Geospatial characterization of immune cell distributions and dynamics across the microenvironment in renal cell carcinoma

Abstract

We performed multiplex immunofluorescence (mIF) using an array of myeloid and lymphoid markers on primary tumor samples from 122 patients with RCC. Regions of interest (ROIs) were selected from three distinct tumor zones: the tumor-core, stroma, and tumor-stroma interface. Digital pathologic imaging analysis was leveraged to quantify the geospatial location and distribution of immune cells within the TIME, and these findings were correlated with a variety of tumor molecular profiles. For patients with ccRCC, as clinical stage increased, immunosuppressive M2-like CD163+ TAMs migrated from the tumor compartment and into the stroma at the tumor-stroma interface and became increasingly co-localized with CD8+ T-cells. Furthermore, high CD163+ TAM clustering into the stromal compartment and high CD8+/CD163+ stromal co-localization were independently associated with worse OS and cancer-specific survival (CSS), while accounting for clinical stage. Overall, this data suggests that the pro-tumor effect of M2-like TAMs and their effect on CD8+ T-cells may be dependent on their specific geospatial location and distribution within the TIME -- namely, that M2-like TAMs exert their pro-tumor effect and interact with CD8+ T-cells most effectively from the stromal compartment at the tumor-stroma interface.

Single‐cell transcriptomic analysis in clear cell renal cell carcinoma: Deciphering the role of APP within the tumour microenvironment

“…19 This insight is crucial given the pivotal role of the TME in RCC progression and response to therapy. 20,21 We used single-cell data screening to obtain TAM-related genes and constructed a prognostic model based on TAM-related genes. At present, we are the first prognostic model constructed based on TAM-related genes in the field of renal clear cell carcinoma.…”

“…We discovered that APP expression in TAMs correlates with patient prognosis, suggesting its potential as a novel biomarker in ccRCC 19 . This insight is crucial given the pivotal role of the TME in RCC progression and response to therapy 20,21 . We used single‐cell data screening to obtain TAM‐related genes and constructed a prognostic model based on TAM‐related genes.…”

Radiomics Correlation to CD68+ Tumor-Associated Macrophages in Clear Cell Renal Cell Carcinoma

“…Future radiomics analysis focusing on M2 macrophage markers such as CD163 and CD206 may yield further prognostic value and help reveal the underlying heterogeneous TIMEs. For example, one concurrent work that showed clustering between M2 macrophage and tumor cells may be a marker for poor prognosis [34]. We noticed another concurrent work explored radiomic features associated with response to nivolumab, a specific ICI for metastatic RCC [35].…”

Increased spatial coupling of integrin and collagen IV in the immunoresistant clear cell renal cell carcinoma tumor microenvironment

“…Elements of the tumor immune microenvironment (TIME) have been found to fluctuate as patients with ccRCC progress. 6,7 Large scale efforts to characterize the ccRCC TIME through bulk transcriptomic sequencing provided an understanding of the macro-level immune cell composition within ccRCC.…”

“…Specifically, spatial proteomics has identified the association of certain myeloid cell line clustering within tumoral regions and poor treatment response with immunotherapy (IO), as well as survival outcomes. 6,11,12 These studies utilized spot-based or mini-bulk resolution but does not elucidate cell-cell crosstalk. Recently, high-plex spatial transcriptomics has allowed simultaneous analysis of cell-level variations in gene expression on a single slide.…”