Sorting Transcriptomics Immune Information from Tumor Molecular Features Allows Prediction of Response to Anti-PD1 Therapy in Patients with Advanced Melanoma
Trilla-Fuertes, Lucia (Instituto de Investigación Sanitaria del Hospital Universitario La Paz (Madrid))
Gámez-Pozo, Angelo (Biomedica Molecular Medicine SL (Madrid))
Prado-Vázquez, Guillermo (Biomedica Molecular Medicine SL (Madrid))
López-Vacas, Rocío (Instituto de Investigación Sanitaria del Hospital Universitario La Paz (Madrid))
Zapater-Moros, Andrea (Biomedica Molecular Medicine SL (Madrid))
López-Camacho, Elena (Biomedica Molecular Medicine SL (Madrid))
Lumbreras-Herrera, María I. (Instituto de Investigación Sanitaria del Hospital Universitario La Paz (Madrid))
Soriano, Virtudes (Grupo Español Multidisciplinar de Melanoma (Barcelona))
Garicano, Fernando (Hospital de Galdakao (Usansolo, Biscaia))
Lecumberri, Mª José (Complejo Hospitalario de Navarra)
Rodríguez de la Borbolla, María (Hospital Universitario Virgen de Valme (Sevilla, Andalusia))
Majem, Margarita (Institut d'Investigació Biomèdica Sant Pau)
Pérez-Ruiz, Elisabeth (Hospital Costa del Sol (Marbella, Andalusia))
González-Cao, María (Hospital Quirón Dexeus)
Oramas, Juana (Hospital Universitario de Canarias (La Laguna))
Magdaleno, Alejandra (Hospital Universitario de Elche y Vega Baja (Elx, País Valencià))
Fra, Joaquín (Hospital Universitario Río Hortega (Valladolid, Castella i LLeó))
Martín Carnicero, Alfonso (Hospital Universitario San Pedro (La Rioja))
Corral, Mónica (Hospital Clínico Universitario "Lozano Blesa" de Zaragoza)
Puértolas, Teresa (Hospital Universitario Miguel Servet (Saragossa))
Ramos-Ruiz, Ricardo (Parque Científico de Madrid (Madrid))
Fresno Vara, Juan Ángel (Instituto de Salud Carlos III)
Espinosa, Enrique (Hospital Universitario La Paz (Madrid))
Universitat Autònoma de Barcelona

Data:	2023
Resum:	Immunotherapy based on anti-PD1 antibodies has improved the outcome of advanced melanoma. However, prediction of response to immunotherapy remains an unmet need in the field. Tumor PD-L1 expression, mutational burden, gene profiles and microbiome profiles have been proposed as potential markers but are not used in clinical practice. Probabilistic graphical models and classificatory algorithms were used to classify melanoma tumor samples from a TCGA cohort. A cohort of patients with advanced melanoma treated with PD-1 inhibitors was also analyzed. We established that gene expression data can be grouped in two different layers of information: immune and molecular. In the TCGA, the molecular classification provided information on processes such as epidermis development and keratinization, melanogenesis, and extracellular space and membrane. The immune layer classification was able to distinguish between responders and non-responders to immunotherapy in an independent series of patients with advanced melanoma treated with PD-1 inhibitors. We established that the immune information is independent than molecular features of the tumors in melanoma TCGA cohort, and an immune classification of these tumors was established. This immune classification was capable to determine what patients are going to respond to immunotherapy in a new cohort of patients with advanced melanoma treated with PD-1 inhibitors Therefore, this immune signature could be useful to the clinicians to identify those patients who will respond to immunotherapy.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Computational analysis ; Immunotherapy ; Ielanoma ; Personalized medicine ; Prediction of response
Publicat a:	International journal of molecular sciences, Vol. 24 Núm. 1 (january 2023) , p. 801, ISSN 1422-0067

DOI: 10.3390/ijms24010801
PMID: 36614248

14 p, 1.9 MB

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