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A multiomics disease progression signature of low-risk ccRCC

ABSTRACT: Background: Clear cell renal cell carcinoma (ccRCC) is the most common renal cancer. Identification of ccRCC likely to progress, despite an apparent low risk at the time of surgery, represents a key clinical issue. Methods: From a cohort of adult ccRCC patients (n=443), we selected low-risk tumors progressing within a 5-years average follow-up (progressors: P, n=8) and non-progressing (NP) tumors (n=16). Transcriptome sequencing, miRNA sequencing and proteomics were performed on tissues obtained at surgery. Our work suggests that LXR, FXR and macrophage activation pathways could be critically involved in the inhibition of the progression of low-risk ccRCC. Furthermore, a 10-component classifier could support an early identification of apparently low-risk ccRCC patients.

ORGANISM(S): Homo sapiens

PROVIDER: GSE207557 | GEO | 2022/08/10

REPOSITORIES: GEO

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Project description:Background: In a previous study of patients with assumed benign IgA nephropathy (IgAN), we have shown that 18.6% develop a progressive clinical course, which was not predicted by any known clinical nor histopathological parameters (Knoop 2017). We aim to differentiate between future progressors and non-progressors with assumed benign IgAN by using glomerular transcriptomics from diagnostic kidney biopsies and to investigate whether this can identify drugs to be used in those patients. Methods: We included adult progressors (n=15) and patients with clinical remission (non-progressors, n=21) from our previously reported IgAN cohort. Glomerular cross-sections were obtained through laser-capture microdissection from 10µm archival kidney biopsy sections for mRNA sequencing, using the SMARTer Stranded Total RNA-Seq kit – Pico Input Mammalian (Clontech Laboratories, California, USA). Key results were validated in external cohorts and on the protein level with immunohistochemistry in the discovery cohort and an internal validation cohort. We also compared our transcriptomic findings with an external genome-wide association study (GWAS) dataset (Kiryluk 2021). Results: For the statistical analyses, we studied 8 progressors and 9 non-progressors. Based on the 1,240 differentially expressed glomerular genes from the initial kidney biopsies, we were able to identify future IgAN progressors and non-progressors using a two-component classifier. The classifier predicted disease progression with 88% accuracy, 75% sensitivity and 100% specificity (AUC=0.875), more accurate than the recommended International IgAN Prediction Tool in this specific patient cohort. Among our differentially expressed genes, 10 were also found in the GWAS dataset (Kiryluk 2021). We identified 45 drug targets in the DrugBank database, including angiotensinogen, a target of sparsentan, currently investigated as therapy in IgAN (NCT03762850 and NCT04663204). The findings on upregulation of angiotensinogen, which was independent of blood pressure, were validated in an internal IgAN validation cohort. Interestingly, complement activation did not play a major role in our data. Our work suggests that LXR, FXR and macrophage activation pathways could be critically involved in the inhibition of the progression of low-risk ccRCC. Furthermore, a 10-component classifier could support an early identification of apparently low-risk ccRCC patients.

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A multiomics disease progression signature of low-risk ccRCC

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