Mathieu Lupien

This paper identifies KDM6B interacting with nucleo-adhesome proteins CSRP2 and TGFB1I1 to regulate EMT-related genes in lung cancer cells.

The paper identifies a subgroup of mCRPC characterized by LINE1 repeat DNA elements flanking the AR gene, which drive AR overexpression and confer resistance to androgen signaling inhibitors.

iSHARC is a pipeline for analyzing scMultiome data, enabling quality control, integration, clustering, annotation, and regulatory analysis in cancer studies.

This study identifies distinct gene regulatory networks in hematopoietic stem cells and leukemia stem cells using integrated gene expression and chromatin accessibility data from AML patients and human umbilical cord blood.

The paper identifies two distinct leukemia stem cell subtypes with unique transcriptional, epigenetic, and functional properties using a patient-derived cell model and single-cell analysis.

The paper develops a method to simulate cell-free chromatin for epigenetic biomarker discovery, showing it better reflects tissue chromatin features than plasma-derived samples.

Copy-scAT deconvolutes single-cell chromatin accessibility to infer genetic subclones in cancer, revealing how genetics influence chromatin profiles and subclonal phenotypes.

SMuRF identifies genomic regions enriched for somatic point mutations in user-defined intervals using SNVs from liver cancer data.

C3D predicts 3D genomic interactions between cis-regulatory elements using DNA accessibility correlations, achieving 32.7% accuracy in K562 cells.

This study identifies metabolic adaptations in taxane-resistant triple-negative breast cancer cells that lead to epigenetic vulnerabilities, specifically through altered methionine metabolism and H3K27me3 redistribution.

CREAM identifies COREs from cis-regulatory data, outperforming ROSE, and shows their enrichment near essential genes and TAD boundaries.

The paper identifies gene isoform-based biomarkers predictive of drug response in breast cancer using pharmacogenomic data from CCLE and GDSC.

The paper identifies driver genes in prostate cancer by analyzing convergence of non-coding mutations in regulatory regions using 3D chromatin interactions and genomic data.

Variant Set Enrichment identifies disease-associated functional genomic regions by analyzing the enrichment of variants in annotated regions.