Biology subjects

Wu, E. Y.

Publications and source records attributed to Wu, E. Y..

4 recordsLinked to original sources

bioRxiv2024

Aberrant expression of collagen type X in solid tumor stroma is associated with EMT, immunosuppressive and pro-metastatic pathways, bone marrow stromal cell signatures, and poor survival prognosis

BackgroundCollagen type X (ColX1, encoded by COL10A1) is expressed specifically in the cartilage-to-bone transition, in bone marrow cells, and in osteoarthritic (OA) cartilage. We have previously shown that ColX1 is expressed in breast tumor stroma, correlates with tumor-infiltrating lymphocytes, and predicts poor adjuvant therapy outcomes in ER+/HER2+ breast cancer. However, the underlying molecular mechanisms for these effects are unknown. In this study, we performed bioinformatic analysis of COL10A1-associated gene modules in breast and pancreatic cancer as well as in cells from bone marrow and OA cartilage. These findings provide important insights into the mechanisms of transcriptional and extracellular matrix changes which impact the local stromal microenvironment and tumor progression. MethodsImmunohistochemistry was performed to examine collagen type X expression in solid tumors. WGCNA was used to generate COL10A1-associated gene networks in breast and pancreatic tumor cohorts using RNA-Seq data from The Cancer Genome Atlas. Computational analysis was employed to assess the impact of these gene networks on development and progression of cancer and OA. Data processing and statistical analysis was performed using R and various publicly-available computational tools. ResultsExpression of COL10A1 and its associated gene networks highlights inflammatory and immunosuppressive microenvironments, which identify aggressive breast and pancreatic tumors and contribute to metastatic potential in a sex-dependent manner. Both cancer types are enriched in stroma, and COL10A1 implicates bone marrow-derived fibroblasts as drivers of the epithelial-to-mesenchymal transition (EMT) in these tumors. Heightened expression of COL10A1 and its associated gene networks is correlated with poorer patient outcomes in both breast and pancreatic cancer. Common transcriptional changes and chondrogenic activity are shared between cancer and OA cartilage, suggesting that similar microenvironmental alterations may underlie both diseases. ConclusionsCOL10A1-associated gene networks may hold substantial value as regulators and biomarkers of aggressive tumor phenotypes with implications for therapy development and clinical outcomes. Identification of tumors which exhibit high expression of COL10A1 and its associated genes may reveal the presence of bone marrow-derived stromal microenvironments with heightened EMT capacity and metastatic potential. Our analysis may enable more effective risk assessment and more precise treatment of patients with breast and pancreatic cancer. Research HighlightsO_LIColX highlights features of EMT in breast and pancreatic cancer C_LIO_LIColX gene modules are immunosuppressive and pro-metastatic C_LIO_LIColX-associated gene networks contribute to sex differences in pancreatic cancer C_LIO_LIColX-positive fibroblasts define more aggressive tumors with poorer survival C_LIO_LIColX is emerging as a biomarker for bone marrow-derived cells in cancer C_LI

cancer biology↗

bioRxiv2023

Tree-based differential testing using inferentialuncertainty for RNA-Seq

Identifying differentially expressed transcripts poses a crucial yet challenging problem in transcriptomics. Substantial uncertainty is associated with the abundance estimates of certain transcripts which, if ignored, can lead to the exaggeration of false positives and, if included, may lead to reduced power. Given a set of RNA-Seq samples, TreeTerminus arranges transcripts in a hierarchical tree structure that encodes different layers of resolution for interpretation of the abundance of transcriptional groups, with uncertainty generally decreasing as one ascends the tree from the leaves. We introduce mehenDi, which utilizes the tree structure from TreeTerminus for differential testing. The nodes output by mehenDi, called the selected nodes are determined in a data-driven manner to maximize the signal that can be extracted from the data while controlling for the uncertainty associated with estimating the transcript abundances. The identified selected nodes can include transcripts and inner nodes, with no two nodes having an ancestor/descendant relationship. We evaluated our method on both simulated and experimental datasets, comparing its performance with other tree-based differential methods as well as with uncertainty-aware differential transcript/gene expression methods. Our method detects inner nodes that show a strong signal for differential expression, which would have been overlooked when analyzing the transcripts alone.

bioinformatics↗

bioRxiv2021

Somatic Mutations in Collagens are Associated with a Distinct Tumor Environment and Overall Survival in Gastric Cancer

BackgroundGastric cancer is a heterogeneous disease with poorly understood genetic and microenvironmental factors. Mutations in collagen genes are associated with genetic diseases that compromise tissue integrity, but their role in tumor progression has not been extensively reported. Aberrant collagen expression has been long associated with malignant tumor growth, invasion, chemoresistance, and patient outcomes. We hypothesized that somatic mutations in collagens could functionally alter the tumor extracellular matrix. MethodsWe used publicly available datasets including The Tumor Cancer Genome Atlas (TCGA) to interrogate somatic mutations in collagens in stomach adenocarcinomas. To demonstrate that collagens were significantly mutated above background mutation rates, we used a moderated Kolmogorov-Smirnov test along with combination analysis with a bootstrap approach to define the background accounting for mutation rates. Association between mutations and clinicopathological features was evaluated by Fisher or chi-squared tests. Association with overall survival was assessed by Kaplan-Meier and the Cox-Proportional Hazards Model. Gene Set Enrichment Analysis was used to interrogate pathways. Immunohistochemistry and in situ hybridization tested expression of COL7A1 in stomach tumors. ResultsIn stomach adenocarcinomas, we identified individual collagen genes and sets of collagen genes harboring somatic mutations at a high frequency compared to background in both microsatellite stable, and microsatellite instable tumors in TCGA. Many of the missense mutations resemble the same types of loss of function mutations in collagenopathies that disrupt tissue formation and destabilize cells providing guidance to interpret the somatic mutations. We identified combinations of somatic mutations in collagens associated with overall survival, with a distinctive tumor microenvironment marked by lower matrisome expression and immune cell signatures. Truncation mutations were strongly associated with improved outcomes suggesting that loss of expression of secreted collagens impact tumor progression and treatment response. Germline collagenopathy variants guided interpretation of impactful somatic mutations on tumors. ConclusionsThese observations highlight that many collagens, expressed in non-physiologically relevant conditions in tumors, harbor impactful somatic mutations in tumors, suggesting new approaches for classification and therapy development in stomach cancer. In sum, these findings demonstrate how classification of tumors by collagen mutations identified strong links between specific genotypes and the tumor environment. Research HighlightsCollagen mutations are prevalent in stomach cancer Collagen somatic missense mutations resemble collagenopathy mutations Collagen mutations associate with overall survival in stomach cancer Tumors with collagen mutations have distinct molecular pathways and tumor microenvironments

cancer biology↗

bioRxiv2020

NPC1-mTORC1 signaling Couples Cholesterol Sensing to Organelle Homeostasis and is a Targetable Pathway in Niemann-Pick type C

Lysosomes promote cellular homeostasis through macromolecular hydrolysis within their lumen and metabolic signaling by the mTORC1 kinase on their limiting membranes. Both hydrolytic and signaling functions require precise regulation of lysosomal cholesterol content. In Niemann-Pick type C (NPC), loss of the cholesterol exporter, NPC1, causes cholesterol accumulation within lysosomes, leading to mTORC1 hyperactivation, disrupted mitochondrial function and neurodegeneration. The compositional and functional alterations in NPC lysosomes, and how aberrant cholesterol-mTORC1 signaling contributes to organelle pathogenesis are not understood. Through proteomic profiling of NPC lysosomes, we find pronounced proteolytic impairment compounded with hydrolase depletion and enhanced membrane damage. Genetic and pharmacologic mTORC1 inhibition restores lysosomal proteolysis without correcting cholesterol storage, implicating aberrant mTORC1 as a pathogenic driver downstream of cholesterol accumulation. Consistently, mTORC1 inhibition ameliorates mitochondrial dysfunction in a neuronal model of NPC. Thus, cholesterol-mTORC1 signaling controls organelle homeostasis and is a targetable pathway in NPC.

cell biology↗