Multi-Phenotypic Patient-Derived Organoid Model for Predicting Recurrence in Epithelial Ovarian Cancer = 상피성 난소암의 재발 예측을 위한 다중 표현형 환자 유래 오가노이드 모델

다국어 초록 (Multilingual Abstract)

We developed the Cancer Organoid Functional Assay （COFA）, a multiphenotypic patient-derived organoid （PDO）-based framework incorporating organoid growth dynamics, functional drug response, and FIGO stage for recurrence risk stratification in epithelial ovarian cancer （EOC）. Among 184 collected EOC tissue specimens, 123 patients with evaluable PDO assays and follow-up data were analyzed. The analysis identified PDO growth rate as an informative phenotypic component, particularly for recurrence risk modeling in R1/R2 patients with residual disease. The logistic recurrence index stratified 2-year recurrence-free survival （RFS） in the overall cohort （71.4% versus 36.5%）, R0 patients （81.3% versus 43.0%）, and R1/R2 patients （52.9% versus 12.5%）. Bootstrap validation of the logistic index showed optimism-corrected AUROCs of 0.664 and 0.632 in R0 and R1/R2 patients, respectively. The Cox-based RFS prognostic index also stratified COFA-sensitive and COFA-resistant groups （65.3% versus 35.2%）, with optimism-corrected C-indexes of 0.647 and 0.648, supporting COFA as a candidate functional profiling approach

목차 (Table of Contents)

• 제1장 ABSTRACT 8
• 1. Background 8
• 제2장 Results 11
• 2.1 The PDO growth rate effect on recurrence prediction after first-line chemotherapy 11
• 2.2 Cancer organoid-based HTS using 384-pillar/well plate and 3D cell spotter 13
• 2.3 Drug response measurements with cell growth rate using EOC cell lines 15
• 2.4 Similarity verification of PDO through pathological biomarkers and genomic analysis 17
• 2.5 Recurrence prediction of all 123 patients with EOC following first-line chemotherapy using the CODRP index 20
• 2.6 Recurrence prediction following first-line chemotherapy in patients with non-residual cancer after surgery (R0) using CODRP 22
• 2.7 Recurrence prediction following first-line chemotherapy in patients with residual cancer after surgery (R1 and R2) using CODRP 24
• 2.8 Bootstrap-based internal validation of the CODRP model following first-line chemotherapy 32
• 제3장 Discussion 34
• 3. Discussion 34
• 제4장 Methods 38
• 4.1 Ovarian cancer cell line culture 38
• 4.2 Assessment of disease recurrence in patients with EOC 38
• 4.3 Experimental protocol for PDC isolation from the tumor tissues of patients with EOC 39
• 4.4 Development of disposable nozzle-type cell spotter 45
• 4.5 Preparation of the 384-pillar/well plates 46
• 4.6 PDO-based HTS using 384-pillar/well plate and 3D cell spotter 46
• 4.7 PDO growth rate measurements 47
• 4.8 The PDO drug response measurement for CODRP 51
• 4.9 Calculation of parameters in PDO-based HTS 52
• 4.10 Calculation of CODRP index using multivariable logistic regression analysis 55
• 제5장 References 63
• 5. References 63