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Tirella, A.

Publications and source records attributed to Tirella, A..

2 recordsLinked to original sources
bioRxiv2025

A data-informed approach for engineering in-vitro experiment design to decipher key features of invasive breast cancer cell phenotypes

The intrinsic complexity of biological processes often hides the role of dynamic microenvironmental cues in the development of pathological states. The use of micro-physiological systems (MPS) offers new technological platforms designed to model the dynamics of tissue-specific microenvironments in vitro and to holistically understand healthy and pathological states. In our previous works, we reported on engineering breast critical tumor microenvironment features, including matrix stiffness, pH, and fluid flow, and use the MPSs to study breast cancer cells phenotypes. By studying different microenvironments mimicking normal and tumor breast tissues, we obtained high-dimensional data using two distinctive human breast cell lines (i.e., MDA-MB231, MCF-7) investigating biomarkers commonly used in cancer in vitro models as cell proliferation, epithelial-to-mesenchymal transition (EMT), and breast cancer stem cell markers (B-CSC). We herein report on a new approach used to explore the complexity of MPSs and the high dimensional datasets: we introduce an innovative machine learning (ML) based platform employing unsupervised k-means clustering and feature extraction to identify key markers that differentiated invasive from non-invasive breast cell phenotypes. This novel data-driven approach streamlines experimental design and emphasizes the translational potential of integrating MPS-derived insights with ML to refine prognostic tools and personalize therapeutic strategies. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/643499v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@4f947eorg.highwire.dtl.DTLVardef@9e1aedorg.highwire.dtl.DTLVardef@1f9fe8corg.highwire.dtl.DTLVardef@1b6b91d_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗
bioRxiv2022

Invasion and Secondary Site Colonization as a function of In vitro Primary Tumor Matrix Stiffness

Increased breast tissue stiffness is correlated with breast cancer risk and invasive cancer progression. However, its role in promoting bone metastasis, which shares a large burden of breast cancer deaths, has not yet been understood. To better understand the cause-effect relationship of tissue stiffness on breast cancers metastatic potential, we fabricated three-dimensional (3D) models to mimic breast and bone tissue in vitro. Based on our previous work, we used alginate-based hydrogels allowing precise control over stiffness and composition of extracellular breast tissue matrix; and 3D printed poly-caprolactone (PCL)-composite scaffolds to mimic the bone. The latter were further modified by promoting bone-ECM deposition using Saos-2 cells. After a decellularization step, PCL scaffolds were assembled with alginate-gelatin hydrogels and a novel breast-to-bone in vitro model was established. It was observed that increased stiffness of hydrogel resulted in higher migration and invasion capacity of MDA-MB 231 cells. Additionally, PTHrP and IL-6 expression, both of which are implicated in bone metastasis, were higher when cells from stiff hydrogels were cultured in bone/PCL scaffolds. These breast-to-bone in vitro models pose as a novel non-animal technology to pave the way for incorporating important tissue microenvironmental factors of the disease physiology (e.g. tissue stiffness) and emerge as promising future platforms for monitoring metastatic disease phenotypes and therapeutic efficacy.

bioengineering↗