The combined use of scRNA-seq and network propagation highlights key features of pan-cancer Tumor-Infiltrating T cells
Adèle Mangelinck, Elodie Molitor, Ibtissam Marchiq, Lamine Alaoui, Matthieu Bouaziz, Renan Andrade-Pereira, Hélène Darville, Etienne Becht, Céline Lefebvre
Abstract
Improving the selectivity and effectiveness of drugs represents a crucial issue for future therapeutic developments in immuno-oncology. Traditional bulk transcriptomics faces limitations in this context for the early phase of target discovery as resulting gene expression levels represent the average measure from multiple cell populations. Alternatively, single cell RNA sequencing can dive into unique cell populations transcriptome, facilitating the identification of specific targets. Here, we generated Tumor-Infiltrating regulatory T cells (TI-Tregs) and exhausted T cells (Tex) gene signatures from a single cell RNA-seq pan-cancer T cell atlas.
Introduction
Since the first report of single cell transcriptomics in 2009 [1], this powerful approach has known a tremendous technological and computational development. It is now possible to sequence tens of thousands of cells in parallel and integrate readouts from DNA, RNA and protein with multi-modal single cell methodologies [2, 3]. This enables cellular heterogeneity and interactions to be dissected more precisely than ever before. As such, single cell RNA sequencing (scRNA-seq) has led to ground-breaking discoveries in several fields including development, oncology, immunology, and neuroscience.
Materials and method
Data collection, preprocessing and T cell identification
The scRNA-seq data were collected from previously published datasets, adhering to the following selection criteria: 1) presence of T cells, 2) treatment-naïve patients, 3) solid tumors, and 4) inclusion of at least tumor and blood samples.
Results
Construction of a pan-cancer single cell RNA-seq atlas of T cells
To generate a pan-cancer atlas of T cells, we collected scRNA-seq datasets including multi-tissues and blood samples from treatment-naïve cancer patients. Specifically, we compiled data from colorectal cancer (CRC) [26], intrahepatic cholangiocarcinoma (CHOL) and hepatocellular carcinoma (HCC) [27], head and neck squamous cell carcinoma (HNSCC) [28], non-small cell lung cancer (NSCLC) [26], pancreatic ductal adenocarcinoma (PDAC) [29], renal cell carcinoma (RCC) [26, 30] and uterine corpus endometrial carcinoma (UCEC) [26].
Discussion
Targeting the immune system has shown to be a successful therapeutic approach in cancer with the approval of immune checkpoint inhibitor treatments for many tumor types. As this strategy does not act directly on malignant cells, target identification ensuring specificity and efficiency in immuno-oncology remains challenging. scRNA-seq offers the opportunity to deeply characterize any cell population of interest, opening up immuno-oncology to a wide range of possibilities. However, scRNA-seq also faces limitations such as noise and data sparsity. Here, we evaluated a methodology based on the combined use of scRNA-seq and network propagation for exploring T cell signatures and associated pathway dependencies that could eventually lead to innovative therapeutic strategies in immuno-oncology.
Acknowledgments
The authors would like to thank all members of Servier R&D who supported the initiation and implementation of Patrimony.
Citation: Mangelinck A, Molitor E, Marchiq I, Alaoui L, Bouaziz M, Andrade-Pereira R, et al. (2024) The combined use of scRNA-seq and network propagation highlights key features of pan-cancer Tumor-Infiltrating T cells. PLoS ONE 19(12): e0315980. https://doi.org/10.1371/journal.pone.0315980
Editor: Xiaosheng Tan, Rutgers: Rutgers The State University of New Jersey, UNITED STATES OF AMERICA
Received: June 17, 2024; Accepted: December 3, 2024; Published: December 27, 2024
Copyright: © 2024 Mangelinck et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data analyzed in this study were obtained from Gene Expression Omnibus (GEO) at GSE140228, GSE139555, GSE155698, GSE121636, GSE139324. The single cell RNA-seq pan-cancer T cell atlas generated from these datasets has been deposited in zenodo database under accession number https://zenodo.org/records/13879752.
Funding: Authors are employees at Servier, an international pharmaceutical company governed by a non-profit foundation. The authors declare that Servier only provided financial support in the form of authors’ salaries and did not play a role in the study design, data collection and analysis, decision to publish, nor in the preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
