BALZARINI MONICA GRACIELA
Artículos
Título:
Unveiling the immune infiltrate modulation in cancer and response to immunotherapy by MIXTURE—an enhanced deconvolution method
Autor/es:
FERNÁNDEZ, ELMER A; MAHMOUD, YAMIL D; VEIGAS, FLORENCIA; ROCHA, DARÍO; MIRANDA, MATÍAS; MERLO, JOAQUÍN; BALZARINI, MÓNICA; LUJAN, HUGO D; RABINOVICH, GABRIEL A; GIROTTI, MARÍA ROMINA
Revista:
BRIEFINGS IN BIOINFORMATICS
Editorial:
OXFORD UNIV PRESS
Resumen:
he 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 a