Detalle Publicación

A proteomic atlas of lineage and cancer-polarized expression modules in myeloid cells modeling immunosuppressive tumor-infiltrating subsets

Autores: Blanco, E.; Ibáñez-Vea, M.; Hernández, C.; Drici, L.; Martínez de Morentin, X.; Gato, M.; Ausín, K.; Bocanegra, A.; Zuazo, M.; Chocarro, L.; Arasanz, H.; Fernández-Hinojal, G.; Fernández Irigoyen, Joaquín; Smerdou Picazo, Cristian; Garnica, M.; Echaide, M.; Fernández, L.; Morente, P.; Ramos-Castellanos, P.; Llopiz Khatchikian, Diana Isabel; Santamaría, E.; Larsen, M. R.; Escors, D.; Kochan, G. (Autor de correspondencia)
ISSN: 2075-4426
Volumen: 11
Número: 6
Páginas: 542
Fecha de publicación: 2021
Monocytic and granulocytic myeloid-derived suppressor cells together with tumor-infiltrating macrophages constitute the main tumor-infiltrating immunosuppressive myeloid populations. Due to the phenotypic resemblance to conventional myeloid cells, their identification and purification from within the tumors is technically difficult and makes their study a challenge. We differentiated myeloid cells modeling the three main tumor-infiltrating types together with uncommitted macrophages, using ex vivo differentiation methods resembling the tumor microenvironment. The phenotype and proteome of these cells was compared to identify linage-dependent relationships and cancer-specific interactome expression modules. The relationships between monocytic MDSCs and TAMs, monocytic MDSCs and granulocytic MDSCs, and hierarchical relationships of expression networks and transcription factors due to lineage and cancer polarization were mapped. Highly purified immunosuppressive myeloid cell populations that model tumor-infiltrating counterparts were systematically analyzed by quantitative proteomics. Full functional interactome maps have been generated to characterize at high resolution the relationships between the three main myeloid tumor-infiltrating cell types. Our data highlights the biological processes related to each cell type, and uncover novel shared and differential molecular targets. Moreover, the high numbers and fidelity of ex vivo-generated subsets to their natural tumor-shaped counterparts enable their use for validation of new treatments in high-throughput experiments.