Biology subjects

McCoy, M. D.

Publications and source records attributed to McCoy, M. D..

5 recordsLinked to original sources

bioRxiv2026

Impact of Trastuzumab and Pertuzumab on HER2 Localization and the Estrogen Receptor Cistrome in ER+/HER2+ Breast Cancer

Background: Although anti-HER2 monoclonal antibody therapy with trastuzumab and pertuzumab is highly effective for HER2-positive breast cancer, co-expression of estrogen receptor (ER) significantly reduces pathologic complete response rates. Objective: We investigated how HER2-targeted inhibition affects HER2 cellular localization and ER genomic binding in ER-positive, HER2-positive breast cancer. Methods: We evaluated HER2 localization in a tissue microarray of treatment-naive patients. Using cellular fractionation, immunofluorescence, chromatin immunoprecipitation, and genome-wide profiling (CUT&RUN), we evaluated HER2 localization and ER genomic binding following acute anti-HER2 treatment and in models of treatment resistance. The treatment-induced ER-bound gene signature was assessed for associations with pathologic complete response and survival in breast cancer clinical cohorts. Results: In treatment-naive primary breast tumors, nuclear HER2 inversely correlated with ER levels. In cell models, treatment with trastuzumab and pertuzumab induced nuclear and chromatin accumulation of HER2. Concurrently, acute anti-HER2 treatment displaced ER from canonical estrogen response elements at classical target genes, yet genome-wide profiling revealed redistribution of ER binding toward non-canonical zinc finger motifs adjacent to pro-survival Wnt and RAGE pathway genes (FZD8, RELA). Expression of a drug-induced ER-bound gene signature was significantly higher in tumors of individuals who did not achieve pathologic complete response following neoadjuvant anti-HER2 therapy and significantly correlated with reduced distant metastasis-free survival in clinical cohorts. Conclusions: Anti-HER2 targeted therapy causes dynamic cistromic reprogramming of ER from classical estrogen pathways toward alternative pro-survival networks. These findings implicate ER redistribution as a possible mediator of resistance and highlight novel therapeutic targets in ER-positive, HER2-positive breast cancer.

cancer biology↗

bioRxiv2025

Probability Distribution for Rare Neutral Mutations in Cancers and Application to Dynamic Precision Medicine of Cancer

Cancers exhibit substantial genetic diversity between individual cells. Previous work shows greater overall diversity that also increases more quickly during a patients clinical course than heretofore expected. Rare subclones will harbor pre-existing resistance to any single agent and may cause medium to late term relapse, which may evolve further variants that have simultaneous resistance to non-cross resistant therapies. In this work, we present a probability distribution function (PDF) of the variant allele fraction (VAF), or prevalence, of a rare subclone, derived from previous evolutionary theory. We show that current clinical sequencing protocols fail to detect the vast majority of these rare subclones and that by the time of detection, simultaneous multiple resistance may already evolve. We apply the PDF to simulation of dynamic precision medicine (DPM), an evolutionary guided precision medicine paradigm that attempts to proactively eliminate singly-resistant subclones before they evolve multiple resistance, with significant potential to extend survival. We show the simulated benefit of DPM with perfect information is hampered by an inability to detect rare subclones if they are assumed to be absent when undetectable. However, the DPM benefit is restored if the PDF is used to calculate the likelihood of the subclone being present below the level of detection and incorporated into the DPM simulation and therapy recommendations in a probabilistic fashion. Two other common statistical distributions are less effective. This theoretical advance facilitates DPM and potentially other evolutionary guided approaches to precision cancer medicine in spite of the limitations of clinical sequencing. Significance StatementCancers contain many cells, each genetically unique. These variations can include pre-existing resistance to therapy, enabling relapse. DNA sequencing cannot detect minority cellular populations (subclones) below a certain size, by which time they may have already evolved simultaneous resistance to multiple therapies. We present a mathematical approach that assigns a risk that a subclone is present, and at what prevalence in the cellular population, even when undetected. We simulate applying this approach to dynamic precision medicine (DPM), which attempts to proactively eliminate singly resistant cells before they become resistant to multiple therapies. Using this probability distribution, we can retain the benefit of DPM even when most rare subclones are undetectable, in contrast to just assuming undetected subclones are absent.

systems biology↗

bioRxiv2024

Personalized cancer treatment strategies incorporating irreversible and reversible drug resistance mechanisms

Despite advances in targeted cancer therapy, the promise of precision medicine has been limited by resistance to these treatments. In this study, we propose a mathematical modelling framework incorporating cellular heterogeneity, genetic evolutionary dynamics, and non-genetic plasticity, accounting for both irreversible and reversible drug resistance. Previously we proposed Dynamic Precision Medicine (DPM), a personalized treatment strategy that designed individualized treatment sequences by simulations of irreversible genetic evolutionary dynamics in a heterogeneous tumor. Here we apply DPM to the joint model of reversible and irreversible drug resistance mechanisms, analyze the simulation results and compare the efficacy of various treatment strategies. The results indicate that this enhanced version of DPM results in superior patient outcomes compared with current personalized medicine treatment approaches. Our results provide insights into cancer treatment strategies for heterogeneous tumors with genetic evolutionary dynamics and non-genetic cellular plasticity, potentially leading to improvements in survival time for cancer patients.

systems biology↗

bioRxiv2023

Unraveling Vulnerabilities in Endocrine Therapy-Resistant HER2+/ER+ Breast Cancer

BackgroundBreast tumors overexpressing human epidermal growth factor receptor (HER2) confer intrinsic resistance to endocrine therapy (ET), and patients with HER2/ estrogen receptor-positive (HER2+/HR+) breast cancer (BCa) are less responsive to ET than HER2-/ER+. However, real-world evidence reveals that a large subset of HER2+/ER+ patients receive ET as monotherapy, positioning this treatment pattern as a clinical challenge. In the present study, we developed and characterized two distinct in vitro models of ET-resistant (ETR) HER2+/ER+ BCa to identify possible therapeutic vulnerabilities. MethodsTo mimic ETR to aromatase inhibitors (AI), we developed two long-term estrogen-deprived (LTED) cell lines from BT-474 (BT474) and MDA-MB-361 (MM361). Growth assays, PAM50 molecular subtyping, genomic and transcriptomic analyses, followed by validation and functional studies, were used to identify targetable differences between ET-responsive parental and ETR-LTED HER2+/ER+ cells. ResultsCompared to their parental cells, MM361 LTEDs grew faster, lost ER, and increased HER2 expression, whereas BT474 LTEDs grew slower and maintained ER and HER2 expression. Both LTED variants had reduced responsiveness to fulvestrant. Whole-genome sequencing of the more aggressive MM361 LTED model system identified exonic mutations in genes encoding transcription factors and chromatin modifiers. Single-cell RNA sequencing demonstrated a shift towards non-luminal phenotypes, and revealed metabolic remodeling of MM361 LTEDs, with upregulated lipid metabolism and antioxidant genes associated with ferroptosis, including GPX4. Combining the GPX4 inhibitor RSL3 with anti-HER2 agents induced significant cell death in both the MM361 and BT474 LTEDs. ConclusionsThe BT474 and MM361 AI-resistant models capture distinct phenotypes of HER2+/ER+ BCa and identify altered lipid metabolism and ferroptosis remodeling as vulnerabilities of this type of ETR BCa.

cancer biology↗

bioRxiv2018

SNP2SIM: A modular workflow for standardizing molecular simulation and functional analysis of protein variants

Molecular simulations are used to provide insight into protein structure and function, and have the potential to provide important context when predicting the impact of sequence variation on protein function. In addition to understanding molecular mechanisms and interactions on the atomic scale, translational applications of those approaches include drug screening, development of novel molecular therapies, and treatment planning when selecting targeted therapies. Supporting the continued development of these applications, we have developed the SNP2SIM workflow generates reproducible molecular dynamics and molecular docking simulations for downstream functional variant analysis. Three modules execute molecular dynamics simulations of solvated protein variant structures, analyze the resulting trajectories for unique structural conformations, and bind small molecule ligands to representative variant scaffolds. In addition to availability as a command line workflow, SNP2SIM modules are also available as individual apps on the Seven Bridges Cancer Genomics Cloud.

bioinformatics↗