Biology subjects

Muller, K. E.

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

6 recordsLinked to original sources

bioRxiv2023

Alteration of DNMT1/DNMT3A by eribulin elicits global DNA methylationchanges with potential therapeutic implications for triple-negative breastcancer

Triple-negative breast cancer (TNBC) is an aggressive disease subtype with limited treatment options. Eribulin is a chemotherapeutic approved for the treatment of advanced breast cancer that has been shown to elicit epigenetic changes. We investigated the effect of eribulin treatment on genome-scale DNA methylation patterns in TNBC cells. Following repeated treatment, The results showed that eribulin-induced changes in DNA methylation patterns evident in persister cells. Eribulin also affected the binding of transcription factors to genomic ZEB1 binding sites and regulated several cellular pathways, including ERBB and VEGF signaling and cell adhesion. Eribulin also altered the expression of epigenetic modifiers including DNMT1, TET1, and DNMT3A/B in persister cells. Data from primary human TNBC tumors supported these findings: DNMT1 and DNMT3A levels were altered by eribulin treatment in human primary TNBC tumors. Our results suggest that eribulin modulates DNA methylation patterns in TNBC cells by altering the expression of epigenetic modifiers. These findings have clinical implications for using eribulin as a therapeutic agent.

cancer biology↗

bioRxiv2023

Pharmacological Induction of mesenchymal-epithelial transition chemosensitizes breast cancer cells and prevents metastatic progression

The epithelial-mesenchymal transition (EMT) is a developmental program co-opted by tumor cells that aids the initiation of the metastatic cascade. Tumor cells that undergo EMT are relatively chemoresistant, and there are currently no therapeutic avenues specifically targeting cells that have acquired mesenchymal traits. We show that treatment of mesenchymal-like triple-negative breast cancer (TNBC) cells with the microtubule-destabilizing chemotherapeutic eribulin, which is FDA-approved for the treatment of advanced breast cancer, leads to a mesenchymal-epithelial transition (MET). This MET is accompanied by loss of metastatic propensity and sensitization to subsequent treatment with other FDA-approved chemotherapeutics. We uncover a novel epigenetic mechanism of action that supports eribulin pretreatment as a path to MET induction that curtails metastatic progression and the evolution of therapy resistance. SummaryWhile the advent of targeted therapy has led to vast improvements in outcomes for certain types of breast cancer, a mainstay for triple-negative breast cancer (TNBC) remains cytotoxic chemotherapy. A major clinical hurdle in successfully managing this disease is the eventual development of therapeutic resistance and disease relapse in more aggressive forms. Our data reveal that epigenetic modulation of EMT state using the FDA-approved therapeutic eribulin curtails metastatic propensity of breast tumors and, when administered in the treatment-naive setting, sensitizes to subsequent treatment with other chemotherapeutics.

cancer biology↗

bioRxiv2023

Predicting Distant Recurrences in Invasive Breast Carcinoma Patients Using Clinicopathological Data: A cross-institutional and AI-based study

Breast cancer ranks second in the most common cancer in women worldwide with 30% of cases resulting into recurrence of the disease at distant organs post the treatment. While clinicians have utilized several clinicopathological measurements for prediction of distant recurrences in invasive breast carcinoma (IBC), none of those studies have showcased the potential of combining clinicopathological evaluations of IBC tumors pre and post therapies using machine learning (ML) or artificially intelligent (AI) models to predict the distant recurrence of the disease in respective patients. The goal of our study was to determine whether classification-based ML/AI techniques can predict distant recurrences in IBC patients using key clinicopathological measurements that includes pathological staging of tumor and surrounding lymph nodes deemed both pre- and post-neoadjuvant therapy, imaging-based therapy responses, and the status of adjuvant therapy administered to patients. We trained and tested clinicopathological ML/AI model using dataset from Duke University and validated it using external dataset from Dartmouth Hitchcock Medical Center (DHMC). Random Forest (RF) model performed best compared to C-Support Vector Classifier (SVC) and Multi-Layer Perceptron (MLP) yielding AUC ranging 0.75-1.0 (p<0.002) across both the institutions, thereby demonstrating the cross-institutional portability and validity of ML/AI models in the field of clinical research in cancer.

bioinformatics↗

bioRxiv2022

Lineage plasticity enables low-ER luminal tumors to evolve and gain basal-like traits

Abstract/summaryStratifying breast cancer into specific molecular or histological subtypes aids in therapeutic decision-making and predicting outcomes, however, these subtypes may not be as distinct as previously thought. Patients with luminal-like, Estrogen Receptor (ER)- expressing tumors have better prognosis than patients with more aggressive, triple- negative or basal-like tumors. There is, however, a subset of luminal-like tumors that express lower levels of ER, which exhibit more basal-like features. We have found that breast tumors expressing lower levels of ER, traditionally considered to be luminal-like, represent a distinct subset of breast cancer characterized by the emergence of basal-like features. Lineage tracing of low-ER tumors in the MMTV-PyMT mouse mammary tumor model revealed that basal marker expressing cells arose from normal luminal epithelial cells, suggesting that luminal-to-basal plasticity is responsible for the evolution and emergence of basal-like characteristics. This plasticity allows tumor cells to gain a new lumino-basal phenotype, thus leading to intratumoral lumino-basal heterogeneity. Single- cell RNA sequencing revealed SOX10 as a potential driver for this plasticity, which is known among breast tumors to be almost exclusively expressed in Triple Negative Breast Cancer (TNBC) and was also found to be highly expressed in low-ER tumors. These findings suggest that basal-like tumors may result from the evolutionary progression of luminal tumors with low ER expression.

cancer biology↗

bioRxiv2021

Dynamic plasticity within the EMT spectrum, rather than static mesenchymal traits, drives tumor heterogeneity and metastatic progression of breast cancers

The Epithelial-to-Mesenchymal Transition (EMT) is a developmental cellular program frequently coopted by cancer cells1 and is a key contributor to both heterogeneity in solid tumors2-4 and later stage chemo-resistance and metastasis5,6. Rather than being a switch from an epithelial to a mesenchymal state, increasing evidence points to the existence of intermediate EMT states, wherein cells co-express both epithelial and mesenchymal traits7-13. Multiple stable intermediate EMT states possessing unique characteristics exist across the EMT spectrum7,8,14,15, contributing to the complex heterogeneity of tumors to promote metastasis16. While much work has been carried out identifying and characterizing EMT-inducing transcription factors17-20, the transcriptional and epigenetic networks responsible for the stability and maintenance of the midpoints along the EMT spectrum are poorly defined. In addition, there are currently no approaches to identifying and quantifying intermediate EMT subpopulations within patient tumors to evaluate their prognostic significance. Using clonally isolated derivatives of the SUM149PT breast cancer cell line, we systematically interrogate how each EMT state independently contributes to heterogeneity and influences metastatic progression, uncovering the role of RUNX2 in stabilizing certain intermediate states. Using SUM149PT-derived tumors as a training set, we develop an entropy-based model to quantify phenotypic heterogeneity and EMT status. Remarkably, the majority of cell states captured in the SUM149PT model are represented in a selection of patient tumors, laying the foundation for quantification of epithelial-mesenchymal heterogeneity and understanding the role of the intermediate EMT state in tumor progression.

cancer biology↗

bioRxiv2020

Limiting Self-Renewal of the Basal Compartment Induces Differentiation and Alters Evolution of Mammary Tumors

Differentiation therapy is an approach that utilizes our understanding of the hierarchy of cellular systems to pharmacologically induce a shift towards terminal commitment. While this approach has been a paradigm in treating certain hematological malignancies, efforts to translate this success to solid tumors have proven challenging. In this study we show that activation of PKA drives aberrant mammary differentiation by diminishing the self-renewing potential of the basal compartment. PKA activation results in tumors that are more benign, exhibiting reduced metastatic propensity, loss of tumor-initiating potential and increased sensitivity to chemotherapy. Analysis of tumor histopathology revealed features of overt differentiation with papillary characteristics. Longitudinal single cell profiling at the hyperplasia and tumor stages uncovered an altered path of tumor evolution whereby PKA curtails the emergence of aggressive subpopulations. PKA activation represents a promising approach as an adjuvant to chemotherapy for certain breast cancers, reviving the paradigm of differentiation therapy for solid tumors.

cancer biology↗