Development of a Multiprotein Classifier for the Detection of Early Stage Ovarian Cancer

Kristin L.M. Boylan, Ashley Petersen, Timothy K. Starr, Xuan Pu, Melissa A. Geller, Robert C. Bast, Karen H. Lu, Ugo Cavallaro, Denise C. Connolly, Kevin M. Elias, Daniel W. Cramer, Tanja Pejovic, Amy P.N. Skubitz

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4 Scopus citations


Background: Individual serum biomarkers are neither adequately sensitive nor specific for use in screening the general population for ovarian cancer. The purpose of this study was to develop a multiprotein classifier to detect the early stages of ovarian cancer, when it is most treatable. Methods: The Olink Proseek Multiplex Oncology II panel was used to simultaneously quantify the expression levels of 92 cancer-related proteins in sera. Results: In the discovery phase, we generated a multiprotein classifier that included CA125, HE4, ITGAV, and SEZ6L, based on an analysis of sera from 116 women with early stage ovarian cancer and 336 age-matched healthy women. CA125 alone achieved a sensitivity of 87.9% at a specificity of 95%, while the multiprotein classifier resulted in an increased sensitivity of 91.4%, while holding the specificity fixed at 95%. The performance of the multiprotein classifier was validated in a second cohort comprised of 192 women with early stage ovarian cancer and 467 age-matched healthy women. The sensitivity at 95% specificity increased from 74.5% (CA125 alone) to 79.2% with the multiprotein classifier. In addition, the multiprotein classifier had a sensitivity of 95.1% at 98% specificity for late stage ovarian cancer samples and correctly classified 80.5% of the benign samples using the 98% specificity cutpoint. Conclusions: The inclusion of the proteins HE4, ITGAV, and SEZ6L improved the sensitivity and specificity of CA125 alone for the detection of early stages of ovarian cancer in serum samples. Furthermore, we identified several proteins that may be novel biomarkers of early stage ovarian cancer.

Original languageEnglish (US)
Article number3077
Issue number13
StatePublished - Jul 1 2022

Bibliographical note

Funding Information:
Funding: This work was supported by grants from the Minnesota Ovarian Cancer Alliance (A.P.N. Skubitz and T.K. Starr), the Cookie Laughlin Pilot Study Award from the Rivkin Center (A.P.N. Skubitz), the Randy Shaver Cancer Research and Community Fund (T.K. Starr), NIH Ovarian SPORE grant P50 CA136393-11A1 (T.K. Starr), and the Masonic Cancer Center (T.K. Starr). Research reported in this publication was supported by the NIH grant P30 CA077598 utilizing the Biostatistics and Bioinformatics Core shared resource of the Masonic Cancer Center, University of Minnesota (A. Petersen), and by the National Center for Advancing Translational Sciences of the National Institutes of Health Award Number UL1-TR002494 (A. Petersen). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Funding for R.C. Bast, Jr. and K.H. Lu was provided by the NCI Early Detection Research Network (5 U01 CA200462-02 (R.C. Bast), the MD Anderson Ovarian SPOREs (P50 CA83639; R.C. Bast) and P50CA217685 (R.C. Bast), National Cancer Institute, Department of Health and Human Services; the Cancer Prevention Research Institute of Texas (RP160145; R.C. Bast); Golfers Against Cancer; the Tracey Joe Wilson Foundation; National Foundation for Cancer Research; UT MD Anderson Women’s Moon Shot; and generous donations from the Ann and Henry Zarrow Foundation, the Mossy Foundation, the Roberson Endowment, Stuart and Gaye Lynn Zarrow, Barry Elson, Arthur, and Sandra Williams. Funding for U. Cavallaro was provided by Associazione Italiana Ricerca sul Cancro (IG-21320), Italian Ministry of Health (RF-2016-02362551), Ricerca Corrente and 5x1000 funds, and the Fondazione Istituto Europeo di Oncologia-Centro Cardiologico Monzino. D.C. Connolly and the FCCC Biosample Repository Facility are supported by the FCCC Core Grant NCI P30 CA6927 and NIH S10 ODO21760. Funding for K.M. Elias was provided by the Honorable Tina Brozman Foundation, the Minnesota Ovarian Cancer Alliance, Mighty Moose Foundation, the Deborah and Robert First Foundation, the Saltonstall Fund, the Potter Fund, and the Sperling Fund.

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.


  • early detection
  • ovarian cancer
  • protein biomarkers


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