NGS data-driven pharmacogenomics: preemptive germline testing and renal cell carcinoma predictive response biomarkers

• Autores: Javier Lanillos Manchón
• Directores de la Tesis: Cristina Rodriguez Gonzalez de Antona (dir. tes.), Gonzalo Gómez López (dir. tes.)
• Lectura: En la Universidad Autónoma de Madrid ( España ) en 2023
• Idioma: inglés
• Número de páginas: 104
• Títulos paralelos:
  • Farmacogenómica dirigida por datos de NGS: testado farmacogenético preventivo basados en variación germinal y biomarcadores de respuesta a inmunoterapia en cáncer renal
• Enlaces
  • Tesis en acceso abierto en: Biblos-e Archivo
• Resumen

  • Next Generation Sequencing (NGS) is one of the fundamental technologies for life sciences research in the 21st century, helping to decipher the human genome and its applications in health. The decrease in NGS costs, as well as its strong development, has promoted scientific advances for the clinic, creating the field of genomic medicine. One of the main objectives of genomic medicine is the implementation of pharmacogenomics, which describes the interaction of the genetic variation of individuals with drugs. Pharmacogenomics can reduce adverse drug reactions (ADRs) as well as improve the efficacy of treatments. The vast amount of available NGS data allows the exploration of new pharmacogenetic interactions. For this, the role of bioinformaticians, who combine data analysis skills with knowledge in biology and medicine to manage and interpret NGS data on a large scale, will be essential. One application of pharmacogenetics consists of testing the germinal genetic variation of an individual in order to predict ADRs before the administration of a specific drug or to achieve its maximum efficacy. The pharmacogenetic clinical guidelines are responsible for collecting the necessary information so that the pharmacogenetic testing can be transferred to the clinic. In this way, NGS data derived from genetic diagnosis, such as whole exome sequencing, could be reused to, presumably, obtain preemptive pharmacogenetic utility. However, how exome sequencing is suitable for this purpose has not been thoroughly investigated. In addition, access to genomic data is unequal across different populations, which hinders the development of pharmacogenetics in different genomic medicine initiatives. This Thesis aims to describe how the use of diagnostic exome sequencing data could be useful for preventive pharmacogenetics. For this, the pharmacogenetic profiles of 5001 individuals who underwent genetic diagnosis in Spain and some Latin American countries were analyzed. In addition, this Thesis studies the feasibility of extracting relevant pharmacogenetic variations in the MT-RNR1 gene (encoded in the mitochondrial DNA) from "off-target" data, generated in NGS experiments that do not capture this genetic material. On the whole, we develop bioinformatics strategies in order to provide relevant pharmacogenetic information for the prescription of drugs. Additionally, therapeutic failure is a major problem, especially in cancer treatment. A second application of pharmacogenomics is in oncology, where predictive molecular markers of response contained in the omics profile of the tumor are investigated. Specifically, renal cell carcinoma (RCC) is a type of cancer originating in the kidneys and that is already metastatic at the diagnosis in one third of patients. Antiangiogenic drugs and, more recently, immunotherapy (immune checkpoint inhibitors) have been shown to be beneficial for a subgroup of these patients, prompting the use of molecular biomarkers to improve therapy selection. Although promising biomarkers have been validated in other tumor types for immunotherapy treatment, none are available in RCC, neither for immunotherapy nor for antiangiogenic drugs, or their combination. In the field of predictive biomarkers in RCC, this Thesis focuses on the role of PARP1 gene expression, in combination with mutations in PBRM1, as potential predictive markers for immunotherapy and antiangiogenic therapy. These results exemplify that retrospective analysis of omics data within a clinical context represents a relevant resource to help improve precision medicine in oncology