A Cancer-Specific Qualitative Method for Estimating the Proportion of Tumor-Infiltrating Immune Cells

Abstract

Tumor-infiltrating immune cells are important components in the tumor microenvironment (TME) and different types of these cells exert different effects on tumor development and progression; these effects depend upon the type of cancer involved. Several methods have been developed for estimating the proportion of immune cells using bulk transcriptome data. However, there is a distinct lack of methods that are capable of predicting the immune contexture in specific types of cancer. Furthermore, the existing methods are based on absolute gene expression and are susceptible to experimental batch effects, thus resulting in incomparability across different datasets. In this study, we considered two common neoplasms as examples (colorectal cancer [CRC] and melanoma) and introduced the Tumor-infiltrating Immune Cell Proportion Estimator (TICPE), a cancer-specific qualitative method for estimating the proportion of tumor-infiltrating immune cells. The TICPE was based on the relative expression orderings (REOs) of gene pairs within a sample and is notably insensitive to batch effects. Performance evaluation using public expression data with mRNA mixtures, single-cell RNA-Seq (scRNA-Seq) data, immunohistochemistry data, and simulated bulk RNA-seq samples, indicated that the TICPE can estimate the proportion of immune cells with levels of accuracy that are clearly superior to other methods. Furthermore, we showed that the TICPE could effectively detect prognostic signals in patients with tumors and changes in the fractions of immune cells during immunotherapy in melanoma. In conclusion, our work presented a unique novel method, TICPE, to estimate the proportion of immune cells in specific cancer types and explore the effect of the infiltration of immune cells on the efficacy of immunotherapy and the prognosis of cancer. The source code for TICPE is available at https://github.com/huitingxiao/TICPE.

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 )).…”

Computational identification and clinical validation of a novel risk signature based on coagulation-related lncRNAs for predicting prognosis, immunotherapy response, and chemosensitivity in colorectal cancer patients

“…First, we used the Estimation of STromal and Immune cells in MAlignant Tumours using Expression data (ESTIMATE) algorithm to evaluate the immunity scores of different clusters and risk groups, including estimate, immune, and stromal scores ( 32 ). Subsequently, we downloaded the immune cell infiltration data of TCGA-CRC patients from the TIMER 2.0 database (Tumor IMmune Estimation Resource, https://cistrome.shinyapps.io/timer/ ), including multiple deconvolution algorithms to assess the extent of immune cell infiltration of patients, namely, the Estimating the Proportion of Immune and Cancer cells (EPIC) ( 33 ), the Microenvironment Cell Populations-counter (MCP-counter) ( 34 ), the quantification of the Tumor Immune contexture from human RNA-seq data (quanTIseq) ( 35 ), TIMER ( 36 ), and Xcell ( 37 ) algorithms. We used the GSVA algorithm to evaluate the score of six immune-related gene sets [hematopoietic cell kinase (HCK), lymphocyte-specific protein tyrosine kinase, immunoglobulin G (IgG), major histocompatibility complex (MHC) I and II, and signal transducer and activator of transcription 1 (STAT1)] in different clusters ( 38 ) and compared the expression of different immunomodulatory genes among the clusters and risk groups.…”