Biology subjects

Yeh, J. J.

Publications and source records attributed to Yeh, J. J..

13 recordsLinked to original sources

bioRxiv2026

NEIL2 regulates RNA polymerase II dynamics to suppress transcription replication conflicts in pancreatic cancer

Transcription replication conflicts (TRCs) arise from spatiotemporal interference between replication and transcription machinery and contribute to genome instability in cancer cells. In pancreatic ductal adenocarcinoma (PDAC) cells, oncogenic KRAS drives TRC formation, and base excision repair (BER) contributes to TRC resolution as an adaptive response. Here, we show that TRCs are enriched in pancreatic tumor sections from patients and are associated with malignant progression. We identify NEIL2, a bifunctional DNA glycosylase in the BER pathway, as a critical regulator of TRCs in PDAC cells. Mechanistically, NEIL2 loss causes aberrant RNA polymerase II (RNAPII) occupancy at transcription start sites and downstream of transcription end sites. Dysregulated RNAPII dynamics promote R-loop-dependent and -independent TRCs contributing to replication stress, DNA damage, and impaired cancer cell proliferation. Notably, the DNA-binding motif of NEIL2 necessary for canonical BER function is dispensable for TRC regulation. Together, these findings reveal an unrecognized role for NEIL2 in regulating RNAPII dynamics and mitigating TRCs in pancreatic cancer cells.

molecular biology↗

bioRxiv2026

Context-specific genetic interaction mapping reveals combinatorial KRAS/FGFR dependence in pancreatic cancer

Oncogenic KRAS drives >90% of pancreatic ductal adenocarcinoma (PDAC), and the pan-RAS inhibitor daraxonrasib doubles overall survival, but adaptive and acquired resistance limit the depth and duration of responses. To systematically map combination vulnerabilities, we developed RAS+, a digenic CRISPR knockout library testing 10,296 pairwise perturbations across oncogenic signaling pathways, and screened 12 cell lines, revealing genotype- and lineage-specific interactions. In KRASmut PDAC, the most differentially effective gene pair was KRAS and FRS2, an adapter coupling FGFR signaling to RAS. Combined KRAS/pan-FGFR inhibition was synergistic and cytotoxic, eradicating tumor cells and overcoming resistance in vitro and deepening and prolonging regressions in vivo. Single-cell analysis identified cancer-associated fibroblasts (CAFs) as a primary source of FGF ligands in PDAC, and CAF conditioned media or individual FGFs promoted resistance. These findings define a stroma-to-tumor adaptive FGFR circuit limiting the effects of KRAS inhibition. Thus, combined KRAS/FGFR blockade may be a strategy to improve responses.

cancer biology↗

bioRxiv2026

Vulnerability of triple negative breast cancer to daraxonrasib pan-RAS(on) inhibition

Triple-Negative Breast Cancer (TNBC) is a clinical subtype where aberrant expression of receptor tyrosine kinases (RTKs) leads to the activation of the RAS-RAF-MEK-ERK pathway contributing to tumor progression. Therapies targeting components of the RAF-MEK-ERK pathway in TNBC have been disappointing due to rapid onset of adaptive reprogramming of the RTK landscape leading to pathway reactivation. To overcome the dynamic heterogeneity of RTK expression and their convergence in activating RAS, treatment with the pan-RAS(on) inhibitor daraxonrasib (RMC-6236), which inhibits RAS-GTP regulation of RAF kinases, inhibited proliferation independent of TNBC kinotype or oncogenic RAS mutation in three of four TNBC cell lines in vitro and two patient-derived TNBC xenografts in mice. Daraxonrasib in combination with the MEK inhibitor trametinib prevented reactivation of the RAF-MEK-ERK pathway observed with each inhibitor alone and further enhanced inhibition of tumor growth in TNBC cell lines and xenografts. Inhibiting pan-RAS(on) RAS-GTP, with daraxonrasib, alone or in combination with a MEK inhibitor, provides a new strategy for the treatment of TNBC that inhibits RTK activation of the RAS-RAF-MEK-ERK pathway and tumor proliferation.

cancer biology↗

bioRxiv2025

S3R: Spatially Smooth and Sparse Regression Reveals High-Dimensional Regulatory Networks in Spatial Transcriptomics

Spatial transcriptomics (ST) data demands models that recover how associations among molecular and cellular features change across tissue while contending with noise, collinearity, cell mixing, and thousands of predictors. We present Spatially Smooth Sparse Regression (S3R), a general statistical framework that estimates location-specific coefficients linking a response feature to high-dimensional spatial predictors. S3R unites structured sparsity with a minimum-spanning-tree-guided smoothness penalty, yielding coefficient fields that are coherent within neighborhoods yet permit sharp boundaries. In synthetic data, S3R accurately recovers spatially varying effects, selects relevant predictors, and preserves known boundaries. Applied to Visium-based ST data, S3R recapitulates layer-specific target-TF associations in human dorsolateral prefrontal cortex with concordant layer-wise correlations in matched single-cell data. In acute Haemophilus ducreyi skin infection, S3R converts spot-level gene expression mixtures into cell type-attributed expression fields, revealing per-cell type spatial gradients, and improving concordance of spatially variable gene calls when tests are applied to these demixed fields. In pancreatic ductal adenocarcinoma, S3R builds cross-cell-type, cross-gene co-variation tensors that quantify cell-cell interaction strength at gene-pair resolution and nominate interacting genes whose pathway enrichments align with established stromal-epithelial and immune crosstalk. An efficient implementation scales to large assays, and on a Xenium-based breast cancer dataset, S3R delineates the contributions at gene-gene, local neighborhood, and global context-level to target gene expression. Because responses and predictors in S3R are user-defined, it could flexibly address diverse biological questions within a single, scalable, and interpretable regression framework.

bioinformatics↗

bioRxiv2025

PCSK9 dependent cholesterol acquisition and utilization underlies metastatic organ preference in pancreatic cancer.

To grow at distant sites, metastatic cells must overcome major challenges posed by the unique cellular and metabolic composition of secondary organs1. Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease that metastasizes to the liver and lungs. Despite evidence of metabolic reprogramming away from the primary site, the key drivers that dictate the ability of PDAC cells to colonize the liver or lungs and survive there are undefined. We identified Proprotein Convertase Subtilisin/Kexin Type 9 (PCSK9) as predictive of colonization of the liver versus lungs by integrating datasets describing the metastatic tropism of human PDAC cell lines2, with in vivo metastasis modeling in mice and gene expression correlation analysis. PCSK9 is a negative regulator of low density lipoprotein(LDL)-cholesterol import and, accordingly, PCSK9-low PDAC cells exhibit strong preference for colonizing LDL-rich liver tissue. Cholesterol taken up by liveravid PCSK9-low cells is converted into the signaling oxysterol, 24(S)-hydroxycholesterol, which reprograms the surrounding microenvironment to induce nutrient release from neighboring hepatocytes. Conversely, PCSK9-high, lung-avid PDAC cells rely on transcriptional upregulation of the distal cholesterol synthesis pathway to generate intermediates, chiefly, 7dehydrodesmosterol and 7-dehydrocholesterol, with protective action against ferroptosis, a vulnerability in the oxygen-rich microenvironment of the lung. Increasing PCSK9 levels redirected liver-avid cells to the lung whereas its ablation drove lung-avid cells to the liver, thereby establishing PCSK9 as necessary and sufficient for secondary organ site preference. Our studies reveal PCSK9-driven differential utilization of the distal cholesterol synthesis pathway as a key and potentially actionable driver of metastatic growth in PDAC.

cancer biology↗

bioRxiv2024

Quantitative proteomic mass spectrometry of protein kinases to determine dynamic heterogeneity of the human kinome

The kinome is a dynamic system of kinases regulating signaling networks in cells and dysfunction of protein kinases contributes to many diseases. Regulation of the protein expression of kinases alters cellular responses to environmental changes and perturbations. We configured a library of 672 proteotypic peptides to quantify >300 kinases in a single LC-MS experiment using ten micrograms protein from human tissues including biopsies. This enables absolute quantitation of kinase protein abundance at attomole-femtomole expression levels, requiring no kinase enrichment and less than ten micrograms of starting protein from flash-frozen and formalin fixed paraffin embedded tissues. Breast cancer biopsies, organoids, and cell lines were analyzed using the SureQuant method, demonstrating the heterogeneity of kinase protein expression across and within breast cancer clinical subtypes. Kinome quantitation was coupled with nanoscale phosphoproteomics, providing a feasible method for novel clinical diagnosis and understanding of patient kinome responses to treatment.

biochemistry↗

bioRxiv2024

nSMase2-mediated exosome secretion shapes the tumor microenvironment to immunologically support pancreatic cancer

The pleiotropic roles of nSMase2-generated ceramide include regulation of intracellular ceramide signaling and exosome biogenesis. We investigated the effects of eliminating nSMase2 on early and advanced PDA, including its influence on the microenvironment. Employing the KPC mouse model of pancreatic cancer, we demonstrate that pancreatic epithelial nSMase2 ablation reduces neoplasia and promotes a PDA subtype switch from aggressive basal-like to classical. nSMase2 elimination prolongs survival of KPC mice, hinders vasculature development, and fosters a robust immune response. nSMase2 loss leads to recruitment of cytotoxic T cells, N1-like neutrophils, and abundant infiltration of anti-tumorigenic macrophages in the pancreatic preneoplastic microenvironment. Mechanistically, we demonstrate that nSMase2-expressing PDA cell small extracellular vesicles (sEVs) reduce survival of KPC mice; PDA cell sEVs generated independently of nSMase2 prolong survival of KPC mice and reprogram macrophages to a proinflammatory phenotype. Collectively, our study highlights previously unappreciated opposing roles for exosomes, based on biogenesis pathway, during PDA progression. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=189 SRC="FIGDIR/small/614610v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@b6e6d1org.highwire.dtl.DTLVardef@1ebb41eorg.highwire.dtl.DTLVardef@17e4275org.highwire.dtl.DTLVardef@3d31f1_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2024

Determination of permissive and restraining cancer-associated fibroblast (DeCAF) subtypes

Cancer-associated fibroblast (CAF) subpopulations in pancreatic ductal adenocarcinoma (PDAC) have been identified using single-cell RNA sequencing (scRNAseq) with divergent characteristics, but their clinical relevance remains unclear. We translate scRNAseq-derived CAF cell-subpopulation-specific marker genes to bulk RNAseq data, and develop a single- sample classifier, DeCAF, for the classification of clinically restraining and permissive CAF subtypes. We validate DeCAF in 19 independent bulk transcriptomic datasets across four tumor types (PDAC, mesothelioma, bladder and renal cell carcinoma). DeCAF subtypes have distinct histology features, immune landscapes, and are prognostic and predict response to therapy across cancer types. We demonstrate that DeCAF is clinically replicable and robust for the classification of CAF subtypes in patients for multiple tumor types, providing a better framework for the future development and translation of therapies against permissive CAF subtypes and preservation of restraining CAF subtypes. SignificanceWe introduce a replicable and robust classifier, DeCAF, that delineates the significance of the role of permissive and restraining CAF subtypes in cancer patients. DeCAF is clinically tractable, prognostic and predictive of treatment response in multiple cancer types and lays the translational groundwork for the preclinical and clinical development of CAF subtype specific therapies.

genomics↗

bioRxiv2022

Integrated Single-Dose Kinome Profiling Data is Predictive of Cancer Cell Line Sensitivity to Kinase Inhibitors

Protein kinase activity forms the backbone of cellular information transfer, acting both individually and as part of a broader network, the kinome. Correspondingly, their central role in signaling implicates kinome dysfunction as a common driver of cancer, where numerous kinases have been identified as having a causal or modulating role in cancer development and progression. Driven by their importance, the development of therapies targeting kinases has rapidly grown, with over 70 kinase inhibitors approved for use in the clinic and over double this number currently in clinical trials. Given the growing importance of kinase-targeted therapies, linking the relationship between kinase inhibitor treatment and their effects on downstream cellular phenotype is of clear importance for understanding treatment mechanisms and streamlining compound screening in therapy development. In this work, we combine two large-scale kinome profiling data sets and use them to link inhibitor-kinome interactions with cell line treatment responses (AUC/IC50). We then built computational models on this data set that achieve a high degree of prediction accuracy (R2 of 0.7 and RMSE of 0.9), and were able to identify a set of well-characterized and understudied kinases that significantly affect cell responses. Further, we validated these models experimentally by testing predicted effects in breast cancer cell lines, and extended the model scope by performing additional validation in patient-derived pancreatic cancer cell lines. Overall, these results demonstrate that broad quantification of kinome inhibition state is highly predictive of downstream cellular phenotypes.

bioinformatics↗

bioRxiv2021

Sub-Cluster Identification through Semi-SupervisedOptimization of Rare-cell Silhouettes (SCISSORS) in Single-Cell Sequencing

Single-cell RNA-sequencing (scRNA-seq) has enabled the molecular profiling of thousands to millions of cells simultaneously in biologically heterogenous samples. Currently, common practice in scRNA-seq is to determine cell type labels through unsupervised clustering and the examination of cluster-specific genes. However, even small differences in analysis and parameter choice can greatly alter clustering solutions and thus impose great influence on which cell types are identified. Existing methods largely focus on determining the optimal number of robust clusters, which is not favorable for identifying cells of extremely low abundance due to their subtle contributions towards overall patterns of gene expression. Here we present a carefully designed framework, SCISSORS, which accurately profiles subclusters within major cluster(s) for the identification of rare cell types in scRNA-seq data. SCISSORS employs silhouette scoring for the estimation of heterogeneity of clusters and reveals rare cells in heterogenous clusters by implementing a multi-step, semi-supervised reclustering process. Additionally, SCISSORS provides a method for the identification of marker genes of rare cells, which may be used for further study. SCISSORS is wrapped around the popular Seurat R package and can be easily integrated into existing Seurat pipelines. SCISSORS, including source code and vignettes for two example datasets, is freely available at https://github.com/jrleary/SCISSORS.

bioinformatics↗

bioRxiv2021

Kinome state is predictive of cell viability in pancreatic cancer tumor and stroma cell lines

Numerous aspects of cellular signaling are regulated by the kinome - the network of over 500 protein kinases that guides and modulates information transfer throughout the cell. The key role played by both individual kinases and assemblies of kinases organized into functional subnetworks leads to kinome dysregulation being a key driver of many diseases, particularly cancer. In the case of pancreatic ductal adenocarcinoma (PDAC), a variety of kinases and associated signaling pathways have been identified for their key role in the establishment of disease as well as its progression. However, the identification of additional relevant therapeutic targets has been slow and is further confounded by interactions between the tumor and the surrounding tumor microenvironment. Fundamentally, it is an open question as to the degree to which knowledge of the state of the kinome at the protein level is able to provide insight into the downstream phenotype of the cell. In this work, we attempt to link the state of the kinome, or kinotype, with cell viability in representative PDAC tumor and stroma cell lines. Through the application of both regression and classification models to independent kinome perturbation and kinase inhibitor cell screen data, we find that the inferred kinotype of a cell has a significant and predictive relationship with cell viability. While regression models perform poorly, we find that classification approaches are able to predict drug viability effects. We further find that models are able to identify a set of kinases whose behavior in response to perturbation drive the majority of viability responses in these cell lines. Using the models to predict new compounds with cell viability effects and not in the initial data set, we conducted a validation screen that confirmed the accuracy of the models. These results suggest that characterizing the state of the protein kinome provides significant opportunity for better understanding signaling behavior and downstream cell phenotypes, as well as providing insight into the broader design of potential therapy design for PDAC.

bioinformatics↗

bioRxiv2020

Single cell transcriptome analysis defines novel heterogeneity within the pancreatic ductal tree

Lineage tracing using genetically engineered mouse models is an essential tool for investigating cell-fate decisions of progenitor cells and biology of mature cell types, with relevance to physiology and disease progression. To study disease development, an inventory of an organs cell types and understanding of physiologic function is paramount. Here, we performed single-cell RNA sequencing to examine heterogeneity of murine pancreatic duct cells, pancreatobiliary cells, and intrapancreatic bile duct cells. We describe an epithelial-mesenchymal transitory axis in our three pancreatic duct subpopulations and identify SPP1 as a regulator of this fate decision as well as human duct cell de-differentiation. Our results further identify functional heterogeneity within pancreatic duct subpopulations by elucidating a role for Geminin in accumulation of DNA damage in the setting of chronic pancreatitis. Our findings implicate diverse functional roles for subpopulations of pancreatic duct cells in maintenance of duct cell identity and disease progression and establish a comprehensive road map of murine pancreatic duct cell, pancreatobiliary cell, and intrapancreatic bile duct cell homeostasis. SIGNIFICANCEMurine models are extensively used for pancreatic lineage tracing experiments and investigation of pancreatic disease progression. Here, we describe the transcriptome of murine pancreatic duct cells, intrapancreatic bile duct cells, and pancreatobiliary cells at single cell resolution. Our analysis defines novel heterogeneity within the pancreatic ductal tree and supports the paradigm that more than one population of pancreatic duct cells harbors progenitor capacity. We identify and validate unique functional properties of subpopulations of pancreatic duct cells including an epithelial-mesenchymal transcriptomic axis and roles in chronic pancreatic inflammation.

bioinformatics↗

bioRxiv2019

De novo compartment deconvolution and weight estimation of tumor samples (DECODER)

Tumors are mixtures of different compartments. While global gene expression analysis profiles the average expression of all compartments in a sample, identifying the specific contribution of each compartment remains a challenge. With the increasing recognition of the importance of non-neoplastic components, the ability to breakdown the gene expression contribution of each is critical. To this end, we developed DECODER, an integrated framework which performs de novo deconvolution, and compartment weight estimation for a single sample. We use DECODER to deconvolve 33 TCGA tumor RNA-seq datasets and show that it may be applied to other data types including ATAC-seq. We demonstrate that it can be utilized to reproducibly estimate cellular compartment weights in pancreatic cancer that are clinically meaningful. Application of DECODER across cancer types advances the capability of identifying cellular compartments in an unknown sample and may have implications for identifying the tumor of origin for cancers of unknown primary.

bioinformatics↗