Generic Reporter Sets for Colorimetric Multiplex dPCR Demonstrated with 6-Plex SNP Quantification Panels.

Digital PCR (dPCR) is a powerful method for highly sensitive and precise quantification of nucleic acids. However, designing and optimizing new multiplex dPCR assays using target sequence specific probes remains cumbersome, since fluorescent signals must be optimized for every new target panel. As a solution, we established a generic fluorogenic 6-plex reporter set, based on mediator probe technology, that decouples target detection from signal generation.

Arginine reprograms metabolism in liver cancer via RBM39.

Metabolic reprogramming is a hallmark of cancer. However, mechanisms underlying metabolic reprogramming and how altered metabolism in turn enhances tumorigenicity are poorly understood. Here, we report that arginine levels are elevated in murine and patient hepatocellular carcinoma (HCC), despite reduced expression of arginine synthesis genes.

Transcription factors TEAD2 and E2A globally repress acetyl-CoA synthesis to promote tumorigenesis.

Acetyl-coenzyme A (acetyl-CoA) plays an important role in metabolism, gene expression, signaling, and other cellular processes via transfer of its acetyl group to proteins and metabolites. However, the synthesis and usage of acetyl-CoA in disease states such as cancer are poorly characterized. Here, we investigated global acetyl-CoA synthesis and protein acetylation in a mouse model and patient samples of hepatocellular carcinoma (HCC).

Multi-omics subtyping of hepatocellular carcinoma patients using a Bayesian network mixture model.

Comprehensive molecular characterization of cancer subtypes is essential for predicting clinical outcomes and searching for personalized treatments. We present bnClustOmics, a statistical model and computational tool for multi-omics unsupervised clustering, which serves a dual purpose: Clustering patient samples based on a Bayesian network mixture model and learning the networks of omics variables representing these clusters. The discovered networks encode interactions among all omics variables and provide a molecular characterization of each patient subgroup.

Integrative proteogenomic characterization of hepatocellular carcinoma across etiologies and stages.

Proteogenomic analyses of hepatocellular carcinomas (HCC) have focused on early-stage, HBV-associated HCCs. Here we present an integrated proteogenomic analysis of HCCs across clinical stages and etiologies. Pathways related to cell cycle, transcriptional and translational control, signaling transduction, and metabolism are dysregulated and differentially regulated on the genomic, transcriptomic, proteomic and phosphoproteomic levels.