Next-Generation Sequencing in Breast Cancer Management: A Case Report of Genomic Tumour Evolution over Time

• Autor(a) principal: Batista, MV
• Data de Publicação: 2021
• Outros Autores: Alpuim Costa, D, Borralho, P, Braga, S
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.26/41555
• topic: Neoplasias da Mama; Predisposição Genética para Doença; Breast Neoplasms; Genetic Predisposition to Disease

The clinicopathological breast cancer subtypes are used in clinical practice to better anticipate biological behaviour and guide systemic treatment strategy. In the adjuvant setting, genomic assay recurrence scores became widely available for luminal-like disease. Recently, next-generation sequencing (NGS) platforms have been used, essentially, in more advanced disease setting, in situations refractory to conventional treatment, or even in rare cancers for which there are no established treatment guidelines. Moreover, subpopulations of cancer cells with unique genomes within the same patient may exist across different regions of a tumour or evolve over time, which is called intratumoural heterogeneity. We herein report a case of a 38-year-old woman with breast cancer whose primary and metastatic disease exhibited discordant expression of hormone receptors, with the former being positive and the latter negative. Furthermore, the NGS analysis revealed slight and dynamic changes of mutational profiles between different metastatic lesions, potentially impacting breast cancer management and prognosis. These alterations may reflect tissular and temporal changes in tumour subclones and may also be due to the selective pressure caused by antineoplastic treatment. The use of genomic analyses in order to improve cancer treatment has been studied prospectively with encouraging results. The widespread use of NGS tests in clinical practice also creates new challenges. The most relevant may be to know which genomic alterations detected should be valued and how they should be targeted.