Biology subjects

Boughey, J. C.

Publications and source records attributed to Boughey, J. C..

6 recordsLinked to original sources

bioRxiv2026

The Human Male Mammary Gland has Similar Epithelial Populations to Female but Distinct Composition and Transcriptional Properties

The normal adult male breast has not been characterized at single-cell resolution, leaving the cellular basis of male breast cancer (MBC) biology undefined. Here we present an integrated single-cell RNA sequencing atlas of the adult human breast comprising 174,471 cells from 17 donors (3 male, 14 female), including 18,117 male-derived cells. This revealed that the male breast retains all three epithelial populations, basal (BC), luminal progenitor (LP), and luminal committed cells (LC), but with an increase in LC at the expense of BC and LP across all three male donors. Male LC were distinguished from female by elevated ESR1 and PGR mRNA, enrichment of RNA processing and ribosome biogenesis programs, reduced inflammatory cytokine and growth factor signaling, elevated estradiol gene set enrichment scores, and higher inferred activity of developmental patterning transcription factors. This pattern was observed across differential expression, gene ontology, ligand profiling, and regulon-based analyses, and was not restricted to sex chromosome-linked gene expression. This is consistent with the near-universal estrogen receptor (ER) positivity that characterizes MBC clinically. This atlas provides the first cellular and transcriptional reference for the normal male breast and a resource for investigating sex differences in mammary biology, germline susceptibility variant interpretation, and modeling breast malignancies.

cancer biology↗

bioRxiv2025

Receptor Tyrosine Kinase Profiling Identifies Chronic Constitutive Floodgate Oxidative Signaling in Glutathione-Independent Human Mammary Luminal Progenitor Cells

The human mammary epithelium contains a subset of luminal progenitor (LP) cells that are distinct from basal cells in both lineage potential and redox biology. LPs are uniquely equipped to tolerate oxidative stress through glutathione-independent mechanisms and have been implicated as candidate cells of origin in basal-like breast cancers. In this study, we identify the receptor tyrosine kinase (RTK) cKIT (CD117), as a defining feature of LPs and a key mediator of their expansion. cKIT is developmentally restricted to the LP compartment via Polycomb-mediated epigenetic repression in basal and luminal-committed cells. It is expressed in scattered epithelial cells within both ductal and alveolar regions of resting human mammary glands. Using RTK-engineered MCF10A models, we demonstrate that cKIT ligand/stem cell factor (SCF)-activated wildtype cKIT signaling is sufficient to drive proliferation in the absence of epidermal growth factor (EGF) and that cKIT is responsive not only to canonical ligands but also to hydrogen peroxide (H2O2). In primary human LPs, cKIT is rapidly phosphorylated upon exposure to SCF and H2O2, with concomitant AKT activation. These responses are enhanced when cKIT and EGFR signaling are co-engaged, suggesting a cooperative mitogenic program. In mammary gland, phosphorylation of the antioxidant enzyme PRDX1 is selectively detected in LPs, consistent with a floodgate model of redox signaling in which transient oxidative inactivation of peroxiredoxins (PRDXs) facilitates RTK signaling under elevated intracellular reactive oxygen species conditions. Clinically, elevated cKIT expression is associated with shorter progression-free survival in certain basal-like breast cancer, supporting a link between LP-like redox signaling states and aggressive tumor behavior. Together, these findings define a redox-integrated RTK signaling axis centered on cKIT that drives LP expansion and is associated with poor outcomes in a subset of basal breast cancers. This work establishes a mechanistic framework for targeting redox-responsive progenitor populations in both regenerative and oncologic context.

cancer biology↗

bioRxiv2024

OmicsFootPrint: a framework to integrate and interpret multi-omics data using circular images and deep neural networks

The OmicsFootPrint framework addresses the need for advanced multi-omics data analysis methodologies by transforming data into intuitive two-dimensional circular images and facilitating the interpretation of complex diseases. Utilizing Deep Neural Networks and incorporating the SHapley Additive exPlanations (SHAP) algorithm, the framework enhances model interpretability. Tested with The Cancer Genome Atlas (TCGA) data, OmicsFootPrint effectively classified lung and breast cancer subtypes, achieving high Area Under Curve (AUC) scores-- 0.98{+/-}0.02 for lung cancer subtype differentiation, 0.83{+/-}0.07 for breast cancer PAM50 subtypes, and successfully distinguished between invasive lobular and ductal carcinomas in breast cancer, showcasing its robustness. It also demonstrated notable performance in predicting drug responses in cancer cell lines, with a median AUC of 0.74, surpassing nine existing methods. Furthermore, its effectiveness persists even with reduced training sample sizes. OmicsFootPrint marks an enhancement in multi-omics research, offering a novel, efficient, and interpretable approach that contributes to a deeper understanding of disease mechanisms.

bioinformatics↗

bioRxiv2022

Integration of multi-omics data shows downregulation of mismatch repair, purin, and tublin pathways in AR-negative triple-negative chemotherapy-resistant breast tumors

Triple negative breast cancer (TNBC) patients who fail to achieve a pathological complete response to neoadjuvant chemotherapy (NAC) will likely experience recurrence of the disease within 3-4 years. Prognostic assessment of early recurrence could facilitate clinical decisions and impact disease survival. This study investigated pre- and post-NAC multi-omics data from both an in-house and a public clinical study to identify biomarkers of NAC response. We observed significant transcriptional differences associated with response in the residual disease (post-NAC biopsies), which did not exist in the pre-NAC biopsies. We further refined the post-NAC transcriptional changes to a 17-gene signature and machine learning models were applied to evaluate the signatures diagnostic potential (area under the curve [≥] 0.8). Interestingly, the signature was enriched with down-regulated immune genes and several of these genes demonstrated prognostic potential in basal TNBC tumors. Our 17-gene signature provides additional insight into TNBC recurrence, which warrants further investigation.

cancer biology↗

bioRxiv2020

Quantitative analysis of tyrosine phosphorylation from FFPE tissues reveals patient specific signaling networks

Formalin fixed paraffin embedded (FFPE) tissues are an invaluable source of clinical specimens. Tyrosine phosphorylation (pTyr) plays a fundamental role in cellular processes and is commonly dysregulated in cancer but has not been studied to date in FFPE samples. We describe a method for quantitative analysis of pTyr signaling networks at an unprecedented sensitivity, with hundreds of sites quantified from 1-2 10-m sections of FFPE tissue specimens. Phosphotyrosine profiles of flash frozen and FFPE tissues derived from the same tumors suggest that FFPE tissues preserve pTyr signaling characteristics in PDX tumors and archived clinical specimens. Differential activation of oncogenic proteins was observed in triple negative breast cancer tumors as well as lung cancer tumors, highlighting patient specific oncogenic driving kinases and indicating potential targeted therapies for each patient. These data highlight the capability for direct translational insight from pTyr analysis of small amounts of FFPE tumor tissue specimens.

systems biology↗

bioRxiv2017

PANOPLY: A computational method for identification of promising drugs for a patient based on multidimensional data

PurposeThe majority of cancer patients receive treatments that are minimally informed by omics data. We propose a precision medicine computational framework (PANOPLY: Precision cancer genomic report: single sample inventory) to identify and prioritize drug targets and cancer therapy regimens.\n\nMethodsThe PANOPLY approach integrates clinical data with germline and somatic features obtained from multi-omics platforms, and apply machine learning, and network analysis approaches in the context of the individual patient and matched controls. The PANOPLY workflow employs four steps (i) selection of matched controls to the case of interest (ii) identification of case-specific genomic events (iii) identification of suitable drugs using the driver-gene network and random forest analyses and (iv) provide an integrated multi-omics case report of the patient with prioritization of anti-cancer drugs.\n\nResultsThe PANOPLY workflow can be executed on a stand-alone virtual machine and is also available for download as an R package. We applied the method to an institutional breast cancer neoadjuvant chemotherapy study which collected clinical and genomic data as well as patient-derived xenografts (PDXs) to investigate the prioritization offered by PANOPLY. In a chemotherapy-resistant PDX model, we found that that the prioritized drug, olaparib, was more effective than placebo in treating the tumor (P < 0.05). We also applied PANOPLY to in-house and publicly accessible multi-omics tumor datasets with therapeutic response or survival data available.\n\nConclusionPANOPLY shows promise as a means to prioritize drugs based on clinical and multi-omics data for an individual cancer patient. Additional studies are needed to confirm this approach.

bioinformatics↗