Biology subjects

Wang, L.-J.

Publications and source records attributed to Wang, L.-J..

5 recordsLinked to original sources

bioRxiv2026

Co-repression of Yap1 and Sox9 Abrogates Established Cholangiocarcinoma by Eliminating Transcriptional Compensation

Background/AimsIntrahepatic cholangiocarcinoma (iCCA) represents an unmet clinical need due to its increasing incidence, aggressive biology, and limited treatment options. The extremely low-response rates to current systemic regimens and the emergence of adaptive resistance to targeted therapies underscore the urgent need for alternative therapeutic strategies. Given that the lineage-defining transcription factors SOX9 and YAP1 are central regulators of cholangiocyte and iCCA identity, we investigated their functional roles as potential therapeutic vulnerabilities across multiple preclinical models. MethodsPatient tissue-microarray (TMA) analysis, Sleeping-Beauty hydrodynamic tail vein injection-based iCCA models, and Cre-mediated inducible gene deletion systems were used to investigate the roles of Sox9 and Yap1. Deep-learning-based prediction, RNA-seq, ChIP-seq and immunohistochemistry analyses were performed to delineate transcriptional networks and downstream effectors associated with SOX9/YAP1 signaling. ResultsDual deletion of Sox9 and Yap1 effectively eradicated advanced iCCA while preserving intrahepatic bile ducts, regardless of oncogenic drivers. Mechanistically, SOX9 and YAP1 transcriptionally compensated for each other when one was absent, and ILF2, MGAT5, and WWTR1 were identified as key downstream effectors mediating this compensatory mechanism. Loss of Ilf2, Mgat5, or Taz suppressed iCCA, whereas overexpression of Ilf2 or Taz following Sox9/Yap1 co-deletion restored tumor development, indicating that ILF2 or TAZ can functionally substitute for YAP1 and SOX9 in sustaining iCCA. ConclusionsCo-targeting SOX9 and YAP1 offers a promising and safe broad-spectrum preventive/therapeutic approach for iCCA, potentially overcoming resistance to YAP1 inhibition. The adaptive resistance mechanism identified may extend to other malignancies, providing insights for addressing the advanced resistant to YAP1-TEAD-directed therapies.

cancer biology↗

bioRxiv2024

High-Throughput Empirical and Virtual Screening to Discover Novel Inhibitors of Polyploid Giant Cancer Cells in Breast Cancer

Therapy resistance in breast cancer is increasingly attributed to polyploid giant cancer cells (PGCCs), which arise through whole-genome doubling and exhibit heightened resilience to standard treatments. Characterized by enlarged nuclei and increased DNA content, these cells tend to be dormant under therapeutic stress, driving disease relapse. Despite their critical role in resistance, strategies to effectively target PGCCs are limited, largely due to the lack of high-throughput methods for assessing their viability. Traditional assays lack the sensitivity needed to detect PGCC-specific elimination, prompting the development of novel approaches. To address this challenge, we developed a high-throughput single-cell morphological analysis workflow designed to differentiate compounds that selectively inhibit non-PGCCs, PGCCs, or both. Using this method, we screened a library of 2,726 FDA Phase 1-approved drugs, identifying promising anti-PGCC candidates, including proteasome inhibitors, FOXM1, CHK, and macrocyclic lactones. Notably, RNA-Seq analysis of cells treated with the macrocyclic lactone Pyronaridine revealed AXL inhibition as a potential strategy for targeting PGCCs. Although our single-cell morphological analysis pipeline is powerful, empirically testing all existing compounds is impractical and inefficient. To overcome this limitation, we trained a machine learning model to predict anti-PGCC efficacy in silico, integrating chemical fingerprints and compound descriptions from prior publications and databases. The model demonstrated a high correlation with experimental outcomes and predicted efficacious compounds in an expanded library of over 6,000 drugs. Among the top-ranked predictions, we experimentally validated two compounds as potent PGCC inhibitors. These findings underscore the synergistic potential of integrating high-throughput empirical screening with machine learning-based virtual screening to accelerate the discovery of novel therapies, particularly for targeting therapy-resistant PGCCs in breast cancer.

bioengineering↗

bioRxiv2023

Sex-dependent Differences in the Genomic Profile of Lingual Sensory Neurons in Naive and Tongue-Tumor Bearing Mice

Mechanisms of sex-dependent orofacial pain are widely understudied. A significant gap in knowledge exists about comprehensive regulation of tissue-specific trigeminal sensory neurons in diseased state of both sexes. Using RNA sequencing of FACS sorted retro-labeled sensory neurons innervating tongue tissue, we determined changes in transcriptomic profiles in males and female mice under naive as well as tongue-tumor bearing conditions Our data revealed the following interesting findings: 1) Tongue tissue of female mice was innervated with higher number of trigeminal neurons compared to males; 2) Naive female neurons innervating the tongue exclusively expressed immune cell markers such as Csf1R, C1qa and others, that werent expressed in males. This was validated by Immunohistochemistry. 4) Accordingly, immune cell markers such as Csf1 exclusively sensitized TRPV1 responses in female TG neurons. 3) Male neurons were more tightly regulated than female neurons upon tumor growth and very few differentially expressed genes (DEGs) overlapped between the sexes, 5) Male DEGs contained higher number of transcription factors whereas female DEGs contained higher number of enzymes, cytokines and chemokines. Collectively, this is the first study to characterize the effect of sex as well as of tongue-tumor on global gene expression, pathways and molecular function of tongue-innervating sensory neurons.

neuroscience↗

bioRxiv2022

DepLink: an R Shiny app to systematically link genetic and pharmacologic dependencies of cancer

Large-scale genetic and pharmacologic dependency maps are generated to reveal genetic vulnerabilities and drug sensitivities of cancer. However, user-friendly software is needed to systematically link such maps. Here we present DepLink, an R Shiny server to identify genetic and pharmacologic perturbations that induce similar effects on cell viability or molecular changes. DepLink integrates heterogeneous datasets of genome-wide CRISPR loss-of-function screens, high-throughput pharmacologic screens, and perturbation expression signatures. The datasets are systematically connected by four complementary modules tailored for different query scenarios. In summary, DepLink enables easy navigation, visualization, and linkage of rapidly evolving cancer dependency maps.

bioinformatics↗

bioRxiv2019

An ancestral informative marker panel design for individual ancestry estimation of Hispanic population using whole exome sequencing data

BackgroundEuropeans and American Indians were major genetic ancestry of Hispanics in the U.S. In those ancestral groups, it has markedly different incidence rates and outcomes in many types of cancers. Therefore, the genetic admixture may cause biased genetic association study with cancer susceptibility variants specifically in Hispanics. The incidence rate and genetic mutational pattern of liver cancer have been shown substantial disparity between Hispanic, Asian and non-Hispanic white populations. Currently, ancestry informative marker (AIM) panels have been widely utilized with up to a few hundred ancestry-informative single nucleotide polymorphisms (SNPs) to infer ancestry admixture. Notably, current available AIMs are predominantly located in intron and intergenic regions, while the whole exome sequencing (WES) protocols commonly used in translational research and clinical practice do not contain these markers, thus, the challenge to accurately determine a patients admixture proportion without subject to additional DNA testing.\n\nMethodsHere we designed a bioinformatics pipeline to obtain an AIM panel. The panel infers 3-way genetic admixture from three distinct continental populations (African (AFR), European (EUR), and East Asian (EAS)) constraint within evolutionary-conserved exome regions. Briefly, we extract [~]1 million exonic SNPs from all individuals of three populations in the 1000 Genomes Project. Then, the SNPs were trimmed by their linkage disequilibrium (LD), restricted to biallelic variants only, and assembled as an AIM panel with the top ancestral informativeness statistics based on the In-statistic. The selected AIM panel was applied to training dataset and clinical dataset. Finally, The ancestral proportions of each individual was estimated by STRUCTURE.\n\nResultsIn this study, the optimally selected AIM panel with 250 markers, or the UT-AIM250 panel, was performed with better accuracy as one of the published AIM panels when we tested with 3 ancestral populations (Accuracy: 0.995 {+/-} 0.012 for AFR, 0.997 {+/-} 0.007 for EUR, and 0.994 {+/-} 0.012 for EAS). We demonstrated the utility of UT-AIM250 panel on the admixed American (AMR) of the 1000 Genomes Project and obtained similar results (AFR: 0.085 {+/-} 0.098; EUR: 0.665 {+/-} 0.182; and EAS 0.250 {+/-} 0.205) to previously published AIM panels (Phillips-AIM34: AFR: 0.096 {+/-} 0.127, EUR: 0.575 {+/-} 0.29; and EAS: 0.330 {+/-} 0.315; Wei-AIM278: AFR: 0.070 {+/-} 0.096, EUR: 0.537 {+/-} 0.267, and EAS: 0.393 {+/-} 0.300) with no significant difference (Pearson correlation, P < 10-50, n = 347 samples). Subsequently, we applied UT-AIM250 panel to clinical datasets of self-reported Hispanic patients in South Texas with hepatocellular carcinoma (26 patients). Our estimated admixture proportions from adjacent non-cancer liver tissue data of Hispanics in South Texas is (AFR: 0.065 {+/-} 0.043; EUR: 0.594 {+/-} 0.150; and EAS: 0.341 {+/-} 0.160), with smaller variation due to its unique Texan/Mexican American population in South Texas. Similar admixture proportion from the corresponding tumor tissue we also obtained. In addition, we estimated admixture proportions of entire TCGA-LIHC samples (376 patients) using UT-AIM250 panel. We demonstrated that our AIM panel estimate consistent admixture proportions from DNAs derived from tumor and normal tissues, and 2 possible incorrect reported race/ethnicity, and/or provide race/ethnicity determination if necessary.\n\nConclusionsTaken together, we demonstrated the feasibility of using evolutionary-conserved exome regions to distinguish genetic ancestry descendants based on 3 continental-ancestry proportion, provided a robust and reliable control for sample collection or patient stratification for genetic analysis. R implementation of UT-AIM250 is available at https://github.com/chenlabgccri/UT-AIM250.

bioinformatics↗