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Artículo

Unveiling the immune infiltrate modulation in cancer and response to immunotherapy by MIXTURE—an enhanced deconvolution method

Fernandez, Elmer AndresIcon ; Mahmoud, Yamil DamiánIcon ; Veigas, FlorenciaIcon ; Rocha, Darío Gastón; Miranda, Matías; Merlo, Joaquín PedroIcon ; Balzarini, Mónica; Lujan, Hugo DanielIcon ; Rabinovich, Gabriel AdriánIcon ; Girotti, Maria RominaIcon
Fecha de publicación: 16/12/2020
Editorial: Oxford University Press
Revista: Briefings In Bioinformatics
ISSN: 1467-5463
Idioma: Inglés
Tipo de recurso: Artículo publicado
Clasificación temática:
Ciencias de la Información y Bioinformática

Resumen

The accurate quantification of tumor-infiltrating immune cells turns crucial to uncover their role in tumor immune escape, to determine patient prognosis and to predict response to immune checkpoint blockade. Current state-of-the-art methods that quantify immune cells from tumor biopsies using gene expression data apply computational deconvolution methods that present multicollinearity and estimation errors resulting in the overestimation or underestimation of the diversity of infiltrating immune cells and their quantity. To overcome such limitations, we developed MIXTURE, a new ν-support vector regression-based noise constrained recursive feature selection algorithm based on validated immune cell molecular signatures. MIXTURE provides increased robustness to cell type identification and proportion estimation, outperforms the current methods, and is available to the wider scientific community. We applied MIXTURE to transcriptomic data from tumor biopsies and found relevant novel associations between the components of the immune infiltrate and molecular subtypes, tumor driver biomarkers, tumor mutational burden, microsatellite instability, intratumor heterogeneity, cytolytic score, programmed cell death ligand 1 expression, patients´ survival and response to anti-cytotoxic T-lymphocyte-associated antigen 4 and anti-programmed cell death protein 1 immunotherapy.
Palabras clave: IMMUNE INFILTRATE , DECONVOLUTION , RNASEQ , CANCER
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info:eu-repo/semantics/restrictedAccess Excepto donde se diga explícitamente, este item se publica bajo la siguiente descripción: Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Unported (CC BY-NC-SA 2.5)
Identificadores
URI: http://hdl.handle.net/11336/121874
URL: https://academic.oup.com/bib/advance-article/doi/10.1093/bib/bbaa317/6035270
DOI: http://dx.doi.org/10.1093/bib/bbaa317
Colecciones
Articulos(CIDIE)
Articulos de CENTRO DE INV. Y DESARROLLO EN INMUNOLOGIA Y ENFERMEDADES INFECCIOSAS
Citación
Fernandez, Elmer Andres; Mahmoud, Yamil Damián; Veigas, Florencia; Rocha, Darío Gastón; Miranda, Matías; et al.; Unveiling the immune infiltrate modulation in cancer and response to immunotherapy by MIXTURE—an enhanced deconvolution method; Oxford University Press; Briefings In Bioinformatics; 16-12-2020; 1-17
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