ECE2020 Audio ePoster Presentations Pituitary and Neuroendocrinology (217 abstracts)
Data mining analyses for precision medicine in acromegaly
Montserrat Marques-Pamies 1 , Joan Gil 2 , Mireia Jorda 2 , Miguel Antonio Sampedro-Nunez 3 , Susan Webb 4 , Guillermo Serra 5 , Isabel Salinas Vert 1 , Alberto Blanco 6 , Elena Valassi 4 , Luciana Maria Martel Duguech 4 , Cristina Carrato 7 , Antonio Pico 8 , Araceli García-Martínez 8 , Andreu Simó-Servat 9 , Betina Biagetti 10 , Carles Villabona 11 , Rosa Camara-Gómez 12 , Carmen Fajardo-Montañana 13 , Cristina Lamas 14 , Clara Alvarez 15 , Ignacio Bernabeu 16 , Monica Marazuela 3 & Manel Puig-Domingo 1,2
1Germans Trias i Pujol University Hospital, Endocrinology and Nutrition, Badalona, Spain; 2Germans Trias i Pujol Research Institute, Endocrinology and Nutrition, Badalona, Spain; 3Hospital de la Princesa, Universidad autónoma de Madrid, Instituto Princesa, Department of Endocrinology, Madrid, Spain; 4CIBERER U747, ISCIII, Research Center for Pituitary Diseases, Hospital Sant Pau, IIB-SPau, Universitat Autònoma de Barcelona, Department of Endocrinology/Medicine, Barcelona, Spain; 5Son Espases University Hospital, Departament of Endocrinology, Palma, Spain; 6Germans Trias i Pujol University Hospital, Department of Neurosurgery, Badalona, Spain; 7Germans Trias i Pujol University Hospital, Departament of Pathology, Badalona, Spain; 8Hospital General Universitario de Alicante-Institute for Health and Biomedical Research (ISABIAL), Alacant, Spain; 9Mútua Terrassa University Hospital, Departament of Endocrinology, Terrassa, Spain; 10Vall d’Hebron University Hospital, Departament of Endocrinology, Barcelona, Spain; 11Bellvitge University Hospital, Departament of Endocrinology, L’Hospitalet de Llobregat, Spain; 12Hospital Universitario y Politécnico de La Fe, Departament of Endocrinology, València, Spain; 13University Public Hospital of La Ribera, Departament of Endocrinology, Alcira, Spain; 14Hospital General Universitario de Albacete, Departament of Endocrinology, Albacete, Spain; 15, CIMUS, University of Santiago de Compostela-Instituto de Investigación Sanitaria, Department of Physiology, Santiago de Compostela, Spain; 166Complejo Hospitalario Universitario de Santiago de Compostela (CHUS)-SERGAS, Endocrinology Division, Santiago de Compostela, Spain
Since the first somatostatin receptor ligand (SRL) was used to treat acromegaly, predicting which patients could benefit from their use has become crucial to avoid months of ineffective treatment for non-responding patients. Although many biomarkers linked to SRL response have been identified, there is no consensus criterion on how to prescribe according to biomarker levels. In this study, we evaluate previously reported biomarkers using more exhaustive and accurate methods than those used in previous analyses to provide better predictive tools from the data. Using advanced mathematical modelling and artificial intelligence, a more accurate acromegaly patient stratification was obtained regarding their ability to respond to SRL. Our results show an association between extrasellar growth and high BMI for SRL non-responding patients. Furthermore, we provide different models of patient stratification. The mathematical algorithms generated achieved a much higher cross‐validatedaccuracy when the population is fragmented according to relevant clinical characteristics. Considering all the models, we proposed a patient stratification based on the extrasellar growth of the tumor and the expression of E-cadherin, GHRL, IN1-GHRL, SSTR5 and RKIP with accuracies that stand between 71 to 95%. This new strategy of data mining is necessary if we want to implement personalized medicine in acromegaly and requires an interdisciplinary effort between computer science, mathematics, biology and medicine. This new methodology opens a door to more precise personalized medicine for acromegaly patients.
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Endocrine Abstracts
ISSN 1470-3947 (print) | ISSN 1479-6848 (online)
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