Millora en l'eficiència del tractament de l'acromegàlia gràcies a la implementació d'un algoritme predictiu i personalitzat que inclou informació molecular i clínica

• Autores: Joan Gil Ortega
• Directores de la Tesis: Mireia Jordà Ramos (dir. tes.), Manuel Puig Domingo (codir. tes.)
• Lectura: En la Universitat Autònoma de Barcelona ( España ) en 2020
• Idioma: inglés
• Tribunal Calificador de la Tesis: Gérald Raverot (presid.), Pedro L Fernandez Ruiz (secret.), Mireia Mora Porta (voc.)
• Programa de doctorado: Programa de Doctorado en Medicina por la Universidad Autónoma de Barcelona
• Materias:
  • Ciencias médicas
    • Medicina interna
      • Endocrinología
• Enlaces
  • Tesis en acceso abierto en: TDX
• Resumen

  • Actual pharmacologic treatment in acromegaly is currently based upon assay-error strategy. The prompt biochemical control of the disease is essential to reduces comorbidities and mortality. Fortunately, several drugs have been developed over the years to treat acromegaly being first generation somatostatin receptor ligands (SRLs), the first-line treatment. However, up to 50% of patients do not respond adequately to SRLs, which delays biochemical control for months or even a year. The main objective of this thesis was to evaluate the potential usefulness of different molecular markers as predictors of response to SRLs and elaborate a new treatment algorithm accordingly. We taught advantage of the REMAH cohort of several nodes in Spain to collect 100 acromegaly samples and performed molecular analysis. We measured molecular expression by RT-qPCR, measured protein by IHC and; quantified CpG methylation and evaluated mutations by sanger sequencing. Furthermore, we were able to stratify the SRLs respond in the majority of the cases and collected clinical associated data too. Taking all that into account, we have been able to validate reported biomarkers (SSTR2, Ki-67, E-cadherin and RORC) associated to SRLs response, describe the association of the epithelial-mesenchymal transition and SRLs in somatotropinomas, molecularly characterize the SRLs improvement after tumor debulking in large GH-producing tumors and define treatment algorithm based on molecular expression through data mining approaches. We conclude presenting treatment algorithms for new diagnosed acromegaly patients that will benefit from personalized medicine using IHC or more complex RNA quantification approaches to overcome the assay-error strategy in acromegaly treatment.