Building and evaluation of bioinformatic pipeline for determination of clonal profiles in myelodysplastic syndrome
| Autor: | Ildar M. Barkhatov, Artem Tishkov, N.V. Petukhova, Dmitrii S. Bug, Alena Andreevna Prikhodko, Evgeny Bakin, Elena V. Morozova, Ivan S. Moiseev |
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| Rok vydání: | 2020 |
| Předmět: |
Cytopenia
Control and Optimization Sequencing data Myeloid leukemia Computational biology Disease Biology medicine.disease DNA sequencing Computer Science Applications Human-Computer Interaction Control and Systems Engineering Dysplasia medicine In patient Software Selection (genetic algorithm) Information Systems |
| Zdroj: | Information and Control Systems. :50-59 |
| ISSN: | 2541-8610 1684-8853 |
| Popis: | Introduction: There is growing evidence of a connection between tumor clonal profile and its clinical impact. However, there is a lack of a feasible and reliable method for clonal profiling in actual clinical practice. Myelodysplastic syndrome is a clonal hematopoietic stem cell disorder characterized by morphological dysplasia, cytopenia and a high risk of evolution to acute myeloid leukemia. The clinical outcome of myelodysplastic syndrome is greatly heterogeneous; therefore, specific examination of clonal profiles is needed to resolve the prognosis of patients with such complex disorders. Purpose: Development of a pipeline specifically for determining the clonal profiles in patients with myelodysplastic syndrome on the basis of target next-generation sequencing data. Results: The pipeline was developed and evaluated on a set of 35 patients with high-risk myelodysplastic syndrome. It is possible to use the target sequencing data in order to assess the heterogeneity of clonal profiles and characterize their genetic features. This approach allows you to identify the consistency between a specific individual profile and the disease prognosis, which can be critical for the treatment decision. Herein, the characterization and analysis of clonal profiles are presented. Practical relevance: The information about relation patterns between clonal profile characteristics (number of subclones, mutations-per-clone rate) and clinical outcome can be used by doctors in current practice for a more accurate therapy selection depending on the identified individual specificity of the disease. |
| Databáze: | OpenAIRE |
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