TY - JOUR
T1 - Prospective Validation of an Ex Vivo, Patient-Derived 3D Spheroid Model for Response Predictions in Newly Diagnosed Ovarian Cancer
AU - Shuford, Stephen
AU - Wilhelm, Christine
AU - Rayner, Melissa
AU - Elrod, Ashley
AU - Millard, Melissa
AU - Mattingly, Christina
AU - Lotstein, Alina
AU - Smith, Ashley M.
AU - Guo, Qi Jin
AU - O’Donnell, Lauren
AU - Elder, Jeffrey
AU - Puls, Larry
AU - Weroha, S. John
AU - Hou, Xiaonan
AU - Zanfagnin, Valentina
AU - Nick, Alpa
AU - Stany, Michael P.
AU - Maxwell, G. Larry
AU - Conrads, Thomas
AU - Sood, Anil K.
AU - Orr, David
AU - Holmes, Lillia M.
AU - Gevaert, Matthew
AU - Crosswell, Howland E.
AU - DesRochers, Teresa M.
N1 - Publisher Copyright:
© 2019, The Author(s).
PY - 2019/12/1
Y1 - 2019/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85070779492&partnerID=8YFLogxK
U2 - 10.1038/s41598-019-47578-7
DO - 10.1038/s41598-019-47578-7
M3 - Article
C2 - 31371750
AN - SCOPUS:85070779492
SN - 2045-2322
VL - 9
JO - Scientific Reports
JF - Scientific Reports
IS - 1
M1 - 11153
ER -