Leveraging the Epigenomic Landscape to identify Histology-Specific Master transcription factors and to functionally annotate risk loci in renal cell carcinoma
Genome binding/occupancy profiling by high throughput sequencing Expression profiling by high throughput sequencing
Summary
We performed histone (ChIP-seq, the Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq), and RNA sequencing (RNA-seq) on 42 fresh frozen RCC tumor samples (24 ccRCC, 6 pRCC, 12 chRCC). We integrated 153 unique data sets to gain a holistic view of gene regulation in RCC subtypes and 50 candidate histology-specific master transcription factors (TF) to define RCC histologic subtypes. We also integrated RCC GWAS risk SNPs with H3K27ac ChIP-seq and ATAC-seq data and revealed that risk variants were significantly enriched in allelically imbalanced peaks
Overall design
We performed histone ChIP-seq, ATAC-seq, and RNA sequencing (RNA-seq) to annoate the epigenetic landscape then untegrate that data to nominate candidate master TFs important to maintain cellular identity. We manipulated the expression of two of these candidate TFs and evaluated RNA expression as a readout. We also GWAS risk loci with epigenetically marked peaks. This submission corresponds to ChIP-seq component of study. The RNA-seq samples were included on Dec 13, 2022.
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