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.

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.

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.

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.

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.

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.

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.

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.

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.

Transcriptomics and Metabolomics Integration Reveals Redox-Dependent Metabolic Rewiring in Breast Cancer Cells.

Rewiring glucose metabolism toward aerobic glycolysis provides cancer cells with a rapid generation of pyruvate, ATP, and NADH, while pyruvate oxidation to lactate guarantees refueling of oxidized NAD+ to sustain glycolysis. CtPB2, an NADH-dependent transcriptional co-regulator, has been proposed to work as an NADH sensor, linking metabolism to epigenetic transcriptional reprogramming. By integrating metabolomics and transcriptomics in a triple-negative human breast cancer cell line, we show that genetic and pharmacological down-regulation of CtBP2 strongly reduces cell proliferation by modulating the redox balance, nucleotide synthesis, ROS generation, and scavenging. Our data highlight the critical role of NADH in controlling the oncogene-dependent crosstalk between metabolism and the epigenetically mediated transcriptional program that sustains energetic and anabolic demands in cancer cells.

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.

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.

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: .

The Multi-Level Mechanism of Action of a Pan-Ras Inhibitor Explains its Antiproliferative Activity on Cetuximab-Resistant Cancer Cells.

Ras oncoproteins play a crucial role in the onset, maintenance, and progression of the most common and deadly human cancers. Despite extensive research efforts, only a few mutant-specific Ras inhibitors have been reported. We show that cmp4-previously identified as a water-soluble Ras inhibitor- targets multiple steps in the activation and downstream signaling of different Ras mutants and isoforms. Binding of this pan-Ras inhibitor to an extended Switch II pocket on HRas and KRas proteins induces a conformational change that down-regulates intrinsic and GEF-mediated nucleotide dissociation and exchange and effector binding. A mathematical model of the Ras activation cycle predicts that the inhibitor severely reduces the proliferation of different Ras-driven cancer cells, effectively cooperating with Cetuximab to reduce proliferation even of Cetuximab-resistant cancer cell lines. Experimental data confirm the model prediction, indicating that the pan-Ras inhibitor is an appropriate candidate for medicinal chemistry efforts tailored at improving its currently unsatisfactory affinity.

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.

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.

Profiling and Targeting of Energy and Redox Metabolism in Grade 2 Bladder Cancer Cells with Different Invasiveness Properties.

Bladder cancer is one of the most prevalent deadly diseases worldwide. Grade 2 tumors represent a good window of therapeutic intervention, whose optimization requires high resolution biomarker identification. Here we characterize energy metabolism and cellular properties associated with spreading and tumor progression of RT112 and 5637, two Grade 2 cancer cell lines derived from human bladder, representative of luminal-like and basal-like tumors, respectively. The two cell lines have similar proliferation rates, but only 5637 cells show efficient lateral migration. In contrast, RT112 cells are more prone to form spheroids. RT112 cells produce more ATP by glycolysis and OXPHOS, present overall higher metabolic plasticity and are less sensitive than 5637 to nutritional perturbation of cell proliferation and migration induced by treatment with 2-deoxyglucose and metformin. On the contrary, spheroid formation is less sensitive to metabolic perturbations in 5637 than RT112 cells. The ability of metformin to reduce, although with different efficiency, cell proliferation, sphere formation and migration in both cell lines, suggests that OXPHOS targeting could be an effective strategy to reduce the invasiveness of Grade 2 bladder cancer cells.

Natural Products Attenuating Biosynthesis, Processing, and Activity of Ras Oncoproteins: State of the Art and Future Perspectives.

RAS genes encode signaling proteins, which, in mammalian cells, act as molecular switches regulating critical cellular processes as proliferation, growth, differentiation, survival, motility, and metabolism in response to specific stimuli. Deregulation of Ras functions has a high impact on human health: gain-of-function point mutations in RAS genes are found in some developmental disorders and thirty percent of all human cancers, including the deadliest. For this reason, the pathogenic Ras variants represent important clinical targets against which to develop novel, effective, and possibly selective pharmacological inhibitors. Natural products represent a virtually unlimited resource of structurally different compounds from which one could draw on for this purpose, given the improvements in isolation and screening of active molecules from complex sources. After a summary of Ras proteins molecular and regulatory features and Ras-dependent pathways relevant for drug development, we point out the most promising inhibitory approaches, the known druggable sites of wild-type and oncogenic Ras mutants, and describe the known natural compounds capable of attenuating Ras signaling. Finally, we highlight critical issues and perspectives for the future selection of potential Ras inhibitors from natural sources.

Disruption of redox homeostasis for combinatorial drug efficacy in K-Ras tumors as revealed by metabolic connectivity profiling.

BACKGROUND: Rewiring of metabolism induced by oncogenic K-Ras in cancer cells involves both glucose and glutamine utilization sustaining enhanced, unrestricted growth. The development of effective anti-cancer treatments targeting metabolism may be facilitated by the identification and rational combinatorial targeting of metabolic pathways. METHODS: We performed mass spectrometric metabolomics analysis in vitro and in vivo experiments to evaluate the efficacy of drugs and identify metabolic connectivity. RESULTS: We show that K-Ras-mutant lung and colon cancer cells exhibit a distinct metabolic rewiring, the latter being more dependent on respiration. Combined treatment with the glutaminase inhibitor CB-839 and the PI3K/aldolase inhibitor NVP-BKM120 more consistently reduces cell growth of tumor xenografts. Maximal growth inhibition correlates with the disruption of redox homeostasis, involving loss of reduced glutathione regeneration, redox cofactors, and a decreased connectivity among metabolites primarily involved in nucleic acid metabolism. CONCLUSIONS: Our findings open the way to develop metabolic connectivity profiling as a tool for a selective strategy of combined drug repositioning in precision oncology.

Tipifarnib as a Precision Therapy for HRAS-Mutant Head and Neck Squamous Cell Carcinomas.

Tipifarnib is a potent and highly selective inhibitor of farnesyltransferase (FTase). FTase catalyzes the posttranslational attachment of farnesyl groups to signaling proteins that are required for localization to cell membranes. Although all RAS isoforms are FTase substrates, only HRAS is exclusively dependent upon farnesylation, raising the possibility that HRAS-mutant tumors might be susceptible to tipifarnib-mediated inhibition of FTase. Here, we report the characterization of tipifarnib activity in a wide panel of HRAS-mutant and wild-type head and neck squamous cell carcinoma (HNSCC) xenograft models. Tipifarnib treatment displaced both mutant and wild-type HRAS from membranes but only inhibited proliferation, survival, and spheroid formation of HRAS-mutant cells. In vivo, tipifarnib treatment induced tumor stasis or regression in all six HRAS-mutant xenografts tested but displayed no activity in six HRAS wild-type patient-derived xenograft (PDX) models. Mechanistically, drug treatment resulted in the reduction of MAPK pathway signaling, inhibition of proliferation, induction of apoptosis, and robust abrogation of neovascularization, apparently via effects on both tumor cells and endothelial cells. Bioinformatics and quantitative image analysis further revealed that FTase inhibition induces progressive squamous cell differentiation in tipifarnib-treated HNSCC PDXs. These preclinical findings support that HRAS represents a druggable oncogene in HNSCC through FTase inhibition by tipifarnib, thereby identifying a precision therapeutic option for HNSCCs harboring HRAS mutations.