Biology subjects

Varley, K. E.

Publications and source records attributed to Varley, K. E..

8 recordsLinked to original sources

bioRxiv2026

Molecular heterogeneity and differential metabolic signatures in thymic adipocytes

Adipocyte depots throughout the body are physiologically and molecularly distinct. With age, adipocytes increase in and around aged thymi. However, thymic adipocytes completely lack molecular characterization. We developed and optimized methods to isolate adipocyte nuclei from mouse thymi of different ages and sexes. Single-nucleus multiomic analysis of male and female mice aged 4-9 months reveals that thymic adipocytes are heterogeneous, with at least two distinct populations. One subpopulation harbors a transcription and chromatin signature consistent with beige/brown fat. A larger subpopulation more strongly resembles classic white adipose tissue and expresses genes associated with epithelial-to-mesenchymal transition (EMT) and antigen presentation. Analysis of differentially open chromatin in the white compared to beige adipose population identifies binding sites for Foxn1 and HIF-1/Arnt, consistent with a situation in which thymic white adipose cells emerge from thymic epithelial cells, possibly under hypoxic conditions. Immunofluorescence microscopy confirmed the expression of UCP1 protein in cells within the thymic parenchyma, most prominently in subcapsular cortical regions. This resource reveals a complex milieu of thymic adipocytes and identifies multiple avenues for directly probing their ontogeny, dynamics and functional significance.

immunology↗

bioRxiv2024

A precision oncology-focused deep learning framework for personalized selection of cancer therapy

Precision oncology matches tumors to targeted therapies based on the presence of actionable molecular alterations. However, most tumors lack actionable alterations, restricting treatment options to cytotoxic chemotherapies for which few data-driven prioritization strategies currently exist. Here, we report an integrated computational/experimental treatment selection approach applicable for both chemotherapies and targeted agents irrespective of actionable alterations. We generated functional drug response data on a large collection of patient-derived tumor models and used it to train ScreenDL, a novel deep learning-based cancer drug response prediction model. ScreenDL leverages the combination of tumor omic and functional drug screening data to predict the most efficacious treatments. We show that ScreenDL accurately predicts response to drugs with diverse mechanisms, outperforming existing methods and approved biomarkers. In our preclinical study, this approach achieved superior clinical benefit and objective response rates in breast cancer patient-derived xenografts, suggesting that testing ScreenDL in clinical trials may be warranted.

cancer biology↗

bioRxiv2024

Dnmt3a-mediated de novo methylation balances Th1 and Tfh memory cell plasticity and functionality

Following acute viral infection, naive CD4+ T cells differentiate into T follicular helper (Tfh) and T helper 1 (Th1) cells that generate long-lived memory cells. However, it is unclear how memory Tfh and Th1 cells maintain their lineage commitment. We demonstrate that Tfh and Th1 lineages acquire distinct Dnmt3a-dependent de novo DNA methylation programs that are preserved into memory. Dnmt3a deletion impairs lineage commitment and functionality of memory Th1 and Tfh cells, resulting in aberrant Runx1 upregulation that represses germinal center Tfh cell differentiation. In contrast, transient pharmacological DNA methyltransferase inhibition during priming impairs repression of Tfh-associated genes while properly silencing Runx1, and results in enhanced Tfh cell functionality in primary and secondary responses to viral infections. Together, these findings demonstrate that Dnmt3a-mediated epigenetic programing is required to enforce T helper lineage commitment and preserve Tfh and Th1-specific functions during the recall response to infection, and reveal novel strategies to improve long-lived adaptive immunity against infectious diseases. SUMMARYThis article demonstrates that Dnmt3a-dependent epigenetic programing regulates functionality and plasticity of Th1 and Tfh memory cells. Furthermore, early pharmacological inhibition of such programing enhances GC Tfh cell differentiation, suggesting novel strategies for modulating the immune response to viral infections.

immunology↗

bioRxiv2024

3D genomic analysis reveals novel enhancer-hijacking caused by complex structural alterations that drive oncogene overexpression

Cancer genomes are composed of many complex structural alterations on chromosomes and extrachromosomal DNA (ecDNA), making it difficult to identify non-coding enhancer regions that are hijacked to activate oncogene expression. Here, we describe a 3D genomics-based analysis called HAPI (Highly Active Promoter Interactions) to characterize enhancer hijacking. HAPI analysis of HiChIP data from 34 cancer cell lines identified enhancer hijacking events that activate both known and potentially novel oncogenes such as MYC, CCND1, ETV1, CRKL, and ID4. Furthermore, we found enhancer hijacking among multiple oncogenes from different chromosomes, often including MYC, on the same complex amplicons such as ecDNA. We characterized a MYC-ERBB2 chimeric ecDNA, in which ERBB2 heavily hijacks MYCs enhancers. Notably, CRISPRi of the MYC promoter led to increased interaction of ERBB2 with MYC enhancers and elevated ERBB2 expression. Our HAPI analysis tool provides a robust strategy to detect enhancer hijacking and reveals novel insights into oncogene activation.

cancer biology↗

bioRxiv2022

TXNIP loss expands Myc-dependent transcriptional programs by increasing Myc genomic binding

c-Myc protooncogene places a demand on glucose uptake to drive glucose-dependent biosynthetic pathways. To achieve this demand, c-Myc protein (Myc henceforth) drives the expression of glucose transporters and represses the expression of Thioredoxin Interacting Protein (TXNIP), which is a potent negative regulator of glucose uptake. A Mychigh/TXNIPlow gene signature is clinically significant as it correlates with poor clinical prognosis in Triple-Negative Breast Cancer (TNBC) but not in other subtypes of breast cancer. To better understand how TXNIP function contributes to the aggressive behavior of TNBC, we generated TXNIP null MDA-MB-231 (231:TKO) cells for our study. We show here that TXNIP loss drives a transcriptional program that resembles those driven by Myc and increases global Myc genome occupancy. TXNIP loss allows Myc to invade the promoters and enhancers of target genes that are potentially relevant to cell transformation. Together, these findings suggest that TXNIP is a broad repressor of Myc genomic binding. The increase in Myc genomic binding in the 231:TKO cells expands the Myc-dependent transcriptome we identified in parental MDA-MB-231 cells. This expansion of Myc-dependent transcription following TXNIP loss occurs without an apparent increase in Mycs intrinsic capacity to activate transcription and without increasing Myc levels. Together, our findings suggest that TXNIP loss mimics Myc overexpression, connecting Myc genomic binding and transcriptional programs to the metabolic signals that control TXNIP expression.

cancer biology↗

bioRxiv2021

A breast cancer patient-derived xenograft and organoid platform for drug discovery and precision oncology

Model systems that recapitulate the complexity of human tumors and the reality of variable treatment responses are urgently needed to better understand cancer biology and to develop more effective cancer therapies. Here we report development and characterization of a large bank of patient-derived xenografts (PDX) and matched organoid cultures from tumors that represent some of the greatest unmet needs in breast cancer research and treatment. These include endocrine-resistant, treatment-refractory, and metastatic breast cancers and, in some cases, multiple tumor collections from the same patients. The models can be grown long-term with high fidelity to the original tumors. We show that development of matched PDX and PDX-derived organoid (PDxO) models facilitates high-throughput drug screening that is feasible and cost-effective, while also allowing in vivo validation of results. Our data reveal consistency between drug screening results in organoids and drug responses in breast cancer PDX. Moreover, we demonstrate the feasibility of using these patient-derived models for precision oncology in real time with patient care, using a case of a triple negative breast cancer with early metastatic recurrence as an example. Our results uncovered an FDA-approved drug with high efficacy against the models. Treatment with the PDxO-directed therapy resulted in a complete response for the patient and a progression-free survival period more than three times longer than her previous therapies. This work provides valuable new methods and resources for functional precision medicine and drug development for human breast cancer. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=189 HEIGHT=200 SRC="FIGDIR/small/433268v2_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@134d5cdorg.highwire.dtl.DTLVardef@1c0a419org.highwire.dtl.DTLVardef@556a4forg.highwire.dtl.DTLVardef@1b68c9c_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2019

STAT3 and GR cooperate to drive basal-like triple negative breast cancer gene expression and proliferation.

Breast cancers can be divided into subtypes with different prognoses and treatment responses based on global gene expression differences. Luminal breast cancer gene expression and proliferation are driven by the transcription factors Estrogen Receptor (ER), FOXA1 and GATA3. Targeting ER is the most effective therapy for treating luminal breast cancer because ER is the master regulator of the luminal gene expression program. In contrast, it is unclear which transcription factors are responsible for driving the gene expression signature that defines basal-like triple negative breast cancer, and there are no targeted therapies approved to treat this aggressive subtype of the disease. This study utilized integrated analysis of DNA methylation, chromatin accessibility, transcription factor binding, and gene expression in large collections of breast cancer cell lines and patient tumors to identify transcription factors responsible for the basal-like gene expression program. The results of this study indicate that glucocorticoid receptor (GR) and signal transducer and activator of transcription 3 (STAT3) bind to the same genomic regulatory regions that are specifically open and unmethylated in basal-like breast cancer. These transcription factors cooperate to regulate expression of hundreds of genes in the basal-like gene expression signature and these downstream genes are associated with poor prognosis in patients. Furthermore, combination treatment with small molecule drugs that inhibit both transcription factors leads to synergistic decreases in cell proliferation in cell lines and patient-derived organoid models. This study demonstrates that GR and STAT3 cooperate to regulate the basal-like breast cancer gene expression program and provides the basis for improved therapy for basal-like triple negative breast cancer through rational combination of STAT3 and GR inhibitors.

cancer biology↗

bioRxiv2016

Resolving the Functional Significance of BRCA1 RING Domain Missense Substitutions

Part 1Development and calibration of suitably accurate functional assays for BRCA1 RING domain and BRCT domain missense substitutions could dramatically accelerate clinical classification of rare missense substitutions observed in that gene. Leveraging data from 68,000 full sequence tests of BRCA1 and BRCA2, plus data from the limited number of already classified BRCA1 RING domain missense substitutions, we used logistic regression and related techniques to evaluate three BRCA1 RING domain assays. These were recently described high throughput yeast 2-hybrid and E3 ubiquitin ligase assays, plus a newly developed mammalian 2-hybrid assay. While there were concerns about the accuracy of the yeast 2-hybrid assay and the indirect nature of the ubiquitin ligase assay, the mammalian 2-hybrid assay had excellent correlation with existing missense substitution classifications. After calibration, this assay contributed to classification of one newly reported BRCA1 missense substitution. In principal, the mammalian 2-hybrid assay could be converted to a high-throughput format that would likely retain suitable accuracy. Part 2How does one achieve clinically applicable classification of the vast majority of all possible sequence variants in disease susceptibility genes? BRCA1 is a high-risk susceptibility gene for breast and ovarian cancer. Pathogenic protein truncating variants are scattered across the open reading frame, but all known missense substitutions that are pathogenic because of missense dysfunction are located in either the amino-terminal RING domain or the carboxy-terminal BRCT domain. Heterodimerization of the BRCA1 and BARD1 RING domains is a molecularly defined obligate activity. Hence, we tested every BRCA1 RING domain missense substitution that can be created by a single nucleotide change for heterodimerization with BARD1 in a Mammalian 2-hybrid (M2H) assay. Downstream of the M2H laboratory assay, we addressed three additional challenges: assay calibration, validation thereof, and integration of the calibrated results with other available data such as computational evidence and patient/population observational data to achieve clinically applicable classification. Overall, we found that about 20% of BRCA1 RING domain missense substitutions are pathogenic. Using a Bayesian point system for data integration and variant classification, we achieved clinical classification of about 89% of observed missense substitutions. Moreover, among missense substitutions not present in the human observational data used here, we find an additional 47 with concordant computational and functional assay evidence in favor of pathogenicity; these are particularly likely to be classified as Likely Pathogenic once human observational data become available.

genetics↗