A YAP-centered mechanotransduction loop drives collective breast cancer cell invasion.

Dense and aligned Collagen I fibers are associated with collective cancer invasion led by protrusive tumor cells, leader cells. In some breast tumors, a population of cancer cells (basal-like cells) maintain several epithelial characteristics and express the myoepithelial/basal cell marker Keratin 14 (K14). Emergence of leader cells and K14 expression are regarded as interconnected events triggered by Collagen I, however the underlying mechanisms remain unknown. Using breast carcinoma organoids, we show that Collagen I drives a force-dependent loop, specifically in basal-like cancer cells. The feed-forward loop is centered around the mechanotransducer Yap and independent of K14 expression. Yap promotes a transcriptional program that enhances Collagen I alignment and tension, which further activates Yap. Active Yap is detected in invading breast cancer cells in patients and required for collective invasion in 3D Collagen I and in the mammary fat pad of mice. Our work uncovers an essential function for Yap in leader cell selection during collective cancer invasion.

Multimodal Techniques to Study Tumor Growth, Basement Membrane Breaching, and Invasion in 3D Matrices.

Cancer-derived organoids and three-dimensional (3D) extracellular matrix (ECM) are taking center stage as in vitro models to study neoplastic cell behavior, since they recapitulate the heterogeneous cellular composition of tumors and their extracellular environment. In combination with imaging and molecular/biochemical techniques, 3D organoid models have contributed substantially to our knowledge about the cellular and molecular mechanisms that regulate the growth of tumors and invasion into the surrounding tissue. We here outline a set of protocols that describe culturing of cancer-derived organoids in 3D matrices and various strategies that allow modeling of tumor growth, tumor cell penetration into basement membranes, and invasion into Collagen I-rich ECM. Furthermore, we specify protocols for subsequent handling of organoids cultured in 3D ECM for confocal microscopy and analysis of gene expression at the protein and mRNA level. Although we here use breast cancer-derived organoids, these protocols can be directly applied or adapted for organoids derived from other cancer types or healthy tissues. Thus, in addition to investigating cell behavior of multiple cancer types, the combination of protocols described here may be used to study processes such as cell differentiation and migration during homeostasis and normal development.

Adenosine A2a Receptor Antagonism Restores Additive Cytotoxicity by Cytotoxic T Cells in Metabolically Perturbed Tumors.

Cytotoxic T lymphocytes (CTL) are antigen-specific effector cells with the ability to eradicate cancer cells in a contact-dependent manner. Metabolic perturbation compromises the CTL effector response in tumor subregions, resulting in failed cancer cell elimination despite the infiltration of tumor-specific CTLs. Restoring the functionality of these tumor-infiltrating CTLs is key to improve immunotherapy. Extracellular adenosine is an immunosuppressive metabolite produced within the tumor microenvironment. Here, by applying single-cell reporter strategies in 3D collagen cocultures in vitro and progressing tumors in vivo, we show that adenosine weakens one-to-one pairing of activated effector CTLs with target cells, thereby dampening serial cytotoxic hit delivery and cumulative death induction. Adenosine also severely compromised CTL effector restimulation and expansion. Antagonization of adenosine A2a receptor (ADORA2a) signaling stabilized and prolonged CTL-target cell conjugation and accelerated lethal hit delivery by both individual contacts and CTL swarms. Because adenosine signaling is a near-constitutive confounding parameter in metabolically perturbed tumors, ADORA2a targeting represents an orthogonal adjuvant strategy to enhance immunotherapy efficacy.

Evaluating Cancer Care Networks; A Case Study of a Lung Cancer Care Network.

BACKGROUND: Networks are promoted as an organizational form that enables integrated care as well as enhanced patient outcomes. However, implementing networks is complex. It is therefore important to evaluate the quality and effectiveness of networks to ensure it is worth developing and maintaining them. This article describes the development of an evaluation tool for cancer care networks and the results of a pilot study with a regional lung cancer care network. METHODS: This study used a combination of qualitative and quantitative evaluation methods. The qualitative evaluation was based on a framework with 10 standards for the organization of an oncological (tumor-specific) care network. Data for the quantitative evaluation were obtained from the Dutch Cancer Registry. The evaluation was performed at a network of three hospitals collaborating in the field of lung oncology. RESULTS: The qualitative evaluation framework consisted of 10 standards/questions which were divided into 38 sub-questions. The evaluation showed that in general patients are satisfied with the collaboration in the network. However, some improvement points were found such as the need for more attention for the implementation and periodic evaluation of a regional care pathway. The start of a regional multidisciplinary meeting has been a major step for improving the collaboration. CONCLUSION: An evaluation tool for (lung) cancer care networks was successfully developed and piloted within a cancer care network. The tool has proven to be a useful method for evaluating collaboration within an oncological network. It helped network partners to understand what they see as important and allowed them to learn about their program's dynamics. Improvement opportunities were successfully identified. To keep the tool up to date continuous improvement is needed, following the Plan Do Check Act (PDCA) cycle.

Space, Time, and Size Dependencies of Greenhouse Gas Payback Times of Wind Turbines in Northwestern Europe.

The net greenhouse gas benefits of wind turbines compared to their fossil energy counterparts depend on location-specific wind climatology and the turbines' technological characteristics. Assessing the environmental impact of individual wind parks requires a universal but location-dependent method. Here, the greenhouse gas payback time for 4161 wind turbine locations in northwestern Europe was determined as a function of (i) turbine size and (ii) spatial and temporal variability in wind speed. A high-resolution wind atlas (hourly wind speed data between 1979 and 2013 on a 2.5 by 2.5 km grid) was combined with a regression model predicting the wind turbines' life cycle greenhouse gas emissions from turbine size. The greenhouse gas payback time of wind turbines in northwestern Europe varied between 1.8 and 22.5 months, averaging 5.3 months. The spatiotemporal variability in wind climatology has a particularly large influence on the payback time, while the variability in turbine size is of lesser importance. Applying lower-resolution wind speed data (daily on a 30 by 30 km grid) approximated the high-resolution results. These findings imply that forecasting location-specific greenhouse gas payback times of wind turbines globally is well within reach with the availability of a high-resolution wind climatology in combination with technological information.