Single-cell resolution characterization of myeloid-derived cell states with implication in cancer outcome

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Guimaraes, Gabriela Rapozo ; Maklouf, Giovanna Resk ; Teixeira, Cristiane Esteves ; Santos, Leandro de Oliveira ; Tessarollo, Nayara Gusmao ; de Toledo, Nayara Evelin ; Serain, Alessandra Freitas ; de Lanna, Cristovao Antunes ; Pretti, Marco Antonio ; da Cruz, Jessica Goncalves Vieira ; Falchetti, Marcelo ; Dimas, Mylla M. ; Filgueiras, Igor Salerno ; Cabral-Marques, Otavio ; Ramos, Rodrigo Nalio ; de Macedo, Fabiane Carvalho ; Rodrigues, Fabiana Resende ; Bastos, Nina Carrossini ; da Silva, Jesse Lopes ; da Rocha, Edroaldo Lummertz ; Chaves, Claudia Bessa Pereira ; de Melo, Andreia Cristina ; Moraes-Vieira, Pedro M. M. ; Mori, Marcelo A. ; Boroni, Mariana

• Tipo de documento: Artigo Científico
• Fonte: NATURE COMMUNICATIONS; v. 15, n. 1, p. 23-pg., 2024-07-07.

Resumo

Tumor-associated myeloid-derived cells (MDCs) significantly impact cancer prognosis and treatment responses due to their remarkable plasticity and tumorigenic behaviors. Here, we integrate single-cell RNA-sequencing data from different cancer types, identifying 29 MDC subpopulations within the tumor microenvironment. Our analysis reveals abnormally expanded MDC subpopulations across various tumors and distinguishes cell states that have often been grouped together, such as TREM2+ and FOLR2+ subpopulations. Using deconvolution approaches, we identify five subpopulations as independent prognostic markers, including states co-expressing TREM2 and PD-1, and FOLR2 and PDL-2. Additionally, TREM2 alone does not reliably predict cancer prognosis, as other TREM2+ macrophages show varied associations with prognosis depending on local cues. Validation in independent cohorts confirms that FOLR2-expressing macrophages correlate with poor clinical outcomes in ovarian and triple-negative breast cancers. This comprehensive MDC atlas offers valuable insights and a foundation for futher analyses, advancing strategies for treating solid cancers. Tumour-associated myeloid cells have been linked to patient outcome and treatment response in multiple cancer types. Here, the authors use deconvolution of single cell RNA-sequencing data to identify myeloid populations which are prognostic across cancer types. (AU)

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• Modalidade de apoio: Bolsas no Brasil - Jovens Pesquisadores
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• Modalidade de apoio: Bolsas no Brasil - Programa Capacitação - Treinamento Técnico