Biology subjects

Sweeney, C. J.

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

8 recordsLinked to original sources

bioRxiv2025

EZH2-TTP-mTORC1 Axis Drives Phenotypic Plasticity and Therapeutic Vulnerability in Lethal Prostate Cancer

Phenotypic plasticity is a recognized mechanism of therapeutic resistance in prostate cancer (PCa), however current knowledge of driver mechanisms and therapeutic interventions are limited. Using genetically engineered mouse models (GEMMs) devoid of Pten and Rb1, we previously demonstrated the chromatin reprogramming factor enhancer of zeste homolog 2 (EZH2) as an important regulator of alternative transcription programs promoting phenotypic plasticity. Here, using a multi-omics approach we demonstrate that EZH2 regulates multilineage cell states dependent on the RNA binding protein Tristetraprolin (TTP) that mediates RNA stability and activation of translation. Combined chemical inhibition of EZH2 and PI3K/mTORC1 resulted in superior anti-tumor activity in murine and human phenotypic plastic models and was most significant when this combination was used with castration or enzalutamide. Together, these data indicate phenotypic plasticity dependence on coordination between EZH2, TTP and mTORC1 signaling that represent novel therapeutic dependencies for this lethal PCa phenotype. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=194 SRC="FIGDIR/small/669104v1_ufig1.gif" ALT="Figure 1"> View larger version (66K): org.highwire.dtl.DTLVardef@5ef713org.highwire.dtl.DTLVardef@af402forg.highwire.dtl.DTLVardef@19eedcforg.highwire.dtl.DTLVardef@10ee421_HPS_FORMAT_FIGEXP M_FIG C_FIG Significance StatementEZH2 plays a key role in driving cell state transitions in neuroendocrine prostate cancer (NEPC), guiding cancer cells towards a more aggressive, therapy-resistant cell type. This transformation creates a specific vulnerability, as NEPC cells become highly reliant on both EZH2 and PI3K/mTORC1-translation signaling. Targeting this dependency, we demonstrate that combining EZH2 with PI3K/mTORC1 inhibition provides effective suppression of NEPC cell growth, offering a promising therapeutic strategy for treating this challenging and aggressive prostate cancer subtype.

cancer biology↗

bioRxiv2025

Increasing Stemness Drives Prostate Cancer Progression, Plasticity, Therapy Resistance and Poor Patient Survival

BackgroundCancer progression is often accompanied by dedifferentiation and acquisition of stem cell-like properties (stemness). In prostate cancer (PCa), lineage plasticity and therapy resistance remain major clinical challenges, yet a unified quantitative transcriptomic framework connecting stemness, androgen receptor (AR) signaling, castration resistance, and disease progression across the PCa continuum is lacking. MethodsWe integrated 87,339 transcriptomic profiles from 33 preclinical and clinical datasets spanning the PCa continuum from normal prostate and treatment-naive primary PCa (Pri-PCa) to PCa treated with neoadjuvant ADT (nADT) and metastatic castration-resistant PCa (mCRPC), with single-cell RNA-seq analyses encompassing 115,197 cells. Cancer stemness was quantified using a transcriptome-derived mRNA-based Stemness Index (mRNAsi; hereafter Stemness), and a 12-gene PCa-Stem signature was developed to capture PCa-specific stemness. Stemness, PCa-Stem, canonical AR activity (c_AR-A), castration-reprogrammed AR activity (cr_AR-A), RB1-loss, PTEN-loss, and MYC activity signatures were quantified across cohorts. Functional validation included MYC inhibition and representative PCa-Stem signature gene depletion in PCa models. Clinical prognostic significance was evaluated in independent patient cohorts. ResultsThe Stemness score and c_AR-A increased concordantly during early prostate tumorigenesis but diverged with PCa progression: as Gleason grade increased, c_AR-A declined while Stemness continually increased. mCRPC exhibited the highest Stemness and lowest c_AR-A, a pattern recapitulated in Pten/Rb1/Trp53-deficient mouse models. Global Stemness increased progressively across the PCa continuum, was enriched in aggressive PAM50-LumB and PCS1 subtypes, associated with proliferative and lineage plasticity programs, and predicted poor patient survival. The newly derived 12-gene PCa-Stem signature provided a PCa-specific molecular representation of Stemness and tracked disease progression and poor patient survival. Network analyses identified a coordinated mitotic regulatory program linking MYC activity, RB1-loss, cr_AR-A, and the PCa-Stem signature. Spatial and single-cell transcriptomic analyses localized the PCa-Stem program to lineage plasticity-related epithelial cells and demonstrated progressive expansion of PCa-Stem epithelial cells during PCa progression. Functional perturbation of representative PCa-Stem signature genes, as well as genetic and pharmacological MYC inhibition, consistently suppressed Stemness-associated phenotypes in diverse PCa models. ConclusionsCancer Stemness quantitatively captures PCa aggressiveness, lineage plasticity, treatment resistance, disease progression, and poor patient survival. cr_AR-A, RB1 loss, and MYC activation cooperate to reinforce the high-Stemness state and therapy resistance in mCRPC. Collectively, our work establishes a trajectory-integrated transcriptomic framework defining cancer Stemness as a quantifiable molecular and clinical determinant of PCa aggressiveness, lineage plasticity, disease progression, therapy resistance and patient survival.

cancer biology↗

bioRxiv2022

Loss of tristetraprolin activates NF-κB induced phenotypic plasticity and primes transition to lethal prostate cancer

Phenotypic plasticity is a hallmark of cancer and increasingly realized as a mechanism of resistance in androgen indifferent prostate tumors. It is critical to identify mechanisms and actionable targets driving phenotypic plasticity. Here, we report that loss of tristetraprolin (TTP, gene ZFP36), an RNA binding protein that regulates mRNA stability increases NF-{kappa}B activation and is associated with higher rates of aggressive disease and early recurrence in primary prostate cancer (PCa). We examined the clinical and biological impact of ZFP36 loss combined with PTEN loss, a known driver of PCa. Combined loss of PTEN and ZFP36 expression was associated with increased risk of recurrence in multiple independent primary PCa cohorts, and significantly reduced overall survival and time to progression following castration in genetically engineered mouse models. ZFP36 loss alters the cell state that is driven by PTEN loss, demonstrated by positive enrichment of gene sets including EMT, inflammation, TNF/NF-{kappa}B, IL6-JAK/STAT3. ZFP36 loss also induces enrichment of multiple gene sets involved in cell migration, chemotaxis, and proliferation. Use of the NF-{kappa}B inhibitor dimethylaminoparthenolide induced significant therapeutic responses in tumors with PTEN and ZFP36 co-loss and reversed castration resistance. This work identifies a novel molecular mechanism driving phenotypic plasticity and castration resistance through loss of ZFP36 expression, that can be reversed by inhibition of NF-{kappa}B activity.

cancer biology↗

bioRxiv2021

Genetic determinants of chromatin reveal prostate cancer risk mediated by context-dependent gene regulation

Methods that link genetic variation to steady-state gene expression levels, such as expression quantitative trait loci (eQTLs), are widely used to functionally annotate trait-associated variants, but they are limited in identifying context-dependent effects on transcription. To address this challenge, we developed the cistrome-wide association study (CWAS), a framework for nominating variants that impact traits through their effects on chromatin state. CWAS associates the genetic determinants of cistromes (e.g., the genome-wide profiles of transcription factor binding sites or histone modifications) with traits using summary statistics from genome-wide association studies (GWAS). We performed CWASs of prostate cancer and androgen-related traits, using a reference panel of 307 prostate cistromes from 165 individuals. CWAS nominated susceptibility regulatory elements or androgen receptor (AR) binding sites at 52 out of 98 known prostate cancer GWAS loci and implicated an additional 17 novel loci. We functionally validated a subset of our results using CRISPRi and in vitro reporter assays. At 28 of the 52 risk loci, CWAS identified regulatory mechanisms that are not observable via eQTLs, implicating genes with complex or context-specific regulation that are overlooked by current approaches that relying on steady-state transcript measurements. CWAS genes include transcription factors that govern prostate development such as NKX3-1, HOXB13, GATA2, and KLF5. Moreover, CWAS boosts discovery power in modestly sized GWAS, identifying novel genetic associations mediated through AR binding for androgen-related phenotypes, including resistance to prostate cancer therapy. CWAS is a powerful and biologically interpretable paradigm for studying variants that influence traits by affecting context-dependent transcriptional regulation.

genetics↗

bioRxiv2020

Global Root Traits (GRooT) Database

MotivationTrait data are fundamental to quantitatively describe plant form and function. Although root traits capture key dimensions related to plant responses to changing environmental conditions and effects on ecosystem processes, they have rarely been included in large-scale comparative studies and global models. For instance, root traits remain absent from nearly all studies that define the global spectrum of plant form and function. Thus, to overcome conceptual and methodological roadblocks preventing a widespread integration of root trait data into large-scale analyses we created the Global Root Trait (GRooT) Database. GRooT provides ready-to-use data by combining the expertise of root ecologists with data mobilization and curation. Specifically, we (i) determined a set of core root traits relevant to the description of plant form and function based on an assessment by experts, (ii) maximized species coverage through data standardization within and among traits, and (iii) implemented data quality checks. Main types of variables containedGRooT contains 114,222 trait records on 38 continuous root traits. Spatial location and grainGlobal coverage with data from arid, continental, polar, temperate, and tropical biomes. Data on root traits derived from experimental studies and field studies. Time period and grainData recorded between 1911 and 2019 Major taxa and level of measurementGRooT includes root trait data for which taxonomic information is available. Trait records vary in their taxonomic resolution, with sub-species or varieties being the highest and genera the lowest taxonomic resolution available. It contains information for 184 sub-species or varieties, 6,214 species, 1,967 genera and 254 families. Due to variation in data sources, trait records in the database include both individual observations and mean values. Software formatGRooT includes two csv file. A GitHub repository contains the csv files and a script in R to query the database.

ecology↗

bioRxiv2020

The fungal collaboration gradient dominates the root economics space in plants

Plant economics run on carbon and nutrients instead of money. Leaf strategies aboveground span an economic spectrum from live fast and die young to slow and steady, but the economy defined by root strategies belowground remains unclear. Here we take a holistic view of the belowground economy, and show that root-mycorrhizal collaboration can short circuit a one-dimensional economic spectrum, providing an entire space of economic possibilities. Root trait data from 1,781 species across the globe confirm a classical fast-slow conservation gradient but show that most variation is explained by an orthogonal collaboration gradient, ranging from do-it-yourself resource uptake to outsourcing of resource uptake to mycorrhizal fungi. This broadened root economics space provides a solid foundation for predictive understanding of belowground responses to changing environmental conditions. One Sentence SummaryCollaboration broadens the root economics space ranging from do-it-yourself resource acquisition to outsourcing to mycorrhizal partners.

ecology↗

bioRxiv2019

Targeting EZH2 Increases Therapeutic Efficacy of Check-Point Blockade in Models of Prostate Cancer

Prostate cancers are considered immunologically cold tumors given the very few patients who respond to checkpoint inhibitor therapy (CPI). Recently, enrichment of interferon (IFN) response genes predicts a favorable response to CPI across various disease sites. The enhancer of zeste homolog-2 (EZH2) is over-expressed in prostate cancer and is known to negatively regulate IFN response genes. Here, we demonstrate that inhibition of EZH2 catalytic activity in prostate cancer models derepresses expression of double-strand RNA (dsRNA), associated with upregulation of genes involved in antigen presentation, Th-1 chemokine signaling, and interferon (IFN) response, including PD-L1. Similarly, application of a novel EZH2 derived gene signature to human prostate sample analysis indicated an inverse correlation between tumor EZH2 activity/expression with T-cell inflamed and IFN gene signatures and PD-L1 expression. EZH2 inhibition combined with PD-1 CPI significantly enhances antitumor response that is dependent on up-regulation of tumor PD-L1 expression. Further, combination therapy significantly increases intratumoral trafficking of activated CD8+ T-cells and M1 tumor associated macrophages (TAMs) with concurrent loss of M2 TAMs. Our study identifies EZH2 as a potent inhibitor of antitumor immunity and responsiveness to CPI. This data suggests EZH2 inhibition as a novel therapeutic direction to enhance prostate cancer response to PD-1 CPI.

cancer biology↗

bioRxiv2016

A community-based collaboration to build prediction models for short-term discontinuation of docetaxel in metastatic castration-resistant prostate cancer patients

BackgroundDocetaxel has a demonstrated survival benefit for metastatic castration-resistant prostate cancer (mCRPC). However, 10-20% of patients discontinue docetaxel prematurely because of toxicity-induced adverse events, and managing risk factors for toxicity remains an ongoing challenge for health care providers and patients. Prospective identification of high-risk patients for early discontinuation has the potential to assist clinical decision-making and can improve the design of more efficient clinical trials. In partnership with Project Data Sphere (PDS), a non-profit initiative facilitating clinical trial data-sharing, we designed an open-data, crowdsourced DREAM (Dialogue for Reverse Engineering Assessments and Methods) Challenge for developing models to predict early discontinuation of docetaxel\n\nMethodsData from the comparator arms of four phase III clinical trials in first-line mCRPC were obtained from PDS, including 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 598 patients treated with docetaxel, prednisone/prednisolone, and placebo in the VENICE trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, and 528 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Early discontinuation was defined as treatment stoppage within three months due to adverse treatment effects. Over 150 clinical features including laboratory values, medical history, lesion measures, prior treatment, and demographic variables were curated and made freely available for model building for all four trials. The ASCENT2, VENICE, and MAINSAIL trial data sets formed the training set that also included patient discontinuation status. The ENTHUSE 33 trial, with patient discontinuation status hidden, was used as an independent validation set to evaluate model performance. Prediction performance was assessed using area under the precision-recall curve (AUPRC) and the Bayes factor was used to compare the performance between prediction models.\n\nResultsThe frequency of early discontinuation was similar between training (ASCENT2, VENICE, and MAINSAIL) and validation (ENTHUSE 33) sets, 12.3% versus 10.4% of docetaxel-treated patients, respectively. In total, 34 independent teams submitted predictions from 61 different models. AUPRC ranged from 0.088 to 0.178 across submissions with a random model performance of 0.104. Seven models with comparable AUPRC scores (Bayes factor [≤]; 3) were observed to outperform all other models. A post-challenge analysis of risk predictions generated by these seven models revealed three distinct patient subgroups: patients consistently predicted to be at high-risk or low-risk for early discontinuation and those with discordant risk predictions. Early discontinuation events were two-times higher in the high-versus low-risk subgroup and baseline clinical features such as presence/absence of metastatic liver lesions, and prior treatment with analgesics and ACE inhibitors exhibited statistically significant differences between the high- and low-risk subgroups (adjusted P < 0.05). An ensemble-based model constructed from a post-Challenge community collaboration resulted in the best overall prediction performance (AUPRC = 0.230) and represented a marked improvement over any individual Challenge submission. A\n\nFindingsOur results demonstrate that routinely collected clinical features can be used to prospectively inform clinicians of mCRPC patients risk to discontinue docetaxel treatment early due to adverse events and to the best of our knowledge is the first to establish performance benchmarks in this area. This work also underscores the \"wisdom of crowds\" approach by demonstrating that improved prediction of patient outcomes is obtainable by combining methods across an extended community. These findings were made possible because data from separate trials were made publicly available and centrally compiled through PDS.

bioinformatics↗