Patient-derived organoids as individual patient models for chemoradiation response prediction in gastrointestinal malignancies. (January 2021)

• Record Type: Journal Article
• Authors: Le Compte, Maxim; Komen, Niels; Joye, Ines; Peeters, Marc; Prenen, Hans; Smits, Evelien; Deben, Christophe; de Maat, Michiel
• Is Part Of: Critical reviews in oncology/hematology. Volume 157(2021)
• Publication Date: 2021-01
• Subjects: Esophageal cancer -- Rectal cancer -- Chemoradiation -- Neoadjuvant treatment -- Patient-derived organoid -- Response prediction; Oncology -- Periodicals; Hematology -- Periodicals; 616.994
• DOI: 10.1016/j.critrevonc.2020.103190
• Languages: English
• ISSNs: 1040-8428

Abstract

Graphical abstract: Highlights: It remains a challange to prospectively distinguish chemoradioresistant from -sensitive patients. Patient-derived esophageal and rectal tumor organoids have great potential to predict chemoradiotherapy responses. The clinical implementation of patient-derived tumor organoids as predictive models is still hampered by several issues. Abstract: Chemoradiotherapy (CRT) is an important treatment modality for specific gastrointestinal (GI) cancers, as it has been shown to improve clinical outcomes. Recent developments in the neoadjuvant setting such as wait-and-see strategies for rectal as well as for esophageal cancers have even proven that CRT might be an effective organ-sparing treatment. However, due to molecular heterogeneity, only a subset of patients will show a complete response to CRT, which addresses the need for an individualized treatment approach. In recent years, the demand for more physiologically relevant predictive in vitro models has fostered the development of patient-derived tumor organoids. In this review, we describe the current treatment options for patients with GI cancers who are treated with (neo)adjuvant CRT. Furthermore, we provide an in-depth discussion of the organoid technology in the context of predicting CRT response for GI cancers as well as possible challenges for clinical implementation.