Estimation of immune cell content in tumour tissue using single-cell RNA-seq data

Schelker, Max; Feau, Sonia; Du, Jinyan; Ranu, Nav; Klipp, Edda; MacBeath, Gavin; Schoeberl, Birgit; Raue, Andreas

doi:10.1038/s41467-017-02289-3

Cited by 231 publications

(206 citation statements)

References 36 publications

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“…So far, gene expression signature patterns used for deconvolution were obtained from transcriptome data of purified leukocyte fractions. Recently, single‐cell sequencing enabled the definition of these patterns based on analysis of single cells in the tumor microenvironment . We agree with the authors of these approaches that using transcriptome data collected for single cells in the tumor microenvironment will increase the accuracy of the deconvolution of bulk transcriptomics data.…”

Section: Discussionsupporting

confidence: 76%

Integration of Omics Data Sources to Inform Mechanistic Modeling of Immune‐Oncology Therapies: A Tutorial for Clinical Pharmacologists

Lazarou¹,

Chelliah²,

Small³

et al. 2020

Clin Pharma and Therapeutics

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Application of contemporary molecular biology techniques to clinical samples in oncology resulted in the accumulation of unprecedented experimental data. These “omics” data are mined for discovery of therapeutic target combinations and diagnostic biomarkers. It is less appreciated that omics resources could also revolutionize development of the mechanistic models informing clinical pharmacology quantitative decisions about dose amount, timing, and sequence. We discuss the integration of omics data to inform mechanistic models supporting drug development in immuno‐oncology. To illustrate our arguments, we present a minimal clinical model of the Cancer Immunity Cycle (CIC), calibrated for non‐small cell lung carcinoma using tumor microenvironment composition inferred from transcriptomics of clinical samples. We review omics data resources, which can be integrated to parameterize mechanistic models of the CIC. We propose that virtual trial simulations with clinical Quantitative Systems Pharmacology platforms informed by omics data will be making increasing impact in the development of cancer immunotherapies.

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Section: Discussionsupporting

confidence: 76%

Integration of Omics Data Sources to Inform Mechanistic Modeling of Immune‐Oncology Therapies: A Tutorial for Clinical Pharmacologists

Lazarou¹,

Chelliah²,

Small³

et al. 2020

Clin Pharma and Therapeutics

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“…In this regard, single-cell RNA-seq can help to reconstruct the expression fingerprints of the different cells of the tumor microenvironment [21, 66]. …”

Section: Challenges In the Quantification Of Tumor-infiltrating Immunmentioning

confidence: 99%

Quantifying tumor-infiltrating immune cells from transcriptomics data

Finotello

Trajanoski

2018

Cancer Immunol Immunother

303

265

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By exerting pro- and anti-tumorigenic actions, tumor-infiltrating immune cells can profoundly influence tumor progression, as well as the success of anti-cancer therapies. Therefore, the quantification of tumor-infiltrating immune cells holds the promise to unveil the multi-faceted role of the immune system in human cancers and its involvement in tumor escape mechanisms and response to therapy. Tumor-infiltrating immune cells can be quantified from RNA sequencing data of human tumors using bioinformatics approaches. In this review, we describe state-of-the-art computational methods for the quantification of immune cells from transcriptomics data and discuss the open challenges that must be addressed to accurately quantify immune infiltrates from RNA sequencing data of human bulk tumors.Electronic supplementary materialThe online version of this article (10.1007/s00262-018-2150-z) contains supplementary material, which is available to authorized users.

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“…In the tumor microenvironment, immune and stromal cells are two major types of non-tumor components that are valuable for the diagnosis and prognosis of tumors (27). The ESTIMATE algorithm is a useful tool to extract the genetic information of immune and stromal cells from a complex tumor microenvironment in order to identify the degree of infiltration of immune and stromal cells (28). Based on ESTIMATE scores, the present study aimed to evaluate the association between gene expression signatures of immune/stromal infiltration and RCC outcomes.…”

Section: Discussionmentioning

confidence: 99%

Prognostic and predictive value of immune/stromal-related gene biomarkers in renal cell carcinoma

et al. 2020

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Immune/stromal-associated genes may be promising biomarkers for cancer diagnosis and the determination of clinical cancer treatment options. The aim of the present study was to identify prognostic stromal/immune-associated genes in renal cell carcinoma (RCC). RCC gene expression data (885 cases) were obtained from The Cancer Genome Atlas database. Immune/stromal scores were calculated by using the ESTIMATE package in R. Immune/stromal scores were significantly associated with Tumor-Node-Metastasis stage, clinical stage and overall survival rate (P<0.05). There were 419 differentially expressed genes (DEGs) based on immune scores and 738 DEGs based on stromal scores. Among these DEGs, 406 DEGs based on stromal scores and 252 DEGs based on immune scores were significantly associated with overall survival rate (P<0.05). The biological functions of these DEGs were primarily enriched in the 'immune response' and 'regulation of cell migration and proliferation'. These DEGs were observed in a protein-protein interaction network. A LASSO Cox regression model was used to build a prognostic 6 gene-based classifier, including the IL21R, ATP6V1C2, GBP1, P2RY10, GBP4 and TNNC2 genes [area under the curve (AUC) =0.776]. The predictive model which combined this classifier with clinical prognostic factors had a high accuracy in predicting patient survival in RCC (combined AUC =0.899). Taken together, these results demonstrated that there are significant associations between immune/stromal scores and clinicopathological staging. A set of tumor microenvironmentassociated genes that have powerful prognostic value in patients with RCC were identified in the present study.

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Estimation of immune cell content in tumour tissue using single-cell RNA-seq data

Cited by 231 publications

References 36 publications

Integration of Omics Data Sources to Inform Mechanistic Modeling of Immune‐Oncology Therapies: A Tutorial for Clinical Pharmacologists

Integration of Omics Data Sources to Inform Mechanistic Modeling of Immune‐Oncology Therapies: A Tutorial for Clinical Pharmacologists

Quantifying tumor-infiltrating immune cells from transcriptomics data

Prognostic and predictive value of immune/stromal-related gene biomarkers in renal cell carcinoma

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