Hepatic nutrient and hormone signaling to mTORC1 instructs the postnatal metabolic zonation of the liver.

The metabolic functions of the liver are spatially organized in a phenomenon called zonation, linked to the differential exposure of portal and central hepatocytes to nutrient-rich blood. The mTORC1 signaling pathway controls cellular metabolism in response to nutrients and insulin fluctuations. Here we show that simultaneous genetic activation of nutrient and hormone signaling to mTORC1 in hepatocytes results in impaired establishment of postnatal metabolic and zonal identity of hepatocytes. Mutant hepatocytes fail to upregulate postnatally the expression of Frizzled receptors 1 and 8, and show reduced Wnt/ß-catenin activation. This defect, alongside diminished paracrine Wnt2 ligand expression by endothelial cells, underlies impaired postnatal maturation. Impaired zonation is recapitulated in a model of constant supply of nutrients by parenteral nutrition to piglets. Our work shows the role of hepatocyte sensing of fluctuations in nutrients and hormones for triggering a latent metabolic zonation program.

PanDrugs2: prioritizing cancer therapies using integrated individual multi-omics data.

Genomics studies routinely confront researchers with long lists of tumor alterations detected in patients. Such lists are difficult to interpret since only a minority of the alterations are relevant biomarkers for diagnosis and for designing therapeutic strategies. PanDrugs is a methodology that facilitates the interpretation of tumor molecular alterations and guides the selection of personalized treatments. To do so, PanDrugs scores gene actionability and drug feasibility to provide a prioritized evidence-based list of drugs. Here, we introduce PanDrugs2, a major upgrade of PanDrugs that, in addition to somatic variant analysis, supports a new integrated multi-omics analysis which simultaneously combines somatic and germline variants, copy number variation and gene expression data. Moreover, PanDrugs2 now considers cancer genetic dependencies to extend tumor vulnerabilities providing therapeutic options for untargetable genes. Importantly, a novel intuitive report to support clinical decision-making is generated. PanDrugs database has been updated, integrating 23 primary sources that support >74K drug-gene associations obtained from 4642 genes and 14 659 unique compounds. The database has also been reimplemented to allow semi-automatic updates to facilitate maintenance and release of future versions. PanDrugs2 does not require login and is freely available at https://www.pandrugs.org/.

Genomic and immune landscape Of metastatic pheochromocytoma and paraganglioma.

The mechanisms triggering metastasis in pheochromocytoma/paraganglioma are unknown, hindering therapeutic options for patients with metastatic tumors (mPPGL). Herein we show by genomic profiling of a large cohort of mPPGLs that high mutational load, microsatellite instability and somatic copy-number alteration burden are associated with ATRX/TERT alterations and are suitable prognostic markers. Transcriptomic analysis defines the signaling networks involved in the acquisition of metastatic competence and establishes a gene signature related to mPPGLs, highlighting CDK1 as an additional mPPGL marker. Immunogenomics accompanied by immunohistochemistry identifies a heterogeneous ecosystem at the tumor microenvironment level, linked to the genomic subtype and tumor behavior. Specifically, we define a general immunosuppressive microenvironment in mPPGLs, the exception being PD-L1 expressing MAML3-related tumors. Our study reveals canonical markers for risk of metastasis, and suggests the usefulness of including immune parameters in clinical management for PPGL prognostication and identification of patients who might benefit from immunotherapy.

A user guide for the online exploration and visualization of PCAWG data.

The Pan-Cancer Analysis of Whole Genomes (PCAWG) project generated a vast amount of whole-genome cancer sequencing resource data. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumor types, we provide a user's guide to the five publicly available online data exploration and visualization tools introduced in the PCAWG marker paper. These tools are ICGC Data Portal, UCSC Xena, Chromothripsis Explorer, Expression Atlas, and PCAWG-Scout. We detail use cases and analyses for each tool, show how they incorporate outside resources from the larger genomics ecosystem, and demonstrate how the tools can be used together to understand the biology of cancers more deeply. Together, the tools enable researchers to query the complex genomic PCAWG data dynamically and integrate external information, enabling and enhancing interpretation.

Global Transcriptomic Profiling of the Bone Marrow Stromal Microenvironment during Postnatal Development, Aging, and Inflammation.

Bone marrow (BM) stromal cells provide the regulatory framework for hematopoiesis and contribute to developmental stage-specific niches, such as those preserving hematopoietic stem cells. Despite advances in our understanding of stromal function, little is known about the transcriptional changes that this compartment undergoes throughout lifespan and during adaptation to stress. Using RNA sequencing, we perform transcriptional analyses of four principal stromal subsets, namely CXCL12-abundant reticular, platelet-derived growth factor receptor (PDGFR)-α+Sca1+, sinusoidal, and arterial endothelial cells, from early postnatal, adult, and aged mice. Our data reveal (1) molecular fingerprints defining cell-specific anatomical and functional features, (2) a radical reprogramming of pro-hematopoietic, immune, and matrisomic transcriptional programs during the transition from juvenile stages to adulthood, and (3) the aging-driven progressive upregulation of pro-inflammatory gene expression in stroma. We further demonstrate that transcriptomic pathways elicited in vivo by prototypic microbial molecules are largely recapitulated during aging, thereby supporting the inflammatory basis of age-related adaptations of BM hematopoietic function.

Oncogenic Rag GTPase signaling enhances B cell activation and drives follicular lymphoma sensitive to pharmacological inhibition of mTOR.

The humoral immune response demands that B cells undergo a sudden anabolic shift and high cellular nutrient levels which are required to sustain the subsequent proliferative burst. Follicular lymphoma (FL) originates from B cells that have participated in the humoral response, and 15% of FL samples harbor point, activating mutations in RRAGC, an essential activator of mTORC1 downstream of the sensing of cellular nutrients. The impact of recurrent RRAGC mutations in B cell function and lymphoma is unexplored. RRAGC mutations, targeted to the endogenous locus in mice, confer a partial insensitivity to nutrient deprivation, but strongly exacerbate B cell responses and accelerate lymphomagenesis, while creating a selective vulnerability to pharmacological inhibition of mTORC1. This moderate increase in nutrient signaling synergizes with paracrine cues from the supportive T cell microenvironment that activates B cells via the PI3K-Akt-mTORC1 axis. Hence, Rragc mutations sustain induced germinal centers and murine and human FL in the presence of decreased T cell help. Our results support a model in which activating mutations in the nutrient signaling pathway foster lymphomagenesis by corrupting a nutrient-dependent control over paracrine signals from the T cell microenvironment.

The use of PanDrugs to prioritize anticancer drug treatments in a case of T-ALL based on individual genomic data.

BACKGROUND: Acute T-cell lymphoblastic leukaemia (T-ALL) is an aggressive disorder derived from immature thymocytes. The variability observed in clinical responses on this type of tumours to treatments, the high toxicity of current protocols and the poor prognosis of patients with relapse or refractory make it urgent to find less toxic and more effective therapies in the context of a personalized medicine of precision. METHODS: Whole exome sequencing and RNAseq were performed on DNA and RNA respectively, extracted of a bone marrow sample from a patient diagnosed with tumour primary T-ALL and double negative thymocytes from thymus control samples. We used PanDrugs, a computational resource to propose pharmacological therapies based on our experimental results, including lists of variants and genes. We extend the possible therapeutic options for the patient by taking into account multiple genomic events potentially sensitive to a treatment, the context of the pathway and the pharmacological evidence already known by large-scale experiments. RESULTS: As a proof-of-principle we used next-generation-sequencing technologies (Whole Exome Sequencing and RNA-Sequencing) in a case of diagnosed Pro-T acute lymphoblastic leukaemia. We identified 689 disease-causing mutations involving 308 genes, as well as multiple fusion transcript variants, alternative splicing, and 6652 genes with at least one principal isoform significantly deregulated. Only 12 genes, with 27 pathogenic gene variants, were among the most frequently mutated ones in this type of lymphoproliferative disorder. Among them, 5 variants detected in CTCF, FBXW7, JAK1, NOTCH1 and WT1 genes have not yet been reported in T-ALL pathogenesis. CONCLUSIONS: Personalized genomic medicine is a therapeutic approach involving the use of an individual's information data to tailor drug therapy. Implementing bioinformatics platform PanDrugs enables us to propose a prioritized list of anticancer drugs as the best theoretical therapeutic candidates to treat this patient has been the goal of this article. Of note, most of the proposed drugs are not being yet considered in the clinical practice of this type of cancer opening up the approach of new treatment possibilities.

In Silico Drug Prescription for Targeting Cancer Patient Heterogeneity and Prediction of Clinical Outcome.

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.

vulcanSpot: a tool to prioritize therapeutic vulnerabilities in cancer.

MOTIVATION: Genetic alterations lead to tumor progression and cell survival but also uncover cancer-specific vulnerabilities on gene dependencies that can be therapeutically exploited. RESULTS: vulcanSpot is a novel computational approach implemented to expand the therapeutic options in cancer beyond known-driver genes unlocking alternative ways to target undruggable genes. The method integrates genome-wide information provided by massive screening experiments to detect genetic vulnerabilities associated to tumors. Then, vulcanSpot prioritizes drugs to target cancer-specific gene dependencies using a weighted scoring system based on well known drug-gene relationships and drug repositioning strategies. AVAILABILITY AND IMPLEMENTATION: http://www.vulcanspot.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In vivo phosphoproteomics reveals kinase activity profiles that predict treatment outcome in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) lacks prognostic and predictive markers. Here, we use high-throughput phosphoproteomics to build a functional TNBC taxonomy. A cluster of 159 phosphosites is upregulated in relapsed cases of a training set (n = 34 patients), with 11 hyperactive kinases accounting for this phosphoprofile. A mass-spectrometry-to-immunohistochemistry translation step, assessing 2 independent validation sets, reveals 6 kinases with preserved independent prognostic value. The kinases split the validation set into two patterns: one without hyperactive kinases being associated with a >90% relapse-free rate, and the other one showing ≥1 hyperactive kinase and being associated with an up to 9.5-fold higher relapse risk. Each kinase pattern encompasses different mutational patterns, simplifying mutation-based taxonomy. Drug regimens designed based on these 6 kinases show promising antitumour activity in TNBC cell lines and patient-derived xenografts. In summary, the present study elucidates phosphosites and kinases implicated in TNBC and suggests a target-based clinical classification system for TNBC.

Author Correction: STAT3 labels a subpopulation of reactive astrocytes required for brain metastasis.

In the version of this article originally published, the names of three authors were incorrect. The authors were listed as "Coral Fustero-Torres", "Elena Pineiro" and "Melchor Sánchez-Martínez". Their respective names are "Coral Fustero-Torre", "Elena Piñeiro-Yáñez" and "Melchor Sanchez-Martinez". The errors have been corrected in the print, HTML and PDF versions of this article.

STAT3 labels a subpopulation of reactive astrocytes required for brain metastasis.

The brain microenvironment imposes a particularly intense selective pressure on metastasis-initiating cells, but successful metastases bypass this control through mechanisms that are poorly understood. Reactive astrocytes are key components of this microenvironment that confine brain metastasis without infiltrating the lesion. Here, we describe that brain metastatic cells induce and maintain the co-option of a pro-metastatic program driven by signal transducer and activator of transcription 3 (STAT3) in a subpopulation of reactive astrocytes surrounding metastatic lesions. These reactive astrocytes benefit metastatic cells by their modulatory effect on the innate and acquired immune system. In patients, active STAT3 in reactive astrocytes correlates with reduced survival from diagnosis of intracranial metastases. Blocking STAT3 signaling in reactive astrocytes reduces experimental brain metastasis from different primary tumor sources, even at advanced stages of colonization. We also show that a safe and orally bioavailable treatment that inhibits STAT3 exhibits significant antitumor effects in patients with advanced systemic disease that included brain metastasis. Responses to this therapy were notable in the central nervous system, where several complete responses were achieved. Given that brain metastasis causes substantial morbidity and mortality, our results identify a novel treatment for increasing survival in patients with secondary brain tumors.

PanDrugs: a novel method to prioritize anticancer drug treatments according to individual genomic data.

BACKGROUND: 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. RESULTS: We present PanDrugs, a new computational methodology to guide the selection of personalized treatments in cancer patients using the variant lists provided by genome-wide sequencing analyses. PanDrugs offers the largest database of drug-target associations available from well-known targeted therapies to preclinical drugs. Scoring data-driven gene cancer relevance and drug feasibility PanDrugs interprets genomic alterations and provides a prioritized evidence-based list of anticancer therapies. Our tool represents the first drug prescription strategy applying a rational based on pathway context, multi-gene markers impact and information provided by functional experiments. Our approach has been systematically applied to TCGA patients and successfully validated in a cancer case study with a xenograft mouse model demonstrating its utility. CONCLUSIONS: PanDrugs is a feasible method to identify potentially druggable molecular alterations and prioritize drugs to facilitate the interpretation of genomic landscape and clinical decision-making in cancer patients. Our approach expands the search of druggable genomic alterations from the concept of cancer driver genes to the druggable pathway context extending anticancer therapeutic options beyond already known cancer genes. The methodology is public and easily integratable with custom pipelines through its programmatic API or its docker image. The PanDrugs webtool is freely accessible at http://www.pandrugs.org .

Pancreas Cancer Precision Treatment Using Avatar Mice from a Bioinformatics Perspective.

Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of cancer-related death among solid malignancies. Unfortunately, PDAC lethality has not substantially decreased over the past 20 years. This aggressiveness is related to the genomic complexity and heterogeneity of PDAC, but also to the absence of an effective screening for the detection of early-stage tumors and a lack of efficient therapeutic options. Therefore, there is an urgent need to improve the arsenal of anti-PDAC drugs for an effective treatment of these patients. Patient-derived xenograft (PDX) mouse models represent a promising strategy to personalize PDAC treatment, offering a bench testing of candidate treatments and helping to select empirical treatments in PDAC patients with no therapeutic targets. Moreover, bioinformatics-based approaches have the potential to offer systematic insights into PDAC etiology predicting putatively actionable tumor-specific genomic alterations, identifying novel biomarkers and generating disease-associated gene expression signatures. This review focuses on recent efforts to individualize PDAC treatments using PDX models. Additionally, we discuss the current understanding of the PDAC genomic landscape and the putative druggable targets derived from mutational studies. PDAC molecular subclassifications and gene expression profiling studies are reviewed as well. Finally, latest bioinformatics methodologies based on somatic variant detection and prioritization, in silico drug response prediction, and drug repositioning to improve the treatment of advanced PDAC tumors are also covered.

PheoSeq: A Targeted Next-Generation Sequencing Assay for Pheochromocytoma and Paraganglioma Diagnostics.

Genetic diagnosis is recommended for all pheochromocytoma and paraganglioma (PPGL) cases, as driver mutations are identified in approximately 80% of the cases. As the list of related genes expands, genetic diagnosis becomes more time-consuming, and targeted next-generation sequencing (NGS) has emerged as a cost-effective tool. This study aimed to optimize targeted NGS in PPGL genetic diagnostics. A workflow based on two customized targeted NGS assays was validated to study the 18 main PPGL genes in germline and frozen tumor DNA, with one of them specifically directed toward formalin-fixed paraffin-embedded tissue. The series involved 453 unrelated PPGL patients, of whom 30 had known mutations and were used as controls. Partial screening using Sanger had been performed in 275 patients. NGS results were complemented with the study of gross deletions. NGS assay showed a sensitivity ≥99.4%, regardless of DNA source. We identified 45 variants of unknown significance and 89 pathogenic mutations, the latter being germline in 29 (7.2%) and somatic in 58 (31.7%) of the 183 tumors studied. In 37 patients previously studied by Sanger sequencing, the causal mutation could be identified. We demonstrated that both assays are an efficient and accurate alternative to conventional sequencing. Their application facilitates the study of minor PPGL genes, and enables genetic diagnoses in patients with incongruent or missing clinical data, who would otherwise be missed.

MicroRNA deep-sequencing reveals master regulators of follicular and papillary thyroid tumors.

MicroRNA deregulation could be a crucial event in thyroid carcinogenesis. However, current knowledge is based on studies that have used inherently biased methods. Thus, we aimed to define in an unbiased way a list of deregulated microRNAs in well-differentiated thyroid cancer in order to identify diagnostic and prognostic markers. We performed a microRNA deep-sequencing study using the largest well-differentiated thyroid tumor collection reported to date, comprising 127 molecularly characterized tumors with follicular or papillary patterns of growth and available clinical follow-up data, and 17 normal tissue samples. Furthermore, we integrated microRNA and gene expression data for the same tumors to propose targets for the novel molecules identified. Two main microRNA expression profiles were identified: one common for follicular-pattern tumors, and a second for papillary tumors. Follicular tumors showed a notable overexpression of several members of miR-515 family, and downregulation of the novel microRNA miR-1247. Among papillary tumors, top upregulated microRNAs were miR-146b and the miR-221~222 cluster, while miR-1179 was downregulated. BRAF-positive samples displayed extreme downregulation of miR-7 and -204. The identification of the predicted targets for the novel molecules gave insights into the proliferative potential of the transformed follicular cell. Finally, by integrating clinical follow-up information with microRNA expression, we propose a prediction model for disease relapse based on expression of two miRNAs (miR-192 and let-7a) and several other clinicopathological features. This comprehensive study complements the existing knowledge about deregulated microRNAs in the development of well-differentiated thyroid cancer and identifies novel markers associated with recurrence-free survival.