Published on November 4th, 2022, a collaborative effort between the team led by Han Luo and Heng Xu from West China Hospital, Sichuan University, and the team of Jihwan Park from Gwangju Institute of Science and Technology resulted in a groundbreaking article titled "Pan-cancer Single-Cell Analysis Reveals the Heterogeneity and Plasticity of Cancer-Associated Fibroblasts in the Tumor Microenvironment" This research, featured in the esteemed journal Nature Communications (IF=17.694), delves into a comprehensive pan-cancer analysis involving 226 samples across ten distinct solid cancer types. The primary objective of the study was to profile the intricacies of the tumor microenvironment (TME) at a single-cell resolution. The findings provided illuminating insights into the commonalities and plasticity inherent in the heterogeneous population of Cancer-associated Fibroblasts (CAFs) across different cancer types. This work contributes significantly to our understanding of the dynamic nature of the TME, and the pivotal role played by CAFs in the progression and heterogeneity of various cancers.
Introduction The tumour microenvironment (TME) is a complex and ever-changing landscape seen within tumours. It consists of both cancerous cells and many non-cancerous stromal components. The TME has a substantial impact on several biological aspects of cancer including tumour advancement, treatment resistance, stimulation of blood vessel growth, and spread of cancer to other parts of the body [1]. Fibroblasts, as a type of stromal cell, stand out as the primary component in the TME, playing a pivotal role in supporting cancer [2]. Cancer-associated fibroblasts (CAFs) exhibit a range of functions beside direct interaction with malignant epithelial cells. CAFs contribute significantly to the formation of the TME, which signifies their role and presents a potential target for therapeutic intervention. Insights into CAFs diversity, behavior and adaptability across different forms of cancer will provide more comprehensive understanding on treating the disease [3-4] To explore this intricate matter, a research study by Luo et al., (2022) utilized single-cell RNA sequencing (scRNA-seq) technology. The authors analysed the TME by merging public and in-house scRNA-seq data from eleven prevalent solid cancer types. This allows them to examine the complex interactions and flexibility between various stromal cell types. The research specifically emphasizes the common characteristics and adaptability of cancer-associated fibroblasts (CAFs) across various cancer scenarios, which further provides insight into their potential significance in the field of cancer biology [5]
Materials and Methods The study utilized single-cell RNA-seq data processing, including alignment and quantification against the GRCh38 human reference genome using Cell Ranger software [6]. The Seurat package was employed for further analysis, involving data filtering, normalization, and identification of variably expressed genes. Clustering analysis, batch correction, and visualization were performed using Seurat functions. Cell–cell interaction analysis was conducted using CellphonedDB [7], and trajectory analysis employed the Monocle algorithm [8]. Enrichment analysis, SCENIC analysis [9], NicheNet analysis [10], similarity analysis, survival analysis, spatial transcriptomics, and multiplexed immunofluorescence analysis were also applied, revealing insights into the heterogeneity, interactions, and functional states of cell populations in the tumor microenvironment.
Results
TME Landscape Illustrated using scRNA-seq The study resulted in a transcriptional atlas, comprising 226 samples from 10 common solid cancer types, reveals 34 TME-related clusters, categorizing cell components into fibroblasts, lymphocytes, myeloid cells, endothelial cells, and plasma cells. Notable observations included heterogeneity among APOE+ tumor-associated macrophages (TAMs), tissue-specific distribution of endothelial cells leading to the classification of tumor endothelial cells(TECs), and distinct transcriptional profiles of TECs suggesting roles in angiogenesis, tumor growth, and immune modulation. Immune cell heterogeneity, epithelial cell characteristics, and shared signaling pathways across cancer types were also identified. Generalized Activation of CAFs and its interaction with TME and Epithelia: The study also revealed that three CAF subtypes (CAFmyo, CAFinfla, CAFadi) exhibit specific gene expressions, linked to tumorigenesis, epithelial-mesenchymal transition, and myogenesis. An activation trajectory from normal fibroblasts to CAFs reveals three states, with CAFstate3 showing characteristics of both pro-angiogenic and immunomodulation-related genes. The study emphasizes the diverse effects CAFs may have on the TME. The study also explored interactions between CAFs and the TME, emphasizing their role in modulating the immune TME. CAFs influence natural killer cells (NKC) and T cells, with CAF state3 exhibiting superior interactions. Reciprocal communications between different CAF states and immune cells were observed, including interactions with tumor-infiltrating B cells and myeloid components. Interactions with endothelial cells, especially involving angiogenesis-related pathways, and communication with epithelial cells, including potential implications for checkpoint blockade immunotherapy, were highlighted.
Characterization of the Plasticity of Fibroblasts via Pan-cancer Analysis The study also identified three shared CAF clusters and proposing potential alternative origins. Antigen-presenting CAFs (CAFap) show interactions with T-cell clusters, suggesting a transitional position between CAFs and TAMs. Fibroblast-like peripheral nerve cells (CAFPN) are associated with perineural invasion and poor prognosis. Fibroblasts potentially derived from endothelial cells through endothelial-mesenchymal transition (EndMT) are identified (CAFEndMT), associated with poor prognosis across various cancers.
The Triple Interplay between CAFs, TECs, and TAMs in the TME and Conclusion In conclusion, this study revealed the intricate interplay among cancer-associated fibroblasts (CAFs), tumor endothelial cells (TECs), and tumor-associated macrophages (TAMs) in the tumor microenvironment (TME). Notably, CAFs undergoing Endothelial-Mesenchymal Transition (EndMT) play a significant role in tumor angiogenesis. Key genes like CD44, SPP1, and APOE are implicated in the interactions between CAFEndMT and TAMs. Spatial analyses confirm the proximity of pro-angiogenic SPP1+ TAMs to CAFEndMT, suggesting their role in intratumoral angiogenesis. Single-cell profiling across various cancers revealed the plasticity of fibroblasts and their association with diverse functions, including angiogenesis, immune modulation, and epithelial-mesenchymal transition (EMT). Differentiated CAF states, especially CAF state3, predicted worse outcomes post-immunotherapy, indicating potential stratification for patients. The study also uncovers diverse CAF origins, with most originating from local normal fibroblasts and three minor clusters from endothelial cells, peripheral nerves, and macrophages. Novogene's advanced bioinformatics tools played a crucial role in this exploration. The findings contribute significantly to our understanding of CAFs and the TME, providing valuable insights for cancer research and potential therapeutic interventions. Despite valuable insights, the study acknowledges the need for experimental validations and a deeper understanding of CAF dynamics and mechanisms.
Reference
- B. Arneth, “Tumor microenvironment,” Medicina (Lithuania). 2020. doi: 10.3390/medicina56010015. R. Kalluri, “The biology and function of fibroblasts in cancer,” Nature Reviews Cancer. 2016. doi: 10.1038/nrc.2016.73.
- R. A. Glabman, P. L. Choyke, and N. Sato, “Cancer-Associated Fibroblasts: Tumorigenicity and Targeting for Cancer Therapy,” Cancers. 2022. doi: 10.3390/cancers14163906.
- T. Liu, L. Zhou, D. Li, T. Andl, and Y. Zhang, “Cancer-associated fibroblasts build and secure the tumor microenvironment,” Frontiers in Cell and Developmental Biology. 2019. doi: 10.3389/fcell.2019.00060.
- H. Luo et al., “Pan-cancer single-cell analysis reveals the heterogeneity and plasticity of cancer-associated fibroblasts in the tumor microenvironment,” Nat. Commun., vol. 13, no. 1, p. 6619, 2022, doi: 10.1038/s41467-022-34395-2.
- V. Marx, “Method of the Year: spatially resolved transcriptomics,” Nat. Methods, vol. 18, no. 1, pp. 9–14, 2021, doi: 10.1038/s41592-020-01033-y.
- M. Efremova, M. Vento-Tormo, S. A. Teichmann, and R. Vento-Tormo, “CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes,” Nat. Protoc., 2020, doi: 10.1038/s41596-020-0292-x.
- C. Trapnell et al., “The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells,” Nat. Biotechnol., 2014, doi: 10.1038/nbt.2859. S. Aibar et al., “SCENIC: Single-cell regulatory network inference and clustering,” Nat. Methods, 2017, doi: 10.1038/nmeth.4463.
- R. Browaeys, W. Saelens, and Y. Saeys, “NicheNet: modeling intercellular communication by linking ligands to target genes,” Nat. Methods, 2020, doi: 10.1038/s41592-019-0667-5.
- Y. Zhang et al., “Elucidating minimal residual disease of paediatric B-cell acute lymphoblastic leukaemia by single-cell analysis,” Nat. Cell Biol., 2022, doi: 10.1038/s41556-021-00814-7.
- S. Newman et al., “Genomes for kids: The scope of pathogenic mutations in pediatric cancer revealed by comprehensive dna and rna sequencing,” Cancer Discov., 2021, doi: 10.1158/2159-8290.CD-20-1631.
