Back

Patient-Derived Organoids Functionally Stratify Epithelial Ovarian Cancer into Clinically Relevant Chemotherapy Response Phenotypes

Ragothaman, S.; Reddy, R.; Sajan, S. C.; John, L. A.; Biju, V.; Y, V.; Sankaran, S.; Ranade, R. R.; P.K, S.

2026-07-07 cancer biology
10.64898/2026.06.10.731260 bioRxiv
Show abstract

Background: Ovarian cancer (OC) exhibits substantial heterogeneity in response to platinum-based chemotherapy, resulting in variable clinical outcomes and frequent recurrence. Current biomarkers, including serum CA-125 kinetics and BRCA mutational status, incompletely predict therapeutic response. We investigated whether patient-derived organoids (PDOs) could functionally stratify chemotherapy sensitivity and better reflect patient-specific clinical behaviour. Methods: Twenty patients with OC treated between January 2024 and May 2026 were included, from whom fourteen PDO lines were successfully established. Eight PDOs with robust low-passage expansion and comprehensive longitudinal follow-up underwent functional profiling against carboplatin, paclitaxel, olaparib, and doxorubicin. Drug responses were assessed using half-maximal inhibitory concentration (IC50) and area under the curve (AUC) analyses and integrated with radiological response, serum CA-125 kinetics, BRCA status, and progression-free survival (PFS). Results: Clinical outcomes varied considerably despite similar platinum-taxane regimens. Although post-treatment CA-125 reduction was associated with prolonged PFS, neither CA-125 kinetics nor BRCA mutational status consistently predicted therapeutic response. PDO-guided functional stratification segregated tumours into four clinically relevant platinum-taxane response phenotypes: dual-sensitive, platinum-sensitive/taxane-resistant, platinum-resistant/taxane-sensitive, and dual-resistant. These functional categories closely mirrored radiological response, CA-125 normalisation, and disease progression patterns. PDOs exhibiting low IC50 and AUC values were associated with durable clinical benefit, whereas resistant PDOs tracked with persistent disease and early recurrence. Conclusions: PDO-guided functional stratification captures clinically meaningful therapeutic heterogeneity in OC and complements conventional biomarkers by directly measuring tumour-specific drug susceptibility. Prospective integration of PDO testing may facilitate patient-specific therapeutic selection and support functional precision oncology approaches in ovarian cancer.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
British Journal of Cancer
49 papers in training set
Top 0.1%
30.9%
2
Cancers
213 papers in training set
Top 0.4%
9.6%
3
Molecular Cancer Therapeutics
40 papers in training set
Top 0.1%
7.2%
4
npj Precision Oncology
53 papers in training set
Top 0.3%
4.8%
50% of probability mass above
5
Clinical Cancer Research
64 papers in training set
Top 0.4%
4.3%
6
Cancer Research Communications
51 papers in training set
Top 0.4%
3.2%
7
International Journal of Cancer
49 papers in training set
Top 0.3%
3.1%
8
Cancer Research
130 papers in training set
Top 1%
2.6%
9
eBioMedicine
183 papers in training set
Top 2%
2.4%
10
Genome Medicine
183 papers in training set
Top 2%
2.4%
11
Cell Reports Medicine
153 papers in training set
Top 2%
2.1%
12
Scientific Reports
3612 papers in training set
Top 54%
1.7%
13
Communications Medicine
113 papers in training set
Top 2%
1.7%
14
PLOS ONE
5266 papers in training set
Top 52%
1.4%
15
BMC Cancer
67 papers in training set
Top 1%
1.4%
16
Nature Communications
5641 papers in training set
Top 49%
1.3%
17
eLife
5828 papers in training set
Top 58%
1.1%
18
JCI Insight
277 papers in training set
Top 6%
1.1%
19
Cancer Medicine
26 papers in training set
Top 0.8%
1.1%
20
Frontiers in Oncology
103 papers in training set
Top 3%
1.0%
21
npj Breast Cancer
23 papers in training set
Top 0.4%
1.0%
22
Annals of Oncology
14 papers in training set
Top 0.2%
1.0%
23
Molecular Oncology
55 papers in training set
Top 1%
0.8%
24
JCO Clinical Cancer Informatics
22 papers in training set
Top 0.8%
0.6%
25
Science Advances
1243 papers in training set
Top 33%
0.6%