Steve Gallinger

The paper uses whole-genome and transcriptome sequencing to identify two consensus cancer subtypes in biliary tract cancer, revealing divergent molecular landscapes that better explain tumor heterogeneity than anatomical classification.

The paper identifies four epithelial transcription states in pancreatic cancer and links their origins to early developmental processes.

This study identifies novel genetic risk loci for pancreatic ductal adenocarcinoma in African ancestry individuals through admixture mapping and GWAS.

This study characterizes morphological classes in pancreatic cancer using transcriptomic, genomic, and clinical data to identify molecular associations and prognostic implications.

This study analyzes ctDNA shedding in pancreatic cancer using paired tissue and plasma whole genomes, finding that subclones contribute significantly to early dissemination and ctDNA levels.

This study integrates spatial proteomics and genomics in PDAC to identify tumour-immune-stroma interactions and their genomic associations, showing multi-omics models improve patient survival prediction.

This study identifies cell-free DNA methylation markers for early detection and prognosis of pancreatic cancer with high accuracy and survival stratification.

This study identifies PDAC cancer cell subpopulations using multiplexed glycan immunofluorescence and shows consistent aberrant glycan signatures in both tumor and blood samples, improving detection beyond CA19-9 alone.

This study identifies MICAL2 as a super enhancer-associated gene that promotes pancreatic cancer growth and metastasis through multiple molecular mechanisms.

This study shows that eliminating nSMase2 in pancreatic cancer reduces tumor growth, promotes a less aggressive tumor subtype, and enhances immune responses by altering exosome production and macrophage polarization.

The paper shows adenomas from individuals with biallelic MUTYH or NTHL1 variants have mutational signatures similar to CRCs, suggesting adenomas can be used for variant classification and cancer prevention.

The paper evaluates multiple next-generation sequencing features to predict DNA mismatch repair status in tumors, finding that combining features improves accuracy across different cancer types.

This paper conducts a multi-ancestry GWAS meta-analysis of suicide attempt, identifying 12 genome-wide significant loci and finding shared genetic risks with ADHD, smoking, and risk tolerance.

This study identifies genetic loci associated with suicide attempts and shows shared genetic architecture with psychiatric disorders and non-psychiatric risk factors.

This study identifies 13 new genetic loci specific to proximal or distal colorectal cancer subtypes, revealing partly distinct genetic architectures for these tumor locations.

The paper uses a multi-layered approach combining GWAS, spatial autocorrelation, and Hi-C to identify novel genetic variants associated with pancreatic cancer susceptibility.

This paper develops a Bayesian method to detect copy number variants and assess their association with disease in large-scale studies, finding deletions near MYC and TUSC3 linked to pancreatic cancer.

This study identifies cell types associated with brain complex traits using GWAS and single-cell transcriptomics, revealing novel roles for oligodendrocytes in Parkinson's disease.

The paper estimates genetic correlations between eating disorder and substance-use-related phenotypes using GWAS data, finding both positive and negative associations.

This study uses Mendelian randomization to show that higher physical activity levels are associated with lower risks of breast and colorectal cancer.

The paper develops a prognostic model for pancreatic ductal adenocarcinoma using meta-analysis of transcriptomic data, identifying gene pairs associated with early mortality after surgery.

This study quantifies genetic correlations between six solid cancers and other diseases using GWAS data, finding shared heritability and functional enrichment in regulatory regions.

The paper compares whole-genome data from primary pancreatic cancer, xenografts, and organoids to assess genetic concordance across different models.

DriverPower identifies cancer driver mutations by combining mutational burden and functional impact scores, outperforming existing methods in coding and non-coding driver discovery.