Abstract
Objectives
Tumor markers are well-known for being important tools in the support of diagnosis, monitoring of treatment efficacy and follow-up of cancers. CA 125, CA 15-3 and HE 4 have demonstrated potential efficacy in other clinical indications. The main objective was to evaluate the biological variation of these glycoproteins using two different immunoassays in an apparently healthy Caucasian population.
Methods
Nineteen healthy volunteers including 11 women and 8 men were sampled weekly for 5 consecutive weeks. Samples were analyzed in duplicate on Lumipulse® G600II (Fujirebio) and on the Cobas e602 (Roche Diagnostics) analyzers. After assessment of normality, exclusion of outliers and analysis of homogeneity of variance, analytical variation (CVA), within-subject biological variation (CVI) and between-subject biological variation (CVG) were determined using a nested ANOVA.
Results
CVA, CVI and CVG were determined on both analyzers and both genders. For CA 125, the CVA ranges from 1.0 to 3.4%, the CVI from 5.7 to 13.8% and the CVG from 32.2 to 42.9%. For CA 15-3, the CVA is between 1.1 and 3.4%, the CVI between 3.9 and 6.5% and the CVG between 43.7 and 196.9%. Lastly, HE 4 has CVA values between 1.4 and 2.4%, CVI between 5.1 and 10.5% and CVG between 7.1 and 12.6%.
Conclusions
Our study provided updated data on the biological variation of CA 125, HE 4 and CA 15-3. These data allow to improve the clinical interpretation and thus the management of the patient.
-
Research funding: None declared.
-
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
-
Competing interests: Authors state no conflict of interest.
-
Informed consent: Informed consent was obtained from all individuals included in this study.
-
Ethical approval: The study protocol was approved by the ethics committee of our institution (Cliniques Universitaires Saint-Luc, Brussels, Belgium; 2019/04SEP/388).
References
1. Ricos, C, Alvarez, V, Cava, F, Garcia-Lario, JV, Hernandez, A, Jimenez, CV, et al.. Current databases on biological variation: pros, cons and progress. Scand J Clin Lab Invest 1999;59:491–500. https://doi.org/10.1080/00365519950185229.Search in Google Scholar PubMed
2. Bushi, S, Trebicka, A. The role of tumor markers for evaluation the course of chemotherapy. ARC J Cancer Sci 2021;7:1–5.10.20431/2455-6009.0701001Search in Google Scholar
3. Duffy, MJ. Tumor markers in clinical practice: a review focusing on common solid cancers. Med Princ Pract 2012;22:4–11. https://doi.org/10.1159/000338393.Search in Google Scholar PubMed PubMed Central
4. Duffy, M, Evoy, D, McDermott, E. CA 15-3: uses and limitation as a biomarker for breast cancer. Clin Chim Acta 2010;411:1869–74. https://doi.org/10.1016/j.cca.2010.08.039.Search in Google Scholar PubMed
5. Dochez, V, Caillon, H, Vaucel, E, Dimet, J, Winer, N, Ducarme, G. Biomarkers and algorithms for diagnosis of ovarian cancer: CA125, HE4, RMI and ROMA, a review. J Ovarian Res 2019;12:1–9. https://doi.org/10.1186/s13048-019-0503-7.Search in Google Scholar PubMed PubMed Central
6. Qi, Z, Zhang, L, Chen, Y, Ma, X, Gao, X, Du, J, et al.. Biological variations of seven tumor markers. Clin Chim Acta 2015;450:233–6. https://doi.org/10.1016/j.cca.2015.08.026.Search in Google Scholar PubMed
7. Ricos, C, Iglesias, N, Garcia-Lario, JV, Simon, M, Cava, F, Hernandez, A, et al.. Within-subject biological variation in disease : collated data and clinical consequences. Ann Clin Biochem 2007;44:342–52. https://doi.org/10.1258/000456307780945633.Search in Google Scholar PubMed
8. Nunez, J, De le Espriella, R, Minana, G, Santas, E, Llacer, P, Nunez, E, et al.. Antigen carbohydrate 125 as a biomarker in heart failure: a narrative review. Eur J Heart Failure 2021;23:1445–57. https://doi.org/10.1002/ejhf.2295.Search in Google Scholar PubMed
9. Socolov, R, Socolov, D, Sindilar, A, Pavaleanu, I. An update on the biological markers of endometriosis. Minerva Ginecol 2017;69:462–7. https://doi.org/10.23736/s0026-4784.17.04046-1.Search in Google Scholar PubMed
10. Li, M, Wu, Z, Tudahun, I, Liu, N, Lin, Q, Liu, J, et al.. High serum carbohydrate antigen (CA) 125 level is associated with poor prognosis in patients with light-chain cardiac amyloidosis. Front Cardiovasc Med 2021;8:1–11. https://doi.org/10.3389/fcvm.2021.692083.Search in Google Scholar PubMed PubMed Central
11. Ricci, A, Mariotta, S, Bronzetti, E, Bruno, P, Vismara, L, De Dominicis, C, et al.. Serum CA 15-3 is increased in pulmonary fibrosis. Sarcoidosis Vasc Diffuse Lung Dis 2009;26:54–63.Search in Google Scholar
12. Okada, M, Suzuki, K, Nakanishi, T, Nakashima, M. Serum levels of KL-6 are positively correlated with those of CA15-3 in patients with interstitial pneumonia associated with collagen diseases [1]. Respirology 2006;11:509–10. https://doi.org/10.1111/j.1440-1843.2006.00881.x.Search in Google Scholar PubMed
13. Batlle, M, Ribera, J-M, Oriol, A, Pastor, C, Mate, JL, Fernandez-Avilés, F, et al.. Usefulness of tumor markers CA 125 and CA 15.3 at diagnosis and during follow-up in non-Hodgkin’s lymphoma: study of 200 patients. Leuk Lymphoma 2005;46:1471–6. https://doi.org/10.1080/10428190500204781.Search in Google Scholar PubMed
14. Allison, SJ. HE4 – a biomarker and target in renal fibrosis. Nat Rev Nephrol 2013;9:124. https://doi.org/10.1038/nrneph.2013.2.Search in Google Scholar PubMed
15. Wan, J, Wang, Y, Cai, G, Liang, J, Yue, C, Wang, F, et al.. Elevated serum concentrations of HE4 as a novel biomarker of disease severity and renal fibrosis in kidney disease. Oncotarget 2016;7:67748–59. https://doi.org/10.18632/oncotarget.11682.Search in Google Scholar PubMed PubMed Central
16. Fraser, C, Harris, E. Generation and application of data on biological variation in clinical chemistry. Crit Rev Clin Lab Sci 1989;27:409–37. https://doi.org/10.3109/10408368909106595.Search in Google Scholar PubMed
17. Carobene, A, Strollo, M, Jonker, N, Barla, G, Bartlett, WA, Sandberg, S, et al.. Sample collections from healthy volunteers for biological variation estimates’ update: a new project undertaken by the working group on biological variation established by the european federation of clinical chemistry and laboratory medicine. Clin Chem Lab Med 2016;54:1599–608. https://doi.org/10.1515/cclm-2016-0035.Search in Google Scholar PubMed
18. Braga, F, Panteghini, M. Generation of data on within-subject biological variation in laboratory medicine: an update. Crit Rev Clin Lab Sci 2016;53:313–25. https://doi.org/10.3109/10408363.2016.1150252.Search in Google Scholar PubMed
19. Bartlett, WA, Braga, F, Carobene, A, Coşkun, A, Prusa, R, Fernandez-Calle, P, et al.. A checklist for critical appraisal of studies of biological variation. Clin Chem Lab Med 2015;53:879–85. https://doi.org/10.1515/cclm-2014-1127.Search in Google Scholar PubMed
20. Aarsand, AK, Røraas, T, Fernandez-Calle, P, Ricos, C, Díaz-Garzón, J, Jonker, N, et al.. The biological variation data critical appraisal checklist: a standard for evaluating studies on biological variation. Clin Chem 2018;64:501–14. https://doi.org/10.1373/clinchem.2017.281808.Search in Google Scholar PubMed
21. Ceriotti, F, Marco, JDG, Fernandez-Calle, P, Maregnani, A, Aarsand, AK, Coskun, A, et al.. The European Biological Variation Study (EuBIVAS): weekly biological variation of cardiac troponin i estimated by the use of two different high-sensitivity cardiac troponin i assays. Clin Chem Lab Med 2020;58:1741–7. https://doi.org/10.1515/cclm-2019-1182.Search in Google Scholar PubMed
22. Fernández-Calle, P, Díaz-Garzón, J, Bartlett, W, Sandberg, S, Braga, F, Beatriz, B, et al.. Biological variation estimates of thyroid related measurands - meta-analysis of BIVAC compliant studies. Clin Chem Lab Med 2022;60:483–93. https://doi.org/10.1515/cclm-2021-0904.Search in Google Scholar PubMed
23. Aarsand, AK, Fernandez-Calle, P, Webster, C, Coskun, A, Gonzalez-lao, E, Diaz-Garzon, J. The EFLM biological variation database. Available from: https://emea01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fbiologicalvariation.eu%2F&data=05%7C01%7C%7C396894a3bc0e429d8e8d08db1d994483%7C84df9e7fe9f640afb435aaaaaaaaaaaa%7C1%7C0%7C638136316334082776%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=QRZbgqtwnvVJwDKANFfNxauoDoTUq6GdJB4xS58t0a8%3D&reserved=0 [Accessed 23 Jan 2023].Search in Google Scholar
24. Coşkun, A, Aarsand, AK, Sandberg, S, Guerra, E, Locatelli, M, Díaz-Garzón, J, et al.. Within- and between-subject biological variation data for tumor markers based on the European Biological Variation Study. Clin Chem Lab Med 2022;60:543–52. https://doi.org/10.1515/cclm-2021-0283.Search in Google Scholar PubMed
25. Braga, F, Ferraro, S, Mozzi, R, Panteghini, M. The importance of individual biology in the clinical use of serum biomarkers for ovarian cancer. Clin Chem Lab Med 2014;52:1625–31. https://doi.org/10.1515/cclm-2014-0097.Search in Google Scholar PubMed
26. Dittadi, R, Peloso, L, Gion, M. Witin-subject biological variation in disease: the case of tumour markers. Ann Clin Biochem 2008;45:226–8.10.1258/acb.2007.072441Search in Google Scholar PubMed
27. Soletormos, G, Schioler, V, Nielsen, D, Skovsgaard, T, Dombernowsky, P. Interpretation of results for tumor markers on the basis of analytical imprecision and biological variation. Clin Chem 1993;39:2077–83. https://doi.org/10.1093/clinchem/39.10.2077.Search in Google Scholar
28. Bon, GG, Kenemans, P, Dekker, JJ, Hompes, PG, Verstraeten, RA, Van Kamp, GJ, et al.. Fluctuations in CA 125 and CA 15-3 serum concentrations during spontaneous ovulatory cycles. Hum Reprod 1999;14:566–70. https://doi.org/10.1093/humrep/14.2.566.Search in Google Scholar PubMed
29. Anastasi, E, Granato, T, Marchei, G, Viggiani, V, Colaprisca, B, Comploj, S, et al.. Ovarian tumor marker HE4 is differently expressed during the phases of the menstrual cycle in healthy young women. Tumor Biol 2010;31:411–5. https://doi.org/10.1007/s13277-010-0049-1.Search in Google Scholar PubMed
30. Tuxen, MK, Solétormos, G, Peterson, P, Schioler, V, Dombernowsky, P. Assessment of biological variation and analytical imprecision of CA 125, CEA, and TPA in relation to monitoring of ovarian cancer. Gynecol Oncol 1999;74:12–22. https://doi.org/10.1006/gyno.1999.5414.Search in Google Scholar PubMed
31. Marques-Garcia, F, Boned, B, González-Lao, E, Braga, F, Carobene, A, Coskun, A, et al.. Critical review and meta-analysis of biological variation estimates for tumor markers. Clin Chem Lab Med 2022;60:494–504. https://doi.org/10.1515/cclm-2021-0725.Search in Google Scholar PubMed
32. Dittadi, R, Fabricio, A, Gion, M. Biogical variation and reference change value as decision criteria in clinical use of tumor biomarkers. Are they really useful? Clin Chem Lab Med 2022;60:136–7. https://doi.org/10.1515/cclm-2022-0047.Search in Google Scholar PubMed
33. Van Rossum, HH, Meng, QH, Ramanathan, LV, Holdenrieder, S. A word of caution on using tumor biomarker reference change values to guide medical decisions and the need for alternatives. Clin Chem Lab Med 2022;60:553–5. https://doi.org/10.1515/cclm-2021-0933.Search in Google Scholar PubMed
34. Carobene, A, Aarsand, AK, Bartlett, WA, Coskun, A, Diaz-Garzon, J, Fernandez-Calle, P, et al.. The European biological variation study (EuBIVAS): a summary report. Clin Chem Lab Med 2022;60:505–17. https://doi.org/10.1515/cclm-2021-0370.Search in Google Scholar PubMed
35. wytze, O. Gross overestimation of total allowable error based on biological variation. Clin Chem 2011;57:1334–6. https://doi.org/10.1373/clinchem.2011.165308.Search in Google Scholar PubMed
© 2023 Walter de Gruyter GmbH, Berlin/Boston
