CBFA2T2 is associated with a cancer stem cell state in renal cell carcinoma

Accurate allocation of multimapped reads enables regulatory element analysis at repeats

Transposable elements (TEs) and other repetitive regions have been shown to contain gene regulatory elements, including transcription factor binding sites. However, regulatory elements harbored by repeats have proven difficult to characterize using short-read sequencing assays such as ChIP-seq or ATAC-seq. Most regulatory genomics analysis pipelines discard "multimapped" reads that align equally well to multiple genomic locations. Because multimapped reads arise predominantly from repeats, current analysis pipelines fail to detect a substantial portion of regulatory events that occur in repetitive regions. To address this shortcoming, we developed Allo, a new approach to allocate multimapped reads in an efficient, accurate, and user-friendly manner. Allo combines probabilistic mapping of multimapped reads with a convolutional neural network that recognizes the read distribution features of potential peaks, offering enhanced accuracy in multimapping read assignment. Allo also provides read-level output in the form of a corrected alignment file, making it compatible with existing regulatory genomics analysis pipelines and downstream peak-finders. In a demonstration application on CTCF ChIP-seq data, we show that Allo results in the discovery of thousands of new CTCF peaks. Many of these peaks contain the expected cognate motif and/or serve as TAD boundaries. We additionally apply Allo to a diverse collection of ENCODE ChIP-seq data sets, resulting in multiple previously unidentified interactions between transcription factors and repetitive element families. Finally, we show that Allo may be particularly beneficial in identifying ChIP-seq peaks at centromeres, near segmentally duplicated genes, and in younger TEs, enabling new regulatory analyses in these regions.

A novel seven-gene signature as Prognostic Biomarker in Hepatocellular Carcinoma

Purpose: Our study is designed to develop and certify a promising prognostic signature for hepatocellular carcinoma (HCC).

Materials and methods: We retrospectively analyzed mRNA expression profiles and clinicopathological data fetched from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. We formulated a prognostic seven-gene signature composed of differentially expressed mRNAs (DEmRNAs) between HCC and nonneoplastic tissues through univariate Cox regression analysis. The receiver operating characteristic (ROC) curve, survival analysis and multivariate Cox regression analysis as well as nomograms were utilized to assess the prognostic performance of the seven-gene signature.

Results: The risk score based on a seven-gene signature categorized HCC subjects into a high- and low-risk group. There was significantly discrepant overall survival (OS) between patients in both groups and the corresponding ROC curve revealed a satisfactory predictive performance in HCC survival in both TCGA and GSE76427 cohort. Multivariate Cox regression analysis demonstrated that a seven-gene signature was an independently prognostic factor for HCC. Nomograms combining this prognostic signature with significant clinical characteristics conferred a crucial reference to predict the 1-,3- and 5 years OS.

Conclusions: Our study defined a promising seven-gene signature and nomogram model to forecast the OS of HCC patients, which is instrumental in clinical decision and personalized therapy.

Study of Cathepsin B inhibition in VEGFR TKI treated human renal cell carcinoma xenografts

Several therapeutic options are available for metastatic RCC, but responses are almost never complete, and resistance to therapy develops in the vast majority of patients. Consequently, novel treatments are needed to combat resistance to current therapies and to improve patient outcomes. We have applied integrated transcriptome and proteome analyses to identify cathepsin B (CTSB), a cysteine proteinase of the papain family, as one of the most highly upregulated gene products in established human RCC xenograft models of resistance to vascular endothelial growth factor receptor (VEGFR) tyrosine kinase inhibitors (TKI). We used established RCC models to test the significance of CTSB in the progression of renal cancer. Our evaluation of CTSB showed that stable CTSB knockdown suppressed RCC growth in vitro and in vivo. Stable over-overexpression of wild-type CTSB (CTSB^wt/hi), but not of an CTSB active site mutant (CTSB^N298A), rescued cell growth in CTSB knockdown cells and abolished the efficacy of VEGFR TKI treatment. Genome-wide transcriptome profiling of CTSB knockdown cells demonstrated significant effects on multiple metabolic and stem cell-related pathways, with ALDHA1A (ALDH1) as one of the most significantly downregulated genes. Importantly, survival analysis across 16 major TCGA cancers revealed that CTSB overexpression is associated with low rates of three and five year patient survival rates (P = 2.5e-08, HR = 1.4). These data strongly support a contribution of CTSB activity to RCC cell growth and tumorigenicity. They further highlight the promise of CTSB inhibition in development of novel combination therapies designed to improve efficacy of current TKI treatments of metastatic RCC.