bioRxiv Science⌕ Search

Biology subjects

Benson, J. A.

Publications and source records attributed to Benson, J. A..

2 recordsLinked to original sources

Multi-Omics Analysis of Fibroblasts from the Invasive Tumor Edge Reveals that Tumor-Stroma Crosstalk Induces O-glycosylation of the CDK4-pRB Axis

The invasive leading edge represents a potential gateway for tumor invasion. We hypothesize that crosstalk between tumor and stromal cells within the tumor microenvironment (TME) results in the activation of key biological pathways depending on their location in the tumor (edge vs core). Here, we highlight phenotypic differences between Tumor-Adjacent-Fibroblasts (TAFs) from the invasive edge and Cancer-Associated Fibroblasts (CAFs) from the tumor core, established from human lung adenocarcinomas. We use an innovative multi-omics approach that includes genomics, proteomics and, O-glycoproteomics to characterize crosstalk between TAFs and cancer cells. Our analysis shows that O-glycosylation, an essential post-translational modification resulting from sugar metabolism, alters key biological pathways including the CDK4-pRB axis in the stroma, and indirectly modulates pro-invasive features of cancer cells. In summary, aside from improving the efficacy of CDK4 inhibitors anti-cancer agents, the O-glycoproteome poses a new consideration for important biological processes involved in tumor-stroma crosstalk.

cancer biology↗

Mapping Lung Cancer Epithelial-Mesenchymal Transition States and Trajectories with Single-Cell Resolution

Elucidating a continuum of epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET) states in clinical samples promises new insights in cancer progression and drug response. Using mass cytometry time-course analysis, we resolve lung cancer EMT states through TGF{beta}-treatment and identify through TGF{beta}-withdrawal, an MET state previously unrealized. We demonstrate significant differences between EMT and MET trajectories using a novel computational tool (TRACER) for reconstructing trajectories between cell states. Additionally, we construct a lung cancer reference map of EMT and MET states referred to as the EMT-MET STAte MaP (STAMP). Using a neural net algorithm, we project clinical samples onto the EMT-MET STAMP to characterize their phenotypic profile with single-cell resolution in terms of our in vitro EMT-MET analysis. In summary, we provide a framework that can be extended to phenotypically characterize clinical samples in the context of in vitro studies showing differential EMT-MET traits related to metastasis and drug sensitivity.

systems biology↗