Chen Hong

This study uses pan-cancer phosphoproteomics to identify therapeutic targets and improve precision oncology by analyzing 1,998 tumor samples and developing new scores for kinase activity and immune response.

The paper introduces REMIND-Cancer Pipeline and pSNV Hunter to identify and prioritize functional promoter SNVs, including lowly recurrent and singleton mutations, using multi-omics data from the PCAWG dataset.

The paper identifies previously overlooked activating promoter mutations in cancer using a computational workflow applied to the PCAWG dataset, validating several candidates through luciferase assays.

The paper analyzes whole-genome sequencing data from cancer samples to identify genomic footprints of telomere maintenance mechanisms, finding specific telomere variant repeat patterns associated with ATRX/DAXX mutations.

The paper introduces the Cancer LncRNA Census (CLC), a curated resource of lncRNAs with causative roles in cancer, and shows their genomic and evolutionary features.

ActivePathways integrates multivariate omics data to identify enriched pathways, revealing cancer driver genes and prognostic pathways in breast cancer.

The paper identifies and characterizes coding and non-coding driver mutations across 2,500 cancer genomes, revealing new candidates and questioning previous findings.