Using organoid models to predict chemotherapy efficacy: the future of precision oncology?. Issue 6 (2nd November 2019)

• Record Type: Journal Article
• Authors: Forsythe, Steven; Pu, Tracey; Skardal, Aleksander
• Is Part Of: Expert review of precision medicine and drug development. Volume 4:Issue 6(2019)
• Page Start: 317
• Page End: 336
• Publication Date: 2019-11-02
• Subjects: Personalized medicine -- organoids -- cancer -- chemotherapy; Personalized medicine -- Periodicals; Drug development -- Periodicals; 615.19005
• DOI: 10.1080/23808993.2019.1685868
• Languages: English
• ISSNs: 2380-8993

Abstract

ABSTRACT: Introduction : Cancer related causes of death remain a global health issue in both developing and developed countries. Traditionally, therapies have been utilized to indiscriminately treat patients, with therapy approval based on statistical significance from clinical trials. However, cancer is a heterogeneous disease, with many characteristics that affect individual patient response to treatment. Recently, the rise of personalized medicine has coincided with the increased technological capabilities to analyze individuals to provide tailored regimens. However, the ability to determine optimal therapy for a given patient has been based on genomic analysis of therapy targets, with no method to confirm these predicted responses. Areas Covered : In this review, we summarize the efforts in utilizing patient-derived organoids, (PDOs), to analyze tumor response toward treatment for the potential of influencing a clinician's decision in individual patients. Recent literature suggests an increasing interest in the field, with incorporation of tumors not typically utilized for in vitro study due to difficulties in establishing models. Expert Opinion : The potential applications of this field are diverse in scope. Further research could provide advanced models to mimic patient therapy responses, including immunotherapy and the use of microfluidic tumor-on-a-chip devices in tandem with PDOs to further increase physiological accuracy of cancer models.