Estudi de l'evolució i el tractament del càncer des d'una perspectiva de la biologia de sistemes

Cancer can be defined as a family of complex disease characterized by a cellular hyperproliferation and the capacity of the cell to invade surrounding tissue and metastasize to other organs in the body. The development and progression of cancer is a complex multistep process in which cells gradually acquire mutations altering the cellular mechanisms until the cell becomes malignant. Cancer develops and progresses through the acquisition of the above mutations or alterations across biological levels. Downstream of these alterations are expression changes in many genes at each stage of the disease. Sets of genes (also called signatures whose differential expression or profiles have prognostic or predictive (in terms of prediction of drug-response) values have been identified for almost every type of cancer. In some cases, several signatures have proved to be useful in independent evaluations, although, intriguingly, their overlap in gene identities was minimal. Then, integrative approaches using different types of gene and protein relationships have demonstrated the existence of biological convergence among apparently disparate gene sets. Moreover, integrating data from he network of known protein-protein interactions (hereafter interactome network) has been shown to improve the reproducibility and accuracy of prognostic signatures. However, in this scenario, the network topological patterns linked to the dynamic molecular alterations that characterize cancer development and progression, and treatment response, remain unknown. Several key genetic, transcriptomic and molecular determinants of cancer therapeutic response have been identified in recent years. Specific genetic alterations have been demonstrated to mediate the existence and/or promote the acquisition of therapeutic resistance. In addition, sets of transcripts have been identified whose profiles have predictive value for prognosis and/or treatment benefit. Successively, the integration and modeling of data from different types of molecular interactions has been shown to enhance understanding of the mechanisms of cancer progression and therapy response. In this scenario, however, cancer patients all too frequently show no or only modest benefit from a given therapy. The persistence of this fundamental clinical problem has been partially attributed to the lack of specific biomarkers. However, the identification of a comprehensive measure of cancer cell activity may support the interpretation of therapy efficacy. At a cancer system level, several studies have shown extensive molecular rewiring and increased signaling entropy, which may endorse the characteristic robustness of cancer. Given these observations, an a priori understanding of therapeutic response should be supported by an integrated measure of cancer network activity. In this study, firstly, we hypothesized that the features of dynamism and robustness intrinsic to cancer should also be present at different biological levels and, in particular, evident within the topology of he interactome network. To assess this hypothesis we analyzed the impact of cancer-related expression changes- including cancer development, progression, response to treatments, and targeted perturbation-in the interactome network using the concept of cascading failures. The results of these analyses associate robustness with cancer and identify autophagy as an opposite condition. Next, we hypothesized that a comprehensive measure of cancer cell status or function applicable to predicting therapy response should integrate and/or partially reflect the fundamental hallmarks of cancer. To address these questions, we have developed a novel weighted network score, which is used to show that cancer network activity is associated with therapeutic response and synergism.El càncer és una família de malalties complexes que es caracteritzen per una proliferació cel·lular descontrolada i il·limitada i la capacitat de les cèl·lules d'invadir teixit veí i metastatitzar cap a teixit i òrgans llunyans. El desenvolupament i progressió d'un càncer és un procés complex de diversos passos en els quals les cèl·lules pateixen i acumulen mutacions gèniques. Aquestes mutacions alteren els mecanismes cel·lulars fins que la cèl·lula cancerosa ha acumulat les alteracions necessàries per proliferar de manera descontrolada i adquirir la capacitat de colonitzar teixits o òrgans llunyans. Les mutacions que pateixen les cèl·lules durant el procés de la carcinogènesi afecten a diferents nivells del sistema cel·lular. Aquestes mutacions alteren els perfils d'expressió gènica i produeixen canvis quantitatius i qualitatius en el producte gènic (ARN i proteïna) que poden alterar les xarxes complexes cel·lulars per les quals es regulen els mecanismes cel·lulars. En els darrers anys, diverses anàlisis, que han integrat dades d'expressió gènica, han sigut útils per identificar signatures de gens pel pronòstic del càncer o determinar teràpies pel seu tractament. Tanmateix, a causa de la diversitat biològica de la cèl·lula, signatures de gens identificades mitjançant estudis independents sovint tenen pocs o cap gen en comú. Amb la integració de dades d'expressió gènica i d'interaccions entre proteïnes, s'ha pogut explicar la diversitat biològica que capturen aquestes anàlisis mitjançant la identificació de signatures aparentment diferents. Malgrat que la xarxa d'interaccions entre proteïnes (xarxa de l'interactoma) aporta informació addicional del sistema, la relació amb la dinàmica molecular del càncer roman desconeguda. Les cèl·lules canceroses sovint responen a la teràpia generant resistència, ja sigui per la pròpia heterogeneïtat de la cèl·lula cancerosa o adquirint noves mutacions o alteracions en els seus mecanismes. Els canvis adquirits poden afectar a processos biològics o vies de senyalització immerses en les xarxes complexes moleculars ---desregulant-les o modificant la seva activitat--- augmentant la robustesa característica del càncer. En aquest sentit, el desenvolupament d'eines per capturar l'activitat de les xarxes moleculars i per les vies que la cèl·lula adquireix resistència o per les quals pot ser vulnerable, pot ser de gran utilitat per identificar teràpies específiques dirigides a dianes moleculars concretes. En aquest treball, per explorar la relació entre la xarxa de l'interactoma i la dinàmica molecular del càncer, s'ha realitzat una anàlisi toplògica de la xarxa de l'interactoma respecte dels estadis del càncer, pertorbació de gens centrals en el càncer i la resposta al tractament. Per fer-ho, s'ha implementat un model de cascada de fallades que modelitza la propagació de càrrega proteica a través de la xarxa. Els resultats obtinguts mostren que la xarxa de l'interactoma és robusta respecte de les condicions del càncer. Per altra banda, per explorar l'activitat de la xarxa de les cèl·lules canceroses i la seva relació a la sensibilitat de les cèl·lules respecta al tractament, s'ha definit una mesura de l'activitat de la xarxa i s'ha aplicat a les xarxes de diferents càncers. Tot seguit, s'han comparat les mesures obtingudes amb la resposta de les cèl·lules canceroses al tractament de diversos fàrmacs. Els resultats obtinguts, mostren que aquesta mesura diferencia fàrmacs segons les seves característiques, per exemple si són citotòxics o tenen dianes específiques, i que prediu combinacions de fàrmacs amb un efecte sinèrgic.Postprint (published version

Radioresistance of mesenchymal glioblastoma initiating cells correlates with patient outcome and is associated with activation of inflammatory program

Glioblastoma (GBM) still remains an incurable disease being radiotherapy (RT) the mainstay treatment. Glioblastoma intra-tumoral heterogeneity and GlioblastomaInitiating Cells (GICs) challenge the design of effective therapies. We investigated GICs and non-GICs response to RT in a paired in-vitro model and addressed molecular programs activated in GICs after RT. Established GICs heterogeneously expressed several GICs markers and displayed a mesenchymal signature. Upon fractionated RT, GICs reported higher radioresistance compared to non-GICs and showed lower α- and β-values, according to the Linear Quadratic Model interpretation of the survival curves. Moreover, a significant correlation was observed between GICs radiosensitivity and patient disease-free survival. Transcriptome analysis of GICs after acquisition of a radioresistant phenotype reported significant activation of Proneural-to-Mesenchymal transition (PMT) and pro-inflammatory pathways, being STAT3 and IL6 the major players. Our findings support a leading role of mesenchymal GICs in defining patient response to RT and provide the grounds for targeted therapies based on the blockade of inflammatory pathways to overcome GBM radioresistance

Integrated genomic and prospective clinical studies show the importance of modular pleiotropy for disease susceptibility, diagnosis and treatment

Background: Translational research typically aims to identify and functionally validate individual, disease-specific genes. However, reaching this aim is complicated by the involvement of thousands of genes in common diseases, and that many of those genes are pleiotropic, that is, shared by several diseases. Methods: We integrated genomic meta-analyses with prospective clinical studies to systematically investigate the pathogenic, diagnostic and therapeutic roles of pleiotropic genes. In a novel approach, we first used pathway analysis of all published genome-wide association studies (GWAS) to find a cell type common to many diseases. Results: The analysis showed over-representation of the T helper cell differentiation pathway, which is expressed in T cells. This led us to focus on expression profiling of CD4(+) T cells from highly diverse inflammatory and malignant diseases. We found that pleiotropic genes were highly interconnected and formed a pleiotropic module, which was enriched for inflammatory, metabolic and proliferative pathways. The general relevance of this module was supported by highly significant enrichment of genetic variants identified by all GWAS and cancer studies, as well as known diagnostic and therapeutic targets. Prospective clinical studies of multiple sclerosis and allergy showed the importance of both pleiotropic and disease specific modules for clinical stratification. Conclusions: In summary, this translational genomics study identified a pleiotropic module, which has key pathogenic, diagnostic and therapeutic roles

Exploring the Link between Germline and Somatic Genetic Alterations in Breast Carcinogenesis

Recent genome-wide association studies (GWASs) have identified candidate genes contributing to cancer risk through low-penetrance mutations. Many of these genes were unexpected and, intriguingly, included well-known players in carcinogenesis at the somatic level. To assess the hypothesis of a germline-somatic link in carcinogenesis, we evaluated the distribution of somatic gene labels within the ordered results of a breast cancer risk GWAS. This analysis suggested frequent influence on risk of genetic variation in loci encoding for “driver kinases” (i.e., kinases encoded by genes that showed higher somatic mutation rates than expected by chance and, therefore, whose deregulation may contribute to cancer development and/or progression). Assessment of these predictions using a population-based case-control study in Poland replicated the association for rs3732568 in EPHB1 (odds ratio (OR) = 0.79; 95% confidence interval (CI): 0.63–0.98; Ptrend = 0.031). Analyses by early age at diagnosis and by estrogen receptor α (ERα) tumor status indicated potential associations for rs6852678 in CDKL2 (OR = 0.32, 95% CI: 0.10–1.00; Precessive = 0.044) and rs10878640 in DYRK2 (OR = 2.39, 95% CI: 1.32–4.30; Pdominant = 0.003), and for rs12765929, rs9836340, rs4707795 in BMPR1A, EPHA3 and EPHA7, respectively (ERα tumor status Pinteraction<0.05). The identification of three novel candidates as EPH receptor genes might indicate a link between perturbed compartmentalization of early neoplastic lesions and breast cancer risk and progression. Together, these data may lay the foundations for replication in additional populations and could potentially increase our knowledge of the underlying molecular mechanisms of breast carcinogenesis

Gasdermin B expression predicts poor clinical outcome in HER2-positive breast cancer

Altres ajuts: This work has been supported by the Community of Madrid (grant S2010/BMD-2303 to GMB), the Breast Cancer Research Foundation (BCRF) to JA. Alba Mota is a predoctoral student supported by a FPU fellowship (Spanish Ministry of Education, Culture and Sport). David Sarrio is a postdoctoral researcher funded by the AECC Scientific Foundation.Around, 30-40% of HER2-positive breast cancers do not show substantial clinical benefit from the targeted therapy and, thus, the mechanisms underlying resistance remain partially unknown. Interestingly, ERBB2 is frequently co-amplified and co-expressed with neighbour genes that may play a relevant role in this cancer subtype. Here, using an in silico analysis of data from 2,096 breast tumours, we reveal a significant correlation between Gasdermin B (GSDMB) gene (located 175 kilo bases distal from ERBB2) expression and the pathological and clinical parameters of poor prognosis in HER2-positive breast cancer. Next, the analysis of three independent cohorts (totalizing 286 tumours) showed that approximately 65% of the HER2-positive cases have GSDMB gene amplification and protein over-expression. Moreover, GSDMB expression was also linked to poor therapeutic responses in terms of lower relapse free survival and pathologic complete response as well as positive lymph node status and the development of distant metastasis under neoadjuvant and adjuvant treatment settings, respectively. Importantly, GSDMB expression promotes survival to trastuzumab in different HER2-positive breast carcinoma cells, and is associated with trastuzumab resistance phenotype in vivo in Patient Derived Xenografts. In summary, our data identifies the ERBB2 co-amplified and co-expressed gene GSDMB as a critical determinant of poor prognosis and therapeutic response in HER2-positive breast cancer

Distinct DNA methylomes of newborns and centenarians

Human aging cannot be fully understood in terms of the constrained genetic setting. Epigenetic drift is an alternative means of explaining age-associated alterations. To address this issue, we performed whole-genome bisulfite sequencing (WGBS) of newborn and centenarian genomes. The centenarian DNA had a lower DNA methylation content and a reduced correlation in the methylation status of neighboring cytosine--phosphate--guanine (CpGs) throughout the genome in comparison with the more homogeneously methylated newborn DNA. The more hypomethylated CpGs observed in the centenarian DNA compared with the neonate covered all genomic compartments, such as promoters, exonic, intronic, and intergenic regions. For regulatory regions, the most hypomethylated sequences in the centenarian DNA were present mainly at CpG-poor promoters and in tissue-specific genes, whereas a greater level of DNA methylation was observed in CpG island promoters. We extended the study to a larger cohort of newborn and nonagenarian samples using a 450,000 CpG-site DNA methylation microarray that reinforced the observation of more hypomethylated DNA sequences in the advanced age group. WGBS and 450,000 analyses of middle-age individuals demonstrated DNA methylomes in the crossroad between the newborn and the nonagenarian/centenarian groups. Our study constitutes a unique DNA methylation analysis of the extreme points of human life at a single-nucleotide resolution level

Tumor xenograft modeling identifies an association between TCF4 loss and breast cancer chemoresistance

Understanding the mechanisms of cancer therapeutic resistance is fundamental to improving cancer care. There is clear benefit from chemotherapy in different breast cancer settings; however, knowledge of the mutations and genes that mediate resistance is incomplete. In this study, by modeling chemoresistance in patientderived xenografts (PDXs), we show that adaptation to therapy is genetically complex and identify that loss of transcription factor 4 (TCF4; also known as ITF2) is associated with this process. A triple-negative BRCA1-mutaied PDX was used to study the genetics of chemoresistance. The PDX was treated in parallel with four chemotherapies for five iterative cycles. Exome sequencing identified few genes with de novo or enriched mutations in common among the different therapies, whereas many common depleted mutations/ genes were observed. Analysis of somatic mutations from The Cancer Genome Atlas (TCGA) supported the prognostic relevance of the identified genes. A mutation in TCF4 was found de novo in all treatments, and analysis of drug sensitivity profiles across cancer cell lines supported the link to chemoresistance. Loss of TCF4 conferred chemoresistance in breast cancer cell models, possibly by altering cell cycle regulation. Targeted sequencing in chemoresistant tumors identified an intronic variant of TCF4 that may represent an expression quantitative trait locus associated with relapse outcome in TCGA. Immunohistochemical studies suggest a common loss of nuclear TCF4 expression post-chemotherapy. Together, these results from tumor xenograft modeling depict a link between altered TCF4 expression and breast cancer chemoresistance

Lymphangioleiomyomatosis biomarkers linked to lung metastatic potential and cell stemness

Lymphangioleiomyomatosis (LAM) is a rare lung-metastasizing neoplasm caused by the proliferation of smooth muscle-like cells that commonly carry loss-of-function mutations in either the tuberous sclerosis complex 1 or 2 (TSC1 or TSC2) genes. While allosteric inhibition of the mechanistic target of rapamycin (mTOR) has shown substantial clinical benefit, complementary therapies are required to improve response and/or to treat specific patients. However, there is a lack of LAM biomarkers that could potentially be used to monitor the disease and to develop other targeted therapies. We hypothesized that the mediators of cancer metastasis to lung, particularly in breast cancer, also play a relevant role in LAM. Analyses across independent breast cancer datasets revealed associations between low TSC1/2 expression, altered mTOR complex 1 (mTORC1) pathway signaling, and metastasis to lung. Subsequently, immunohistochemical analyses of 23 LAM lesions revealed positivity in all cases for the lung metastasis mediators fascin 1 (FSCN1) and inhibitor of DNA binding 1 (ID1). Moreover, assessment of breast cancer stem or luminal progenitor cell biomarkers showed positivity in most LAM tissue for the aldehyde dehydrogenase 1 (ALDH1), integrin-ß3 (ITGB3/CD61), and/or the sex-determining region Y-box 9 (SOX9) proteins. The immunohistochemical analyses also provided evidence of heterogeneity between and within LAM cases. The analysis of Tsc2-deficient cells revealed relative over-expression of FSCN1 and ID1; however, Tsc2-deficient cells did not show higher sensitivity to ID1-based cancer inhibitors. Collectively, the results of this study reveal novel LAM biomarkers linked to breast cancer metastasis to lung and to cell stemness, which in turn might guide the assessment of additional or complementary therapeutic opportunities for LAM

Crystal structure of ethyl (6-hydroxy-1-benzofuran-3-yl)acetate sesquihydrate

In the title hydrate, C12H12O4·1.5H2O, one of the water molecules in the asymmetric unit is located on a twofold rotation axis. The molecule of the benzofuran derivative is essentially planar (r.m.s. deviation for the non-H atoms = 0.021à ), with the ester group adopting a fully extended conformation. In the crystal, O-H�O hydrogen bonds between the water molecules and the hydroxy groups generate a centrosymmetric R6 6(12) ring motif. These R6 6(12) rings are fused, forming a one-dimensional motif extending along the c-axis direction

沃度保兒謨ニ因セル濕疹

Evaluation of CNA-IC50 correlations by known cancer driver mutations. Distributions of PCCs for cancer cell lines with mutated or wild-type proto-oncogenes or tumor suppressors as depicted in each figure. The rank position of negatively correlated drugs (p < 0.10) is shown. (EPS 975 kb