Dataset Information

A novel scoring method based on RNA-Seq immunograms describing individual cancer-immunity interactions.

ABSTRACT: Because of the complexity of cancer-immune system interactions, combinations of biomarkers will be required for predicting individual patient responses to treatment and for monitoring combination strategies to overcome treatment resistance. To this end, the "immunogram" has been proposed as a comprehensive framework to capture all relevant immunological variables. Here, we developed a method to convert transcriptomic data into immunogram scores (IGS). This immunogram includes 10 molecular profiles, consisting of innate immunity, priming and activation, T cell response, interferon ? (IFNG) response, inhibitory molecules, regulatory T cells, myeloid-derived suppressor cells (MDSCs), recognition of tumor cells, proliferation, and glycolysis. Using genes related to these 10 parameters, we applied single-sample gene set enrichment analysis (ssGSEA) to 9417 bulk RNA-Seq data from 9362 cancer patients with 29 different solid cancers in The Cancer Genome Atlas (TCGA). Enrichment scores were z-score normalized (Z) for each cancer type or the entire TCGA cohort. The IGS was defined by the formula IGS = 3 + 1.5 × Z so that patients would be well distributed over a range of scores from 1 to 5. The immunograms constructed in this way for all individual patients in the entire TCGA cohort can be accessed at "The RNA-Seq based Cancer Immunogram Web" (https://yamashige33.shinyapps.io/immunogram/).

SUBMITTER: Kobayashi Y

PROVIDER: S-EPMC7648030 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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A novel scoring method based on RNA-Seq immunograms describing individual cancer-immunity interactions.

Kobayashi Yukari Y Kushihara Yoshihiro Y Saito Noriyuki N Yamaguchi Shigeo S Kakimi Kazuhiro K

Cancer science 20200909 11

Because of the complexity of cancer-immune system interactions, combinations of biomarkers will be required for predicting individual patient responses to treatment and for monitoring combination strategies to overcome treatment resistance. To this end, the "immunogram" has been proposed as a comprehensive framework to capture all relevant immunological variables. Here, we developed a method to convert transcriptomic data into immunogram scores (IGS). This immunogram includes 10 molecular profil ...[more]

PMID: 32810311

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Project description:BackgroundIt is important to understand the composition of cell type and its proportion in intact tissues, as changes in certain cell types are the underlying cause of disease in humans. Although compositions of cell type and ratios can be obtained by single-cell sequencing, single-cell sequencing is currently expensive and cannot be applied in clinical studies involving a large number of subjects. Therefore, it is useful to apply the bulk RNA-Seq dataset and the single-cell RNA dataset to deconvolute and obtain the cell type composition in the tissue.ResultsBy analyzing the existing cell population prediction methods, we found that most of the existing methods need the cell-type-specific gene expression profile as the input of the signature matrix. However, in real applications, it is not always possible to find an available signature matrix. To solve this problem, we proposed a novel method, named DCap, to predict cell abundance. DCap is a deconvolution method based on non-negative least squares. DCap considers the weight resulting from measurement noise of bulk RNA-seq and calculation error of single-cell RNA-seq data, during the calculation process of non-negative least squares and performs the weighted iterative calculation based on least squares. By weighting the bulk tissue gene expression matrix and single-cell gene expression matrix, DCap minimizes the measurement error of bulk RNA-Seq and also reduces errors resulting from differences in the number of expressed genes in the same type of cells in different samples. Evaluation test shows that DCap performs better in cell type abundance prediction than existing methods.ConclusionDCap solves the deconvolution problem using weighted non-negative least squares to predict cell type abundance in tissues. DCap has better prediction results and does not need to prepare a signature matrix that gives the cell-type-specific gene expression profile in advance. By using DCap, we can better study the changes in cell proportion in diseased tissues and provide more information on the follow-up treatment of diseases.

Dataset Information

A novel scoring method based on RNA-Seq immunograms describing individual cancer-immunity interactions.

Publications

A novel scoring method based on RNA-Seq immunograms describing individual cancer-immunity interactions.

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