Background: Cell migration and invasion are essential processes for metastatic dissemination of cancer cells. Significant progress has been made in developing new therapies against oncogenic signaling to eliminate cancer cells and shrink tumors. However, inherent heterogeneity and treatment-induced adaptation to drugs commonly enable subsets of cancer cells to survive therapy. In addition to local recurrence, these cells escape a primary tumor and migrate through the stroma to access the circulation and metastasize to different organs, leading to an incurable disease. As such, therapeutics that block migration and invasion of cancer cells may inhibit or reduce metastasis and significantly improve cancer therapy. This is particularly more important for cancers, such as triple negative breast cancer, that currently lack targeted drugs.
Methods: We used cell migration, 3D invasion, zebrafish metastasis model, and phosphorylation analysis of 43 protein kinases in nine triple negative breast cancer (TNBC) cell lines to study effects of fisetin and quercetin on inhibition of TNBC cell migration, invasion, and metastasis.
Results: Fisetin and quercetin were highly effective against migration of all nine TNBC cell lines with up to 76 and 74% inhibitory effects, respectively. In addition, treatments significantly reduced 3D invasion of highly motile TNBC cells from spheroids into a collagen matrix and their metastasis in vivo. Fisetin and quercetin commonly targeted different components and substrates of the oncogenic PI3K/AKT pathway and significantly reduced their activities. Additionally, both compounds disrupted activities of several protein kinases in MAPK and STAT pathways. We used molecular inhibitors specific to these signaling proteins to establish the migration-inhibitory role of the two phytochemicals against TNBC cells.
Conclusions: We established that fisetin and quercetin potently inhibit migration of metastatic TNBC cells by interfering with activities of oncogenic protein kinases in multiple pathways.
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Multi-omics data integration reveals correlated regulatory features of triple negative breast cancer
Triple negative breast cancer (TNBC) is an aggressive type of breast cancer with very little treatment options. TNBC is very heterogeneous with large alterations in the genomic, transcriptomic, and proteomic landscapes leading to various subtypes with differing responses to therapeutic treatments. We applied a multi-omics data integration method to evaluate the correlation of important regulatory features in TNBC BRCA1 wild-type MDA-MB-231 and TNBC BRCA1 5382insC mutated HCC1937 cells compared with non-tumorigenic epithelial breast MCF10A cells. The data includes DNA methylation, RNAseq, protein, phosphoproteomics, and histone post-translational modification. Data integration methods identified regulatory features from each omics method that had greater than 80% positive correlation within each TNBC subtype. Key regulatory features at each omics level were identified distinguishing the three cell lines and were involved in important cancer related pathways such as TGFβ signaling, PI3K/AKT/mTOR, and Wnt/beta-catenin signaling. We observed overexpression of PTEN, which antagonizes the PI3K/AKT/mTOR pathway, and MYC, which downregulates the same pathway in the HCC1937 cells relative to the MDA-MB-231 cells. The PI3K/AKT/mTOR and Wnt/beta-catenin pathways are both downregulated in HCC1937 cells relative to MDA-MB-231 cells, which likely explains the divergent sensitivities of these cell lines to inhibitors of downstream signaling pathways. The DNA methylation and RNAseq data is freely available via GEO GSE171958 and the proteomics data is available via the ProteomeXchange PXD025238.
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- Award ID(s):
- 1946391
- NSF-PAR ID:
- 10321617
- Date Published:
- Journal Name:
- Molecular Omics
- Volume:
- 17
- Issue:
- 5
- ISSN:
- 2515-4184
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Chemotherapy remains the standard treatment for triple‐negative breast cancer (TNBC); however, chemoresistance compromises its efficacy. The RNA‐binding protein Hu antigen R (HuR) could be a potential therapeutic target to enhance the chemotherapy efficacy. HuR is known to mainly stabilize its target mRNAs, and/or promote the translation of encoded proteins, which are implicated in multiple cancer hallmarks, including chemoresistance. In this study, a docetaxel‐resistant cell subline (231‐TR) was established from the human TNBC cell line MDA‐MB‐231. Both the parental and resistant cell lines exhibited similar sensitivity to the small molecule functional inhibitor of HuR, KH‐3. Docetaxel and KH‐3 combination therapy synergistically inhibited cell proliferation in TNBC cells and tumor growth in three animal models. KH‐3 downregulated the expression levels of HuR targets (e.g., β‐Catenin and BCL2) in a time‐ and dose‐dependent manner. Moreover, KH‐3 restored docetaxel's effects on activating Caspase‐3 and cleaving PARP in 231‐TR cells, induced apoptotic cell death, and caused S‐phase cell cycle arrest. Together, our findings suggest that HuR is a critical mediator of docetaxel resistance and provide a rationale for combining HuR inhibitors and chemotherapeutic agents to enhance chemotherapy efficacy.
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Age is a leading risk factor for developing breast cancer. This may be in part to the time required for acquiring sufficient cancer mutations; however, stromal cells that accumulate in tissues and undergo senescence eventually develop a senescence-associated secretory phenotype that alters the microenvironment to promote cancer. Our focus is on mesenchymal stem cells (MSCs) – stromal cells recruited to tumors due to their natural tropism for inflammatory tissues; MSCs have been shown to enhance the metastatic potential of tumor cells through direct interactions or paracrine signaling within the tumor. In the tumor, MSCs can differentiate into carcinoma-associated fibroblasts that play a central role in tumor growth and matrix remodeling. We recently investigated the molecular and mechanical differences in pre- and post- senescent MSCs and how their interactions with MDA-MB-231 breast cancer cells contribute to malignancy. Our data show post-senescent MSCs are larger and less motile, with more homogeneous mechanical properties than pre-senescent MSCs. In-depth omics analysis revealed differentially regulated genes and peptides including factors related to inflammatory cytokines, cell adhesion to the extracellular matrix, and cytoskeletal regulation. A 3D co-culture model was used to assess the effects of pre- and post- senescent MSCs on collagen matrix remodeling. Although post-senescent MSCs were far less motile than pre-senescent MSCs and less contractile with the matrix, they profoundly altered matrix protein deposition and crosslinking, which resulted in local matrix stiffening effects. Post-senescent MSCs also induced an invasive breast cancer cell phenotype, characterized by increased proliferation and invasion of breast cancer cells. This invasive breast cancer cell behavior was further amplified when MDA-MB-231 was co-cultured with a mixture of pre- and post- senescent MSCs; this result was attributed to matrix remodeling and soluble factor secretion effects of post-senescent MSCs, which enhanced the migration of pre-senescent MSCs allowing them to form tracks in the collagen network for cancer cells to follow. Finally, molecular inhibitors targeting actomyosin contractility and adhesion were used to alter MSC interactions with breast cancer cells. Actin depolymerizing agent and focal adhesion kinase inhibitor were most efficient and completely able to block the effects of post-senescent MSCs on MDA-MB-231 invasion in collagen gels. This comprehensive approach can be used to identify molecular pathways regulating heterotypic interactions of post-senescent MSCs with other cells in the tumor. Furthermore, the local matrix stiffening effect of post-senescent MSCs may play a critical role in breast cancer progression.more » « less
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Abstract Cancer cells are mechanically sensitive to physical properties of the microenvironment, which can affect downstream signaling to promote malignancy, in part through the modulation of metabolic pathways. Fluorescence Lifetime Imaging Microscopy (FLIM) can be used to measure the fluorescence lifetime of endogenous fluorophores, such as the metabolic co-factors NAD(P)H and FAD, in live samples. We used multiphoton FLIM to investigate the changes in cellular metabolism of 3D breast spheroids derived from MCF-10A and MD-MB-231 cell lines embedded in collagen with varying densities (1 vs. 4 mg/ml) over time (Day 0 vs. Day 3). MCF-10A spheroids demonstrated spatial gradients, with the cells closest to the spheroid edge exhibiting FLIM changes consistent with a shift towards oxidative phosphorylation (OXPHOS) while the spheroid core had changes consistent with a shift towards glycolysis. The MDA-MB-231 spheroids had a large shift consistent with increased OXPHOS with a more pronounced change at the higher collagen concentration. The MDA-MB-231 spheroids invaded into the collagen gel over time and cells that traveled the farthest had the largest changes consistent with a shift towards OXPHOS. Overall, these results suggest that the cells in contact with the extracellular matrix (ECM) and those that migrated the farthest had changes consistent with a metabolic shift towards OXPHOS. More generally, these results demonstrate the ability of multiphoton FLIM to characterize how spheroids metabolism and spatial metabolic gradients are modified by physical properties of the 3D ECM.
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Xu, Jinbo (Ed.)Abstract Motivation Motions of transmembrane receptors on cancer cell surfaces can reveal biophysical features of the cancer cells, thus providing a method for characterizing cancer cell phenotypes. While conventional analysis of receptor motions in the cell membrane mostly relies on the mean-squared displacement plots, much information is lost when producing these plots from the trajectories. Here we employ deep learning to classify breast cancer cell types based on the trajectories of epidermal growth factor receptor (EGFR). Our model is an artificial neural network trained on the EGFR motions acquired from six breast cancer cell lines of varying invasiveness and receptor status: MCF7 (hormone receptor positive), BT474 (HER2-positive), SKBR3 (HER2-positive), MDA-MB-468 (triple negative, TN), MDA-MB-231 (TN) and BT549 (TN). Results The model successfully classified the trajectories within individual cell lines with 83% accuracy and predicted receptor status with 85% accuracy. To further validate the method, epithelial–mesenchymal transition (EMT) was induced in benign MCF10A cells, noninvasive MCF7 cancer cells and highly invasive MDA-MB-231 cancer cells, and EGFR trajectories from these cells were tested. As expected, after EMT induction, both MCF10A and MCF7 cells showed higher rates of classification as TN cells, but not the MDA-MB-231 cells. Whereas deep learning-based cancer cell classifications are primarily based on the optical transmission images of cell morphology and the fluorescence images of cell organelles or cytoskeletal structures, here we demonstrated an alternative way to classify cancer cells using a dynamic, biophysical feature that is readily accessible. Availability and implementation A python implementation of deep learning-based classification can be found at https://github.com/soonwoohong/Deep-learning-for-EGFR-trajectory-classification. Supplementary information Supplementary data are available at Bioinformatics online.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/d1mo00117e
