Ronglai Shen

This study shows that chromatin architecture and physical constriction together drive phenotype switching and metastasis in melanoma by altering CTCF positioning and heterochromatin structure.

This paper introduces BayesMAGPIE, a Bayesian method for identifying driver mutations in oncogenic pathways using mutual exclusivity, showing improved accuracy over MAGPIE in simulation and TCGA data.

The paper improves somatic mutation detection in tumor samples without matched normal samples by estimating tumor purity and variant allele frequency, reducing false positive and negative rates.

The paper introduces SpatialTopic, a Bayesian topic model for multiplexed imaging analysis that identifies spatial features and tracks their dynamics across disease progression.

CellGate automates spatial immunophenotyping of multiplexed tissue images with high accuracy, enabling scalable analysis of tumor-immune microenvironments.

The paper uses machine learning to identify cancer driver mutations, finding that methods incorporating protein structure or functional genomic data outperform those using only evolutionary data, and validates their predictions using real-world patient cohorts.

The paper identifies how germline and somatic mutations interact to cause resistance to CDK4/6 inhibitors in breast cancer and proposes a sequential treatment strategy using PARP inhibitors.

The paper introduces MAGPIE, a computational method to identify driver mutations in cancer pathways by analyzing mutual exclusivity patterns, and validates it on melanoma data.

The paper uses topic modeling to analyze T cell dynamics in cancer patients, identifying three latent topics associated with clinical outcomes and immune responses.

This paper introduces survClust, an outcome-weighted clustering algorithm that identifies clinically relevant genomic subtypes in pan-cancer data, outperforming unsupervised methods in detecting subpopulations with aggressive clinical behavior.