Optimizing media composition for patient-derived organoids in ovarian cancer: a narrative review.

BACKGROUND AND OBJECTIVE

Patient-derived organoids (PDOs) are emerging as powerful ex vivo models for studying ovarian cancer (OC) biology and drug response. However, success rates for establishing OC PDOs remain low, partly due to heterogeneous media formulations and inconsistent definitions of "success". This review aims to compare media compositions, summarize derivation efficiencies across studies, and identify cross-cancer insights that may inform optimization of OC PDO culture conditions.

METHODS

A structured PubMed search was performed using the terms "ovarian cancer PDOs" and "ovarian cancer patient-derived organoids". Studies were included if they were primary research articles, published from 2018 onward, did not use commercial PDO kits, and provided detailed descriptions of media composition and culture methods. Ten studies met all criteria and were analyzed for media components, tissue sources, derivation efficiencies, and definitions of success.

KEY CONTENT AND FINDINGS

Across 10 foundational studies, success rates ranged from 13% to 65%, influenced by tissue type, histology, and variable definitions of success. Media compositions shared core components such as advanced DMEM/F12, B27, GlutaMAX, nicotinamide, and A83-01, but differed significantly in Wnt/BMP modulators, growth factors, hormonal additives, and inhibitors. Evidence suggests that high-Wnt conditions support PDOs in colorectal and pancreatic cancers but may hinder long-term growth in HGSOC. Cross-cancer comparisons highlighted potentially transferable strategies, particularly from TP53-mutant pancreatic and colorectal PDO systems.

CONCLUSIONS

OC PDO establishment remains challenging due to inconsistent culture conditions and varying success definitions. Standardizing media formulations, harmonizing reporting practices, and applying insights from other TP53-mutant cancers may improve reproducibility and clinical applicability. Tailored, subtype-specific optimization is likely necessary to enhance PDO derivation and utility in precision oncology.