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1. Tracking the Progression of Triple Negative Mammary Tumors over Time by Chemometric Analysis of Urinary Volatile Organic Compounds

   https://doi.org/10.3390/cancers13061462  

   Woollam, Mark; Wang, Luqi; Grocki, Paul; Liu, Shengzhi; Siegel, Amanda P.; Kalra, Maitri; Goodpaster, John V.; Yokota, Hiroki; Agarwal, Mangilal (March 2021, Cancers)

   null (Ed.)

   Previous studies have shown that volatile organic compounds (VOCs) are potential biomarkers of breast cancer. An unanswered question is how urinary VOCs change over time as tumors progress. To explore this, BALB/c mice were injected with 4T1.2 triple negative murine tumor cells in the tibia. This typically causes tumor progression and osteolysis in 1–2 weeks. Samples were collected prior to tumor injection and from days 2–19. Samples were analyzed by headspace solid phase microextraction coupled to gas chromatography–mass spectrometry. Univariate analysis identified VOCs that were biomarkers for breast cancer; some of these varied significantly over time and others did not. Principal component analysis was used to distinguish Cancer (all Weeks) from Control and Cancer Week 1 from Cancer Week 3 with over 90% accuracy. Forward feature selection and linear discriminant analysis identified a unique panel that could identify tumor presence with 94% accuracy and distinguish progression (Cancer Week 1 from Cancer Week 3) with 97% accuracy. Principal component regression analysis also demonstrated that a VOC panel could predict number of days since tumor injection (R2 = 0.71 and adjusted R2 = 0.63). VOC biomarkers identified by these analyses were associated with metabolic pathways relevant to breast cancer. 

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2. Primary Human Breast Cancer‐Associated Endothelial Cells Favor Interactions with Nanomedicines

   https://doi.org/10.1002/adma.202403986  

   Wang, Lin; Sheth, Vinit; Liu, Kaili; Panja, Prasanta; Frickenstein, Alex N; He, Yuxin; Yang, Wen; Thomas, Abigail G; Jamei, Mohammad Hasan; Park, Jeesoo; et al (May 2024, Advanced Materials)

   Abstract Cancer nanomedicines predominately rely on transport processes controlled by tumor‐associated endothelial cells to deliver therapeutic and diagnostic payloads into solid tumors. While the dominant role of this class of endothelial cells for nanoparticle transport and tumor delivery is established in animal models, the translational potential in human cells needs exploration. Using primary human breast cancer as a model, the differential interactions of normal and tumor‐associated endothelial cells with clinically relevant nanomedicine formulations are explored and quantified. Primary human breast cancer‐associated endothelial cells exhibit up to ≈2 times higher nanoparticle uptake than normal human mammary microvascular endothelial cells. Super‐resolution imaging studies reveal a significantly higher intracellular vesicle number for tumor‐associated endothelial cells, indicating a substantial increase in cellular transport activities. RNA sequencing and gene expression analysis indicate the upregulation of transport‐related genes, especially motor protein genes, in tumor‐associated endothelial cells. Collectively, the results demonstrate that primary human breast cancer‐associated endothelial cells exhibit enhanced interactions with nanomedicines, suggesting a potentially significant role for these cells in nanoparticle tumor delivery in human patients. Engineering nanoparticles that leverage the translational potential of tumor‐associated endothelial cell‐mediated transport into human solid tumors may lead to the development of safer and more effective clinical cancer nanomedicines. 

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3. Visual Intratumor Heterogeneity and Breast Tumor Progression

   https://doi.org/10.3390/cancers16132294  

   Li, Yao; Alsten, Sarah_C Van; Lee, Dong Neuck; Kim, Taebin; Calhoun, Benjamin C; Perou, Charles M; Wobker, Sara E; Marron, J S; Hoadley, Katherine A; Troester, Melissa A (July 2024, Cancers)

   High intratumoral heterogeneity is thought to be a poor prognostic indicator. However, the source of heterogeneity may also be important, as genomic heterogeneity is not always reflected in histologic or ‘visual’ heterogeneity. We aimed to develop a predictor of histologic heterogeneity and evaluate its association with outcomes and molecular heterogeneity. We used VGG16 to train an image classifier to identify unique, patient-specific visual features in 1655 breast tumors (5907 core images) from the Carolina Breast Cancer Study (CBCS). Extracted features for images, as well as the epithelial and stromal image components, were hierarchically clustered, and visual heterogeneity was defined as a greater distance between images from the same patient. We assessed the association between visual heterogeneity, clinical features, and DNA-based molecular heterogeneity using generalized linear models, and we used Cox models to estimate the association between visual heterogeneity and tumor recurrence. Basal-like and ER-negative tumors were more likely to have low visual heterogeneity, as were the tumors from younger and Black women. Less heterogeneous tumors had a higher risk of recurrence (hazard ratio = 1.62, 95% confidence interval = 1.22–2.16), and were more likely to come from patients whose tumors were comprised of only one subclone or had a TP53 mutation. Associations were similar regardless of whether the image was based on stroma, epithelium, or both. Histologic heterogeneity adds complementary information to commonly used molecular indicators, with low heterogeneity predicting worse outcomes. Future work integrating multiple sources of heterogeneity may provide a more comprehensive understanding of tumor progression. 

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4. Three-dimensional models of breast cancer–fibroblasts interactions

   https://doi.org/10.1177/1535370220917366  

   Singh, Sunil; Tran, Sydnie; Putman, Justin; Tavana, Hossein (May 2020, Experimental Biology and Medicine)

   Tumor microenvironment is a complex niche consisting of cancer cells and stromal cells in a network of extracellular matrix proteins and various soluble factors. Dynamic interactions among cellular and non-cellular components of the tumor microenvironment regulate tumor initiation and progression. Fibroblasts are the most abundant stromal cell type and dynamically interact with cancer cells both in primary tumors and in metastases. Cancer cells activate resident fibroblasts to produce and secrete soluble signaling molecules that support proliferation, migration, matrix invasion, and drug resistance of cancer cell and tumor angiogenesis. In recent years, various forms of three-dimensional tumor models have been developed to study tumor–stromal interactions and to identify anti-cancer drugs that block these interactions. There is currently a technological gap in development of tumor models that are physiologically relevant, scalable, and allow convenient, on-demand addition of desired components of the tumor microenvironment. In this review, we discuss three studies from our group that focus on developing bioengineered models to study tumor-stromal signaling. We will present these studies chronologically and based on their increasing complexity. We will discuss the validation of the models using a CXCL12-CXCR4 chemokine-receptor signaling present among activated fibroblasts and breast cancer cells in solid tumors, highlight the advantages and shortcomings of the models, and conclude with our perspectives on their applications. Impact statement Tumor stroma plays an important role in progression of cancers to a fatal metastatic disease. Modern treatment strategies are considering targeting tumor stroma to improve outcomes for cancer patients. A current challenge to develop stroma-targeting therapeutics is the lack of preclinical physiologic tumor models. Animal models widely used in cancer research lack human stroma and are not amenable to screening of chemical compounds for cancer drug discovery. In this review, we outline in vitro three-dimensional tumor models that we have developed to study the interactions among cancer cells and stromal cells. We describe development of the tumor models in a modular fashion, from a spheroid model to a sophisticated organotypic model, and discuss the importance of using correct physiologic models to recapitulate tumor-stromal signaling. These biomimetic tumor models will facilitate understanding of tumor-stromal signaling biology and provide a scalable approach for testing and discovery of cancer drugs. 

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5. Employing Raman Spectroscopy and Machine Learning for the Identification of Breast Cancer

   https://doi.org/10.1186/s12575-024-00255-0  

   Zhang, Ya; Li, Zheng; Li, Zhongqiang; Wang, Huaizhi; Regmi, Dinkar; Zhang, Jian; Feng, Jiming; Yao, Shaomian; Xu, Jian (December 2024, Biological Procedures Online)

   Abstract BackgroundBreast cancer poses a significant health risk to women worldwide, with approximately 30% being diagnosed annually in the United States. The identification of cancerous mammary tissues from non-cancerous ones during surgery is crucial for the complete removal of tumors. ResultsOur study innovatively utilized machine learning techniques (Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN)) alongside Raman spectroscopy to streamline and hasten the differentiation of normal and late-stage cancerous mammary tissues in mice. The classification accuracy rates achieved by these models were 94.47% for RF, 96.76% for SVM, and 97.58% for CNN, respectively. To our best knowledge, this study was the first effort in comparing the effectiveness of these three machine-learning techniques in classifying breast cancer tissues based on their Raman spectra. Moreover, we innovatively identified specific spectral peaks that contribute to the molecular characteristics of the murine cancerous and non-cancerous tissues. ConclusionsConsequently, our integrated approach of machine learning and Raman spectroscopy presents a non-invasive, swift diagnostic tool for breast cancer, offering promising applications in intraoperative settings. 

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