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 physiologic matrix stiffness affects the quantity and protein cargo of small extracellular vesicles (EV) produced by cancer cells, which in turn aid cancer cell dissemination. Primary patient breast tissue released by cancer cells on matrices that model human breast tumors (25 kPa; stiff EVs) feature increased adhesion molecule presentation (ITGα2ß1, ITGα6ß4, ITGα6ß1, CD44) compared with EVs from softer normal tissue (0.5 kPa; soft EVs), which facilitates their binding to extracellular matrix proteins including 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. Moreover, normal, resident lung fibroblasts treated with stiff and soft EVs change their gene expression profiles to adopt a cancer-associated fibroblast phenotype. These findings show that EV quantity, cargo, and function depend heavily on the mechanical properties of the extracellular microenvironment. SIGNIFICANCE: Here we show that the quantity, cargo, and function of breast cancer-derived EVs vary with mechanical properties of the extracellular microenvironment.

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 (ITGα 2 ß 1 , ITGα 6 ß 4 , ITGα 6 ß 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.

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. Importantly, the percentage of dense breast tissue does not directly correlate with tissue stiffness or straight collagen content.

Use of the p-values as a size-dependent function to address practical differences when analyzing large datasets.

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 for identifying statistically significant differences among groups of observations, a single p-value is computed from sample data. Then, it 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 datasets with large sample sizes results in the rejection of the null hypothesis, making it not meaningful in this specific situation. We propose a new approach to detect differences based on the dependence of the p-value on the sample size. We introduce new descriptive parameters that overcome the effect of the size in the p-value interpretation in the framework of datasets with large sample sizes, reducing the uncertainty in the decision about the existence of biological differences between the compared experiments. The methodology enables 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 methodology is carried out on simulated and experimental data. Code availability at https://github.com/BIIG-UC3M/pMoSS .

Supramolecular Design of Unsymmetric Reverse Bolaamphiphiles for Cell-Sensitive Hydrogel Degradation and Drug Release.

Self-assembly of peptide-based building units into supramolecular nanostructures creates an important class of biomaterials with robust mechanical properties and improved resistance to premature degradation. Yet, upon aggregation, substrate-enzyme interactions are often compromised because of the limited access of macromolecular proteins to the peptide substrate, leading to either a reduction or loss of responsiveness to biomolecular cues. Reported here is the supramolecular design of unsymmetric reverse bolaamphiphiles (RBA) capable of exposing a matrix metalloproteinase (MMP) substrate on the surface of their filamentous assemblies. Upon addition of MMP-2, these filaments rapidly break into fragments prior to reassembling into spherical micelles. Using 3D cell culture, it is shown that drug release is commensurate with cell density, revealing more effective cell killing when more cancer cells are present. This design platform could serve as a cell-responsive therapeutic depot for local chemotherapy.

Engineering Remotely Triggered Liposomes to Target Triple Negative Breast Cancer.

Triple Negative Breast Cancer (TNBC) continues to present a challenge in the clinic, as there is still no approved targeted therapy. TNBC is the worst sub-type of breast cancer in terms of prognosis and exhibits a deficiency in estrogen, progesterone, and human epidermal growth factor 2 (HER2) receptors. One possible option for the treatment of TNBC is chemotherapy. The issue with many chemotherapy drugs is that their effectiveness is diminished due to poor water solubility, and the method of administration directly or with a co-solvent intravenously can lead to an increase in toxicity. The issues of drug solubility can be avoided by using liposomes as a drug delivery carrier. Liposomes are engineered, biological nanoconstructs that possess the ability to encapsulate both hydrophobic and hydrophilic drugs and have been clinically approved to treat cancer. Specific targeting of cancer cell receptors through the use of ligands conjugated to the surface of drug-loaded liposomes could lessen damage to normal, healthy tissue. This study focuses on polyethylene glycol (PEG)-coated, folate conjugated, benzoporphyrin derivative (BPD)-loaded liposomes for treatment via photodynamic therapy (PDT). The folate receptor is over expressed on TNBC cells so these liposomes are targeted for greater uptake into cancer cells. PDT involves remotely irradiating light at 690 nm to trigger BPD, a hydrophobic photosensitive drug, to form reactive oxygen species that cause tumor cell death. BPD also displays a fluorescence signal when excited by light making it possible to image the fluorescence prior to PDT and for theranostics. In this study, free BPD, non-targeted and folate-targeted PEGylated BPD-loaded liposomes were introduced to a metastatic breast cancer cell line (MDA-MB-231) in vitro. The liposomes were reproducibly synthesized and characterized for size, polydispersity index (PDI), zeta potential, stability, and BPD release kinetics. Folate competition tests, fluorescence confocal imaging, and MTT assay were used to observe and quantify targeting effectiveness. The toxicity of BPD before and after PDT in monolayer and 3D in vitro cultures with TNBC cells was observed. This study may contribute to a novel nanoparticle-mediated approach to target TNBC using PDT.

Remotely Triggered Nano-Theranostics For Cancer Applications.

Nanotechnology has enabled the development of smart theranostic platforms that can concurrently diagnose disease, start primary treatment, monitor response, and, if required, initiate secondary treatments. Recent in vivo experiments demonstrate the promise of using theranostics in the clinic. In this paper, we review the use of remotely triggered theranostic nanoparticles for cancer applications, focusing heavily on advances in the past five years. Remote triggering mechanisms covered include photodynamic, photothermal, phototriggered chemotherapeutic release, ultrasound, electro-thermal, magneto-thermal, X-ray, and radiofrequency therapies. Each section includes a brief overview of the triggering mechanism and summarizes the variety of nanoparticles employed in each method. Emphasis in each category is placed on nano-theranostics with in vivo success. Some of the nanotheranostic platforms highlighted include photoactivatable multi-inhibitor nanoliposomes, plasmonic nanobubbles, reduced graphene oxide-iron oxide nanoparticles, photoswitching nanoparticles, multispectral optoacoustic tomography using indocyanine green, low temperature sensitive liposomes, and receptor-targeted iron oxide nanoparticles loaded with gemcitabine. The studies reviewed here provide strong evidence that the field of nanotheranostics is rapidly evolving. Such nanoplatforms may soon enable unique advances in the clinical management of cancer. However, reproducibility in the synthesis procedures of such "smart" platforms that lend themselves to easy scale-up in their manufacturing, as well as the development of new and improved models of cancer that are more predictive of human responses, need to happen soon for this field to make a rapid clinical impact.