Patient-derived organoids as individual patient models for chemoradiation response prediction in gastrointestinal malignancies

IF 5.5 2区 医学 Q1 HEMATOLOGY Critical reviews in oncology/hematology Pub Date : 2021-01-01 DOI:10.1016/j.critrevonc.2020.103190
Maxim Le Compte , Niels Komen , Ines Joye , Marc Peeters , Hans Prenen , Evelien Smits , Christophe Deben , Michiel de Maat
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引用次数: 5

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.

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患者来源的类器官作为预测胃肠道恶性肿瘤放化疗反应的个体患者模型
放化疗(CRT)是特异性胃肠道(GI)癌症的重要治疗方式,因为它已被证明可以改善临床结果。最近新辅助治疗的发展,如直肠癌和食管癌的观望策略,甚至证明了CRT可能是一种有效的器官保留治疗。然而,由于分子的异质性,只有一小部分患者对CRT有完全的反应,这就需要个性化的治疗方法。近年来,对更多生理相关的体外预测模型的需求促进了患者来源的肿瘤类器官的发展。在这篇综述中,我们描述了目前接受(新)辅助CRT治疗的胃肠道癌患者的治疗选择。此外,我们深入讨论了类器官技术在预测胃肠道癌症CRT反应以及临床实施可能面临的挑战的背景下。
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来源期刊
CiteScore
11.00
自引率
3.20%
发文量
213
审稿时长
55 days
期刊介绍: Critical Reviews in Oncology/Hematology publishes scholarly, critical reviews in all fields of oncology and hematology written by experts from around the world. Critical Reviews in Oncology/Hematology is the Official Journal of the European School of Oncology (ESO) and the International Society of Liquid Biopsy.
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