Classification of triple-negative breast cancers through a Boolean network model of the epithelial-mesenchymal transition

Artificial Intelligence for Precision Oncology of Triple-Negative Breast Cancer: Learning from Melanoma

Thanks to new technologies using artificial intelligence (AI) and machine learning, it is possible to use large amounts of data to try to extract information that can be used for personalized medicine. The great challenge of the future is, on the one hand, to acquire masses of biological data that nowadays are still limited and, on the other hand, to develop innovative strategies to extract information that can then be used for the development of predictive models. From this perspective, we discuss these aspects in the context of triple-negative breast cancer, a tumor where a specific treatment is still lacking and new therapies, such as immunotherapy, are under investigation. Since immunotherapy is already in use for other tumors such as melanoma, we discuss the strengths and weaknesses identified in the use of immunotherapy with melanoma to try to find more successful strategies. It is precisely in this context that AI and predictive tools can be extremely valuable. Therefore, the discoveries and advancements in immunotherapy for melanoma provide a foundation for developing effective immunotherapies for triple-negative breast cancer. Shared principles, such as immune system activation, checkpoint inhibitors, and personalized treatment, can be applied to TNBC to improve patient outcomes and offer new hope for those with aggressive, hard-to-treat breast cancer.

Fibrin Stiffness Regulates Phenotypic Plasticity of Metastatic Breast Cancer Cells

The extracellular matrix (ECM)-regulated phenotypic plasticity is crucial for metastatic progression of triple negative breast cancer (TNBC). While ECM faithful cell-based models are available for in situ and invasive tumors, such as cell aggregate cultures in reconstituted basement membrane and in collagenous gels, there are no ECM faithful models for metastatic circulating tumor cells (CTCs). Such models are essential to represent the stage of metastasis where clinical relevance and therapeutic opportunities are significant. Here, CTC-like DU4475 TNBC cells are cultured in mechanically tunable 3D fibrin hydrogels. This is motivated, as in circulation fibrin aids CTC survival by forming a protective coating reducing shear stress and immune cell-mediated cytotoxicity and promotes several stages of late metastatic processes at the interface between circulation and tissue. This work shows that fibrin hydrogels support DU4475 cell growth, resulting in spheroid formation. Furthermore, increasing fibrin stiffness from 57 to 175 Pa leads to highly motile, actin and tubulin containing cellular protrusions, which are associated with specific cell morphology and gene expression patterns that markedly differ from basement membrane or suspension cultures. Thus, mechanically tunable fibrin gels reveal specific matrix-based regulation of TNBC cell phenotype and offer scaffolds for CTC-like cells with better mechano-biological properties than liquid.

The Response of Triple-Negative Breast Cancer to Neoadjuvant Chemotherapy and the Epithelial–Mesenchymal Transition

It would be highly desirable to find prognostic and predictive markers for triple-negative breast cancer (TNBC), a strongly heterogeneous and invasive breast cancer subtype often characterized by a high recurrence rate and a poor outcome. Here, we investigated the prognostic and predictive capabilities of ARIADNE, a recently developed transcriptomic test focusing on the epithelial–mesenchymal transition. We first compared the stratification of TNBC patients obtained by ARIADNE with that based on other common pathological indicators, such as grade, stage and nodal status, and found that ARIADNE was more effective than the other methods in dividing patients into groups with different disease-free survival statistics. Next, we considered the response to neoadjuvant chemotherapy and found that the classification provided by ARIADNE led to statistically significant differences in the rates of pathological complete response within the groups.

Artificial intelligence in breast cancer diagnostics

Since breast cancer deaths are mainly due to metastasis, predicting the risk that a primary tumor will develop metastasis after a first diagnosis is a central issue that could be addressed by artificial intelligence. To overcome the problem posed by limited availability of standardized datasets, algorithms should include biological insight.

Nuclear Biophysical Changes during Human Melanoma Plasticity

Tumor plasticity is an emerging property of tumor cells which allows them to change their phenotype in dependence on the environment. The epithelial-mesenchymal transition plays a crucial role in helping cells acquire a more aggressive phenotype when they are in the mesenchymal state. Herein, we investigated the biophysical changes occurring during phenotypic switching in human melanoma cells, considering the blebbiness of the nuclei, their stiffness, and the involvement of polycombs with lamins. We show that the formation of cellular heterogeneity involves many crucial nuclear changes including the interaction between different types of polycombs with lamins and chromosome accessibility. Altogether, our results shed new light on the molecular mechanisms involved in the formation of a heterogeneous cell population during phenotypic switching. In particular, our results show that phenotypic switching in melanoma involves chromatin remodeling changing the transcriptional activity of cells and consequently their phenotype.

Advancement of cell-penetrating peptides in combating triple-negative breast cancer

Extensive research efforts have been made and are still ongoing in the search for an ideal anti-cancer therapy. Almost all chemotherapeutics require a carrier or vehicle, a drug delivery system that can transport the drug specifically to the targeted cancer cells, sparing normal cells. Cell-penetrating peptides (CPPs) provide an effective and efficient pathway for the intra-cellular transportation of various bioactive molecules in several biomedical therapies. They are now well-recognized as facilitators of intracellular cargo delivery and have excellent potential for targeted anti-cancer therapy. In this review, we explain CPPs, recent progress in the development of new CPPs, and their utilization to transport cargoes such as imaging agents, chemotherapeutics, and short-interfering RNAs (siRNA) into tumor cells, contributing to the advancement of novel tumor-specific delivery systems.

Classification of triple negative breast cancer by epithelial mesenchymal transition and the tumor immune microenvironment

Triple-negative breast cancer (TNBC) accounts for about 15–20% of all breast cancers and differs from other invasive breast cancer types because it grows and spreads rapidly, it has limited treatment options and typically worse prognosis. Since TNBC does not express estrogen or progesterone receptors and little or no human epidermal growth factor receptor (HER2) proteins are present, hormone therapy and drugs targeting HER2 are not helpful, leaving chemotherapy only as the main systemic treatment option. In this context, it would be important to find molecular signatures able to stratify patients into high and low risk groups. This would allow oncologists to suggest the best therapeutic strategy in a personalized way, avoiding unnecessary toxicity and reducing the high costs of treatment. Here we compare two independent patient stratification strategies for TNBC based on gene expression data: The first is focusing on the epithelial mesenchymal transition (EMT) and the second on the tumor immune microenvironment. Our results show that the two stratification strategies are not directly related, suggesting that the aggressiveness of the tumor can be due to a multitude of unrelated factors. In particular, the EMT stratification is able to identify a high-risk population with high immune markers that is, however, not properly classified by the tumor immune microenvironment based strategy.

Single-Cell RNA-Seq Analysis Reveals the Acquisition of Cancer Stem Cell Traits and Increase of Cell-Cell Signaling during EMT Progression

Intermediate cell states (ICSs) during the epithelial-mesenchymal transition (EMT) are emerging as a driving force of cancer invasion and metastasis. ICSs typically exhibit hybrid epithelial/mesenchymal characteristics as well as cancer stem cell (CSC) traits including proliferation and drug resistance. Here, we analyze several single-cell RNA-seq (scRNA-seq) datasets to investigate the relation between several axes of cancer progression including EMT, CSC traits, and cell-cell signaling. To accomplish this task, we integrate computational methods for clustering and trajectory inference with analysis of EMT gene signatures, CSC markers, and cell-cell signaling pathways, and highlight conserved and specific processes across the datasets. Our analysis reveals that "standard" measures of pluripotency often used in developmental contexts do not necessarily correlate with EMT progression and expression of CSC-related markers. Conversely, an EMT circuit energy that quantifies the co-expression of epithelial and mesenchymal genes consistently increases along EMT trajectories across different cancer types and anatomical locations. Moreover, despite the high context specificity of signal transduction across different cell types, cells undergoing EMT always increased their potential to send and receive signals from other cells.

An Integrated View of Virus-Triggered Cellular Plasticity Using Boolean Networks

Virus-related mortality and morbidity are due to cell/tissue damage caused by replicative pressure and resource exhaustion, e.g., HBV or HIV; exaggerated immune responses, e.g., SARS-CoV-2; and cancer, e.g., EBV or HPV. In this context, oncogenic and other types of viruses drive genetic and epigenetic changes that expand the tumorigenic program, including modifications to the ability of cancer cells to migrate. The best-characterized group of changes is collectively known as the epithelial–mesenchymal transition, or EMT. This is a complex phenomenon classically described using biochemistry, cell biology and genetics. However, these methods require enormous, often slow, efforts to identify and validate novel therapeutic targets. Systems biology can complement and accelerate discoveries in this field. One example of such an approach is Boolean networks, which make complex biological problems tractable by modeling data (“nodes”) connected by logical operators. Here, we focus on virus-induced cellular plasticity and cell reprogramming in mammals, and how Boolean networks could provide novel insights into the ability of some viruses to trigger uncontrolled cell proliferation and EMT, two key hallmarks of cancer.