Biology subjects

Sneider, A.

Publications and source records attributed to Sneider, A..

3 recordsLinked to original sources

bioRxiv2023

Small extracellular vesicles promote stiffness-mediated metastasis

Tissue stiffness is a critical prognostic factor in breast cancer and is associated with metastatic progression. Here we show an alternative and complementary hypothesis of tumor progression whereby physiological matrix stiffness affects the quantity and protein cargo of small EVs produced by cancer cells, which in turn drive their metastasis. Primary patient breast tissue produces significantly more EVs from stiff tumor tissue than soft tumor adjacent tissue. EVs released by cancer cells on matrices that model human breast tumors (25 kPa; stiff EVs) feature increased adhesion molecule presentation (ITG2{beta}1, ITG6{beta}4, ITG6{beta}1, CD44) compared to EVs from softer normal tissue (0.5 kPa; soft EVs), which facilitates their binding to extracellular matrix (ECM) protein collagen IV, and a 3-fold increase in homing ability to distant organs in mice. In a zebrafish xenograft model, stiff EVs aid cancer cell dissemination through enhanced chemotaxis. Moreover, normal, resident lung fibroblasts treated with stiff and soft EVs change their gene expression profiles to adopt a cancer associated fibroblast (CAF) phenotype. These findings show that EV quantity, cargo, and function depend heavily on the mechanical properties of the extracellular microenvironment. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/545937v3_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@1be1dbdorg.highwire.dtl.DTLVardef@928710org.highwire.dtl.DTLVardef@1e14e9borg.highwire.dtl.DTLVardef@efdb7f_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2020

Deep Learning Identification of Stiffness Markers in Breast Cancer

While essential to our understanding of solid tumor progression, the study of cell and tissue mechanics has yet to find traction in the clinic. Determining tissue stiffness, a mechanical property known to promote a malignant phenotype in vitro and in vivo, is not part of the standard algorithm for the diagnosis and treatment of breast cancer. Instead, clinicians routinely use mammograms to identify malignant lesions and radiographically dense breast tissue is associated with an increased risk of developing cancer. Whether breast density is related to tumor tissue stiffness, and what cellular and non-cellular components of the tumor contribute the most to its stiffness are not well understood. Through training of a deep learning network and mechanical measurements of fresh patient tissue, we create a bridge in understanding between clinical and mechanical markers. The automatic identification of cellular and extracellular features from hematoxylin and eosin (H&E)-stained slides reveals that global and local breast tissue stiffness best correlate with the percentage of straight collagen. Global breast tissue mechanics correlate weakly with the percentage of blood vessels and fibrotic tissue, and non-significantly with the percentage of fat, ducts, tumor cells, and wavy collagen in tissue. Importantly, the percentage of dense breast tissue does not directly correlate with tissue stiffness or straight collagen content.

cancer biology↗

bioRxiv2019

Confronting p-hacking: addressing p-value dependence on sample size

Biomedical research has come to rely on p-values as a deterministic measure for data-driven decision making. In the largely extended null-hypothesis significance testing (NHST) for identifying statistically significant differences among groups of observations, a single p-value computed from sample data is routinely compared with a threshold, commonly set to 0.05, to assess the evidence against the hypothesis of having non-significant differences among groups, or the null hypothesis. Because the estimated p-value tends to decrease when the sample size is increased, applying this methodology to large datasets results in the rejection of the null hypothesis, making it not directly applicable in this specific situation. Herein, we propose a systematic and easy-to-follow method to detect differences based on the dependence of the p-value on the sample size. The proposed method introduces new descriptive parameters that overcome the effect of the size in the p-value interpretation in the framework of large datasets, reducing the uncertainty in the decision about the existence of biological/clinical differences between the compared experiments. This methodology enables both the graphical and quantitative characterization of the differences between the compared experiments guiding the researchers in the decision process. An in-depth study of the proposed methodology is carried out using both simulated and experimentally obtained data. Simulations show that under controlled data, our assumptions on the p-value dependence on the sample size holds. The results of our analysis in the experimental datasets reflect the large scope of this approach and its interpretability in terms of common decision-making and data characterization tasks. For both simulated and real data, the obtained results are robust to sampling variations within the dataset.

bioinformatics↗