The set of PCR-based laboratory methods for molecular diagnostics in oncology
The development of accessible and sensitive methods for mutations analysis leading to tumor and for evaluation of patients’ individual genetic characteristics is essential for molecular genetic studies both in clinical and epidemiological research. The Central Research Institute of Epidemiology has developed a set of techniques for the qualitative detection of germinal mutations and single nucleotide polymorphisms, as well as for the quantification of the mutant alleles of somatic mutations using pyrosequencing and real-time PCR. The methods allow identifying the most common mutations in the Russian Federation in the BRCA1, BRCA2, NBN, and CHEK2 genes, responsible for hereditary tumor syndromes, and polymorphisms in the TTC34, MIR146A, CYP1A1, MICA, PAX8, MTHFR, HLA-DQB1, and HLA-DQA1 genes associated with an increased risk of cervical cancer, and in genes involved in drug biotransformation. Quantitative methods are designed to determine somatic mutations in the BRAF, KRAS, HRAS, NRAS, JAK2, MPL and CALR genes in the solid tumors and in hematology. The developed set of scientific and diagnostic techniques is being employed for analysis of genetic polymorphisms and mutations in Russian population. Some of the techniques are available as reagent kits and are used in practice.
Authors
- О. П. Дрибноходова (ORCID: https://orcid.org/0000-0003-4918-4907)
- Michael Vinokurov (ORCID: https://orcid.org/0000-0002-4101-0702)
- V. I. Korchagin (ORCID: https://orcid.org/0000-0003-2264-6294)
- К. О. Миронов (ORCID: https://orcid.org/0000-0001-8207-9215)
- В. Г. Акимкин (ORCID: https://orcid.org/0000-0003-4228-9044)
- Elena A. Pozdysheva (ORCID: https://orcid.org/0000-0002-4477-8506)
Institutions
- Central Research Institute of Epidemiology (RU)
- Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing (RU)
Publication Details
- Journal
- Annals of the Russian academy of medical sciences
- Published
- 2024-12-15
- DOI
- https://doi.org/10.15690/vramn17937
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00