Holistic Characterization of Tumor Monocyte-to-Macrophage Differentiation Integrates Distinct Immune Phenotypes in Kidney Cancer

Abstract

The tumor immune microenvironment (TIME) is commonly infiltrated by diverse collections of myeloid cells. Yet, the complexity of myeloid-cell identity and plasticity has challenged efforts to define bona fide populations and determine their connections to T-cell function and their relationship to patient outcome. Here, we have leveraged single-cell RNA-sequencing analysis of several mouse and human tumors and found that monocyte–macrophage diversity is characterized by a combination of conserved lineage states as well as transcriptional programs accessed along the differentiation trajectory. We also found in mouse models that tumor monocyte-to-macrophage progression was profoundly tied to regulatory T cell (Treg) abundance. In human kidney cancer, heterogeneity in macrophage accumulation and myeloid composition corresponded to variance in, not only Treg density, but also the quality of infiltrating CD8+ T cells. In this way, holistic analysis of monocyte-to-macrophage differentiation creates a framework for critically different immune states.

Tumor-associated macrophage heterogeneity is driven by tissue territories in breast cancer

“…However, our clustering was mostly confirmed at the protein level by flow cytometry and recovered the main subsets observed by imaging. We showed that most TAM transcriptomic signatures poorly recovered the M1/M2 dichotomy, as shown recently in other mouse models (Mujal et al, 2022). CD206 is usually associated to M2-like phenotype but is widely expressed by subsets of resident tissue macrophages including those of adipose tissue (Arendt et al, 2013;Silva et al, 2019;Summers et al, 2020;Wentworth et al, 2010).…”

“…Macrophage adaptation to the tumor environment has commonly been discussed in terms of a spectrum of polarization states from anti-tumor M1 to pro-tumor M2 (reviewed in (Locati et al, 2020)). However, the validity of the polarization model in cancer has been widely questioned (Hume and Freeman, 2014;Mujal et al, 2022;Xue et al, 2014). Therefore, a proper understanding of TAM intrinsic diversity related to their spatial localization over time is still warranted.…”

Space-Time Mapping Identifies Concerted Multicellular Patterns and Gene Programs in Healing Wounds and their Conservation in Cancers

“…We focused on two gene products— Arg1 and CD206/Mrc1 that, though often grouped together as a combined signature of ‘M2’ macrophages in vitro 55 , show clear cell subset ( Figure 3B ) and space-time distinct ( Figure 3C ) patterns in wound healing, further confirming results from tumors 56 that they are not obligately part of the same gene network. By scRNAseq data, ‘early’ inflammatory Mono_Mac subpopulations Mono_Mac_1, Mono_Mac_2, and Mono_Mac_3 express notably high levels of Arg1 ( Figures 3B and S3C ).…”

High-dimensional immune cell profiling of cerebrospinal fluid from patients with metastatic breast cancer and leptomeningeal disease

“…Conventional markers of T cell exhaustion are PD-1, TIM3, and LAG3 22 . However, recent evidence suggests that CD38 is a mechanism of acquired resistance to PD-1/PD-L1 blockade, leading to CD8 + T cell exhaustion 23 , 26 and lower survival in patients with kidney cancer 28 . Despite lower overall frequencies of immune infiltrates in the CSF of patients with LMD, there was a subset of CD8 + T cells (CD38 hi TIM3 lo ) that were enriched.…”