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Pesquisa de Deleções/Duplicações em Genes Associados a Cancro Hereditário por MLPA Digital
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Abstract(s)
A presença, na linha germinativa, de Variações do Número de Cópias (CNV) em genes de predisposição para cancro hereditário pode aumentar a suscetibilidade a esta doença. A identificação de uma CNV patogénica ou provavelmente patogénica num doente oncológico tem um impacto significativo na gestão clínica do indivíduo afetado e dos seus familiares. Tradicionalmente, a pesquisa de CNV no diagnóstico molecular de cancro hereditário é realizada apenas para alguns genes através do MLPA (Multiplex Ligation-dependent Probe Amplification) convencional. Nos últimos anos, o desenvolvimento de softwares de análise in silico de CNV com base em dados de NGS (Next-Generation Sequencing) representou um avanço significativo, ao possibilitar a pesquisa de deleções e duplicações em múltiplos genes em simultâneo. No entanto, estas ferramentas apresentam ainda limitações. Dada a relevância de uma análise abrangente que integre o maior número possível de genes relevantes no âmbito da patologia em causa, este trabalho teve como principal objetivo implementar uma nova metodologia de pesquisa de CNV em genes associados a cancro hereditário, que combina os princípios do MLPA convencional com a capacidade da NGS de analisar vários genes em simultâneo: o MLPA digital. Neste estudo, foi realizada a pesquisa de CNV por MLPA digital em amostras de doentes com história clínica e familiar de cancro, seguida de validação dos resultados utilizando outras metodologias de genética molecular e classificação das variantes identificadas segundo as recomendações da CanVIG-UK. O MLPA digital demonstrou ser eficaz na deteção de deleções e duplicações em genes associados a cancro hereditário, apresentando um desempenho adequado para a utilização em laboratórios clínicos, com sensibilidade de 100% e especificidade de 98%. A eficácia dos softwares de pesquisa in silico panelcn.MOPS e DRAGEN Enrichment foi confirmada através da concordância entre os resultados destas ferramentas e do MLPA digital. Foram identificadas cinco variantes patogénicas ou provavelmente patogénicas nos genes APC, BRCA1, BRCA2 e CHEK2, que justificam os fenótipos dos doentes. Este estudo demonstra que o MLPA digital é uma alternativa ao MLPA convencional na primeira fase de pesquisa molecular de CNV germinativas em genes associados a cancro hereditário, permitindo a análise de múltiplos genes em várias amostras em simultâneo.
The presence of germline Copy Number Variations (CNV) in hereditary cancer predisposition genes may increase susceptibility to this disease. The identification of a pathogenic or likely pathogenic CNV in a cancer patient has a significant impact on the clinical management of the affected individual and their family members. Traditionally, CNV screening in the molecular diagnosis of hereditary cancer is conducted only for certain genes using conventional MLPA (Multiplex Ligation-dependent Probe Amplification). In recent years, the development of in silico CNV analysis software based on NGS (Next-Generation Sequencing) data has represented a significant advancement, enabling the screening of deletions and duplications in multiple genes simultaneously. However, these tools still have limitations. Given the relevance of a comprehensive analysis that integrates as many relevant genes as possible within the context of the pathology in question, this work aimed to implement a new methodology for CNV screening in genes associated with hereditary cancer, which combines the principles of conventional MLPA with NGS capability to analyze multiple genes simultaneously: digitalMLPA. In this study, CNV screening was performed using digitalMLPA on samples from patients with a clinical and family history of cancer, followed by validation of the results using other molecular genetics methodologies and classification of the identified variants according to CanVIG-UK recommendations. digitalMLPA proved to be effective in detecting deletions and duplications in genes associated with hereditary cancer, showing adequate performance for use in clinical laboratories, with a sensitivity of 100% and a specificity of 98%. The effectiveness of the in silico software panelcn.MOPS and DRAGEN Enrichment was confirmed through concordance between the results of these tools and digitalMLPA. Five pathogenic or likely pathogenic variants were identified in the APC, BRCA1, BRCA2, and CHEK2 genes, which account for the patients' phenotypes. This study demonstrates that digitalMLPA is an alternative to conventional MLPA in the initial phase of molecular screening of germline CNV in genes associated with hereditary cancer, allowing for the analysis of multiple genes in several samples simultaneously.
The presence of germline Copy Number Variations (CNV) in hereditary cancer predisposition genes may increase susceptibility to this disease. The identification of a pathogenic or likely pathogenic CNV in a cancer patient has a significant impact on the clinical management of the affected individual and their family members. Traditionally, CNV screening in the molecular diagnosis of hereditary cancer is conducted only for certain genes using conventional MLPA (Multiplex Ligation-dependent Probe Amplification). In recent years, the development of in silico CNV analysis software based on NGS (Next-Generation Sequencing) data has represented a significant advancement, enabling the screening of deletions and duplications in multiple genes simultaneously. However, these tools still have limitations. Given the relevance of a comprehensive analysis that integrates as many relevant genes as possible within the context of the pathology in question, this work aimed to implement a new methodology for CNV screening in genes associated with hereditary cancer, which combines the principles of conventional MLPA with NGS capability to analyze multiple genes simultaneously: digitalMLPA. In this study, CNV screening was performed using digitalMLPA on samples from patients with a clinical and family history of cancer, followed by validation of the results using other molecular genetics methodologies and classification of the identified variants according to CanVIG-UK recommendations. digitalMLPA proved to be effective in detecting deletions and duplications in genes associated with hereditary cancer, showing adequate performance for use in clinical laboratories, with a sensitivity of 100% and a specificity of 98%. The effectiveness of the in silico software panelcn.MOPS and DRAGEN Enrichment was confirmed through concordance between the results of these tools and digitalMLPA. Five pathogenic or likely pathogenic variants were identified in the APC, BRCA1, BRCA2, and CHEK2 genes, which account for the patients' phenotypes. This study demonstrates that digitalMLPA is an alternative to conventional MLPA in the initial phase of molecular screening of germline CNV in genes associated with hereditary cancer, allowing for the analysis of multiple genes in several samples simultaneously.
Description
Dissertação de Mestrado em Genética Clínica Laboratorial apresentada à Faculdade de Medicina da Universidade de Coimbra, 2024
Orientador João Gonçalves, Departamento de Genética Humana, INSA
Projeto realizado na Unidade de Genética Molecular do Departamento de Genética Humana, INSA.
Orientador João Gonçalves, Departamento de Genética Humana, INSA
Projeto realizado na Unidade de Genética Molecular do Departamento de Genética Humana, INSA.
Keywords
Doenças Genéticas CNV Cancro Hereditário MLPA Digital Análise in silico Hereditary Cancer Digital MLPA In Silico Analysis
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