Preview

Russian Journal for Personalized Medicine

Advanced search

BIOMARKERS, TYPES AND ROLE IN PERSONALIZED MEDICINE

https://doi.org/10.18705/2782-3806-2022-2-3-6-16

Abstract

The review summarizes current knowledge about biomarkers in personalized medicine. Classification of biomarkers, the process of validation and challenges in implementation are discussed. The role of biobanks for new biomarkers is also included.

About the Author

A. O. Konradi
Almazov National Medical Research Centre, World-Class Research Centre for Personalized Medicine
Russian Federation

Konradi Alexandra O., MD, Dr. Sc., Professor, Academician of the Russian Academy of Science, Deputy General Director for Research, Head of the Research Department of Arterial Hypertension, Head of the Department of Management Organization and Health Economics, Institute of Medical Education

Akkuratova str., 2, Saint Petersburg, 197341



References

1. Biomarkers and Surrogate Endpoints: Preferred Definitions and Conceptual Framework. Clin. Pharmacol. Ther. Mar 2001;69(3): 89–95.

2. Stratification biomarkers in personalized medicine. Summary report. European Commission, DG Research – Brussels, 10–11 June 2010.

3. Morrow DA, de Lemos JA. Benchmarks for the assessment of novel cardiovascular biomarkers. Circulation. 2007; 115:949–952.

4. Pepe MS, Etzioni R, Feng Z, et al. Phases of biomarker development for early detection of cancer. J Natl Cancer Inst 2001; 93: 1054–1061.

5. Coupez D, Hulo P, Touchefeu Y, Denis MG, Bennouna J. KRAS mutations in metastatic colorectal cancer: from a de facto ban on anti-EGFR treatment in the past to a potential biomarker for precision medicine. Expert Opin Biol Ther. 2021:1325–1334. DOI:10.1080/14712598.2021.1967318. Epub 2021 Aug 18. PMID:34378483.

6. Simon R. Clinical trial designs for evaluating the medical utility of prognostic and predictive biomarkers in oncology. Personalized Medicine. 2010, vol. 7, no. 1, pp. 33–47.

7. Chau CH, Rixe O, McLeod H, Figg WD. Validation of analytic methods for biomarkers used in drug development. Clinical Cancer Research. 2008, vol. 14, no. 19, pp. 5967–5976.

8. Erickson HS. Measuring molecular biomarkers in epidemiologic studies: laboratory techniques and biospecimen considerations. Statistics in Medicine. 2012, vol. 31, no. 22, pp. 2400–2413.

9. Pencina MJ, D’Agostino Sr RB, D’Agostino Jr RB, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008; 27: 157–172.

10. Pencina MJ, D’Agostino Sr RB, Steyerberg EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med. 2011; 30: 11–21.

11. de Lemos JA, Drazner MH, Omland T, et al. Association of troponin T detected with a highly sensitive assay and cardiac structure and mortality risk in the general population. JAMA. 2010; 304: 2503–12.

12. Di Angelantonio, E, Chowdhury R, Sarwar N, Ray KK, Gobin R, Saleheen D, Thompson A, Gudnason V, Sattar N, Danesh J. B-type natriuretic peptides and cardiovascular risk: Systematic review and metaanalysis of 40 prospective studies. Circulation. 2009; 120, 2177–2187.

13. Paik S, Shak S, Tang G, et al. A multigene assay to predict recurrence of tamoxifen-treated, nodenegative breast cancer. The New England Journal of Medicine. 2004, vol. 351, no. 27, p. 2817–2826.

14. Pham MX, Teuteberg JJ, Kfoury AG, et al. Geneexpression profiling for rejection surveillance after cardiac transplantation. The New England Journal of Medicine. 2010, vol. 362, no. 20, p. 1890–1900.

15. Pepe MS, Longton G, Anderson GL, Schummer M. Selecting differentially expressed genes from microarray experiments. Biometrics. 2003, vol. 59, no. 1, pp. 133–142.

16. Noma H, Matsui S. Empirical Bayes ranking and selection methods via semiparametric hierarchical mixture models in microarray studies. Statistics in Medicine, 2012.

17. van de Vijver MJ, He YD, van ’t Veer LJ, et al. A geneexpression signature as a predictor of survival in breast cancer. The New England Journal of Medicine. 2002, vol. 347, no. 25, pp. 1999–2009.

18. Monzon FA, Lyons-Weiler M, Buturovic LJ, et al. Multicenter validation of a 1,550-gene expression profile for identification of tumor tissue of origin. Journal of Clinical Oncology. 2009, vol. 27, no. 15, pp. 2503–2508.

19. Simon R. The use of genomics in clinical trial design. Clinical Cancer Research. 2008, vol. 14, no. 19, pp. 5984–5993.

20. Cree IA, Kurbacher CM, Lamont A, et al. A prospective randomized controlled trial of tumour chemosensitivity assay directed chemotherapy versus physician’s choice in patients with recurrent platinumresistant ovarian cancer. Anti-Cancer Drugs. 2007, vol. 18, no. 9, pp. 1093–1101.

21. Cobo M, Isla D, Massuti B, et al. Customizing cisplatin based on quantitative excision repair crosscomplementing 1 mRNA expression: a phase III trial in non-small-cell lung cancer. Journal of Clinical Oncology. 2007, vol. 25, no. 19, pp. 2747–2754.

22. Sargent DJ, Conley BA, Allegra C, Collette L. Clinical trial designs for predictive marker validation in cancer treatment trials. Journal of Clinical Oncology. 2005, vol. 23, no. 9, pp. 2020– 2027.

23. Slamon DJ, Leyland-Jones B, Shak S, et al. Use of chemotherapy plus a monoclonal antibody against her2 for metastatic breast cancer that overexpresses HER2. The New England Journal of Medicine. 2001, vol. 344, no. 11, pp. 783–792.


Review

For citations:


Konradi A.O. BIOMARKERS, TYPES AND ROLE IN PERSONALIZED MEDICINE. Russian Journal for Personalized Medicine. 2022;2(3):6-16. (In Russ.) https://doi.org/10.18705/2782-3806-2022-2-3-6-16

Views: 845


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 2782-3806 (Print)
ISSN 2782-3814 (Online)