An Efficient Method for Vault Nanoparticle Conjugation with Finely Adjustable Amounts of Antibodies and Small Molecules.

Vaults are eukaryotic ribonucleoproteins consisting of 78 copies of the major vault protein (MVP), which assemble into a nanoparticle with an about 60 nm volume-based size, enclosing other proteins and RNAs. Regardless of their physiological role(s), vaults represent ideal, natural hollow nanoparticles, which are produced by the assembly of the sole MVP. Here, we have expressed in Komagataella phaffi and purified an MVP variant carrying a C-terminal Z peptide (vault-Z), which can tightly bind an antibody's Fc portion, in view of targeted delivery. Via surface plasmon resonance analysis, we could determine a 2.5 nM affinity to the monoclonal antibody Trastuzumab (Tz)/vault-Z 1:1 interaction. Then, we characterized the in-solution interaction via co-incubation, ultracentrifugation, and analysis of the pelleted proteins. This showed virtually irreversible binding up to an at least 10:1 Tz/vault-Z ratio. As a proof of concept, we labeled the Fc portion of Tz with a fluorophore and conjugated it with the nanoparticle, along with either Tz or Cetuximab, another monoclonal antibody. Thus, we could demonstrate antibody-dependent, selective uptake by the SKBR3 and MDA-MB 231 breast cancer cell lines. These investigations provide a novel, flexible technological platform that significantly extends vault-Z's applications, in that it can be stably conjugated with finely adjusted amounts of antibodies as well as of other molecules, such as fluorophores, cell-targeting peptides, or drugs, using the Fc portion as a scaffold.

INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation.

Metabolism is directly and indirectly fine-tuned by a complex web of interacting regulatory mechanisms that fall into two major classes. On the one hand, the expression level of the catalyzing enzyme sets the maximal theoretical flux level (i.e., the net rate of the reaction) for each enzyme-controlled reaction. On the other hand, metabolic regulation controls the metabolic flux through the interactions of metabolites (substrates, cofactors, allosteric modulators) with the responsible enzyme. High-throughput data, such as metabolomics and transcriptomics data, if analyzed separately, do not accurately characterize the hierarchical regulation of metabolism outlined above. They must be integrated to disassemble the interdependence between different regulatory layers controlling metabolism. To this aim, we propose INTEGRATE, a computational pipeline that integrates metabolomics and transcriptomics data, using constraint-based stoichiometric metabolic models as a scaffold. We compute differential reaction expression from transcriptomics data and use constraint-based modeling to predict if the differential expression of metabolic enzymes directly originates differences in metabolic fluxes. In parallel, we use metabolomics to predict how differences in substrate availability translate into differences in metabolic fluxes. We discriminate fluxes regulated at the metabolic and/or gene expression level by intersecting these two output datasets. We demonstrate the pipeline using a set of immortalized normal and cancer breast cell lines. In a clinical setting, knowing the regulatory level at which a given metabolic reaction is controlled will be valuable to inform targeted, truly personalized therapies in cancer patients.

Combining denoising of RNA-seq data and flux balance analysis for cluster analysis of single cells.

BACKGROUND: Sophisticated methods to properly pre-process and analyze the increasing collection of single-cell RNA sequencing (scRNA-seq) data are increasingly being developed. On the contrary, the best practices to integrate these data into metabolic networks, aiming at describing metabolic phenotypes within a heterogeneous cell population, have been poorly investigated. In this regard, a critical factor is the presence of false zero values in reactions essential for a fundamental metabolic function, such as biomass or energy production. Here, we investigate the role of denoising strategies in mitigating this problem. METHODS: We applied state-of-the-art denoising strategies - namely MAGIC, ENHANCE, and SAVER - on three public scRNA-seq datasets. We then associated a metabolic flux distribution with every single cell by embedding its noise-free transcriptomics profile in the constraints of the optimization of a core metabolic model. Finally, we used the obtained single-cell optimal metabolic fluxes as features for cluster analysis. We compared the results obtained with different techniques, and with or without the use of denoising. We also investigated the possibility of applying denoising directly on the Reaction Activity Scores, which are metabolic features extracted from the read counts, rather than on the read counts. RESULTS: We show that denoising of transcriptomics data improves the clustering of single cells. We also illustrate that denoising restores important metabolic properties, such as the correlation between cell cycle phase and biomass accumulation, and between the RAS scores of reactions belonging to the same metabolic pathway. We show that MAGIC performs better than ENHANCE and SAVER, and that, denoising applied directly on the RAS matrix could be an effective alternative in removing false zero values from essential metabolic reactions. CONCLUSIONS: Our results indicate that including denoising as a pre-processing operation represents a milestone to integrate scRNA-seq data into Flux Balance Analysis simulations and to perform single-cell cluster analysis with a focus on metabolic phenotypes.

Methotrexate inhibits SARS-CoV-2 virus replication "in vitro".

In early 2020 the new respiratory syndrome COVID-19 (caused by the zoonotic SARS-CoV-2 virus) spread like a pandemic, starting from Wuhan, China, causing severe economic depression. Despite some advances in drug treatments of medical complications in the later stages of the disease, the pandemic's death toll is tragic, as no vaccine or specific antiviral treatment is currently available. By using a systems approach, we identify the host-encoded pathway, which provides ribonucleotides to viral RNA synthesis, as a possible target. We show that methotrexate, an FDA-approved inhibitor of purine biosynthesis, potently inhibits viral RNA replication, viral protein synthesis, and virus release. The effective antiviral methotrexate concentrations are similar to those used for established human therapies using the same drug. Methotrexate should be most effective in patients at the earliest appearance of symptoms to effectively prevent viral replication, diffusion of the infection, and possibly fatal complications.

A framework for validating AI in precision medicine: considerations from the European ITFoC consortium.

BACKGROUND: Artificial intelligence (AI) has the potential to transform our healthcare systems significantly. New AI technologies based on machine learning approaches should play a key role in clinical decision-making in the future. However, their implementation in health care settings remains limited, mostly due to a lack of robust validation procedures. There is a need to develop reliable assessment frameworks for the clinical validation of AI. We present here an approach for assessing AI for predicting treatment response in triple-negative breast cancer (TNBC), using real-world data and molecular -omics data from clinical data warehouses and biobanks. METHODS: The European "ITFoC (Information Technology for the Future Of Cancer)" consortium designed a framework for the clinical validation of AI technologies for predicting treatment response in oncology. RESULTS: This framework is based on seven key steps specifying: (1) the intended use of AI, (2) the target population, (3) the timing of AI evaluation, (4) the datasets used for evaluation, (5) the procedures used for ensuring data safety (including data quality, privacy and security), (6) the metrics used for measuring performance, and (7) the procedures used to ensure that the AI is explainable. This framework forms the basis of a validation platform that we are building for the "ITFoC Challenge". This community-wide competition will make it possible to assess and compare AI algorithms for predicting the response to TNBC treatments with external real-world datasets. CONCLUSIONS: The predictive performance and safety of AI technologies must be assessed in a robust, unbiased and transparent manner before their implementation in healthcare settings. We believe that the consideration of the ITFoC consortium will contribute to the safe transfer and implementation of AI in clinical settings, in the context of precision oncology and personalized care.

Integration of single-cell RNA-seq data into population models to characterize cancer metabolism.

Metabolic reprogramming is a general feature of cancer cells. Regrettably, the comprehensive quantification of metabolites in biological specimens does not promptly translate into knowledge on the utilization of metabolic pathways. By estimating fluxes across metabolic pathways, computational models hold the promise to bridge this gap between data and biological functionality. These models currently portray the average behavior of cell populations however, masking the inherent heterogeneity that is part and parcel of tumorigenesis as much as drug resistance. To remove this limitation, we propose single-cell Flux Balance Analysis (scFBA) as a computational framework to translate single-cell transcriptomes into single-cell fluxomes. We show that the integration of single-cell RNA-seq profiles of cells derived from lung adenocarcinoma and breast cancer patients into a multi-scale stoichiometric model of a cancer cell population: significantly 1) reduces the space of feasible single-cell fluxomes; 2) allows to identify clusters of cells with different growth rates within the population; 3) points out the possible metabolic interactions among cells via exchange of metabolites. The scFBA suite of MATLAB functions is available at https://github.com/BIMIB-DISCo/scFBA, as well as the case study datasets.

A metabolic core model elucidates how enhanced utilization of glucose and glutamine, with enhanced glutamine-dependent lactate production, promotes cancer cell growth: The WarburQ effect.

Cancer cells share several metabolic traits, including aerobic production of lactate from glucose (Warburg effect), extensive glutamine utilization and impaired mitochondrial electron flow. It is still unclear how these metabolic rearrangements, which may involve different molecular events in different cells, contribute to a selective advantage for cancer cell proliferation. To ascertain which metabolic pathways are used to convert glucose and glutamine to balanced energy and biomass production, we performed systematic constraint-based simulations of a model of human central metabolism. Sampling of the feasible flux space allowed us to obtain a large number of randomly mutated cells simulated at different glutamine and glucose uptake rates. We observed that, in the limited subset of proliferating cells, most displayed fermentation of glucose to lactate in the presence of oxygen. At high utilization rates of glutamine, oxidative utilization of glucose was decreased, while the production of lactate from glutamine was enhanced. This emergent phenotype was observed only when the available carbon exceeded the amount that could be fully oxidized by the available oxygen. Under the latter conditions, standard Flux Balance Analysis indicated that: this metabolic pattern is optimal to maximize biomass and ATP production; it requires the activity of a branched TCA cycle, in which glutamine-dependent reductive carboxylation cooperates to the production of lipids and proteins; it is sustained by a variety of redox-controlled metabolic reactions. In a K-ras transformed cell line we experimentally assessed glutamine-induced metabolic changes. We validated computational results through an extension of Flux Balance Analysis that allows prediction of metabolite variations. Taken together these findings offer new understanding of the logic of the metabolic reprogramming that underlies cancer cell growth.

How Epigallocatechin-3-gallate and Tetracycline Interact with the Josephin Domain of Ataxin-3 and Alter Its Aggregation Mode.

Epigallocatechin-3-gallate (EGCG) and tetracycline are two known inhibitors of amyloid aggregation able to counteract the fibrillation of most of the proteins involved in neurodegenerative diseases. We have recently investigated their effect on ataxin-3 (AT3), the polyglutamine-containing protein responsible for spinocerebellar ataxia type 3. We previously showed that EGCG and tetracycline can contrast the aggregation process and toxicity of expanded AT3, although by different mechanisms. Here, we have performed further experiments by using the sole Josephin domain (JD) to further elucidate the mechanism of action of the two compounds. By protein solubility assays and FTIR spectroscopy we have first observed that EGCG and tetracycline affect the JD aggregation essentially in the same way displayed when acting on the full-length expanded AT3. Then, by saturation transfer difference (STD) NMR experiments, we have shown that EGCG binds both the monomeric and the oligomeric JD form, whereas tetracycline can only interact with the oligomeric one. Surface plasmon resonance (SPR) analysis has confirmed the capability of the sole EGCG to bind monomeric JD, although with a KD value suggestive for a non-specific interaction. Our investigations provide new details on the JD interaction with EGCG and tetracycline, which could explain the different mechanisms by which the two compounds reduce the toxicity of AT3.

A personalized osteoarthritic joint-on-a-chip as a screening platform for biological treatments.

Osteoarthritis (OA) is a highly disabling pathology, characterized by synovial inflammation and cartilage degeneration. Orthobiologics have shown promising results in OA treatment thanks to their ability to influence articular cells and modulate the inflammatory OA environment. Considering their complex mechanism of action, the development of reliable and relevant joint models appears as crucial to select the best orthobiologics for each patient. The aim of this study was to establish a microfluidic OA model to test therapies in a personalized human setting. The joint-on-a-chip model included cartilage and synovial compartments, containing hydrogel-embedded chondrocytes and synovial fibroblasts, separated by a channel for synovial fluid. For the cartilage compartment, a Hyaluronic Acid-based matrix was selected to preserve chondrocyte phenotype. Adding OA synovial fluid induced the production of inflammatory cytokines and degradative enzymes, generating an OA microenvironment. Personalized models were generated using patient-matched cells and synovial fluid to test the efficacy of mesenchymal stem cells on OA signatures. The patient-specific models allowed monitoring changes induced by cell injection, highlighting different individual responses to the treatment. Altogether, these results support the use of this joint-on-a-chip model as a prognostic tool to screen the patient-specific efficacy of orthobiologics.

Tumor heterogeneity: preclinical models, emerging technologies, and future applications.

Heterogeneity describes the differences among cancer cells within and between tumors. It refers to cancer cells describing variations in morphology, transcriptional profiles, metabolism, and metastatic potential. More recently, the field has included the characterization of the tumor immune microenvironment and the depiction of the dynamics underlying the cellular interactions promoting the tumor ecosystem evolution. Heterogeneity has been found in most tumors representing one of the most challenging behaviors in cancer ecosystems. As one of the critical factors impairing the long-term efficacy of solid tumor therapy, heterogeneity leads to tumor resistance, more aggressive metastasizing, and recurrence. We review the role of the main models and the emerging single-cell and spatial genomic technologies in our understanding of tumor heterogeneity, its contribution to lethal cancer outcomes, and the physiological challenges to consider in designing cancer therapies. We highlight how tumor cells dynamically evolve because of the interactions within the tumor immune microenvironment and how to leverage this to unleash immune recognition through immunotherapy. A multidisciplinary approach grounded in novel bioinformatic and computational tools will allow reaching the integrated, multilayered knowledge of tumor heterogeneity required to implement personalized, more efficient therapies urgently required for cancer patients.

HspB8 interacts with BAG3 in a "native-like" conformation forming a complex that displays chaperone-like activity.

The HspB8-BAG3 complex plays an important role in the protein quality control acting alone or within multi-components complexes. To clarify the mechanism underlying its activity, in this work we used biochemical and biophysical approaches to study the tendency of both proteins to auto-assemble and to form the complex. Solubility and Thioflavin T assays, Fourier transform infrared spectroscopy and atomic force microscopy analyses clearly showed the tendency of HspB8 to self-assemble at high concentration and to form oligomers in a "native-like" conformation; otherwise, BAG3 aggregates poorly. Noteworthy, also HspB8 and BAG3 associate in a "native-like" conformation, forming a stable complex. Furthermore, the high difference between dissociation constant values of HspB8-HspB8 interaction with respect to the binding to BAG3 obtained by surface plasmon resonance confirms that HspB8 is an obligated partner of BAG3 in vivo. Lastly, both proteins alone or in the complex are able to bind and affect the aggregation of the Josephin domain, the structured domain that triggers the ataxin-3 fibrillation. In particular, the complex displayed higher activity than HspB8 alone. All this considered, we can assert that the two proteins form a stable assembly with chaperone-like activity that could contribute to the physiological role of the complex in vivo.

Progranulin Oncogenic Network in Solid Tumors.

Progranulin is a pleiotropic growth factor with important physiological roles in embryogenesis and maintenance of adult tissue homeostasis. While-progranulin deficiency is associated with a broad range of pathological conditions affecting the brain, such as frontotemporal dementia and neuronal ceroid lipofuscinosis, progranulin upregulation characterizes many tumors, including brain tumors, multiple myeloma, leiomyosarcoma, mesothelioma and epithelial cancers such as ovarian, liver, breast, bladder, adrenal, prostate and kidney carcinomas. The increase of progranulin levels in tumors might have diagnostic and prognostic significance. In cancer, progranulin has a pro-tumorigenic role by promoting cancer cell proliferation, migration, invasiveness, anchorage-independent growth and resistance to chemotherapy. In addition, progranulin regulates the tumor microenvironment, affects the function of cancer-associated fibroblasts, and modulates tumor immune surveillance. However, the molecular mechanisms of progranulin oncogenic function are not fully elucidated. In bladder cancer, progranulin action relies on the activation of its functional signaling receptor EphA2. Notably, more recent data suggest that progranulin can also modulate a functional crosstalk between multiple receptor-tyrosine kinases, demonstrating a more complex and context-dependent role of progranulin in cancer. Here, we will review what is currently known about the function of progranulin in tumors, with a focus on its molecular mechanisms of action and regulation.

Nutritional limitation sensitizes mammalian cells to GSK-3ß inhibitors and leads to growth impairment.

The serine/threonine kinase GSK-3ß was initially described as a key enzyme involved in glucose metabolism, but it is now known to regulate a wide range of biological processes, including proliferation and apoptosis. We previously reported a transformation-dependent cell death induced by glucose limitation in K-ras-transformed NIH3T3. To address the mechanism of this phenomenon, we analyzed GSK-3ß regulation in these cells in conditions of high versus low glucose availability. We found that glucose depletion caused a marked inhibition of GSK-3ß through posttranslational mechanisms and that this inhibition was much less pronounced in normal cells. Further inhibition of GSK-3ß with lithium chloride, combined with glucose shortage, caused specific activation of AMP-activated protein kinase and significant suppression of proliferation in transformed but not normal cells. The cooperative effect of lithium and low glucose availability on cell growth did not seem to depend exclusively on ras pathway activation because two human cell lines, A549 and MDA-MB-231, both harboring an activated ras gene, showed very different sensitivity to lithium. These findings thus provide a rationale to further analyze the biochemical bases for combined glucose deprivation and GSK-3ß inhibition as a new approach to control transformed cell growth.

An acidic loop and cognate phosphorylation sites define a molecular switch that modulates ubiquitin charging activity in Cdc34-like enzymes.

E2 ubiquitin-conjugating enzymes are crucial mediators of protein ubiquitination, which strongly influence the ultimate fate of the target substrates. Recently, it has been shown that the activity of several enzymes of the ubiquitination pathway is finely tuned by phosphorylation, an ubiquitous mechanism for cellular regulation, which modulates protein conformation. In this contribution, we provide the first rationale, at the molecular level, of the regulatory mechanism mediated by casein kinase 2 (CK2) phosphorylation of E2 Cdc34-like enzymes. In particular, we identify two co-evolving signature elements in one of the larger families of E2 enzymes: an acidic insertion in ß4α2 loop in the proximity of the catalytic cysteine and two conserved key serine residues within the catalytic domain, which are phosphorylated by CK2. Our investigations, using yeast Cdc34 as a model, through 2.5 µs molecular dynamics simulations and biochemical assays, define these two elements as an important phosphorylation-controlled switch that modulates opening and closing of the catalytic cleft. The mechanism relies on electrostatic repulsions between a conserved serine phosphorylated by CK2 and the acidic residues of the ß4α2 loop, promoting E2 ubiquitin charging activity. Our investigation identifies a new and unexpected pivotal role for the acidic loop, providing the first evidence that this loop is crucial not only for downstream events related to ubiquitin chain assembly, but is also mandatory for the modulation of an upstream crucial step of the ubiquitin pathway: the ubiquitin charging in the E2 catalytic cleft.

An Optimized Workflow for the Analysis of Metabolic Fluxes in Cancer Spheroids Using Seahorse Technology.

Three-dimensional cancer models, such as spheroids, are increasingly being used to study cancer metabolism because they can better recapitulate the molecular and physiological aspects of the tumor architecture than conventional monolayer cultures. Although Agilent Seahorse XFe96 (Agilent Technologies, Santa Clara, CA, United States) is a valuable technology for studying metabolic alterations occurring in cancer cells, its application to three-dimensional cultures is still poorly optimized. We present a reliable and reproducible workflow for the Seahorse metabolic analysis of three-dimensional cultures. An optimized protocol enables the formation of spheroids highly regular in shape and homogenous in size, reducing variability in metabolic parameters among the experimental replicates, both under basal and drug treatment conditions. High-resolution imaging allows the calculation of the number of viable cells in each spheroid, the normalization of metabolic parameters on a per-cell basis, and grouping of the spheroids as a function of their size. Multivariate statistical tests on metabolic parameters determined by the Mito Stress test on two breast cancer cell lines show that metabolic differences among the studied spheroids are mostly related to the cell line rather than to the size of the spheroid. The optimized workflow allows high-resolution metabolic characterization of three-dimensional cultures, their comparison with monolayer cultures, and may aid in the design and interpretation of (multi)drug protocols.

Scatter-hoarding birds disperse seeds to sites unfavorable for plant regeneration.

Scatter-hoarding birds provide effective long-distance seed dispersal for plants. Transporting seeds far promotes population spread, colonization of new areas, and connectivity between populations. However, whether seeds transported over long distances are deposited in habitats favorable to plant regeneration has rarely been investigated, mainly due to methodological constraints. To investigate dispersal patterns and distances of Swiss stone pine (Pinus cembra) seeds we utilized advances in tracking technology to track the movements of their sole disperser, the spotted nutcracker (Nucifraga caryocatactes). We found routine individual movements between single seed harvesting and seed caching site. Harvesting sites of individual birds overlapped, whereas seed caching sites were separated and located on average 5.3 km away from the harvesting site. Interestingly, most distant caching sites were located at low elevations and in spruce forest, where Swiss stone pine does not naturally occur. This suggests that nutcrackers disperse seeds over long distances but that a large portion of these seeds are cached outside the known pine habitat. Therefore, we conclude that the implications of such long-distance seed dispersal movements for plant populations should be carefully considered in combination with the effects of habitat quality on plant recruitment.

The insulin-like growth factor receptor I promotes motility and invasion of bladder cancer cells through Akt- and mitogen-activated protein kinase-dependent activation of paxillin.

The insulin-like growth factor receptor I (IGF-IR) plays an essential role in transformation by promoting cell growth and protecting cancer cells from apoptosis. Aberrant IGF-IR signaling is implicated in several types of tumors, including carcinomas of the lung, breast, prostate, pancreas, liver, and colon. However, the contribution of the IGF-IR to the development of the transformed phenotype in urothelial cells has not been clearly established. In this study we demonstrated that the IGF-IR is overexpressed in invasive bladder cancer tissues compared with nonmalignant controls. We have investigated the role of the IGF-IR in bladder cancer by using urothelial carcinoma-derived 5637 and T24 cells. Although activation of the IGF-IR did not appreciably affect their growth, it did promote migration and stimulate in vitro wound closure and invasion. These effects required the activation of the Akt and Mitogen-activated protein kinase (MAPK) pathways as well as IGF-I-induced Akt- and MAPK-dependent phosphorylation of paxillin, which relocated at dynamic focal adhesions and was necessary for promoting motility in bladder cancer cells. Our results provide the first evidence for a role of the IGF-IR in motility and invasion of bladder cancer cells and support the hypothesis that the IGF-IR may play a critical role in the establishment of the invasive phenotype in urothelial neoplasia. Thus, the IGF-IR may also serve as a novel biomarker for bladder cancer.

Correction: A microphysiological early metastatic niche on a chip reveals how heterotypic cell interactions and inhibition of integrin subunit ß3 impact breast cancer cell extravasation.

Correction for 'A microphysiological early metastatic niche on a chip reveals how heterotypic cell interactions and inhibition of integrin subunit ß3 impact breast cancer cell extravasation' by Martina Crippa et al., Lab Chip, 2021, DOI: .

A microphysiological early metastatic niche on a chip reveals how heterotypic cell interactions and inhibition of integrin subunit ß3 impact breast cancer cell extravasation.

During metastatic progression multiple players establish competitive mechanisms, whereby cancer cells (CCs) are exposed to both pro- and anti-metastatic stimuli. The early metastatic niche (EMN) is a transient microenvironment which forms in the circulation during CC dissemination. EMN is characterized by the crosstalk among CCs, platelets, leukocytes and endothelial cells (ECs), increasing CC ability to extravasate and colonize secondary tissues. To better understand this complex crosstalk, we designed a human "EMN-on-a-chip" which involves the presence of blood cells as compared to standard metastases-on-chip models, hence providing a microenvironment more similar to the in vivo situation. We showed that CC transendothelial migration (TEM) was significantly increased in the presence of neutrophils and platelets in the EMN-on-a-chip compared to CC alone. Moreover, exploiting the EMN-on-chip in combination with multi-culture experiments, we showed that platelets increased the expression of epithelial to mesenchymal transition (EMT) markers in CCs and that the addition of a clinically approved antiplatelet drug (eptifibatide, inhibiting integrin ß3) impaired platelet aggregation and decreased CC expression of EMT markers. Inhibition of integrin ß3 in the co-culture system modulated the activation of the Src-FAK-VE-cadherin signaling axis and partially restored the architecture of inter-endothelial junctions by limiting VE-cadherinY658 phosphorylation and its nuclear localization. These observations correlate with the decreased CC TEM observed in the presence of integrin ß3 inhibitor. Our EMN-on-a-chip can be easily implemented for drug repurposing studies and to investigate new candidate molecules counteracting CC extravasation.

The driving role of the Cdk5/Tln1/FAKS732 axis in cancer cell extravasation dissected by human vascularized microfluidic models.

BACKGROUND: Understanding the molecular mechanisms of metastatic dissemination, the leading cause of death in cancer patients, is required to develop novel, effective therapies. Extravasation, an essential rate-limiting process in the metastatic cascade, includes three tightly coordinated steps: cancer cell adhesion to the endothelium, trans-endothelial migration, and early invasion into the secondary site. Focal adhesion proteins, including Tln1 and FAK, regulate the cytoskeleton dynamics: dysregulation of these proteins is often associated with metastatic progression and poor prognosis. METHODS: Here, we studied the previously unexplored role of these targets in each extravasation step using engineered 3D in vitro models, which recapitulate the physiological vascular niche experienced by cancer cells during hematogenous metastasis. RESULTS: Human breast cancer and fibrosarcoma cell lines respond to Cdk5/Tln1/FAK axis perturbation, impairing their metastatic potential. Vascular breaching requires actin polymerization-dependent invadopodia formation. Invadopodia generation requires the structural function of FAK and Tln1 rather than their activation through phosphorylation. Our data support that the inhibition of FAKS732 phosphorylation delocalizes ERK from the nucleus, decreasing ERK phosphorylated form. These findings indicate the critical role of these proteins in driving trans-endothelial migration. In fact, both knock-down experiments and chemical inhibition of FAK dramatically reduces lung colonization in vivo and TEM in microfluidic setting. Altogether, these data indicate that engineered 3D in vitro models coupled to in vivo models, genetic, biochemical, and imaging tools represent a powerful weapon to increase our understanding of metastatic progression. CONCLUSIONS: These findings point to the need for further analyses of previously overlooked phosphorylation sites of FAK, such as the serine 732, and foster the development of new effective antimetastatic treatments targeting late events of the metastatic cascade.