Biology subjects

Wolfgang, C. L.

Publications and source records attributed to Wolfgang, C. L..

4 recordsLinked to original sources

bioRxiv2025

SPINK1-COL18A1 Crosstalk Shapes Epigenome and Drives Cancer Stemness in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a devastating cancer with an increasing incidence and extremely dismal prognosis. Discovery and mechanistic understanding of the genetic and epigenetic drivers of PDAC is therefore of critical importance. Here, we uncover serine protease inhibitor Kazal type 1 (SPINK1) as a putative determinant of clinical progression and demonstrate that it can have a non-canonical function as a regulator of epigenomic states of PDAC cells and associated extensive changes in gene expression. We show that SPINK1 expression, which varies in PDAC, is associated with key aggressive phenotypic cancer states and is correlated with the expression of stemness markers both in vitro and in patient samples. Mechanistically, our results strongly suggest that SPINK1 acts through a new signaling axis, by interacting with COL18A1 in the Golgi apparatus, promoting endostatin release and eventually inducing extensive histone H3 modifications. These results reveal a new function of SPINK1 in PDAC and suggest a potential therapeutic route to combat PDAC aggressiveness by targeting the SPINK1-COL18A1-endostatin signaling.

cancer biology↗

bioRxiv2022

Inflammatory Signaling and Fibroblast-Cancer Cell Interactions Transfer from a Harmonized Human Single-cell RNA Sequencing Atlas of Pancreatic Ductal Adenocarcinoma to Organoid Co-Culture

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy characterized by a heterogeneous tumor microenvironment (TME) that is enriched with cancer associated fibroblasts (CAFs)1. Cell-cell interactions involving these CAFs promote an immunosuppressive phenotype with altered inflammatory gene expression. While single-cell transcriptomics provides a tool to dissect the complex intercellular pathways that regulate cancer-associated inflammation in human tumors, complementary experimental systems for mechanistic validation remain limited. This study integrated single-cell data from human tumors and novel organoid co-cultures to study the PDAC TME. We derived a comprehensive atlas of PDAC gene expression from six published human single-cell RNA sequencing (scRNA-seq) datasets2-7 to characterize intercellular signaling pathways between epithelial tumor cells and CAFs that regulate the inflammatory TME. Analysis of the epithelial cell compartment identified global gene expression pathways that modulate inflammatory signaling and are correlated with CAF composition. We then generated patient-derived organoid-CAF co-cultures to serve as a biological model of the cellular interactions learned from human tissue in the atlas. Transfer learning analysis to additional scRNA-seq data of this co-culture system and mechanistic experiments confirmed the epithelial response to fibroblast signaling. This bidirectional approach of complementary computational and in vitro applications provides a framework for future studies identifying important mechanisms of intercellular interactions in PDAC.

cancer biology↗

bioRxiv2019

Assessing aneuploidy with repetitive element sequencing.

We report a sensitive PCR-based assay that can detect aneuploidy in samples containing as little as 3 picograms of DNA. Using a single primer pair, we amplified [~]750,000 amplicons distributed throughout the genome Aneuploidy was detected in 49% of liquid biopsies from a total of 883 non-metastatic cancers of eight different types. Combining aneuploidy with somatic mutation detection and eight standard protein biomarkers yielded a median sensitivity of 80% at 99% specificity.

cancer biology↗

bioRxiv2017

Computational discovery of tissue morphology biomarkers in very long-term survivors with pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest forms of cancer, with an average 5-year survival rate of only 8%. Within PDAC patients, however, there is a small subset of patients who survive >10 years. Deciphering underlying reasons behind prolonged survival could potentially provide new opportunities to treat PDAC; however, no genomic, transcriptomic, proteomic, or clinical signatures have been found to robustly separate this subset of patients. Digital pathology, in combination with machine learning, provides an opportunity to computationally search for tissue morphology patterns associated with disease outcomes. Here, we developed a computational framework to analyze whole-slide images (WSI) of PDAC patient tissue and identify tissue-morphology signatures for very long term surviving patients. Our results indicate that less tissue morphology heterogeneity is significantly linked to better patient survival and that the extra-tumoral space encodes prognostic information for survival. Based on information from morphological heterogeneity in the tumor and its adjacent area, we established a machine learning model with an AUC of 0.94. Our analysis workflow highlighted a quantitative visual-based tissue phenotype analysis that also allows direct interaction with pathology. This study demonstrates a pathway to accelerate the discovery of undetermined tissue morphology associated with pathogenesis states and prognosis and diagnosis of patients by utilizing new computational approaches.

bioinformatics↗