Bollig-Fischer, A. | Search bioRxiv

bioRxiv2022

Hyperglycemic O-GlcNAc transferase activity drives cancer stem cell induction in TNBC

Enhanced glucose metabolism is a feature of almost all cancers, but downstream functional effects of aberrant glucose flux are difficult to mechanistically determine. The objective of this study is to characterize a mechanism by which elevated glucose level drives a tumorigenic pathway in triple negative breast cancer (TNBC). We used chemical biology methods to track how a metabolite of glucose, N-acetylglucosamine (GlcNAc), is linked to the transcriptional regulatory protein tet-methylcytosine dioxygenase 1 (TET1) as an O-linked GlcNAc post translational modification (O-GlcNAc). In this work, we revealed that intracellular protein glycosylation by O-GlcNAc is driven by high glucose levels in TNBC models, including on TET1. A single enzyme, O-GlcNAc transferase (OGT), is responsible for catalyzing protein modification of O-GlcNAc. We showed that OGT activity is higher in TNBC cell lines compared to non-tumor breast cell lines and is associated with hyperglycemia. Furthermore, enhanced OGT activity activated a pathway for cancer stem-like cell (CSC) reprogramming in TNBC cells. In our model, O-GlcNAcylated TET1 upregulated expression of splicing factor TAR-DNA binding protein (TARDBP), which drives CSC induction as well as higher OGT levels. We show that this OGT-TET1-TARDBP axis "feeds-forward" in hyperglycemic conditions both in cell lines and diet-induced obese mice, which displayed higher blood glucose levels and tumor O-GlcNAc levels than lean littermates. This data converges on a novel pathway whereby hyperglycemia drives aberrant OGT activity, activating a pathway for CSC induction in TNBC. Our findings partially explain a key aspect of how obesity is associated with TNBC risk and negative outcomes. Statement of ImplicationThis work presents a novel mechanism to explain how obesity is a risk factor for triple-negative breast cancer via elevated sugar-transfer activity by O-GlcNAc transferase in hyperglycemia, leading to the induction of a cancer stem-like cells and revealing a targetable pathway in obesity-associated tumors.

cancer biology↗

bioRxiv2018

Computational Disease Subtyping based on Joint Analysis of Clinical and Genomic Data

ObjectiveTo investigate application of non-negative tensor decomposition for disease subtype discovery based on joint analysis of clinical and genomic data.\n\nData and MethodsSomatic mutation profiles including 11,996 genes of 503 breast cancer patients from the Cancer Genome Atlas (TCGA) along with 11 clinical variables and markers of these patients were used to construct a binary third-order tensor. CANDECOMP/PARAFAC method was applied to decompose the constructed tensor into rank-one component tensors. Definitions of breast cancer verotypes were constructed from the patient, gene and clinical vectors corresponding to each component tensor. Patient membership proportions in the identified verotypes were utilized in a Cox proportional hazards model to predict their survival.\n\nResultsQualitative evaluation of the verotypes obtained by tensor factorization indicates that they correspond to clinically meaningful breast cancer subtypes. While some components correspond to the known HER2- or ER-positive breast cancer subtypes, other components correspond to a variant of triple negative subtype and a cohort of patients with high mutation load of tumor suppressor genes. Quantitative evaluation indicates that the Cox model utilizing computationally discovered breast cancer verotypes is more accurate (AUC=0.5796) at predicting patient survival than the Cox models utilizing random patient membership proportions in cancer subtypes (AUC=0.4056) as well as patient membership proportions in genotypes (AUC=0.4731) and phenotypes (AUC=0.5047) obtained by non-negative factorization of the somatic mutation and clinical matrices.\n\nConclusionNon-negative factorization of a binary tensor constructed from clinical and genomic data enables high-throughput discovery of breast cancer verotypes that are effective at predicting patient survival.

bioinformatics↗