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

ELMER FERNANDEZ; YAMIL MAHMOUD; FLORENCIA VEIGAS; DARIO ROCHA; MATIAS MIRANDA; JOAQUIN MERLO; MONICA BALZARINI; HUGO LUJAN; GABRIEL RABINOVICH; ROMINA GIROTTI

BRIEFINGS IN BIOINFORMATICS

OXFORD UNIV PRESS

Lugar: Oxford; Año: 2021

1467-5463

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 methodsthat 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 AQ7 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, cytolyticscore, PD-ligand 1 expression, patients? survival and response to anti-cytotoxic T-lymphocyte-associated antigen 4 and anti-programmed cell death protein 1 immunotherapy.Key words: immune infiltrate; deconvolution; RNA sequencing; cancer; digital cytometry; immunotherapy