Biology subjects

Ferraro, R.

Publications and source records attributed to Ferraro, R..

3 recordsLinked to original sources

bioRxiv2025

Mechanical Fingerprints in Breast Cancer Research: A Multimodal Experimental Approach

Breast cancer remains the leading cause of cancer-related mortality among women worldwide. Tumor biomechanics are not merely a symptom: they represent a functional signature with translational relevance in diagnostic, prognostic, and therapeutic resistance. Despite this, few experimental models are engineered to systematically investigate these physical properties across biological systems. Here, this study presents a multimodal biomechanical platform combining engineered 3D breast cancer spheroids with ex vivo tissue analysis to profiling and compare viscoelastic behavior or of healthy and tumoral environments. Rheometry and compression testing revealed a consistent mechanical shift in tumor-derived samples marked by increased stiffness and force-dependent nonlinear behavior, mirroring the ECM remodeling typical of aggressive phenotypes. This increased rigidity may adversely affect chemotherapy effectiveness by hindering drug delivery and altering cellular mechanotransduction. These biomechanical fingerprints enable quantitative discrimination between healthy and cancerous tissues and can serve as a surrogate maker of malignancy. By supporting the development of mechanics-informed diagnostic tools, our platform offers a reproducible, clinically relevant framework to integrate biomechanical screening into translational breast cancer pipelines. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=166 SRC="FIGDIR/small/680332v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@2c87a3org.highwire.dtl.DTLVardef@17cecc8org.highwire.dtl.DTLVardef@9d56d3org.highwire.dtl.DTLVardef@1af509a_HPS_FORMAT_FIGEXP M_FIG C_FIG Translational Impact StatementWe propose a multimodal experimental approach that combines in vitro 3D breast-cancer models and ex vivo tissue analysis to measure and compare the viscoelastic properties of healthy and malignant breast tissues. By using mechanical behaviour as a fingerprint, this framework discriminates tumour tissue from its healthy counterpart. Revealing how tumour stiffness impacts drug delivery and therapy resistance, the approach provides a clinically relevant tool to inform diagnosis and optimise treatment strategies.

bioengineering↗

bioRxiv2023

Hybrid Cellular Automata Modelling Reveals the Effects of Glucose Gradients on Tumour Spheroid Growth

PurposeIn recent years, mathematical models have become instrumental in cancer research, offering insights into tumor growth dynamics, and guiding the development of pharmacological strategies. These models, encompassing diverse biological and physical processes, are increasingly used in clinical settings, showing remarkable predictive precision for individual patient outcomes and therapeutic responses. MethodsMotivated by these advancements, our study introduces an innovative in silico model for simulating tumor growth and invasiveness. The Automated Hybrid Cell emulates critical tumor cell characteristics, including rapid proliferation, heightened motility, reduced cell adhesion, and increased responsiveness to chemotactic signals. This model explores the potential evolution of 3D tumor spheroids by manipulating biological parameters and microenvironment factors, focusing on nutrient availability. ResultsOur comprehensive Global and Local Sensitivity Analyses reveal that tumor growth primarily depends on cell duplication speed and cell-to-cell adhesion, rather than external chemical gradients. Conversely, tumor invasiveness is predominantly driven by chemotaxis. These insights illuminate tumor development mechanisms, providing vital guidance for effective strategies against tumor progression. Our proposed model is a valuable tool for advancing cancer biology research and exploring potential therapeutic interventions. Simple SummaryIn recent years, mathematical models have revolutionized cancer research, illuminating the complex dynamics of tumor growth and aiding drug development. These models, reflecting biological and physical processes, are increasingly used in clinical practice, offering precise patient-specific predictions. Our work introduces an innovative in silico model to simulate tumor growth and invasiveness. The Automated Hybrid Cell, replicating key tumor cell features, enables exploration of 3D tumor spheroid evolution. Sensitivity analyses reveal that tumor growth is primarily influenced by cell replication speed and adhesion, while invasiveness relies on chemotaxis. These insights shed light on tumor development mechanisms, guiding effective strategies against tumor progression. Our model serves as a valuable tool for advancing cancer biology research and potential therapeutic interventions.

bioengineering↗

bioRxiv2023

Thermal suppression of gametogenesis explains historical collapses in larval recruitment

Projections for population viability under climate change are often made using estimates of thermal lethal thresholds. These estimates vary across life history stages and can be valuable for explaining or forecasting shifts in population viability. However, sublethal temperatures can also depress vital rates and shape fluctuations in the reproductive viability of populations. For example, heatwaves may suppress reproduction, leading to recruitment failure before lethal temperatures are reached. Despite a growing awareness of this issue, tying sublethal effects to observed recruitment failure remains a challenge especially in marine environments. We experimentally show that sublethal suppression of female gametogenesis by marine heatwaves can partially explain historical collapses in urchin recruitment. This response differed by sex but was similar between animals from warmer or cooler regions of their range. Overall, we show sublethal thermal sensitivities of reproduction can narrow the thermal envelope for population viability compared to predictions from lethal limits.

ecology↗