Prospective Validation of an Ex Vivo, Patient-Derived 3D Spheroid Model for Response Predictions in Newly Diagnosed Ovarian Cancer

Stephen Shuford, Christine Wilhelm, Melissa Rayner, Ashley Elrod, Melissa Millard, Christina Mattingly, Alina Lotstein, Ashley M. Smith, Qi Jin Guo, Lauren O’Donnell, Jeffrey Elder, Larry Puls, S. John Weroha, Xiaonan Hou, Valentina Zanfagnin, Alpa Nick, Michael P. Stany, G. Larry Maxwell, Thomas Conrads, Anil K. SoodDavid Orr, Lillia M. Holmes, Matthew Gevaert, Howland E. Crosswell, Teresa M. DesRochers

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number11153
JournalScientific reports
Volume9
Issue number1
DOIs
StatePublished - Dec 1 2019

ASJC Scopus subject areas

  • General

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