Mesenchymal progenitor cells (MPCs) have been hypothesized as cells of origin for sarcomas, and c-Fos transcription factor has been showed to act as an oncogene in bone tumors. In this study, we show c-Fos is present in most sarcomas with chondral phenotype, while multiple other genes are related to c-Fos expression pattern. To further define the role of c-Fos in sarcomagenesis, we expressed it in primary human MPCs (hMPCs), immortalized hMPCs and transformed murine MPCs (mMPCs). In immortalized hMPCs, c-Fos expression generated morphological changes, reduced mobility capacity and impaired adipogenic- and osteogenic-differentiation potentials. Remarkably, immortalized hMPCs or mMPCs expressing c-Fos generated tumors harboring a chondrogenic phenotype and morphology. Thus, here we show that c-Fos protein has a key role in sarcomas and that c-Fos expression in immortalized MPCs yields cell transformation and chondrogenic tumor formation.

Senescent cells accumulate in multiple aging-associated diseases, and eliminating these cells has recently emerged as a promising therapeutic approach. Here, we take advantage of the high lysosomal β-galactosidase activity of senescent cells to design a drug delivery system based on the encapsulation of drugs with galacto-oligosaccharides. We show that gal-encapsulated fluorophores are preferentially released within senescent cells in mice. In a model of chemotherapy-induced senescence, gal-encapsulated cytotoxic drugs target senescent tumor cells and improve tumor xenograft regression in combination with palbociclib. Moreover, in a model of pulmonary fibrosis in mice, gal-encapsulated cytotoxics target senescent cells, reducing collagen deposition and restoring pulmonary function. Finally, gal-encapsulation reduces the toxic side effects of the cytotoxic drugs. Drug delivery into senescent cells opens new diagnostic and therapeutic applications for senescence-associated disorders.

Recent observations point towards the existence of a large number of neighborhoods composed of functionally-related gene modules that lie together in the genome. This local component in the distribution of the functionality across chromosomes is probably affecting the own chromosomal architecture by limiting the possibilities in which genes can be arranged and distributed across the genome. As a direct consequence of this fact it is therefore presumable that diseases such as cancer, harboring DNA copy number alterations (CNAs), will have a symptomatology strongly dependent on modules of functionally-related genes rather than on a unique "important" gene.

Clinical interest on human mesenchymal progenitor cells (hMPC) relies on their potential applicability in cell-based therapies. An in vitro characterization is usually performed in order to define MPC potency. However, in vitro predictions not always correlate with in vivo results and thus there is no consensus in how to really assess cell potency. Our goal was to provide an in vivo testing method to define cell behavior before therapeutic usage, especially for bone tissue engineering applications. In this context, we wondered whether bone marrow stromal cells (hBMSC) would proceed in an osteogenic microenvironment. Based on previous approaches, we developed a fibrin/ceramic/BMP-2/hBMSCs compound. We implanted the compound during only 2 weeks in NOD-SCID mice, either orthotopically to assess its osteoinductive property or subcutaneously to analyze its adequacy as a cell potency testing method. Using fluorescent cell labeling and immunohistochemistry techniques, we could ascertain cell differentiation to bone, bone marrow, cartilage, adipocyte and fibrous tissue. We observed differences in cell potential among different batches of hBMSCs, which did not strictly correlate with in vitro analyses. Our data indicate that the method we have developed is reliable, rapid and reproducible to define cell potency, and may be useful for testing cells destined to bone tissue engineering purposes. Additionally, results obtained with hMPCs from other sources indicate that our method is suitable for testing any potentially implantable mesenchymal cell. Finally, we propose that this model could successfully be employed for bone marrow niche and bone tumor studies.

Nanotechnology changed the concept of treatment for a variety of diseases, producing a huge impact regarding drug and gene delivery. Among the different targeted diseases, osteoporosis has devastating clinical and economic consequences. Since current osteoporosis treatments present several side effects, new treatment approaches are needed. Recently, the application of small interfering RNA (siRNA) has become a promising alternative. Wnt/β-catenin signaling pathway controls bone development and formation. This pathway is negatively regulated by sclerostin, which knock-down through siRNA application would potentially promote bone formation. However, the major bottleneck for siRNA-based treatments is the necessity of a delivery vector, bringing nanotechnology as a potential solution. Among the available nanocarriers, mesoporous silica nanoparticles (MSNs) have attracted great attention for intracellular delivery of siRNAs. The mesoporous structure of MSNs permits the delivery of siRNAs together with another biomolecule, achieving a combination therapy. Here, the effectiveness of a new potential osteoporosis treatment based on MSNs is evaluated. The proposed system is effective in delivering SOST siRNA and osteostatin through systemic injection to bone tissue. The nanoparticle administration produced an increase expression of osteogenic related genes improving the bone microarchitecture. The treated osteoporotic mice recovered values of a healthy situation approaching to osteoporosis remission.

In silico drug prescription tools for precision cancer medicine can match molecular alterations with tailored candidate treatments. These methodologies require large and well-annotated datasets to systematically evaluate their performance, but this is currently constrained by the lack of complete patient clinicopathological data. Moreover, in silico drug prescription performance could be improved by integrating additional tumour information layers like intra-tumour heterogeneity (ITH) which has been related to drug response and tumour progression. PanDrugs is an in silico drug prescription method which prioritizes anticancer drugs combining both biological and clinical evidence. We have systematically evaluated PanDrugs in the Genomic Data Commons repository (GDC). Our results showed that PanDrugs is able to establish an a priori stratification of cancer patients treated with Epidermal Growth Factor Receptor (EGFR) inhibitors. Patients labelled as responders according to PanDrugs predictions showed a significantly increased overall survival (OS) compared to non-responders. PanDrugs was also able to suggest alternative tailored treatments for non-responder patients. Additionally, PanDrugs usefulness was assessed considering spatial and temporal ITH in cancer patients and showed that ITH can be approached therapeutically proposing drugs or combinations potentially capable of targeting the clonal diversity. In summary, this study is a proof of concept where PanDrugs predictions have been correlated to OS and can be useful to manage ITH in patients while increasing therapeutic options and demonstrating its clinical utility.

FBXW7 is one of the most frequently mutated tumor suppressors, deficiency of which has been associated with resistance to some anticancer therapies. Through bioinformatics and genome-wide CRISPR screens, we here reveal that FBXW7 deficiency leads to multidrug resistance (MDR). Proteomic analyses found an upregulation of mitochondrial factors as a hallmark of FBXW7 deficiency, which has been previously linked to chemotherapy resistance. Despite this increased expression of mitochondrial factors, functional analyses revealed that mitochondria are under stress, and genetic or chemical targeting of mitochondria is preferentially toxic for FBXW7-deficient cells. Mechanistically, the toxicity of therapies targeting mitochondrial translation such as the antibiotic tigecycline relates to the activation of the integrated stress response (ISR) in a GCN2 kinase-dependent manner. Furthermore, the discovery of additional drugs that are toxic for FBXW7-deficient cells showed that all of them unexpectedly activate a GCN2-dependent ISR regardless of their accepted mechanism of action. Our study reveals that while one of the most frequent mutations in cancer reduces the sensitivity to the vast majority of available therapies, it renders cells vulnerable to ISR-activating drugs.

The mechanistic target of rapamycin complex 1 (mTORC1) integrates cellular nutrient signaling and hormonal cues to control metabolism. We have previously shown that constitutive nutrient signaling to mTORC1 by means of genetic activation of RagA (expression of GTP-locked RagA, or RagAGTP) in mice resulted in a fatal energetic crisis at birth. Herein, we rescue neonatal lethality in RagAGTP mice and find morphometric and metabolic alterations that span glucose, lipid, ketone, bile acid and amino acid homeostasis in adults, and a median lifespan of nine months. Proteomic and metabolomic analyses of livers from RagAGTP mice reveal a failed metabolic adaptation to fasting due to a global impairment in PPARα transcriptional program. These metabolic defects are partially recapitulated by restricting activation of RagA to hepatocytes, and revert by pharmacological inhibition of mTORC1. Constitutive hepatic nutrient signaling does not cause hepatocellular damage and carcinomas, unlike genetic activation of growth factor signaling upstream of mTORC1. In summary, RagA signaling dictates dynamic responses to feeding-fasting cycles to tune metabolism so as to match the nutritional state.

Ellis-van Creveld syndrome protein homolog (Evc) was previously shown to mediate expression of Indian hedgehog (Ihh) downstream targets in chondrocytes. Consequently disruption of the Ihh/Pthrp axis was demonstrated in Evc(-/-) mice, but the full extent of Evc involvement in endochondral development was not totally characterized. Herein we have examined further the Evc(-/-) growth plate in a homogeneous genetic background and show that Evc promotes chondrocyte proliferation, chondrocyte hypertrophy and the differentiation of osteoblasts in the perichondrium, hence implicating Evc in both Pthrp-dependent and Pthrp-independent Ihh functions. We also demonstrate that Evc, which localizes to osteoblast primary cilia, mediates Hedgehog (Hh) signaling in the osteoblast lineage. In spite of this, bone collar development is mildly affected in Evc(-/-) mutants. The onset of perichondrial osteoblastogenesis is delayed at the initial stages of endochondral ossification in Evc(-/-) mice, and in later stages, the leading edge of expression of osteoblast markers and Wnt/β-catenin signaling components is located closer to the primary spongiosa in the Evc(-/-) perichondrium owing to impaired osteoblast differentiation. Additionally we have used Ptch1-LacZ reporter mice to learn about the different types of Hh-responsive cells that are present in the perichondrium of normal and Evc(-/-) mice. Evc mediates Hh target gene expression in inner perichondrial cells, but it is dispensable in the external layers of the perichondrium. Finally, we report cranial base defects in Evc(-/-) mice and reveal that Evc is essential for intrasphenoidal synchondrosis development.

Porous ceramic scaffolds are widely studied in the tissue engineering field due to their potential in medical applications as bone substitutes or as bone-filling materials. Solid free form (SFF) fabrication methods allow fabrication of ceramic scaffolds with fully controlled pore architecture, which opens new perspectives in bone tissue regeneration materials. However, little experimentation has been performed about real biological properties and possible applications of SFF designed 3D ceramic scaffolds. Thus, here the biological properties of a specific SFF scaffold are evaluated first, both in vitro and in vivo, and later scaffolds are also implanted in pig maxillary defect, which is a model for a possible application in maxillofacial surgery. In vitro results show good biocompatibility of the scaffolds, promoting cell ingrowth. In vivo results indicate that material on its own conducts surrounding tissue and allow cell ingrowth, thanks to the designed pore size. Additional osteoinductive properties were obtained with BMP-2, which was loaded on scaffolds, and optimal bone formation was observed in pig implantation model. Collectively, data show that SFF scaffolds have real application possibilities for bone tissue engineering purposes, with the main advantage of being fully customizable 3D structures.

Gene Expression Profile Analysis Suite (GEPAS) is one of the most complete and extensively used web-based packages for microarray data analysis. During its more than 5 years of activity it has continuously been updated to keep pace with the state-of-the-art in the changing microarray data analysis arena. GEPAS offers diverse analysis options that include well established as well as novel algorithms for normalization, gene selection, class prediction, clustering and functional profiling of the experiment. New options for time-course (or dose-response) experiments, microarray-based class prediction, new clustering methods and new tests for differential expression have been included. The new pipeliner module allows automating the execution of sequential analysis steps by means of a simple but powerful graphic interface. An extensive re-engineering of GEPAS has been carried out which includes the use of web services and Web 2.0 technology features, a new user interface with persistent sessions and a new extended database of gene identifiers. GEPAS is nowadays the most quoted web tool in its field and it is extensively used by researchers of many countries and its records indicate an average usage rate of 500 experiments per day. GEPAS, is available at http://www.gepas.org.

The Gene Expression Profile Analysis Suite (GEPAS) has been running for more than four years. During this time it has evolved to keep pace with the new interests and trends in the still changing world of microarray data analysis. GEPAS has been designed to provide an intuitive although powerful web-based interface that offers diverse analysis options from the early step of preprocessing (normalization of Affymetrix and two-colour microarray experiments and other preprocessing options), to the final step of the functional annotation of the experiment (using Gene Ontology, pathways, PubMed abstracts etc.), and include different possibilities for clustering, gene selection, class prediction and array-comparative genomic hybridization management. GEPAS is extensively used by researchers of many countries and its records indicate an average usage rate of 400 experiments per day. The web-based pipeline for microarray gene expression data, GEPAS, is available at http://www.gepas.org.

Epidemiological studies indicate that patients suffering from Alzheimer's disease have a lower risk of developing lung cancer, and suggest a higher risk of developing glioblastoma. Here we explore the molecular scenarios that might underlie direct and inverse co-morbidities between these diseases. Transcriptomic meta-analyses reveal significant numbers of genes with inverse patterns of expression in Alzheimer's disease and lung cancer, and with similar patterns of expression in Alzheimer's disease and glioblastoma. These observations support the existence of molecular substrates that could at least partially account for these direct and inverse co-morbidity relationships. A functional analysis of the sets of deregulated genes points to the immune system, up-regulated in both Alzheimer's disease and glioblastoma, as a potential link between these two diseases. Mitochondrial metabolism is regulated oppositely in Alzheimer's disease and lung cancer, indicating that it may be involved in the inverse co-morbidity between these diseases. Finally, oxidative phosphorylation is a good candidate to play a dual role by decreasing or increasing the risk of lung cancer and glioblastoma in Alzheimer's disease.

Triple-negative breast cancer (TNBC) is characterized by aggressiveness and high rates of metastasis. The identification of relevant biomarkers is crucial to improve outcomes for TNBC patients. Membrane type 1-matrix metalloproteinase (MT1-MMP) could be a good candidate because its expression has been reported to correlate with tumor malignancy, progression and metastasis. Moreover, single-domain variable regions (VHHs or Nanobodies) derived from camelid heavy-chain-only antibodies have demonstrated improvements in tissue penetration and blood clearance, important characteristics for cancer imaging. Here, we have developed a nanobody-based PET imaging strategy for TNBC detection that targets MT1-MMP. A llama-derived library was screened against the catalytic domain of MT1-MMP and a panel of specific nanobodies were identified. After a deep characterization, two nanobodies were selected to be labeled with gallium-68 (68Ga). ImmunoPET imaging with both ([68Ga]Ga-NOTA-3TPA14 and [68Ga]Ga-NOTA-3CMP75) in a TNBC mouse model showed precise tumor-targeting capacity in vivo with high signal-to-background ratios. (68Ga)Ga-NOTA-3CMP75 exhibited higher tumor uptake compared to (68Ga)Ga-NOTA-3TPA14. Furthermore, imaging data correlated perfectly with the immunohistochemistry staining results. In conclusion, we found a promising candidate for nanobody-based PET imaging to be further investigated as a diagnostic tool in TNBC.

Pancreatic ductal adenocarcinoma (PDAC) continues to be one of the deadliest cancers for which optimal diagnostic tools are still greatly needed. Identification of PDAC-specific molecular markers would be extremely useful to improve disease diagnosis and follow-up. MT1-MMP has long been involved in pancreatic cancer, especially in tumour invasion and metastasis. In this study, we aim to ascertain the suitability of MT1-MMP as a biomarker for positron emission tomography (PET) imaging. Two probes were assessed and compared for this purpose, an MT1-MMP-specific binding peptide (MT1-AF7p) and a specific antibody (LEM2/15), labelled, respectively, with 68Ga and with 89Zr. PET imaging with both probes was conducted in patient-derived xenograft (PDX), subcutaneous and orthotopic, PDAC mouse models, and in a cancer cell line (CAPAN-2)-derived xenograft (CDX) model. Both radiolabelled tracers were successful in identifying, by means of PET imaging techniques, tumour tissues expressing MT1-MMP although they did so at different uptake levels. The 89Zr-DFO-LEM2/15 probe showed greater specific activity compared to the 68Ga-labelled peptide. The mean value of tumour uptake for the 89Zr-DFO-LEM2/15 probe (5.67 ± 1.11%ID/g, n=28) was 25-30 times higher than that of the 68Ga-DOTA-AF7p ones. Tumour/blood ratios (1.13 ± 0.51 and 1.44 ± 0.43 at 5 and 7 days of 89Zr-DFO-LEM2/15 after injection) were higher than those estimated for 68Ga-DOTA-AF7p probes (of approximately tumour/blood ratio = 0.5 at 90 min after injection). Our findings strongly point out that (i) the in vivo detection of MT1-MMP by PET imaging is a promising strategy for PDAC diagnosis and (ii) labelled LEM2/15 antibody is a better candidate than MT1-AF7p for PDAC detection.

A high percentage of patients with brain metastases frequently develop neurocognitive symptoms; however, understanding how brain metastasis co-opts the function of neuronal circuits beyond a tumor mass effect remains unknown. We report a comprehensive multidimensional modeling of brain functional analyses in the context of brain metastasis. By testing different preclinical models of brain metastasis from various primary sources and oncogenic profiles, we dissociated the heterogeneous impact on local field potential oscillatory activity from cortical and hippocampal areas that we detected from the homogeneous inter-model tumor size or glial response. In contrast, we report a potential underlying molecular program responsible for impairing neuronal crosstalk by scoring the transcriptomic and mutational profiles in a model-specific manner. Additionally, measurement of various brain activity readouts matched with machine learning strategies confirmed model-specific alterations that could help predict the presence and subtype of metastasis.

Chronic lymphocytic leukaemia is the most prevalent leukaemia in Western countries. It is an incurable disease characterized by a highly variable clinical course. Chronic lymphocytic leukaemia is an ideal model for studying clonal heterogeneity and dynamics during cancer progression, response to therapy and/or relapse because the disease usually develops over several years. Here we report an analysis by deep sequencing of sequential samples taken at different times from the affected organs of two patients with 12- and 7-year disease courses, respectively. One of the patients followed a linear pattern of clonal evolution, acquiring and selecting new mutations in response to salvage therapy and/or allogeneic transplantation, while the other suffered loss of cellular tumoral clones during progression and histological transformation.

The Gene Expression Profile Analysis Suite, GEPAS, has been running for more than three years. With >76,000 experiments analysed during the last year and a daily average of almost 300 analyses, GEPAS can be considered a well-established and widely used platform for gene expression microarray data analysis. GEPAS is oriented to the analysis of whole series of experiments. Its design and development have been driven by the demands of the biomedical community, probably the most active collective in the field of microarray users. Although clustering methods have obviously been implemented in GEPAS, our interest has focused more on methods for finding genes differentially expressed among distinct classes of experiments or correlated to diverse clinical outcomes, as well as on building predictors. There is also a great interest in CGH-arrays which fostered the development of the corresponding tool in GEPAS: InSilicoCGH. Much effort has been invested in GEPAS for developing and implementing efficient methods for functional annotation of experiments in the proper statistical framework. Thus, the popular FatiGO has expanded to a suite of programs for functional annotation of experiments, including information on transcription factor binding sites, chromosomal location and tissues. The web-based pipeline for microarray gene expression data, GEPAS, is available at http://www.gepas.org.

We present a new version of Babelomics, a complete suite of web tools for functional analysis of genome-scale experiments, with new and improved tools. New functionally relevant terms have been included such as CisRed motifs or bioentities obtained by text-mining procedures. An improved indexing has considerably speeded up several of the modules. An improved version of the FatiScan method for studying the coordinate behaviour of groups of functionally related genes is presented, along with a similar tool, the Gene Set Enrichment Analysis. Babelomics is now more oriented to test systems biology inspired hypotheses. Babelomics can be found at http://www.babelomics.org.

Large-sequencing cancer genome projects have shown that tumors have thousands of molecular alterations and their frequency is highly heterogeneous. In such scenarios, physicians and oncologists routinely face lists of cancer genomic alterations where only a minority of them are relevant biomarkers to drive clinical decision-making. For this reason, the medical community agrees on the urgent need of methodologies to establish the relevance of tumor alterations, assisting in genomic profile interpretation, and, more importantly, to prioritize those that could be clinically actionable for cancer therapy.