Welcome to the repository for the research paper titled "Single-cell resolution characterization of myeloid-derived cell states with implication in cancer outcome." Here, you'll find the code used to generate the figures and perform analyses associated with our study.
Tumor-associated myeloid-derived cells (MDCs) play a critical role in cancer prognosis and treatment responses due to their remarkable plasticity and tumorigenic behaviors. In this study, we utilized single-cell RNA-Sequencing data from seven different cancers to identify 29 distinct MDC subpopulations within the tumor microenvironment. Our analysis not only uncovered abnormally expanded MDC subpopulations across various tumors but also distinguished between cell states that have historically been grouped together, such as TREM2+ and FOLR2+ subpopulations.
- Integration of single-cell data revealed 29 MDC subpopulations across multiple cancers.
- Identification of five independent prognostic markers within these subpopulations, including states co-expressing TREM2 and PD-1, and FOLR2 and PDL-2.
- TREM2 alone does not reliably predict cancer prognosis, as its association varies depending on local cues.
- Validation in independent cohorts confirmed poor clinical outcomes associated with FOLR2-expressing macrophages in ovarian and triple-negative breast cancers.
This repository contains:
- Code for generating figures:
Panels_Notebooks/ - Code for analyses:
scRNA-seq/,Clinical_Impact/,Deconvolution_Analysis/
The bioinformatics analysis was conducted by:
- Gabriela Rapozo
- Giovanna Maklouf
- Cristiane Esteves
- Leandro Santos
- Nayara Toledo
- Cristóvão Lanna
- Marco Antônio Pretti
Under the supervision of Mariana Boroni, Principal Investigator of the Bioinformatics and Computational Biology Laboratory at the Brazilian National Cancer Institute.
This comprehensive MDC atlas provides valuable insights and serves as a foundation for novel analyses, ultimately advancing strategies for treating solid cancers. Thank you for your interest in our research!