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1. Deep learning-based classification of breast cancer cells using transmembrane receptor dynamics

   https://doi.org/10.1093/bioinformatics/btab581  

   Kim, Mirae; Hong, Soonwoo; Yankeelov, Thomas E; Yeh, Hsin-Chih; Liu, Yen-Liang (August 2021, Bioinformatics)

   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. 

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2. Functional assessment of migration and adhesion to quantify cancer cell aggression

   https://doi.org/10.1039/D4SM01351D  

   Mehanna, Lauren E; Boyd, James D; Walker, Chloe G; Osborne, Adrianna R; Grady, Martha E; Berron, Brad J (April 2025, Soft Matter)

   During epithelial-to-mesenchymal transition (EMT), cancer cells lose their cell–cell adhesion junctions as they become more metastatic, altering cell motility and focal adhesion disassembly associated with increased detachment from the primary tumor and a migratory response into nearby tissue and vasculature. Current in vitro strategies characterizing a cell's metastatic potential heavily favor quantifying the presence of cell adhesion biomarkers through biochemical analysis; however, mechanical cues such as adhesion and motility directly relate to cell metastatic potential without needing to first identify a cell specific biomarker for a particular type of cancer. This paper presents a comprehensive comparison of two functional metrics of cancer aggression, wound closure migration velocity and cell detachment from a culture surface, for three pairs of epithelial cancer cell lines (breast, endometrium, tongue tissue origins). It was found that one functional metric alone was not sufficient to categorize the cancer cell lines; instead, both metrics were necessary to identify functional trends and accurately place cells on the spectrum of metastasis. On average, cell lines with low metastatic potential (MCF-7, Ishikawa, and Cal-27) were more aggressive through wound closure migration compared to loss of cell adhesion. On the other hand, cell lines with high metastatic potential (MDA-MB-231, KLE, and SCC-25) were on average more aggressive through loss of cell adhesion compared to wound closure migration. This trend was true independent of the tissue type where the cells originated, indicating that there is a relationship between metastatic potential and the predominate type of cancer aggression. Our work presents one of the first combined studies relating cell metastatic potential to functional migration and adhesion metrics across cancer cell lines from selected tissue origins, without needing to identify tissue-specific biomarkers to achieve success. Using functional metrics provides powerful clinical relevancy for future predictive tools of cancer metastasis. 

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3. Mesenchymal Stem Cell Aging and Senescence Associated Extracellular Matrix Contributions to Breast Cancer Progression

   Ghosh, Deepraj; Quach, Nhat; Pena, Carolina; Xuan, Botai; Lee, Amy; Dawson, Michelle (February 2019, Gordon Research Conference: Physical Science of Cancer)

   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. 

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4. Metabolomics revealed the influence of breast cancer on lymphatic endothelial cell metabolism, metabolic crosstalk, and lymphangiogenic signaling in co-culture

   https://doi.org/10.1038/s41598-020-76394-7  

   Acevedo-Acevedo, Suehelay; Millar, Douglas C.; Simmons, Aaron D.; Favreau, Peter; Cobra, Paulo F.; Skala, Melissa; Palecek, Sean P. (December 2020, Scientific Reports)

   Abstract Breast cancer metastasis occurs via blood and lymphatic vessels. Breast cancer cells ‘educate’ lymphatic endothelial cells (LECs) to support tumor vascularization and growth. However, despite known metabolic alterations in breast cancer, it remains unclear how lymphatic endothelial cell metabolism is altered in the tumor microenvironment and its effect in lymphangiogenic signaling in LECs. We analyzed metabolites inside LECs in co-culture with MCF-7, MDA-MB-231, and SK-BR-3 breast cancer cell lines using $$^1\hbox {H}$$ 1 H nuclear magnetic resonance (NMR) metabolomics, Seahorse, and the spatial distribution of metabolic co-enzymes using optical redox ratio imaging to describe breast cancer-LEC metabolic crosstalk. LECs co-cultured with breast cancer cells exhibited cell-line dependent altered metabolic profiles, including significant changes in lactate concentration in breast cancer co-culture. Cell metabolic phenotype analysis using Seahorse showed LECs in co-culture exhibited reduced mitochondrial respiration, increased reliance on glycolysis and reduced metabolic flexibility. Optical redox ratio measurements revealed reduced NAD(P)H levels in LECs potentially due to increased NAD(P)H utilization to maintain redox homeostasis. $$^{13}\hbox {C}$$ 13 C -labeled glucose experiments did not reveal lactate shuttling into LECs from breast cancer cells, yet showed other $$^{13}\hbox {C}$$ 13 C signals in LECs suggesting internalized metabolites and metabolic exchange between the two cell types. We also determined that breast cancer co-culture stimulated lymphangiogenic signaling in LECs, yet activation was not stimulated by lactate alone. Increased lymphangiogenic signaling suggests paracrine signaling between LECs and breast cancer cells which could have a pro-metastatic role. 

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5. Dose-Dependent Growth Delay of Breast Cancer Xenografts in the Bone Marrow of Mice Treated with Ra-223: The Role of Bystander Effects and Their Potential for Therapy

   https://doi.org/DOI: 10.2967  

   Leung, C. N.; Canter, B. S.; Rajon, D.; Back, T. A.; Fritton, J. C.; Azzam, E. I.; Howell, R. W. (January 2020, NM Journal of nuclear medicine)

   The role of radiation-induced bystander effects in radiation therapy remains unclear. With renewed interest in therapy with alpha-particle emitters, and their potential for sterilizing disseminated tumor cells (DTCs), it is critical to determine the contribution of bystander effects to the overall response so they can be leveraged for maximum clinical benefit. Methods: Female Foxn1(nu) athymic nude mice were administered 0, 50, or 600 kBq/kg (RaCl2)-Ra-223 to create bystander conditions. At 24 hours after administration, MDA-MB-231 or MCF-7 human breast cancer cells expressing luciferase were injected into the tibial marrow compartment. Tumor burden was tracked weekly via bioluminescence. Results: The MDA-MB-231 xenografts were observed to have a 10-day growth delay in the 600 kBq/kg treatment group only. In contrast, MCF-7 cells had 7-and 65-day growth delays in the 50 and 600 kBq/kg groups, respectively. Histologic imaging of the tibial marrow compartment, alpha-camera imaging, and Monte Carlo dosimetry modeling revealed DTCs both within and beyond the range of the alpha-particles emitted from Ra-223 in bone for both MCF-7 and MDA-MB-231 cells. Conclusion: Taken together, these results support the participation of Ra-22(3)-induced antiproliferative/cytotoxic bystander effects in delayed growth of DTC xenografts. They indicate that the delay depends on the injected activity and therefore is dose-dependent. They suggest using (RaCl2)-Ra-223 as an adjuvant treatment for select patients at early stages of breast cancer. 

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