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Although reprogramming of cellular metabolism is a hallmark of cancer, little is known about how metabolic reprogramming contributes to early stages of transformation. Here, we show that the histone deacetylase SIRT6 regulates tumor initiation during intestinal cancer by controlling glucose metabolism. Loss of SIRT6 results in an increase in the number of intestinal stem cells (ISCs), which translates into enhanced tumor initiating potential in APC min mice. By tracking down the connection between glucose metabolism and tumor initiation, we find a metabolic compartmentalization within the intestinal epithelium and adenomas, where a rare population of cells exhibit features of Warburg-like metabolism characterized by high pyruvate dehydrogenase kinase (PDK) activity. Our results show that these cells are quiescent cells expressing +4 ISCs and enteroendocrine markers. Active glycolysis in these cells suppresses ROS accumulation and enhances their stem cell and tumorigenic potential. Our studies reveal that aerobic glycolysis represents a heterogeneous feature of cancer, and indicate that this metabolic adaptation can occur in non-dividing cells, suggesting a role for the Warburg effect beyond biomass production in tumors., (© 2022. The Author(s).)

Background: Patient-derived xenografts (PDX) mice models play an important role in preclinical trials and personalized medicine. Sharing data on the models is highly valuable for numerous reasons - ethical, economical, research cross validation etc. The EurOPDX Consortium was established 8 years ago to share such information and avoid duplicating efforts in developing new PDX mice models and unify approaches to support preclinical research. EurOPDX Data Portal is the unified data sharing platform adopted by the Consortium., Main Body: In this paper we describe the main features of the EurOPDX Data Portal ( https://dataportal.europdx.eu/ ), its architecture and possible utilization by researchers who look for PDX mice models for their research. The Portal offers a catalogue of European models accessible on a cooperative basis. The models are searchable by metadata, and a detailed view provides molecular profiles (gene expression, mutation, copy number alteration) and treatment studies. The Portal displays the data in multiple tools (PDX Finder, cBioPortal, and GenomeCruzer in future), which are populated from a common database displaying strictly mutually consistent views., (short) Conclusion: EurOPDX Data Portal is an entry point to the EurOPDX Research Infrastructure offering PDX mice models for collaborative research, (meta)data describing their features and deep molecular data analysis according to users' interests., (© 2022. The Author(s).)

Patient-derived xenografts (PDXs) are obtained by transplanting fragments of a patient's tumour into immunodeficient mice. Growth and propagation of PDXs allows correlating therapeutic response in vivo with extensive, multi-dimensional molecular annotation, leading to identification of predictive biomarkers. PDXs are increasingly recognised as clinically relevant models of cancer for several reasons, of which the main is the possibility of studying the behaviour of cancer cells in a natural microenvironment, where they interact with stromal components accrued from the mouse host. PDXs maintain close similarities with the tumour of origin, in terms of tissue architecture, molecular features and response to treatments. Indeed, preclinical trials in PDXs have been shown to match and also anticipate data obtained in patients. Exploration of more complex processes like metastatic evolution and antitumour immune responses is actively pursued with PDXs, as new generations of host models emerge on the horizon., (Copyright © 2020 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Semaphorins are a family of molecular signals that guide cell migration and are implicated in the regulation of cancer cells. In particular, transmembrane semaphorins are postulated to act as both ligands ("forward" mode) and signaling receptors ("reverse" mode); however, reverse semaphorin signaling in cancer is relatively less understood. Here, we identified a previously unknown function of transmembrane semaphorin 4C (Sema4C), acting in reverse mode, to elicit nonconventional TGF-β/BMP receptor activation and selective SMAD1/5 phosphorylation. Sema4C coimmunoprecipitated with TGFBRII and BMPR1, supporting its role as modifier of this pathway. Sema4C reverse signaling led to the increased abundance of ID1/3 transcriptional factors and to extensive reprogramming of gene expression, which suppressed the typical features of the epithelial-mesenchymal transition in invasive carcinoma cells. This phenotype was nevertheless coupled with burgeoning metastatic behavior in vivo, consistent with evidence that Sema4C expression correlates with metastatic progression in human breast cancers. Thus, Sema4C reverse signaling promoted SMAD1/5- and ID1/3-dependent gene expression reprogramming and phenotypic plasticity in invasive cancer cells., (Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Each cancer is a complex system with unique molecular features determining its dynamics, such as its prognosis and response to therapies. Understanding the role of these biological traits is fundamental in order to personalize cancer clinical care according to the characteristics of each patient's disease. To achieve this, translational researchers propagate patients' samples through in vivo and in vitro cultures to test different therapies on the same tumor and to compare their outcomes with the molecular profile of the disease. This in turn generates information that can be subsequently translated into the development of predictive biomarkers for clinical use. These large-scale experiments generate huge collections of hierarchical data (i.e. experimental trees) with relative annotations that are extremely difficult to analyze. To address such issues in data analyses, we came up with the Semalytics data framework, the core of an analytical platform that processes experimental information through Semantic Web technologies. Semalytics allows (i) the efficient exploration of experimental trees with irregular structures together with their annotations. Moreover, (ii) the platform links its data to a wider open knowledge base (i.e. Wikidata) to add an extended knowledge layer without the need to manage and curate those data locally. Altogether, Semalytics provides augmented perspectives on experimental data, allowing the generation of new hypotheses, which were not anticipated by the user a priori. In this work, we present the data core we created for Semalytics, focusing on its semantic nucleus and on how it exploits semantic reasoning and data integration to tackle issues of this kind of analyses. Finally, we describe a proof-of-concept study based on the examination of several dozen cases of metastatic colorectal cancer in order to illustrate how Semalytics can help researchers generate hypotheses about the role of genes alterations in causing resistance or sensitivity of cancer cells to specific drugs., (© The Author(s) 2019. Published by Oxford University Press.)

Effective stratification of cancer patients on the basis of their molecular make-up is a key open challenge. Given the altered and heterogenous nature of cancer metabolism, we here propose to use the overall expression of central carbon metabolism as biomarker to characterize groups of patients with important characteristics, such as response to ad-hoc therapeutic strategies and survival expectancy. To this end, we here introduce the data integration framework named Metabolic Reaction Enrichment Analysis (MaREA), which strives to characterize the metabolic deregulations that distinguish cancer phenotypes, by projecting RNA-seq data onto metabolic networks, without requiring metabolic measurements. MaREA computes a score for each network reaction, based on the expression of the set of genes encoding for the associated enzyme(s). The scores are first used as features for cluster analysis and then to rank and visualize in an organized fashion the metabolic deregulations that distinguish cancer sub-types. We applied our method to recent lung and breast cancer RNA-seq datasets from The Cancer Genome Atlas and we were able to identify subgroups of patients with significant differences in survival expectancy. We show how the prognostic power of MaREA improves when an extracted and further curated core model focusing on central carbon metabolism is used rather than the genome-wide reference network. The visualization of the metabolic differences between the groups with best and worst prognosis allowed to identify and analyze key metabolic properties related to cancer aggressiveness. Some of these properties are shared across different cancer (sub) types, e.g., the up-regulation of nucleic acid and amino acid synthesis, whereas some other appear to be tumor-specific, such as the up- or down-regulation of the phosphoenolpyruvate carboxykinase reaction, which display different patterns in distinct tumor (sub)types. These results might be soon employed to deliver highly automated diagnostic and prognostic strategies for cancer patients., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Patient-derived tumor xenograft (PDX) mouse models have emerged as an important oncology research platform to study tumor evolution, mechanisms of drug response and resistance, and tailoring chemotherapeutic approaches for individual patients. The lack of robust standards for reporting on PDX models has hampered the ability of researchers to find relevant PDX models and associated data. Here we present the PDX models minimal information standard (PDX-MI) for reporting on the generation, quality assurance, and use of PDX models. PDX-MI defines the minimal information for describing the clinical attributes of a patient's tumor, the processes of implantation and passaging of tumors in a host mouse strain, quality assurance methods, and the use of PDX models in cancer research. Adherence to PDX-MI standards will facilitate accurate search results for oncology models and their associated data across distributed repository databases and promote reproducibility in research studies using these models. Cancer Res; 77(21); e62-66. ©2017 AACR ., (©2017 American Association for Cancer Research.)

Patient-derived xenografts (PDXs) have emerged as an important platform to elucidate new treatments and biomarkers in oncology. PDX models are used to address clinically relevant questions, including the contribution of tumour heterogeneity to therapeutic responsiveness, the patterns of cancer evolutionary dynamics during tumour progression and under drug pressure, and the mechanisms of resistance to treatment. The ability of PDX models to predict clinical outcomes is being improved through mouse humanization strategies and the implementation of co-clinical trials, within which patients and PDXs reciprocally inform therapeutic decisions. This Opinion article discusses aspects of PDX modelling that are relevant to these questions and highlights the merits of shared PDX resources to advance cancer medicine from the perspective of EurOPDX, an international initiative devoted to PDX-based research.

The development of resistance remains a major obstacle to long-term disease control in cancer patients treated with targeted therapies. In BRAF-mutant mouse models, we demonstrate that although targeted inhibition of either BRAF or VEGF initially suppresses the growth of BRAF-mutant tumors, combined inhibition of both pathways results in apoptosis, long-lasting tumor responses, reduction in lung colonization, and delayed onset of acquired resistance to the BRAF inhibitor PLX4720. As well as inducing tumor vascular normalization and ameliorating hypoxia, this approach induces remodeling of the extracellular matrix, infiltration of macrophages with an M1-like phenotype, and reduction in cancer-associated fibroblasts. At the molecular level, this therapeutic regimen results in a de novo transcriptional signature, which sustains and explains the observed efficacy with regard to cancer progression. Collectively, our findings offer new biological rationales for the management of clinical resistance to BRAF inhibitors based on the combination between BRAF V 600E inhibitors with anti-angiogenic regimens., (© 2016 The Authors. Published under the terms of the CC BY 4.0 license.)

We propose a new multi-network-based strategy to integrate different layers of genomic information and use them in a coordinate way to identify driving cancer genes. The multi-networks that we consider combine transcription factor co-targeting, microRNA co-targeting, protein-protein interaction and gene co-expression networks. The rationale behind this choice is that gene co-expression and protein-protein interactions require a tight coregulation of the partners and that such a fine tuned regulation can be obtained only combining both the transcriptional and post-transcriptional layers of regulation. To extract the relevant biological information from the multi-network we studied its partition into communities. To this end we applied a consensus clustering algorithm based on state of art community detection methods. Even if our procedure is valid in principle for any pathology in this work we concentrate on gastric, lung, pancreas and colorectal cancer and identified from the enrichment analysis of the multi-network communities a set of candidate driver cancer genes. Some of them were already known oncogenes while a few are new. The combination of the different layers of information allowed us to extract from the multi-network indications on the regulatory pattern and functional role of both the already known and the new candidate driver genes.

Background: Ionizing radiation (IR) is effectively used in cancer therapy. However, in subsets of patients, a few radioresistant cancer cells survive and cause disease relapse with metastatic progression. The MET oncogene encodes the hepatocyte growth factor (HGF) receptor and is known to drive "invasive growth", a regenerative and prosurvival program unduly activated in metastasis., Methods: Human tumor cell lines (MDA-MB-231, MDA-MB-435S, U251) were subjected to therapeutic doses of IR. MET mRNA, and protein expression and signal transduction were compared in treated and untreated cells, and the involvement of the DNA-damage sensor ataxia telangiectasia mutated (ATM) and the transcription factor nuclear factor kappa B (NF-κB) in activating MET transcription were analyzed by immunoblotting, chromatin immunoprecipitation, and use of NF-κB silencing RNA (siRNA). Cell invasiveness was measured in wound healing and transwell assays, and cell survival was measured in viability and clonogenic assays. MET was inhibited by siRNA or small-molecule kinase inhibitors (PHA665752 or JNJ-38877605). Combinations of MET-targeted therapy and radiotherapy were assessed in MDA-MB-231 and U251 xenografts (n = 5-6 mice per group). All P values were from two-sided tests., Results: After irradiation, MET expression in cell lines was increased up to fivefold via activation of ATM and NF-κB. MET overexpression increased ligand-independent MET phosphorylation and signal transduction, and rendered cells more sensitive to HGF. Irradiated cells became more invasive via a MET-dependent mechanism that was further enhanced in the presence of HGF. MET silencing by siRNA or inhibition of its kinase activity by treatment with PHA665752 or JNJ-38877605 counteracted radiation-induced invasiveness, promoted apoptosis, and prevented cells from resuming proliferation after irradiation in vitro. Treatment with MET inhibitors enhanced the efficacy of IR to stop the growth of or to induce the regression of xenografts (eg, at day 13, U251 xenografts, mean volume increase relative to mean tumor volume at day 0: vehicle = 438%, 5 Gy IR = 151%, 5 Gy IR + JNJ-38877605 = 76%; difference, IR vs JNJ-38877604 + IR = 75%, 95% CI = 59% to 91%, P = .01)., Conclusion: IR induces overexpression and activity of the MET oncogene through the ATM-NF-κB signaling pathway; MET, in turn, promotes cell invasion and protects cells from apoptosis, thus supporting radioresistance. Drugs targeting MET increase tumor cell radiosensitivity and prevent radiation-induced invasiveness.

The copper(II) complex A0 induces a type of non-apoptotic cell death also known as paraptosis. Paraptosis involves extensive endoplasmic reticulum vacuolization in the absence of caspase activation. A wide panel of human cancer cell lines was used to demonstrate differences in cytotoxicity by the paraptosis-inducing drug A0 and the metal-based pro-apoptotic drug cisplatin. Gene expression profiling of the human fibrosarcoma HT1080 cells showed that, while cisplatin induced p53 targets, A0 up-regulated genes involved in the unfolded protein response (UPR) and response to heavy metals. The cytotoxic effects of A0 were associated with inhibition of the ubiquitin-proteasome system and accumulation of ubiquitinylated proteins, in a manner dependent on protein synthesis. Cycloheximide inhibited the accumulation of ubiquitinylated proteins and hampered A0-induced cell death process. The occurrence of the UPR during A0-induced death process was shown by the increased abundance of spliced XBP1 mRNA, transient eIF2alpha phosphorylation, and a series of downstream events, including attenuation of global protein synthesis and increased expression of ATF4, CHOP, BIP, and GADD34. Mouse embryonic fibroblasts expressing a mutant eIF2alpha, which could not be phosphorylated, were more resistant to A0 than wild type cells, pointing to a pro-death role of eIF2alpha phosphorylation. A0 may thus represent the prototypical member of a new class of compounds that cause paraptotic cell death via mechanisms involving eIF2alpha phosphorylation and the UPR.

Tumor onset and progression require the accumulation of many genetic and epigenetic lesions. In some cases, however, cancer cells rely on only one of these lesions to maintain their malignant properties, and this dependence results in tumor regression upon oncogene inactivation ("oncogene addiction"). Determining which nodes of the many networks operative in the transformed phenotype specifically mediate this response to oncogene neutralization is crucial to identifying the vulnerabilities of cancer. Using the Met receptor as the major model system, we combined multiplex phosphoproteomics, genome-wide expression profiling, and functional assays in various cancer cells addicted to oncogenic receptor tyrosine kinases. We found that Met blockade affected a limited subset of Met downstream signals: Little or no effect was observed for several pathways downstream of Met; instead, only a restricted and pathway-specific signature of transducers and transcriptional effectors downstream of Ras or phosphoinositide 3-kinase (PI3K) was inactivated. An analogous signature was also generated by inhibition of epidermal growth factor receptor in a different cellular context, suggesting a stereotyped response that likely is independent of receptor type or tissue origin. Biologically, Met inhibition led to cell-cycle arrest. Inhibition of Ras-dependent signals and PI3K-dependent signals also resulted in cell-cycle arrest, whereas cells in which Met was inhibited proliferated when Ras or PI3K signaling was active. These findings uncover "dominant" and "recessive" nodes among the numerous oncogenic networks regulated by receptor tyrosine kinases and active in cancer, with the Ras and PI3K pathways as determinants of therapeutic response.

Mutations in oncogenes and tumor suppressor genes are responsible for tumorigenesis and represent favored therapeutic targets in oncology. We exploited homologous recombination to knock-in individual cancer mutations in the genome of nontransformed human cells. Sequential introduction of multiple mutations was also achieved, demonstrating the potential of this strategy to construct tumor progression models. Knock-in cells displayed allele-specific activation of signaling pathways and mutation-specific phenotypes different from those obtainable by ectopic oncogene expression. Profiling of a library of pharmacological agents on the mutated cells showed striking sensitivity or resistance phenotypes to pathway-targeted drugs, often matching those of tumor cells carrying equivalent cancer mutations. Thus, knock-in of single or multiple cancer alleles provides a pharmacogenomic platform for the rational design of targeted therapies.

We present here an experimental pipeline for the systematic identification and functional characterization of genes with high potential diagnostic and therapeutic value in human cancer. Complementary competences and resources have been brought together in the TRANSFOG Consortium to reach the following integrated research objectives: 1) execution of cancer-oriented genomic screenings on tumor tissues and experimental models and merging of the results to generate a prioritized panel of candidate genes involved in cancer progression and metastasis; 2) setup of systems for high-throughput delivery of full-length cDNAs, for gain-of-function analysis of the prioritized candidate genes; 3) collection of vectors and oligonucleotides for systematic, RNA interference-mediated down-regulation of the candidate genes; 4) adaptation of existing cell-based and model organism assays to a systematic analysis of gain and loss of function of the candidate genes, for identification and preliminary validation of novel potential therapeutic targets; 5) proteomic analysis of signal transduction and protein-protein interaction for better dissection of aberrant cancer signaling pathways; 6) validation of the diagnostic potential of the identified cancer genes towards the clinical use of diagnostic molecular signatures; 7) generation of a shared informatics platform for data handling and gene functional annotation. The results of the first three years of activity of the TRANSFOG Consortium are also briefly presented and discussed.

In human patients, blood coagulation disorders often associate with cancer, even in its early stages. Recently, in vitro and in vivo experimental models have shown that oncogene expression, or inactivation of tumour suppressor genes, upregulate genes that control blood coagulation. These studies suggest that activation of blood clotting, leading to peritumoral fibrin deposition, is instrumental in cancer development. Fibrin can indeed build up a provisional matrix, supporting the invasive growth of neoplastic tissues and blood vessels. Interference with blood coagulation can thus be considered as part of a multifaceted therapeutic approach to cancer.