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Understanding the effects of obesity on the immune profile of renal cell carcinoma (RCC) patients is critical, given the rising use of immunotherapies to treat advanced disease and recent reports of differential cancer immunotherapy outcomes with obesity. Here, we evaluated multiple immune parameters at the genetic, soluble protein, and cellular levels in peripheral blood and renal tumors from treatment-naive clear cell RCC (ccRCC) subjects (n = 69), to better understand the effects of host obesity (Body Mass Index "BMI" ≥ 30 kg/m2) in the absence of immunotherapy. Tumor-free donors (n = 38) with or without obesity were used as controls. In our ccRCC cohort, increasing BMI was associated with decreased percentages of circulating activated PD-1+CD8+ T cells, CD14+CD16neg classical monocytes, and Foxp3+ regulatory T cells (Tregs). Only CD14+CD16neg classical monocytes and Tregs were reduced when obesity was examined as a categorical variable. Obesity did not alter the percentages of circulating IFNγ+ CD8 T cells or IFNγ+, IL-4+, or IL-17A+ CD4 T cells in ccRCC subjects. Of 38 plasma proteins analyzed, six (CCL3, IL-1β, IL-1RA, IL-10, IL-17, and TNFα) were upregulated specifically in ccRCC subjects with obesity versus tumor-free controls with obesity. IGFBP-1 was uniquely decreased in ccRCC subjects with obesity versus non-obese ccRCC subjects. Immunogenetic profiling of ccRCC tumors revealed that 93% of examined genes were equivalently expressed and no changes in cell type scores were found in stage-matched tumors from obesity category II/III versus normal weight (BMI ≥ 35 kg/m2 versus 18.5-24.9 kg/m2, respectively) subjects. Intratumoral PLGF and VEGF-A proteins were elevated in ccRCC subjects with obesity. Thus, in ccRCC patients with localized disease, obesity is not associated with widespread detrimental alterations in systemic or intratumoral immune profiles. The effects of combined obesity and immunotherapy administration on immune parameters remains to be determined.
Pubmed ID: 32469992 RIS Download
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THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5,2022.Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role.
View all literature mentionsSoftware for single-cell flow cytometry analysis. Its functions include management, display, manipulation, analysis and publication of the data stream produced by flow and mass cytometers.
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