Charlotte K.Y. Ng

This paper identifies a distinct HCC subgroup defined by high regeneration-associated stress, showing vulnerability to ATR inhibitors and proposing LIVRESS as a biomarker for treatment stratification.

This study identifies a SWI/SNF-targeting therapy effective against a double-negative prostate cancer subtype, showing it reduces viability in both AR-dependent and WNT-dependent models.

This study identifies zonation disruption, specifically aberrant β-catenin expansion, as a novel driver of hepatocellular carcinoma in chronic liver disease, revealing a distinct human HCC subtype with therapeutic implications.

The paper shows that loss of PI5P4K slows cancer progression in prostate basal cells by disrupting lipid metabolism in a Pten mutant model.

This study performs a meta-analysis of lncRNA associations with hepatocellular carcinoma, finding limited reproducibility and frequent conflicts in reported roles and expression patterns.

PipeIT2 is a somatic variant calling workflow for Ion Torrent sequencing data that identifies mutations without germline control, achieving >95% recall for variants with >10% allele fraction.

This study develops a statistical framework to identify synthetic lethal gene pairs from cancer perturbation screens and validates novel interactions in hepatocellular carcinoma.

The paper introduces bnClustOmics, a Bayesian network mixture model for multi-omics clustering, identifies three HCC subtypes with survival associations, and learns cluster-specific molecular networks.

scROSHI identifies cell types without training data by using pre-existing gene sets and hierarchical relationships, outperforming methods in low-data and high-diversity scenarios.

APSiC identifies novel cancer genes and non-genetic drivers from perturbation screens, revealing context-dependent oncogenic mechanisms and functional roles in specific cancers.

SCIM matches single-cell measurements across technologies by learning a technology-invariant latent space and applying bipartite matching, achieving high accuracy in real-world datasets.

This study evaluates the reliability of whole-exome sequencing in detecting intratumor genetic heterogeneity, finding that technical noise accounts for a significant proportion of apparent genetic differences.

This study characterizes genomic, transcriptomic, and epigenomic artifacts in FFPE tissues and provides best practices to mitigate their impact on molecular analysis.