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Research areas
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Introduction of high-throughput analyses into medical research and diagnostics, development of new diagnostic techniques
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Identification and analysis of novel diagnostic and prognostic markers and therapeutical targets
Main objectives
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Introduction of high-throughput analyses of human genomes (whole genome/exom sequencing, transcriptome profiling, microRNA detection, SNP analysis, etc.). Utilization of these technologies in medicine and development of diagnostic tests based on high-throughput methods
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Mutational analyses of human cells in relation to cancer, neuromuscular, neurodegenerative, metabolic and skin disorders; the detection of novel prognostic markers
Content of research
Application of genomic approaches in cancer research and diagnostics
The transformation of normal to malignant cells may be caused by many different mechanisms that share a single common feature – the alteration of genetic information and subsequent disruption of cellular regulatory mechanisms, which thus lead to uncontrolled proliferation. Some of these genetic alterations have already been described and are routinely analysed in oncological diagnostics, e.g. TP53, ATM or BRCA gene mutations or specific translocations occurring in leukemias, lymphomas and other tumours. The importance of many other genomic aberrations found in tumours and their influence on the malignant potential of transformed cells should be analysed as well as the impact of individual genetic variants on tumour behaviour. Novel technologies including high-resolution SNP microarrays and high-throughput genome sequencing (massive parallel sequencing) provide fast and complex analysis of the human genome. These methods will be used to characterise genetic information of the patient‘s malignant and non-malignant cells to reveal the mechanisms of cellular transformation.
The expected outputs of this work package are:
1) the identification of recurrent genomic alterations in hematological and other malignancies, which could be used in cancer diagnostics and as a potential therapeutic target,
2) the analysis of the influence of the host genome on disease progression,
3) the characterisation of the regulatory pathways disrupted in tumour cells. The outcome of these studies is expected to be used as an initial point for focused research as well as for direct use in diagnostics of haematological and oncological malignancies.
Technologies used: massive parallel DNA sequencing (next-generation high-throughput sequencing), SNP mapping (high resolution SNP microarrays), gene expression profiling (microarrays, QRT-PCR), FACS, bioinformatic processing.
Brochure about the FP7 project "Next Generation Sequencing platform for targeted Personalized Therapy of Leukemia" to download can be found HERE.
CURRENT RESEARCH INFRASTRUCTURE
Molecular – biological diagnostic and research laboratory equipped with instrumentation for routine diagnostic tasks such as PCR, qPCR, DNA electrophoresis and DNA sequencing as well as equipment serving mainly for research needs such as microarray scanners (short and long oligonucleotide platforms) or nucleofector for high-efficient transfections.
1. Genomic analysis of leukemic cells using next generation sequencing approaches
1. Analysis of genomic data in patients with chronic lymphocytic leukemia
Supervisor: Mgr. Karla Plevová, Ph.D.
Consultants: Mgr. Kamila Réblová, Ph.D., Mgr. Karol Pál
Annotation:
With the advent of massively parallel sequencing (MPS), many challenges in molecular biology and medicine have emerged including the discovery of novel molecular mechanisms and biological networks. Due to the accelerated development in genomics and computational technologies it is possible to study molecular processes at various levels ranging from primary DNA structure, through genome organization, to its expression in a comprehensive manner. Among other biological contexts this is especially important in human diseases, such as cancer. In the proposed PhD project, a PhD candidate will perform advanced bioinformatics analyses of genomic data obtained using MPS in cases with chronic lymphocytic leukemia (CLL). The student will focus on discovery of genetic and expression patterns related to specific disease conditions, namely CLL early development, and accumulation of adverse genomic defects leading to refractory disease. In particular, tumor signatures will be extracted from DNA and RNA sequencing data in an attempt to provide a detailed picture of molecular processes underlying CLL clonal evolution. It is expected that the student will acquire good knowledge of existing solutions, design his/her own bioinformatics tools and employ computing approaches based on machine learning, if necessary. Obtained data will be validated using available independent datasets and will serve as a basis for consequent wet-lab experiments aiming to define novel clinically relevant biomarkers for better disease stratification and management.
Keywords: chronic lymphocytic leukemia, cancer genome, clonal evolution, biomarkers, mutation signature, expression pattern, tumor interactions, bioinformatics tools, computing technologies
Supervisor: prof. RNDr. Šárka Pospíšilová, Ph.D.
Consultants: RNDr. Jitka Malčíková, Ph.D., MVDr. Boris Tichý, Ph.D.
Annotation:
The development of human leukemias is closely associated with the genomic changes in relevant blood and bone marrow cells. The amount and type of these changes significantly influences the disease development, patient prognosis and therapy response. The aim of the PhD study will be focused on detailed genomic analysis of B-lymphocytes derived from patients with lymphoproliferative disorders, e.g. with chronic lymphocytic leukemia (CLL), acute lymphoblastic leukemia (ALL) or lymphomas. The novel genomic approaches including high-throughput next-generation sequencing, SNP microarrays or quantitative / digital PCR will be used to characterize genetic information of the patient‘s malignant and non-malignant cells to reveal the mechanisms of cellular transformation and detect the evolution of B- or T-cell clones during the disease development. The novel technologies could also enable to discover novel diagnostic and prognostic markers, which would facilitate the administration of the patient treatment.
2. Identification of genomic lesions in NGS data of leukemic cells for clinical applications
Supervisor: Mgr. Vojtěch Bystrý, Ph.D.
Consultants: prof. RNDr. Šárka Pospíšilová, Ph.D.
Annotation:
Human lymphoproliferative disorders such as leukemias are known to be associated with a characteristic genomic alterations, such as chromosomal translocations typically affecting a limited number of recurrent genes with several variable partner genes. The presence of such fusion genes might be instrumental for the development of the disease and its recognition and identification an important prognostic marker. Even more importantly fusion genes can be targeted by chemotherapy and/or new drugs, therefore genomic profiling has the potential to identify new potentially druggable targets. The aim of the PhD study will be to work with NGS systems and to analyse the resulting data with the focus on detection and characterisation of genetic abnormalities associated with leukemia. Student should also design and implement an automated system for identification, storing and classification of the genomic aberrations.
3. High throughput sequencing data analysis of IG/TR rearranged genes in leukemia clinical research
Supervisor: Mgr. Vojtěch Bystrý, Ph.D.
Consultants: prof. RNDr. Šárka Pospíšilová, Ph.D.
Annotation:
The immunoglobulin superfamily of proteins is a group of recognition and signalling proteins expressed on cell surfaces. Analysis of immunoglobulin (IG) and T cell receptor (TR) repertoires – or immunoprofiling, has been instrumental in understanding a wide range of diseases with an underlying immune basis, such as lymphomas, infections, and allergies. With the introduction of next generation sequencing (NGS) methods, much more detailed insights are now available. However, this also poses unique challenges for sequence analysis due to the enormous inherent complexity, huge diversity and temporal variation of immune responses combined with imprecise and bias-prone nature of current NGS technologies. The aim of the PhD study will be to design, implement and apply in silico analytical methods for processing of NGS data from leukemia patient samples. The focus will be the recognition and quantification of repertoire abnormalities in general and the clonality assessment and MRD monitoring in particular.
