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

Abstract

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.

Evaluating the impact of age on immune checkpoint therapy biomarkers

“…The large number of primary tumor transcriptional profiles across disease subtypes available from TCGA provides a unique cohort to estimate the impact of age on tumor immune cell composition. We apply the MIXTURE immune-cell-type deconvolution algorithm ( Fernández et al, 2021 ) to infer the absolute proportions of immune cell types from RNA-seq data derived from pan-cancer TCGA samples. The algorithm provides an absolute proportion that describes the portion of total immune content that a particular immune cell type makes up in a sample but is normalized to be comparable across all samples in the dataset by multiplying the inferred relative proportion by a scaling factor that measures the total immune content in the sample.…”

“…Immune Cell Type Deconvolution from Bulk RNA-Sequencing Data The MIXTURE algorithm (Ferná ndez et al, 2021) builds on the nu-Support Vector Regression framework used by CIBER-SORT (Newman et al, 2015) for particular use with noisy tumor samples. MIXTURE applies Recursive Feature Selection to make the cell type deconvolution more robust to noise and collinearity, and was thus designed to improve performance on tumor data.…”

Identification of circadian clock genes as regulators of immune infiltration in Hepatocellular Carcinoma

“…Accumulating data have shown that immune cells for tumor infiltration in the tumor microenvironment play an important role in tumor development 34 , 35 . However, it remains unclear how CCGs influence immune cell infiltration.…”

Fourteen years of cellular deconvolution: methodology, applications, technical evaluation and outstanding challenges

“…In total, we review 39 reference-based methods (MuSiC ( 79 ), DWLS ( 80 ), AdRoit ( 112 ), spatialDWLS ( 113 ), Scaden ( 81 ), LinDeconSeq ( 109 ), DigitalDLSorter ( 82 ), AutoGeneS ( 114 ), RNA-Sieve ( 83 ), DecOT ( 111 ), BayICE ( 94 ), DeconPeaker ( 73 ), SCDC ( 84 ), DAISM-DNN ( 115 ), CPM ( 85 ), MOMF ( 86 ), BisqueRef ( 116 ), deconvSeq ( 101 ), DeCompress ( 87 ), DeMixT ( 117 ), CIBERSORT ( 107 , 108 ), MethylResolver ( 104 ), MIXTURE ( 105 ), FARDEEP ( 118 ), NITUMID ( 110 ), MySort ( 119 ), PREDE ( 57 ), quanTIseq ( 106 ), DeconRNASeq ( 120 ), DCQ ( 88 ), dtangle ( 102 ), DESeq2’s unmix ( 121 ), ARIC ( 100 ), EMeth ( 122 ), ImmuCellAI ( 89 ), EPIC ( 103 ), TICPE ( 90 ), BayesPrism ( 98 ), Bseq-SC ( 99 )), 10 reference-free approaches (Linseed ( 123 ), TOAST ( 91 , 92 ), debCAM ( 124 ), CellDistinguisher ( 125 ), deconf ( 126 ), BayCount ( 127 ), BayesCCE ( 74 ), ReFACTor ( 93 ), DeconICA ( 128 ), SMC ( 97 )) and 4 semi-reference-free techniques (Deblender ( 95 ), MCP-counter ( 129 ), BisqueMarker ( 116 ), DSA ( 96 )).…”