Abstract
Objectives
Numerous prognostic models have been proposed for ovarian cancer, extending from single serological factors to complex gene-expression signatures. Nonetheless, these models have not been routinely translated into clinical practice. We constructed a robust and readily calculable model for predicting surgical outcome and prognosis of ovarian cancer patients by exploiting commonly available clinico-pathological factors and three selected serum parameters.
Methods
Serum CA125, human epididymis protein 4 (HE4) and mesothelin (MSL) were quantified by Lumipulse® G chemiluminescent enzyme immunoassay (Fujirebio) in a total of 342 serum samples from 190 ovarian cancer patients, including 152 paired pre- and post-operative samples.
Results
Detection of pre-operative HE4 and CA125 was the optimal marker combination for blood-based prediction of surgical outcome (AUC=0.86). We constructed a prognostic model, computed by serum levels of pre-operative CA125, post-operative HE4, post-operative MSL and surgical outcome. Prognostic performance of our model was superior to any of these parameters alone and was independent from BRCA1/2 mutational status. We subsequently transformed our model into a prognostic risk index, stratifying patients as “lower risk” or “higher risk”. In “higher risk” patients, relapse or death was predicted with an AUC of 0.89 and they had a significantly shorter progression free survival (HR: 9.74; 95 % CI: 5.95–15.93; p<0.0001) and overall survival (HR: 5.62; 95 % CI: 3.16–9.99; p<0.0001) compared to “lower risk” patients.
Conclusions
We present a robust predictive/prognostic model for ovarian cancer, which could readily be implemented into routine diagnostics in order to identify ovarian cancer patients at high risk of recurrence.
Funding source: Fujirebio Europe, Gent, Belgium
Acknowledgments
The authors want to thank Dr. M. Stevense (TU Dresden) for language editing. We further thank L. Vernoux (Fujirebio) for excellent statistical and technical assistance.
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Research ethics: The study was approved by the Local Research Ethics Committee at the Technische Universität Dresden, Germany (file number: EK74032013) and was performed in accordance with good clinical practice guidelines, national laws and the Declaration of Helsinki.
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Informed consent: Informed consent was obtained from all individuals included in this study.
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Author contributions: JDK, DMK, PW, TL made substantial contributions to the conception and design of the study. JDK and DMK contributed to the experimental work or to the acquisition of data and to the analysis/interpretation of the results. JDK and DMK, TL and PW were involved in drafting the manuscript, creating figures or revising the manuscript. All authors read and approved the manuscript in its final version. All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Competing interests: COI disclosures have been uploaded for each author.
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Research funding: The study was supported by Fujirebio Europe, Gent, Belgium by supplying Lumipulse® G chemiluminescent enzyme immunoassays.
References
1. Torre, LA, Trabert, B, DeSantis, CE, Miller, KD, Samimi, G, Runowicz, CD, et al.. Ovarian cancer statistics, 2018. CA Cancer J Clin 2018;68:284–96. https://doi.org/10.3322/caac.21456.Search in Google Scholar PubMed PubMed Central
2. du Bois, A, Quinn, M, Thigpen, T, Vermorken, J, Avall-Lundqvist, E, Bookman, M, et al.. 2004 consensus statements on the management of ovarian cancer: final document of the 3rd International Gynecologic Cancer Intergroup Ovarian Cancer Consensus Conference (GCIG OCCC 2004). Ann Oncol 2005;16(8 Suppl):viii7–12. https://doi.org/10.1093/annonc/mdi961.Search in Google Scholar PubMed
3. Karam, A, Ledermann, JA, Kim, JW, Sehouli, J, Lu, K, Gourley, C, et al.. Fifth Ovarian Cancer Consensus Conference of the Gynecologic Cancer InterGroup: first-line interventions. Ann Oncol 2017;28:711–7. https://doi.org/10.1093/annonc/mdx011.Search in Google Scholar PubMed
4. Stuart, GC, Kitchener, H, Bacon, M, duBois, A, Friedlander, M, Ledermann, J, et al.. 2010 Gynecologic Cancer InterGroup (GCIG) consensus statement on clinical trials in ovarian cancer: report from the Fourth Ovarian Cancer Consensus Conference. Int J Gynecol Cancer 2011;21:750–5. https://doi.org/10.1097/igc.0b013e31821b2568.Search in Google Scholar PubMed
5. Ray-Coquard, I, Pautier, P, Pignata, S, Perol, D, Gonzalez-Martin, A, Berger, R, et al.. Olaparib plus bevacizumab as first-line maintenance in ovarian cancer. N Engl J Med 2019;381:2416–28. https://doi.org/10.1056/nejmoa1911361.Search in Google Scholar
6. Gonzalez-Martin, A, Pothuri, B, Vergote, I, DePont Christensen, R, Graybill, W, Mirza, MR, et al.. Niraparib in patients with newly diagnosed advanced ovarian cancer. N Engl J Med 2019;381:2391–402. https://doi.org/10.1056/nejmoa1910962.Search in Google Scholar
7. Waldron, L, Haibe-Kains, B, Culhane, AC, Riester, M, Ding, J, Wang, XV, et al.. Comparative meta-analysis of prognostic gene signatures for late-stage ovarian cancer. J Natl Cancer Inst 2014;106:dju049. https://doi.org/10.1093/jnci/dju049.Search in Google Scholar PubMed PubMed Central
8. Tothill, RW, Tinker, AV, George, J, Brown, R, Fox, SB, Lade, S, et al.. Novel molecular subtypes of serous and endometrioid ovarian cancer linked to clinical outcome. Clin Cancer Res 2008;14:5198–208. https://doi.org/10.1158/1078-0432.ccr-08-0196.Search in Google Scholar PubMed
9. Laios, A, Katsenou, A, Tan, YS, Johnson, R, Otify, M, Kaufmann, A, et al.. Feature selection is critical for 2-year prognosis in advanced stage high grade serous ovarian cancer by using machine learning. Cancer Control 2021;28:10732748211044678. https://doi.org/10.1177/10732748211044678.Search in Google Scholar PubMed PubMed Central
10. Wimberger, P, Wehling, M, Lehmann, N, Kimmig, R, Schmalfeldt, B, Burges, A, et al.. Influence of residual tumor on outcome in ovarian cancer patients with FIGO stage IV disease: an exploratory analysis of the AGO-OVAR (Arbeitsgemeinschaft Gynaekologische Onkologie Ovarian Cancer Study Group). Ann Surg Oncol 2010;17:1642–8. https://doi.org/10.1245/s10434-010-0964-9.Search in Google Scholar PubMed
11. Menon, U, Gentry-Maharaj, A, Burnell, M, Singh, N, Ryan, A, Karpinskyj, C, et al.. Ovarian cancer population screening and mortality after long-term follow-up in the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS): a randomised controlled trial. Lancet 2021;397:2182–93. https://doi.org/10.1016/s0140-6736(21)00731-5.Search in Google Scholar
12. Hertlein, L, Stieber, P, Kirschenhofer, A, Krocker, K, Nagel, D, Lenhard, M, et al.. Human epididymis protein 4 (HE4) in benign and malignant diseases. Clin Chem Lab Med 2012;50:2181–8. https://doi.org/10.1515/cclm-2012-0097.Search in Google Scholar
13. Sonoda, G, Palazzo, J, du Manoir, S, Godwin, AK, Feder, M, Yakushiji, M, et al.. Comparative genomic hybridization detects frequent overrepresentation of chromosomal material from 3q26, 8q24, and 20q13 in human ovarian carcinomas. Genes Chromosomes Cancer 1997;20:320–8. https://doi.org/10.1002/(sici)1098-2264(199712)20:4<320::aid-gcc2>3.0.co;2-3.10.1002/(SICI)1098-2264(199712)20:4<320::AID-GCC2>3.0.CO;2-3Search in Google Scholar
14. Galgano, MT, Hampton, GM, Frierson, HFJr. Comprehensive analysis of HE4 expression in normal and malignant human tissues. Mod Pathol 2006;19:847–53. https://doi.org/10.1038/modpathol.3800612.Search in Google Scholar
15. Drapkin, R, von Horsten, HH, Lin, Y, Mok, SC, Crum, CP, Welch, WR, et al.. Human epididymis protein 4 (HE4) is a secreted glycoprotein that is overexpressed by serous and endometrioid ovarian carcinomas. Cancer Res 2005;65:2162–9. https://doi.org/10.1158/0008-5472.can-04-3924.Search in Google Scholar
16. Bast, RCJr, Badgwell, D, Lu, Z, Marquez, R, Rosen, D, Liu, J, et al.. New tumor markers: CA125 and beyond. Int J Gynecol Cancer 2005;15(3 Suppl):274–81. https://doi.org/10.1111/j.1525-1438.2005.00441.x.Search in Google Scholar
17. Rump, A, Morikawa, Y, Tanaka, M, Minami, S, Umesaki, N, Takeuchi, M, et al.. Binding of ovarian cancer antigen CA125/MUC16 to mesothelin mediates cell adhesion. J Biol Chem 2004;279:9190–8. https://doi.org/10.1074/jbc.m312372200.Search in Google Scholar
18. Gubbels, JA, Belisle, J, Onda, M, Rancourt, C, Migneault, M, Ho, M, et al.. Mesothelin-MUC16 binding is a high affinity, N-glycan dependent interaction that facilitates peritoneal metastasis of ovarian tumors. Mol Cancer 2006;5:50. https://doi.org/10.1186/1476-4598-5-50.Search in Google Scholar
19. Scholler, N, Garvik, B, Hayden-Ledbetter, M, Kline, T, Urban, N. Development of a CA125-mesothelin cell adhesion assay as a screening tool for biologics discovery. Cancer Lett 2007;247:130–6. https://doi.org/10.1016/j.canlet.2006.03.029.Search in Google Scholar
20. Bruney, L, Conley, KC, Moss, NM, Liu, Y, Stack, MS. Membrane-type I matrix metalloproteinase-dependent ectodomain shedding of mucin16/CA-125 on ovarian cancer cells modulates adhesion and invasion of peritoneal mesothelium. Biol Chem 2014;395:1221–31. https://doi.org/10.1515/hsz-2014-0155.Search in Google Scholar
21. Giordano, G, Ferioli, E, Tafuni, A. The role of mesothelin expression in serous ovarian carcinoma: impacts on diagnosis, prognosis, and therapeutic targets. Cancers (Basel) 2022;14:2283. https://doi.org/10.3390/cancers14092283.Search in Google Scholar PubMed PubMed Central
22. Liu, X, Chan, A, Tai, CH, Andresson, T, Pastan, I. Multiple proteases are involved in mesothelin shedding by cancer cells. Commun Biol 2020;3:728. https://doi.org/10.1038/s42003-020-01464-5.Search in Google Scholar PubMed PubMed Central
23. FIGO Committee on Gynecologic Oncology. Current FIGO staging for cancer of the vagina, fallopian tube, ovary, and gestational trophoblastic neoplasia. Int J Gynaecol Obstet 2009;105:3–4. https://doi.org/10.1016/j.ijgo.2008.12.015.Search in Google Scholar PubMed
24. Prat, J, Oncology FCoG. Staging classification for cancer of the ovary, fallopian tube, and peritoneum. Int J Gynaecol Obstet 2014;124:1–5. https://doi.org/10.1016/j.ijgo.2013.10.001.Search in Google Scholar PubMed
25. Link, T, Passek, S, Wimberger, P, Frank, K, Vassileva, YD, Kramer, M, et al.. Serum calretinin as an independent predictor for platinum resistance and prognosis in ovarian cancer. Int J Cancer 2020;146:2608–18. https://doi.org/10.1002/ijc.32676.Search in Google Scholar PubMed
26. Klotz, DM, Link, T, Wimberger, P, Kuhlmann, JD. Prognostic relevance of longitudinal HGF levels in serum of patients with ovarian cancer. Mol Oncol 2021;15:3626–38. https://doi.org/10.1002/1878-0261.12949.Search in Google Scholar PubMed PubMed Central
27. Feng, LY, Liao, SB, Li, L. Preoperative serum levels of HE4 and CA125 predict primary optimal cytoreduction in advanced epithelial ovarian cancer: a preliminary model study. J Ovarian Res 2020;13:17. https://doi.org/10.1186/s13048-020-0614-1.Search in Google Scholar PubMed PubMed Central
28. Braicu, EI, Fotopoulou, C, Van Gorp, T, Richter, R, Chekerov, R, Hall, C, et al.. Preoperative HE4 expression in plasma predicts surgical outcome in primary ovarian cancer patients: results from the OVCAD study. Gynecol Oncol 2013;128:245–51. https://doi.org/10.1016/j.ygyno.2012.11.023.Search in Google Scholar PubMed
29. Huang, CY, Cheng, WF, Lee, CN, Su, YN, Chien, SC, Tzeng, YL, et al.. Serum mesothelin in epithelial ovarian carcinoma: a new screening marker and prognostic factor. Anticancer Res 2006;26:4721–8.Search in Google Scholar
30. Shi, M, Mu, Y, Zhang, H, Liu, M, Wan, J, Qin, X, et al.. MicroRNA-200 and microRNA-30 family as prognostic molecular signatures in ovarian cancer: a meta-analysis. Medicine (Baltim) 2018;97:e11505. https://doi.org/10.1097/md.0000000000011505.Search in Google Scholar
31. Wang, Y, Li, BX, Li, X. Identification and validation of angiogenesis-related gene expression for predicting prognosis in patients with ovarian cancer. Front Oncol 2021;11:783666. https://doi.org/10.3389/fonc.2021.783666.Search in Google Scholar PubMed PubMed Central
32. Cancer Genome Atlas Research N. Integrated genomic analyses of ovarian carcinoma. Nature 2011;474:609–15. https://doi.org/10.1038/nature10166.Search in Google Scholar PubMed PubMed Central
33. Konecny, GE, Wang, C, Hamidi, H, Winterhoff, B, Kalli, KR, Dering, J, et al.. Prognostic and therapeutic relevance of molecular subtypes in high-grade serous ovarian cancer. J Natl Cancer Inst 2014;106:dju249. https://doi.org/10.1093/jnci/dju249.Search in Google Scholar PubMed PubMed Central
34. Millstein, J, Budden, T, Goode, EL, Anglesio, MS, Talhouk, A, Intermaggio, MP, et al.. Prognostic gene expression signature for high-grade serous ovarian cancer. Ann Oncol 2020;31:1240–50. https://doi.org/10.1016/j.annonc.2020.05.019.Search in Google Scholar PubMed PubMed Central
35. Hellstrom, I, Hellstrom, KE. SMRP and HE4 as biomarkers for ovarian carcinoma when used alone and in combination with CA125 and/or each other. Adv Exp Med Biol 2008;622:15–21. https://doi.org/10.1007/978-0-387-68969-2_2.Search in Google Scholar PubMed
36. Hellstrom, I, Hellstrom, KE. fTwo novel biomarkers, mesothelin and HE4, for diagnosis of ovarian carcinoma. Expert Opin Med Diagn 2011;5:227–40. https://doi.org/10.1517/17530059.2011.559459.Search in Google Scholar PubMed PubMed Central
37. Abdel-Azeez, HA, Labib, HA, Sharaf, SM, Refai, AN. HE4 and mesothelin: novel biomarkers of ovarian carcinoma in patients with pelvic masses. Asian Pac J Cancer Prev 2010;11:111–6.Search in Google Scholar
38. Moore, K, Colombo, N, Scambia, G, Kim, BG, Oaknin, A, Friedlander, M, et al.. Maintenance olaparib in patients with newly diagnosed advanced ovarian cancer. N Engl J Med 2018;379:2495–505. https://doi.org/10.1056/nejmoa1810858.Search in Google Scholar
Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/cclm-2023-0314).
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