Rangell, L. | Search bioRxiv

bioRxiv2023

The renal capsule: a vibrant and adaptive cell environment of the kidney in homeostasis, disease and aging

The kidney is a complex organ that governs many physiological parameters. It is roughly divided into three parts, the renal pelvis, medulla, and cortex. Covering the cortex is the renal capsule, a serosal tissue that provides protection and forms a barrier for the kidney. Serosal tissues of many organs have been recently shown to play a vital role in homeostasis and disease. Analyses of the cells that reside in these tissues have identified distinct cell types with unique phenotypes. Here, we characterized this niche and found that it is mainly comprised of fibroblasts and macrophages, but also includes other diverse cell types. Characterizing renal capsule-associated macrophages, we found that they consist of a distinct subset (i.e., TLF+ macrophages) that is nearly absent in the kidney parenchyma. Injury, disease, and other changes that involve the kidney, affected the cell composition of the renal capsule, indicating its dynamic response to changes within the organ parenchyma. Lastly, we studied age-related changes in the renal capsule and found that aging affected the cell composition and inflammatory phenotype of macrophages, increased CD8 T cells and other lymphocyte counts, and promoted a senescence-associated phenotype in fibroblasts. Taken together, our data illustrate the complexity and heterogeneity of the renal capsule and its underlying changes during aging and disease, improving our understanding of the kidney serosa that may be valuable for novel renal therapies.

immunology↗

bioRxiv2023

Automated tumor immunophenotyping predicts clinical benefit from anti-PD-L1 immunotherapy

BackgroundCancer immunotherapy has transformed the clinical approach to patients with malignancies as profound benefits can be seen in a subset of patients. To identify this subset, biomarker analyses increasingly focus on phenotypic and functional evaluation of the tumor microenvironment (TME) to determine if density, spatial distribution, and cellular composition of immune cell infiltrates can provide prognostic and/or predictive information. Attempts have been made to develop standardized methods to evaluate immune infiltrates in the routine assessment of certain tumor types; however, broad adoption of this approach in clinical decision-making is still missing. MethodsWe developed approaches to categorize solid tumors into "Desert", "Excluded" and "Inflamed" types according to the spatial distribution of CD8+ immune effector cells to determine the prognostic and/or predictive implications of such labels. To overcome the limitations of this subjective approach we incrementally developed four automated analysis pipelines of increasing granularity and complexity for density and pattern assessment of immune effector cells. ResultsWe show that categorization based on "manual" observation is predictive for clinical benefit from anti-programmed cell death ligand-1 (PD-L1) therapy in two large cohorts of patients with non-small cell lung cancer (NSCLC) or triple-negative breast cancer (TNBC). For the automated analysis we demonstrate that a combined approach outperforms individual pipelines and successfully relates spatial features to pathologist-based read-outs and patient response to therapy. ConclusionsOur findings suggest tumor immunophenotype (IP) generated by automated analysis pipelines should be evaluated further as potential predictive biomarkers for cancer immunotherapy. What is already known on this topicClinical benefit from checkpoint inhibitor-targeted therapies is realized only in a subset of patients. Robust biomarkers to identify patients who may respond to such therapies are needed. What this study addsWe have developed manual and automated approaches to categorize tumors into immunophenotypes based on the spatial distribution of CD8+ T effector cells that predict clinical benefit from anti-PD-L1 immunotherapy for patients with advanced non-small cell lung cancer or triple-negative breast cancer. How this study might affect research, practice or policyTumor immunophenotypes should be further validated as predictive biomarker for checkpoint inhibitor-targeted therapies in prospective clinical studies.

pathology↗