Salvatore Piscuoglio

The paper identifies an RFX-driven ciliogenic cell state in colorectal cancer that is sensitive to FGFR inhibitors and associated with poor prognosis.

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 uses ribosome profiling to show that steatohepatitis-associated HCC has extensive translational alterations affecting hundreds of genes and revealing novel translated regions.

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.

The paper integrates single-cell transcriptomes and spatial transcriptomics to identify therapeutic targets in pancreatic cancer by analyzing cell states and ligand-receptor interactions in tumoroids.

This study shows eosinophils inhibit CRC metastasis by blocking SPP1+ macrophage differentiation, using transcriptomics and a murine model.

This study identifies RPL39L as an efficiency factor in cotranslational folding of alpha helical proteins by analyzing its expression, ribosome structure, and impact on translation dynamics.

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.

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.