Abstract
Patient-derived cancer organoids have taken a prominent role in pre-clinical and translational research and have been generated for most common solid tumors. Cancer organoids have been shown to retain key genetic and phenotypic characteristics of their tissue of origin, tumor subtype and maintain intratumoral heterogeneity and therefore have the potential to be used as predictors for individualized treatment response. In this review, we highlight studies that have used cancer organoids to compare the efficacy of standard-of-care and targeted combination treatments with clinical patient response. Furthermore, we review studies using cancer organoids to identify new anti-cancer treatments using drug screening. Finally, we discuss the current limitations and improvements needed to understand the full potential of cancer organoids as avatars for clinical management of cancer therapy.
Highlights
Cancer remains one of the leading causes of death in the 21st century
All but one of the patients used for organoid derivation showed poor clinical response to chemotherapy which was recapitulated in the corresponding cancer organoids, though organoids from the patient that showed clinical response were not available for drug testing [13]
This study shows that this treatment response can potentially be predicted in cancer organoids, regardless of PD-L1 status though no correlation towards individual patient response could be made [54]
Summary
Cancer remains one of the leading causes of death in the 21st century. Despite enormous progress in the identification of mechanisms of tumor progression and treatment resistance and the development of new tumor targeted treatments many patients do not get cured. The main challenge remains to achieve accurate patient selection. Tumor biopsies used for clinical diagnostics do not capture the extensive intratumoral heterogeneity that masks emergent tumor clones with intrinsic or acquired resistance. This can result in patients receiving suboptimal treatment, or overtreatment leading to long-lasting harmful side-effects. The development of specific biomarkers and predictive model systems are essential to personalized treatment leading to more durable responses with fewer side effects
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