatment response and eventually usedina larger series to develop a new molecular outcome predictor for advanced cHL.