Prognostic and predictive biomarkers for somatostatin analogs, peptide receptor radionuclide therapy and serotonin pathway targets in neuroendocrine tumours. (November 2018) - Barriuso, Jorge; Custodio, Ana; Afonso, Ruth; Alonso, Vicente; Astudillo, Aurora; Capdevila, Jaume; García-Carbonero, Rocío; Grande, Enrique; Jimenez-Fonseca, Paula; Marazuela, Mónica; Rodríguez-Antona, Cristina; Aller, Javier
Highlights: NETs are a heterogeneous group of neoplasms with different prognosis and response to therapy. The majority of NETs overexpress somatostatin receptors and they can be targeted with SSAs and PRRT. Ki-67, SSTR, chromogranin A, SSTR scintigraphy and the PCR-based NETest are the most widely studied markers so far. There is extensive research on molecular markers but no validated ones are ready for practice yet. The most promising way to bring out biomarkers is to incorporate them into clinical trials. Abstract: Neuroendocrine tumours (NETs) are a heterogeneous group of neoplasms regarding their molecular biology, clinical behaviour, prognosis and response to therapy. Several attempts to establish robust predictive biomarkers have failed. Neither tissue markers nor blood borne ones have proven to be successful yet. Circulating tumour cells (CTCs) as "liquid biopsies" could provide prognostic information at the time a therapeutic decision needs to be made and could be an attractive tool for tumour monitoring throughout the treatment period. However, "liquid biopsies" are far from becoming the standard biomarker in NETs. Promising results have been presented over the last few years using a novel biomarker candidate, a multianalyte algorithm analysis PCR-based test (NETest). New technologies will open the field to different ways of approaching the biomarker conundrum in NETs. However, the complications derived from being a heterogeneous group of malignancies will remain with us forever. In summary, there is an unmet need to incorporate new biomarker candidates into clinical research trials to obtain a robust prospective validation under the most demanding scenario.