Cell facilitation promotes growth and survival under drug pressure in breast cancer

Simulations probe the role of space in the interplay between drug-sensitive and drug-resistant cancer cells

The interplay between drug-sensitive and drug-resistant cancer cells has been observed to impact cell-to-cell interactions in experimental settings. However, the role that space plays in these interactions remains unclear. In this study, we develop mathematical models to investigate how spatial factors affect cell-to-cell competition between drug-sensitive and drug-resistant cancer cells in silico. We develop two baseline models to study cells from the epithelial FaDu cell line subjected to two drugs, specifically the ATR inhibitor ceralasertib and the PARP inhibitor olaparib, that target DNA damage response pathways. Our baseline models are: (1) a temporally resolved ordinary differential equation (ODE) model, and (2) a spatio-temporally resolved agent-based model (ABM). The models simulate cells in well-mixed and spatially structured cell systems, respectively. The ODE model is calibrated against in vitro data and is thereafter mapped onto the baseline ABM which, in turn, is extended to enable a simulation-based investigation on how spatial factors impact cell-to-cell competition. Simulation results from the extended ABMs demonstrate that the in silico treatment responses are simultaneously affected by: (i) the initial spatial cell configurations, (ii) the initial fraction of drug-resistant cells, (iii) the drugs to which cells express resistance, (iv) drug combinations, (v) drug doses, and (vi) the doubling time of drug-resistant cells compared to the doubling time of drug-sensitive cells. These results reveal that spatial structures of the simulated cancer cells affect both cell-to-cell interactions, and the impact that these interactions have on the ensuing population dynamics. This leads us to suggest that the role that space plays in cell-to-cell interactions should be further investigated and quantified in experimental settings.

Drug response-based precision therapeutic selection for tamoxifen-resistant triple-positive breast cancer

Breast cancer adaptability to the drug environment reduces the chemotherapeutic response and facilitates acquired drug resistance. Cancer-specific therapeutics can be more effective against advanced-stage cancer than standard chemotherapeutics. To extend the paradigm of cancer-specific therapeutics, clinically relevant acquired tamoxifen-resistant MCF-7 proteome was deconstructed to identify possible druggable targets (N = 150). Twenty-eight drug inhibitors were used against identified druggable targets to suppress non-resistant (NC) and resistant cells (RC). First, selected drugs were screened using growth-inhibitory response against NC and RC. Seven drugs were shortlisted for their time-dependent (10-12 days) cytotoxic effect and further narrowed to three effective drugs (e.g., cisplatin, doxorubicin, and hydroxychloroquine). The growth-suppressive effectiveness of selected drugs was validated in the complex spheroid model (progressive and regressive). In the progressive model, doxorubicin (RC: 83.64 %, NC: 54.81 %), followed by cisplatin (RC: 76.66 %, NC: 68.94 %) and hydroxychloroquine (RC: 68.70 %, NC: 61.78 %) showed a significant growth-suppressive effect. However, in fully grown regressive spheroid, after 4th drug treatment, cisplatin significantly suppressed RC (84.79 %) and NC (40.21 %), while doxorubicin and hydroxychloroquine significantly suppressed only RC (76.09 and 76.34 %). Our in-depth investigation effectively integrated the expression data with the cancer-specific therapeutic investigation. Furthermore, our three-step sequential drug-screening approach unbiasedly identified cisplatin, doxorubicin, and hydroxychloroquine as an efficacious drug to target heterogeneous cancer cell populations. SIGNIFICANCE STATEMENT: Hormonal-positive BC grows slowly, and hormonal-inhibitors effectively suppress the oncogenesis. However, development of drug-resistance not only reduces the drug-response but also increases the chance of BC aggressiveness. Further, alternative chemotherapeutics are widely used to control advanced-stage BC. In contrast, we hypothesized that, compared to standard chemotherapeutics, cancer-specific drugs can be more effective against resistant-cancer. Although cancer-specific treatment identification is an uphill battle, our work shows proteome data can be used for drug selection. We identified multiple druggable targets and, using ex-vivo methods narrowed multiple drugs to disease-condition-specific therapeutics. We consider that our investigation successfully interconnected the expression data with the functional disease-specific therapeutic investigation and selected drugs can be used for effective resistant treatment with higher therapeutic response.

A novel combination therapy for ER+ breast cancer suppresses drug resistance via an evolutionary double-bind

Chemotherapy remains a commonly used and important treatment option for metastatic breast cancer. A majority of ER+ metastatic breast cancer patients ultimately develop resistance to chemotherapy, resulting in disease progression. We hypothesized that an "evolutionary double-bind", where treatment with one drug improves the response to a different agent, would improve the effectiveness and durability of responses to chemotherapy. This approach exploits vulnerabilities in acquired resistance mechanisms. Evolutionary models can be used in refractory cancer to identify alternative treatment strategies that capitalize on acquired vulnerabilities and resistance traits for improved outcomes. To develop and test these models, ER+ breast cancer cell lineages sensitive and resistant to chemotherapy are grown in spheroids with varied initial population frequencies to measure cross-sensitivity and efficacy of chemotherapy and add-on treatments such as disulfiram combination treatment. Different treatment schedules then assessed the best strategy for reducing the selection of resistant populations. We developed and parameterized a game-theoretic mathematical model from this in vitro experimental data, and used it to predict the existence of a double-bind where selection for resistance to chemotherapy induces sensitivity to disulfiram. The model predicts a dose-dependent re-sensitization (a double-bind) to chemotherapy for monotherapy disulfiram.

Mechanisms of sensitivity and resistance to CDK4/CDK6 inhibitors in hormone receptor-positive breast cancer treatment

Cell cycle dysregulation is a hallmark of cancer that promotes eccessive cell division. Cyclin-dependent kinase 4 (CDK4) and cyclin-dependent kinase 6 (CDK6) are key molecules in the G1-to-S phase cell cycle transition and are crucial for the onset, survival, and progression of breast cancer (BC). Small-molecule CDK4/CDK6 inhibitors (CDK4/6i) block phosphorylation of tumor suppressor Rb and thus restrain susceptible BC cells in G1 phase. Three CDK4/6i are approved for the first-line treatment of patients with advanced/metastatic hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) BC in combination with endocrine therapy (ET). Though this has improved the clinical outcomes for survival of BC patients, there is no established standard next-line treatment to tackle drug resistance. Recent studies suggest that CDK4/6i can modulate other distinct effects in both BC and breast stromal compartments, which may provide new insights into aspects of their clinical activity. This review describes the biochemistry of the CDK4/6-Rb-E2F pathway in HR+ BC, then discusses how CDK4/6i can trigger other effects in BC/breast stromal compartments, and finally outlines the mechanisms of CDK4/6i resistance that have emerged in recent preclinical studies and clinical cohorts, emphasizing the impact of these findings on novel therapeutic opportunities in BC.

Cell-state transitions and density-dependent interactions together explain the dynamics of spontaneous epithelial-mesenchymal heterogeneity

Cancer cell populations comprise phenotypes distributed among the epithelial-mesenchymal (E-M) spectrum. However, it remains unclear which population-level processes give rise to the observed experimental distribution and dynamical changes in E-M heterogeneity, including (1) differential growth, (2) cell-state switching, and (3) population density-dependent growth or state-transition rates. Here, we analyze the necessity of these three processes in explaining the dynamics of E-M population distributions as observed in PMC42-LA and HCC38 breast cancer cells. We find that, while cell-state transition is necessary to reproduce experimental observations of dynamical changes in E-M fractions, including density-dependent growth interactions (cooperation or suppression) better explains the data. Further, our models predict that treatment of HCC38 cells with transforming growth factor β (TGF-β) signaling and Janus kinase 2/signal transducer and activator of transcription 3 (JAK2/3) inhibitors enhances the rate of mesenchymal-epithelial transition (MET) instead of lowering that of E-M transition (EMT). Overall, our study identifies the population-level processes shaping the dynamics of spontaneous E-M heterogeneity in breast cancer cells.

Enhancing breast cancer treatment: mesoporous dopamine nanoparticles in synergy with chrysin for photothermal therapy

Introduction

Breast cancer is one of the most prevalent cancers, primarily affecting women. Among its subtypes, estrogen receptor-positive (ER+) breast cancer is particularly common. Inhibiting estrogen's effects is crucial for treating ER+ breast cancer, but current therapies often have significant side effects and limitations. Chrysin, a natural flavonoid, has shown potential in reducing estrogen receptor expression, but its poor water solubility hampers clinical application. This study explores the use of mesoporous dopamine nanoparticles (mPDA) to enhance the delivery and efficacy of Chrysin, combined with photothermal therapy (PTT), for breast cancer treatment.

Methods

Chrysin-loaded mPDA nanoparticles (Chrysin@mPDA) were synthesized and characterized for their morphology, drug-loading efficiency, stability, and photothermal properties. Network pharmacology was used to predict Chrysin's mechanisms in breast cancer, which were validated through gene expression analysis in cell experiments. The therapeutic efficacy of Chrysin@mPDA with and without PTT was evaluated in a mouse model of breast cancer, with tumor volume and weight measured. Immunohistochemical analysis was conducted to assess estrogen receptor expression and immune cell infiltration in tumor tissues.

Results

Chrysin@mPDA nanoparticles demonstrated a high drug-loading capacity and excellent stability. Photothermal studies confirmed the nanoparticles' ability to generate heat upon laser exposure, significantly enhancing Chrysin release in acidic conditions with laser irradiation. Network pharmacology identified key target genes affected by Chrysin, including ESR1, BRCA1, CTNNB1, and BAX, which were validated through qPCR. In vivo, the combination of Chrysin@mPDA and PTT significantly reduced tumor volume and weight, decreased estrogen receptor-positive cells, and increased infiltration of CD3+CD4+ and CD3+CD8+ T cells in tumor tissues.

Discussion

The study highlights the potential of Chrysin-loaded mPDA nanoparticles combined with PTT as an effective strategy for breast cancer treatment. This approach addresses the limitations of Chrysin's solubility and enhances its therapeutic efficacy through synergistic mechanisms. The dual action of Chrysin in modulating gene expression and PTT in inducing localized hyperthermia and immune response suggests a promising avenue for improved breast cancer prognosis and reduced recurrence.

Proteomics Analysis of Interactions between Drug-Resistant and Drug-Sensitive Cancer Cells: Comparative Studies of Monoculture and Coculture Cell Systems

Cell-cell interactions, which allow cells to communicate with each other through molecules in their microenvironment, are critical for the growth, health, and functions of cells. Previous studies show that drug-resistant cells can interact with drug-sensitive cells to elevate their drug resistance level, which is partially responsible for cancer recurrence. Studying protein targets and pathways involved in cell-cell communication provides essential information for fundamental cell biology studies and therapeutics of human diseases. In the current studies, we performed direct coculture and indirect coculture of drug-resistant and drug-sensitive cell lines, aiming to investigate intracellular proteins responsible for cell communication. Comparative studies were carried out using monoculture cells. Shotgun bottom-up proteomics results indicate that the P53 signaling pathway has a strong association with drug resistance mechanisms, and multiple TP53-related proteins were upregulated in both direct and indirect coculture systems. In addition, cell-cell communication pathways, including the phagosome and the HIF-signaling pathway, contribute to both direct and indirect coculture systems. Consequently, AK3 and H3-3A proteins were identified as potential targets for cell-cell interactions that are relevant to drug resistance mechanisms. We propose that the P53 signaling pathway, in which mitochondrial proteins play an important role, is responsible for inducing drug resistance through communication between drug-resistant and drug-sensitive cancer cells.

Se-Pair Search for Deciphering Selenium-Encoded Peptide and a Pyrolysis-Thermolysis Dietary Model for Minimizing Loss of "KKSe(M)R" during Processing

Dietary deficiency of selenium is a global health hazard. Supplementation of organic selenopeptides via food crops is a relatively safe approach. Selenopeptides with heterogeneous selenium-encoded isotopes or a poorly fragmented peptide backbone remain unidentified in site-specific selenoproteomic analysis. Herein, we developed the Se-Pair Search, a UniProtKB-FASTA-independent peptide-matching strategy, exploiting the fragmentation patterns of shared peptide backbones in selenopeptides to optimize spectral interpretation, along with developing new selenosite assignment schemes (steps 1-3) to standardize selenium-localization data reporting for the selenoproteome community and thereby facilitating the discovery of unexpected selenopeptides. Using selenium-biofortified rice under cooking, fermentation, and high-temperature and high-pressure processing conditions as a pyrolysis-thermolysis dietary model, we probed the single-molecule-level kinetic evolution of the novel selenopeptide "KKSe(M)R" with qual-quantitative information on graph-theory-oriented localization calculations, abundance patterns, activation energy, and rate constants at a selenoproteome-wide scale. We ground-truth-annotated thirteen pyrolysis-thermolysis products and inferred four pyrolysis-thermolysis pathways to characterize the formation reactivity of the main intermediate variables of KKSe(M)R and constructed an advanced probe-type ultrasound technique prior to pyrolysis-thermolysis conditions for minimizing loss of KKSe(M)R during processing. Importantly, we reveal the unappreciated pyro-excitation diversion of KKSe(M)R at pyrolysis-thermolysis time and temperature matrices. These findings provide pioneering theoretical guidance for controlling dietary selenium supplementation within the safety thresholds.

The multifaceted roles of GSDME-mediated pyroptosis in cancer: therapeutic strategies and persisting obstacles

Pyroptosis is a novel regulated cell death (RCD) mode associated with inflammation and innate immunity. Gasdermin E (GSDME), a crucial component of the gasdermin (GSDM) family proteins, has the ability to convert caspase-3-mediated apoptosis to pyroptosis of cancer cells and activate anti-tumor immunity. Accumulating evidence indicates that GSDME methylation holds tremendous potential as a biomarker for early detection, diagnosis, prognosis, and treatment of tumors. In fact, GSDME-mediated pyroptosis performs a dual role in anti-tumor therapy. On the one side, pyroptotic cell death in tumors caused by GSDME contributes to inflammatory cytokines release, which transform the tumor immune microenvironment (TIME) from a 'cold' to a 'hot' state and significantly improve anti-tumor immunotherapy. However, due to GSDME is expressed in nearly all body tissues and immune cells, it can exacerbate chemotherapy toxicity and partially block immune response. How to achieve a balance between the two sides is a crucial research topic. Meanwhile, the potential functions of GSDME-mediated pyroptosis in anti-programmed cell death protein 1 (PD-1) therapy, antibody-drug conjugates (ADCs) therapy, and chimeric antigen receptor T cells (CAR-T cells) therapy have not yet been fully understood, and how to improve clinical outcomes persists obscure. In this review, we systematically summarize the latest research regarding the molecular mechanisms of pyroptosis and discuss the role of GSDME-mediated pyroptosis in anti-tumor immunity and its potential applications in cancer treatment.

GeneMAP: A discovery platform for metabolic gene function

Organisms maintain metabolic homeostasis through the combined functions of small molecule transporters and enzymes. While many of the metabolic components have been well-established, a substantial number remains without identified physiological substrates. To bridge this gap, we have leveraged large-scale plasma metabolome genome-wide association studies (GWAS) to develop a multiomic Gene-Metabolite Associations Prediction (GeneMAP) discovery platform. GeneMAP can generate accurate predictions, even pinpointing genes that are distant from the variants implicated by GWAS. In particular, our work identified SLC25A48 as a genetic determinant of plasma choline levels. Mechanistically, SLC25A48 loss strongly impairs mitochondrial choline import and synthesis of its downstream metabolite, betaine. Rare variant testing and polygenic risk score analyses have elucidated choline-relevant phenomic consequences of SLC25A48 dysfunction. Altogether, our study proposes SLC25A48 as a mitochondrial choline transporter and provides a discovery platform for metabolic gene function.