PURPOSE: Early detection would significantly decrease the mortality rate of ovarian cancer. In this study, we characterize and validate the combination of six serum biomarkers that discriminate between disease-free and ovarian cancer patients with high efficiency. EXPERIMENTAL DESIGN: We analyzed 362 healthy controls and 156 newly diagnosed ovarian cancer patients. Concentrations of leptin, prolactin, osteopontin, insulin-like growth factor II, macrophage inhibitory factor, and CA-125 were determined using a multiplex, bead-based, immunoassay system. All six markers were evaluated in a training set (181 samples from the control group and 113 samples from OC patients) and a test set (181 sample control group and 43 ovarian cancer). RESULTS: Multiplex and ELISA exhibited the same pattern of expression for all the biomarkers. None of the biomarkers by themselves were good enough to differentiate healthy versus cancer cells. However, the combination of the six markers provided a better differentiation than CA-125. Four models with <2% classification error in training sets all had significant improvement (sensitivity 84%-98% at specificity 95%) over CA-125 (sensitivity 72% at specificity 95%) in the test set. The chosen model correctly classified 221 out of 224 specimens in the test set, with a classification accuracy of 98.7%. CONCLUSIONS: We describe the first blood biomarker test with a sensitivity of 95.3% and a specificity of 99.4% for the detection of ovarian cancer. Six markers provided a significant improvement over CA-125 alone for ovarian cancer detection. Validation was performed with a blinded cohort. This novel multiplex platform has the potential for efficient screening in patients who are at high risk for ovarian cancer.
PURPOSE: Early detection would significantly decrease the mortality rate of ovarian cancer. In this study, we characterize and validate the combination of six serum biomarkers that discriminate between disease-free and ovarian cancerpatients with high efficiency. EXPERIMENTAL DESIGN: We analyzed 362 healthy controls and 156 newly diagnosed ovarian cancerpatients. Concentrations of leptin, prolactin, osteopontin, insulin-like growth factor II, macrophage inhibitory factor, and CA-125 were determined using a multiplex, bead-based, immunoassay system. All six markers were evaluated in a training set (181 samples from the control group and 113 samples from OC patients) and a test set (181 sample control group and 43 ovarian cancer). RESULTS: Multiplex and ELISA exhibited the same pattern of expression for all the biomarkers. None of the biomarkers by themselves were good enough to differentiate healthy versus cancer cells. However, the combination of the six markers provided a better differentiation than CA-125. Four models with <2% classification error in training sets all had significant improvement (sensitivity 84%-98% at specificity 95%) over CA-125 (sensitivity 72% at specificity 95%) in the test set. The chosen model correctly classified 221 out of 224 specimens in the test set, with a classification accuracy of 98.7%. CONCLUSIONS: We describe the first blood biomarker test with a sensitivity of 95.3% and a specificity of 99.4% for the detection of ovarian cancer. Six markers provided a significant improvement over CA-125 alone for ovarian cancer detection. Validation was performed with a blinded cohort. This novel multiplex platform has the potential for efficient screening in patients who are at high risk for ovarian cancer.
Authors: Siwen Hu-Lieskovan; Srabani Bhaumik; Kavita Dhodapkar; Jean-Charles J B Grivel; Sumati Gupta; Brent A Hanks; Sylvia Janetzki; Thomas O Kleen; Yoshinobu Koguchi; Amanda W Lund; Cristina Maccalli; Yolanda D Mahnke; Ruslan D Novosiadly; Senthamil R Selvan; Tasha Sims; Yingdong Zhao; Holden T Maecker Journal: J Immunother Cancer Date: 2020-12 Impact factor: 13.751
Authors: Celeste Leigh Pearce; Jennifer A Doherty; David J Van Den Berg; Kirsten Moysich; Chris Hsu; Kara L Cushing-Haugen; David V Conti; Susan J Ramus; Aleksandra Gentry-Maharaj; Usha Menon; Simon A Gayther; Paul D P Pharoah; Honglin Song; Susanne K Kjaer; Estrid Hogdall; Claus Hogdall; Alice S Whittemore; Valerie McGuire; Weiva Sieh; Jacek Gronwald; Krzysztof Medrek; Anna Jakubowska; Jan Lubinski; Georgia Chenevix-Trench; Jonathan Beesley; Penelope M Webb; Andrew Berchuck; Joellen M Schildkraut; Edwin S Iversen; Patricia G Moorman; Christopher K Edlund; Daniel O Stram; Malcolm C Pike; Roberta B Ness; Mary Anne Rossing; Anna H Wu Journal: Hum Mol Genet Date: 2011-03-21 Impact factor: 6.150
Authors: Brian Nolen; Liudmila Velikokhatnaya; Adele Marrangoni; Koen De Geest; Aleksey Lomakin; Robert C Bast; Anna Lokshin Journal: Gynecol Oncol Date: 2010-03-24 Impact factor: 5.482
Authors: Ilana Chefetz; Ayesha B Alvero; Jennie C Holmberg; Noah Lebowitz; Vinicius Craveiro; Yang Yang-Hartwich; Gang Yin; Lisa Squillace; Marta Gurrea Soteras; Paulomi Aldo; Gil Mor Journal: Cell Cycle Date: 2013-01-16 Impact factor: 4.534
Authors: Rikki Cannioto; Michael J LaMonte; Harvey A Risch; Chi-Chen Hong; Lara E Sucheston-Campbell; Kevin H Eng; J Brian Szender; Jenny Chang-Claude; Barbara Schmalfeldt; Ruediger Klapdor; Emily Gower; Albina N Minlikeeva; Gary R Zirpoli; Elisa V Bandera; Andrew Berchuck; Daniel Cramer; Jennifer A Doherty; Robert P Edwards; Brooke L Fridley; Ellen L Goode; Marc T Goodman; Estrid Hogdall; Satoyo Hosono; Allan Jensen; Susan Jordan; Susanne K Kjaer; Keitaro Matsuo; Roberta B Ness; Catherine M Olsen; Sara H Olson; Celeste Leigh Pearce; Malcolm C Pike; Mary Anne Rossing; Elizabeth A Szamreta; Pamela J Thompson; Chiu-Chen Tseng; Robert A Vierkant; Penelope M Webb; Nicolas Wentzensen; Kristine G Wicklund; Stacey J Winham; Anna H Wu; Francesmary Modugno; Joellen M Schildkraut; Kathryn L Terry; Linda E Kelemen; Kirsten B Moysich Journal: Cancer Epidemiol Biomarkers Prev Date: 2016-05-06 Impact factor: 4.254