Abstract

Update on Prognostic and Predictive Biomarkers for Pancreatic Neuroendocrine Tumors

Neuroendocrine tumors (NETs) describe a heterogeneous group of tumors with a wide range of morphologic, functional, and behavioral characteristics. These tumors are often indolent but they have the potential to cause symptoms through release of hormones and through local or distant spread. Pancreatic neuroendocrine tumors (pNET) represent a subset of NETs and they have a unique pattern of symptoms and disease progression. Due to the heterogeneity of this disease it is important to identify reliable markers to help guide prognosis and predict response to therapy. The recent approval of two new agents in the treatment of advanced pNET has raised additional interest in the significance of molecular markers in this disease. At the 2012 American Society of Clinical Oncology (ASCO) Annual Meeting, several investigators reported on collaborative efforts to develop clinically useful biomarkers for this disease. Choti et al. (Abstract #4126) reported data about the functional characteristics of NETs and pNETs among a multi-institutional cohort finding that a substantial minority of patients have functional symptoms even when the disease is localized. Faggiano et al. (Abstract #1604) looked at a wide spectrum of NETs in several referral centers and reported on predictors of both short term and long term survival in this setting. Yao et al. (Abstract #4014) reported analysis of predictive biomarkers among patients in the RADIANT-2 trial which looked at the role of the mTOR inhibitor, everolimus compared to placebo in NET. Fischer et al. (Abstract #4128) looked at a novel biomarker, placental growth factor (PlGF) and evaluated the role of serum and tissue levels of this protein as a predictive marker. Finally, Khan et al. (Abstract #4123) investigated the role of circulating tumor cells in NET (and pNET) and found that this may have potential as a prognostic marker and an early marker of response to therapy. The authors review and summarize these abstracts in this article.


Author(s):

Muhammad Wasif Saif, Paul Eliezer Oberstein



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