Prospective Validation of an Ex Vivo, Patient-Derived 3D Spheroid Model for Response Predictions in Newly Diagnosed Ovarian Cancer
Open accessAbstract
Although 70-80% of newly diagnosed ovarian cancer patients respond to first-line therapy, almost all relapse and five-year survival remains below 50%. One strategy to increase five-year survival is prolonging time to relapse by improving first-line therapy response. However, no biomarker today can accurately predict individual response to therapy. In this study, we present analytical and prospective clinical validation of a new test that utilizes primary patient tissue in 3D cell culture to make patient-specific response predictions prior to initiation of treatment in the clinic. Test results were generated within seven days of tissue receipt from newly diagnosed ovarian cancer patients obtained at standard surgical debulking or laparoscopic biopsy. Patients were followed for clinical response to chemotherapy. In a study population of 44, the 32 test-predicted Responders had a clinical response rate of 100% across both adjuvant and neoadjuvant treated populations with an overall prediction accuracy of 89% (39 of 44, p < 0.0001). The test also functioned as a prognostic readout with test-predicted Responders having a significantly increased progression-free survival compared to test-predicted Non-Responders, p = 0.01. This correlative accuracy establishes the test's potential to benefit ovarian cancer patients through accurate prediction of patient-specific response before treatment.
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Patient-derived spheroids grown in 384-well ULA plates derived from primary debulking surgery formed over 24 h and after a further 72 h of growth predicted patient response to first-line adjuvant chemotherapies with 89% accuracy.
“Patient-derived spheroids grown in 384-well ULA plates derived from primary debulking surgery formed over 24 h and after a further 72 h of growth predicted patient response to first-line adjuvant chemotherapies with 89% accuracy (Shuford et al., 2019).”
From Three-dimensional modelling of ovarian cancer: from cell lines to organoids for discovery and personalized medicine (2022) · cites this paper as Shuford et al., 2019
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References 46 · Cited by 66
- Authors
- Shuford, S., Wilhelm, C., Rayner, M., Elrod, A., Millard, M., Mattingly, C., Lotstein, A., Smith, A. M., Guo, Q. J., O’Donnell, L., Elder, J., Puls, L., Weroha, S. J., Hou, X., Zanfagnin, V., Nick, A., Stany, M. P., Maxwell, G. L., Conrads, T., Sood, A. K., Orr, D., Holmes, L. M., Gevaert, M., Crosswell, H. E., & DesRochers, T. M.
- Affiliations
- Kiyatec (United States) (US)
- University of South Carolina (US)
- Medical University of South Carolina (US)
- Prisma Health (US)
- Mayo Clinic (US)
- Saint Thomas Health (US)
- Inova Fairfax Hospital (US)
- The University of Texas MD Anderson Cancer Center (US)
- Corresponding author
- DesRochers, Teresa M.
- ORCID
- Venue
- Scientific Reports
- Published
- 2019-08-01
- Volume
- 9
- Issue
- 1
- Pages
- 11153-11153
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC6671958
- OpenAlex
- W2965335596
- Cited by
- 66
- References
- 46
- Citations by year
- 2026: 7 · 2025: 10 · 2024: 3 · 2023: 14 · 2022: 8 · 2021: 14 · 2020: 10
- FWCI
- 7.65
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Institutes of Health (CA136393)
- National Cancer Institute (P50CA136393)
- Topics
- Health Sciences › Medicine › Reproductive Medicine › Ovarian cancer diagnosis and treatment
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › BRCA gene mutations in cancer
- Health Sciences › Medicine › Surgery › Intraperitoneal and Appendiceal Malignancies
- Keywords
- ovarian cancer, 3D spheroid model, patient-derived tumor tissue, predictive biomarkers, chemotherapy response prediction, first-line therapy, neoadjuvant chemotherapy, adjuvant chemotherapy, progression-free survival, prognostic factors, ex vivo cell culture
- MeSH terms
- Progression-Free Survival, Female, Humans, Middle Aged, Models, Biological, Ovarian Neoplasms, Prognosis, Tumor Cells, Cultured, Treatment Outcome, Spheroids, Cellular, Precision Medicine
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