ArticleFrontiers in immunology2021
A Cancer-Specific Qualitative Method for Estimating the Proportion of Tumor-Infiltrating Immune Cells.
Article in Frontiers in immunology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 3 citations in OpenAlex.
- Fourteen years of cellular deconvolution: methodology, applications, technical evaluation and outstanding challenges.Nucleic acids research · 2024Review
- DeepDecon accurately estimates cancer cell fractions in bulk RNA-seq data.Patterns (New York, N.Y.) · 2024Article
- 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.Frontiers in immunology · 2023Article
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Authors and funding
10 authors at 1 institution in 1 country.
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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.
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