Structural model for formation of packages of clinical and diagnostic tests to organize personalized medical care for patients with malignant tumors of the prostate gland
- Authors: Andreev D.A.1, Zavyalov A.A.1
-
Affiliations:
- Research Institute for Healthcare Organization and Medical Management of Moscow Healthcare Department
- Issue: Vol 27, No 1 (2021)
- Pages: 45-55
- Section: Clinical medicine
- Submitted: 27.06.2021
- Accepted: 27.06.2021
- Published: 29.06.2021
- URL: https://medjrf.com/0869-2106/article/view/72261
- DOI: https://doi.org/10.17816/0869-2106-2021-27-1-45-55
- ID: 72261
Cite item
Abstract
BACKGROUND: Prostate carcinoma is a serious social and economic problem; it ranks second among the most frequently diagnosed malignant tumors worldwide and it ranks sixth in the structure of causes of death from cancer in men. The correct organization of prevention and screening as well as the use of the latest methods of clinical instrumental and molecular analysis at all stages of treatment and diagnostic process can fundamentally improve the outcomes of the disease.
AIM: This study aimed to analyze of the best international experience in the development of basic clinical and instrumental test packages that determine the personalized choice of medical prescriptions for the treatment and diagnostic process of prostate cancer.
METHODS: The study included papers published after January 1, 2008, with an emphasis on the analysis of results posted in the last 2 years in the PubMed/Medline electronic database as reliable sources of information.
RESULTS AND DISCUSSION: This study explored the basic structural model of the formation of clinical and diagnostic packages at the implementation of a personalized treatment and diagnostic process in prostate cancer. The main biomarkers of prostate cancer used in clinical practice have been identified, and the informative value of the newest biomarker tests has been established. Diagnostic tools that are ready for wider implementation in oncological practice are predictive models, such as the 4K algorithm, Score, SelectMDx, Stockholm-3 model, justifying the need to perform a prostate biopsy in a particular patient. Some promising biomarker characteristics of prostate cancer, assessed at the nonclinical, experimental stage, have been demonstrated.
CONCLUSIONS: This study found that (1) Revision of the algorithms for personalized treatment is becoming an important element in the provision of patient-oriented cancer care. (2) Periodic reassessment of the “individual portrait” of the tumor process is necessary for the correct organization of treatment and diagnostic measures in a particular patient. (3) Finally, for the implementation of the whole range of possibilities of individual treatment, close interaction with the representatives of various medical specialties in the framework of the implementation of translational medicine programs in oncologyis important.
Full Text
About the authors
Dmitry A. Andreev
Research Institute for Healthcare Organization and Medical Management of Moscow Healthcare Department
Author for correspondence.
Email: dmitry.email08@gmail.com
ORCID iD: 0000-0003-0745-9474
MD, Cand. Sci. (Med.)
Russian Federation, 9, Sharikopodshipnikovskaya st., 115088, MoscowAleksander A. Zavyalov
Research Institute for Healthcare Organization and Medical Management of Moscow Healthcare Department
Email: azav06@mail.ru
ORCID iD: 0000-0003-1825-1871
ResearcherId: A-7169-2017
МD, Dr. Sci. (Med.), Professor
Russian Federation, 9, Sharikopodshipnikovskaya st., 115088, MoscowReferences
- Culp MB, Soerjomataram I, Efstathiou JA, et al. Recent Global Patterns in Prostate Cancer Incidence and Mortality Rates. Eur Urol. 2020;77(1):38–52. doi: 10.1016/j.eururo.2019.08.005
- Belkora J, Chan JM, Cooperberg MR, et al. Development and pilot evaluation of a personalized decision support intervention for low risk prostate cancer patients. Cancer Med. 2020. Vol. 9, N 1. P. 125–132. doi: 10.1002/cam4.2685
- Rais-Bahrami S, Gordetsky JB. Personalized prostate cancer care. Transl Androl Urol. 2018;7(Suppl 4):S383.doi: 10.21037/tau.2018.08.24
- Aoun F, Rassy EE, Assi T, Kattan J. Personalized treatment of prostate cancer: better knowledge of the patient, the disease and the medicine. Future Oncol. 2016;12(20):2359–2361.doi: 10.2217/fon-2016-0292
- Lomas DJ, Ahmed HU. All change in the prostate cancer diagnostic pathway. Nat Rev Clin Oncol. 2020;17(6):372–381.doi: 10.1038/s41571-020-0332-z
- Klotz L. Active surveillance in intermediate-risk prostate cancer. BJU Int. 2020;125(3):346–354. doi: 10.1111/bju.14935
- Costello AJ. Considering the role of radical prostatectomy in 21st century prostate cancer care. Nat Rev Urol. 2020;17(3):177–188.doi: 10.1038/s41585-020-0287-y
- Conran CA, Brendler CB, Xu J. Personalized prostate cancer care: from screening to treatment. Asian J Androl. 2016;18(4): 505–508. doi: 10.4103/1008-682X.179529
- Nassir AM. A piece in prostate cancer puzzle: Future perspective of novel molecular signatures. Saudi J Biol Sci. 2020;27(4): 1148–1154. doi: 10.1016/j.sjbs.2020.02.003
- Connors LM. Genomics to personalize care of prostate cancer. J Am Assoc Nurse Pract. 2020;32(2):106–108.doi: 10.1097/JXX.0000000000000390
- Al Olama AA, Kote-Jarai Z, Berndt SI, et al. A meta-analysis of 87,040 individuals identifies 23 new susceptibility loci for prostate cancer. Nat Genet. 2014;46(10):1103–1109. doi: 10.1038/ng.3094
- Kader AK, Sun J, Reck BH, et al. Potential impact of adding genetic markers to clinical parameters in predicting prostate biopsy outcomes in men following an initial negative biopsy: findings from the REDUCE trial. Eur Urol. 2012;62(6):953–961.doi: 10.1016/j.eururo.2012.05.006
- Kovac E, Carlsson SV, Lilja H, et al. Association of Baseline Prostate-Specific Antigen Level With Long-term Diagnosis of Clinically Significant Prostate Cancer Among Patients Aged 55 to 60 Years: A Secondary Analysis of a Cohort in the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. JAMA Netw Open. 2020;3(1):e1919284. doi: 10.1001/jamanetworkopen.2019.19284
- Zheng SL, Sun J, Wiklund F, et al. Cumulative association of five genetic variants with prostate cancer. N Engl J Med. 2008;358(9):910–919. doi: 10.1056/NEJMoa075819
- Liss MA, Xu J, Chen H, Kader AK. Prostate genetic score (PGS-33) is independently associated with risk of prostate cancer in the PLCO trial. Prostate. 2015;75(12):1322–1328.doi: 10.1002/pros.23012
- Robinson D, Van Allen EM, Wu YM, et al. Integrative clinical genomics of advanced prostate cancer. Cell. 2015;161(5):1215–1228.doi: 10.1016/j.cell.2015.05.001
- Sokoll LJ, Ellis W, Lange P, et al. A multicenter evaluation of the PCA3 molecular urine test: pre-analytical effects, analytical performance, and diagnostic accuracy. Clin Chim Acta. 2008; 389(1–2):1–6. doi: 10.1016/j.cca.2007.11.003
- Leyten GH, Hessels D, Jannink SA, et al. Prospective multicentre evaluation of PCA3 and TMPRSS2-ERG gene fusions as diagnostic and prognostic urinary biomarkers for prostate cancer. Eur Urol. 2014;65(3):534–542. doi: 10.1016/j.eururo.2012.11.014
- Loeb S, Sokoll LJ, Broyles DL, et al. Prospective multicenter evaluation of the Beckman Coulter Prostate Health Index using WHO calibration. J Urol. 2013;189(5):1702–1706.doi: 10.1016/j.juro.2012.11.149
- Vickers AJ, Gupta A, Savage CJ, et al. A panel of kallikrein marker predicts prostate cancer in a large, population-based cohort followed for 15 years without screening. Cancer Epidemiol Biomarkers Prev. 2011;20(2):255–261.doi: 10.1158/1055-9965.EPI-10-1003
- Salagierski M, Schalken JA. Molecular diagnosis of prostate cancer: PCA3 and TMPRSS2:ERG gene fusion. J Urol. 2012;187(3):795–801. doi: 10.1016/j.juro.2011.10.133
- Wei JT, Feng Z, Partin AW, et al. Can urinary PCA3 supplement PSA in the early detection of prostate cancer? J Clin Oncol. 2014;32(36):4066–4072. doi: 10.1200/JCO.2013.52.8505
- Fernandez-Serra A, Casanova-Salas I, Rubio L, et al. [Update on the diagnosis of PCa in urine. The current role of urine markers]. Arch Esp Urol. 2015;68(3):240–249.
- Tomlins SA, Day JR, Lonigro RJ, et al. Urine TMPRSS2:ERG Plus PCA3 for Individualized Prostate Cancer Risk Assessment. Eur Urol. 2016;70(1):45–53. doi: 10.1016/j.eururo.2015.04.039
- Rodriguez JF, Eggener SE. Prostate Cancer and the Evolving Role of Biomarkers in Screening and Diagnosis. Radiol Clin North Am. 2018;56(2):187–196. doi: 10.1016/j.rcl.2017.10.002
- Filella X, Gimenez N. Evaluation of [-2] proPSA and Prostate Health Index (phi) for the detection of prostate cancer: a systematic review and meta-analysis. Clin Chem Lab Med. 2013;51(4):729–739. doi: 10.1515/cclm-2012-0410
- Borque-Fernando A, Rubio-Briones J, Esteban LM, et al. Role of the 4Kscore test as a predictor of reclassification in prostate cancer active surveillance. Prostate Cancer Prostatic Dis. 2019;22(1):84–90.doi: 10.1038/s41391-018-0074-5
- Osses DF, Roobol MJ, Schoots IG. Prediction Medicine: Biomarkers, Risk Calculators and Magnetic Resonance Imaging as Risk Stratification Tools in Prostate Cancer Diagnosis. Int J Mol Sci. 2019;20(7). doi: 10.3390/ijms20071637
- Moller A, Olsson H, Gronberg H, et al. The Stockholm3 blood-test predicts clinically-significant cancer on biopsy: independent validation in a multi-center community cohort. Prostate Cancer Prostatic Dis. 2019;22(1):137–142. doi: 10.1038/s41391-018-0082-5
- Van Neste L, Hendriks RJ, Dijkstra S, et al. Detection of High-grade Prostate Cancer Using a Urinary Molecular Biomarker-Based Risk Score. Eur Urol. 2016;70(5):740–748.doi: 10.1016/j.eururo.2016.04.012
- Leyten GH, Hessels D, Smit FP, et al. Identification of a Candidate Gene Panel for the Early Diagnosis of Prostate Cancer. Clin Cancer Res. 2015;21(13):3061–3070. doi: 10.1158/1078-0432.CCR-14-3334
- Mottet N, Bellmunt J, Bolla M, et al. EAU-ESTRO-SIOG Guidelines on Prostate Cancer. Part 1: Screening, Diagnosis, and Local Treatment with Curative Intent. Eur Urol. 2017;71(4):618–629.doi: 10.1016/j.eururo.2016.08.003
- Sathianathen NJ, Kuntz KM, Alarid-Escudero F, et al. Incorporating Biomarkers into the Primary Prostate Biopsy Setting: A Cost-Effectiveness Analysis. J Urol. 2018;200(6):1215–1220.doi: 10.1016/j.juro.2018.06.016
- Marzouk K, Ehdaie B, Vertosick E, et al. Developing an effective strategy to improve the detection of significant prostate cancer by combining the 4Kscore and multiparametric MRI. Urol Oncol. 2019;37(10):672–677. doi: 10.1016/j.urolonc.2019.07.010
- Wysock JS, Becher E, Persily J, et al. Concordance and Performance of 4Kscore and SelectMDx for Informing Decision to Perform Prostate Biopsy and Detection of Prostate Cancer. Urology. 2020;141:119–124. doi: 10.1016/j.urology.2020.02.032
- Pang B, Zhu Y, Ni J, et al. Extracellular vesicles: the next generation of biomarkers for liquid biopsy-based prostate cancer diagnosis. Theranostics. 2020;10(5):2309–2326.doi: 10.7150/thno.39486
- Wang WW, Sorokin I, Aleksic I, et al. Expression of Small Noncoding RNAs in Urinary Exosomes Classifies Prostate Cancer into Indolent and Aggressive Disease. J Urol. 2020;204(3):466–475. doi: 10.1097/JU.0000000000001020
- Meng L, Li Y, Ren J, et al. Early Stage Biomarkers Screening of Prostate Cancer Based on Weighted Gene Coexpression Network Analysis. DNA Cell Biol. 2019;38(5):468–475.doi: 10.1089/dna.2018.4406
- Brunese L, Mercaldo F, Reginelli A, Santone A. Formal methods for prostate cancer Gleason score and treatment prediction using radiomic biomarkers. Magn Reson Imaging. 2020;66:165–175.doi: 10.1016/j.mri.2019.08.030
- Epstein JI, Zelefsky MJ, Sjoberg DD, et al. A Contemporary Prostate Cancer Grading System: A Validated Alternative to the Gleason Score. Eur Urol. 2016;69(3):428–435.doi: 10.1016/j.eururo.2015.06.046
- Choyke PL. A Grading System for Extraprostatic Extension of Prostate Cancer That We Can All Agree Upon? Radiol Imaging Cancer. 2020;2(1):e190088. doi: 10.1148/rycan.2019190088
- Mehralivand S, Shih JH, Harmon S, et al. A Grading System for the Assessment of Risk of Extraprostatic Extension of Prostate Cancer at Multiparametric MRI. Radiology. 2019;290(3):709–719.doi: 10.1148/radiol.2018181278
- Del Re M, Crucitta S, Restante G, et al. Pharmacogenetics of androgen signaling in prostate cancer: Focus on castration resistance and predictive biomarkers of response to treatment. Crit Rev Oncol Hematol. 2018;125:51–59. doi: 10.1016/j.critrevonc.2018.03.002
- Bernemann C, Krabbe LM, Schrader AJ. Considerations for AR-V7 testing in clinical routine practice. Ann Transl Med. 2019;7(Suppl 8):S378. doi: 10.21037/atm.2019.12.136
- Luo J, Attard G, Balk SP, et al. Role of Androgen Receptor Variants in Prostate Cancer: Report from the 2017 Mission Androgen Receptor Variants Meeting. Eur Urol. 2018;73(5):715–723.doi: 10.1016/j.eururo.2017.11.038
- Julka PK, Verma A, Gupta K. Personalized Treatment Approach to Metastatic Castration-Resistant Prostate Cancer with BRCA2 and PTEN Mutations: A Case Report. Case Rep Oncol. 2020;13(1):55–61. doi: 10.1159/000505182
- Prasad V, Kaestner V, Mailankody S. Cancer Drugs Approved Based on Biomarkers and Not Tumor Type-FDA Approval of Pembrolizumab for Mismatch Repair-Deficient Solid Cancers. JAMA Oncol. 2018;4(2):157–158. doi: 10.1001/jamaoncol.2017.4182
- Slovin SF. Pembrolizumab in Metastatic Castration-Resistant Prostate Cancer: Can an Agnostic Become a Believer? J Clin Oncol. 2020;38(5):381–383. doi: 10.1200/JCO.19.02921
- Varnai R, Koskinen LM, Mantyla LE, et al. Pharmacogenomic Biomarkers in Docetaxel Treatment of Prostate Cancer: From Discovery to Implementation. Genes (Basel). 2019;10(8):599.doi: 10.3390/genes10080599