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Gokmen-Polar, Y.

Publications and source records attributed to Gokmen-Polar, Y..

2 recordsLinked to original sources

GPNMB overexpression- a marker of resistance to CDK4/6 inhibitors

Resistance to cyclin-dependent kinase 4/6 inhibitors remains a major clinical challenge in treating estrogen receptor-positive breast cancer, with no reliable predictive biomarkers currently available for patient selection. To investigate resistance mechanisms, we generated drug-tolerant persisters (DTPs) to abemaciclib and palbociclib in a panel of estrogen receptor-positive breast cancer cell lines. Functional analyses revealed that DTPs showed resistance to CDK4/6 inhibition, maintained G1 arrest, and exhibited increased senescence phenotype. To identify clinically relevant markers of resistance, we compared transcriptomic profiles from DTPs with publicly available gene-expression data from the phase III PEARL trial. Glycoprotein non-metastatic B (GPNMB) emerged as one of the most strongly upregulated transcripts in DTPs, and also was amongst the genes associated with resistance in the PEARL dataset. We further verified that GPNMB overexpression (GPNMB-OE) in sensitive cells conferred resistance to CDK4/6 inhibition, and enhanced migratory capacity. Overexpression of GPNMB drove substantially faster tumor progression and eliminated the growth-inhibitory effect of abemaciclib, which remained highly effective in control tumors. Across all treatment arms, GPNMB-OE tumors failed to respond to CDK4/6 blockade, highlighting a strong resistance phenotype. These results identify GPNMB as a potent promoter of tumor progression and a key mediator of resistance to abemaciclib. Our findings position GPNMB as a potential biomarker and therapeutic target that may help identify patients unlikely to benefit from CDK4/6 inhibition.

cancer biology↗

MorphLink: Bridging Cell Morphological Behaviors and Molecular Dynamics in Multi-modal Spatial Omics

Multi-modal spatial omics data are invaluable for exploring complex cellular behaviors in diseases from both morphological and molecular perspectives. Current analytical methods primarily focus on clustering and classification, and do not adequately examine the relationship between cell morphology and molecular dynamics. Here, we present MorphLink, a framework designed to systematically identify disease-related morphological-molecular interplays. MorphLink has been evaluated across a wide array of datasets, showcasing its effectiveness in extracting and linking interpretable morphological features with various molecular measurements in multi-modal spatial omics analyses. These linkages provide a transparent depiction of cellular behaviors that drive transcriptomic heterogeneity and immune diversity across different regions within diseased tissues, such as cancer. Additionally, MorphLink is scalable and robust against cross-sample batch effects, making it an efficient method for integrative spatial omics data analysis across samples, cohorts, and modalities, and enhancing the interpretation of results for large-scale studies.

bioinformatics↗