1,203 research outputs found

    Cellular interactions in the tumor microenvironment: the role of secretome

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    Over the past years, it has become evident that cancer initiation and progression depends on several components of the tumor microenvironment, including inflammatory and immune cells, fibroblasts, endothelial cells, adipocytes, and extracellular matrix. These components of the tumor microenvironment and the neoplastic cells interact with each other providing pro and antitumor signals. The tumor-stroma communication occurs directly between cells or via a variety of molecules secreted, such as growth factors, cytokines, chemokines and microRNAs. This secretome, which derives not only from tumor cells but also from cancer-associated stromal cells, is an important source of key regulators of the tumorigenic process. Their screening and characterization could provide useful biomarkers to improve cancer diagnosis, prognosis, and monitoring of treatment responses.Agência financiadora Fundação de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) FAPESP 10/51168-0 12/06048-2 13/03839-1 National Council for Scientific and Technological Development (CNPq) CNPq 306216/2010-8 Fundacao para a Ciencia e a Tecnologia (FCT) UID/BIM/04773/2013 CBMR 1334info:eu-repo/semantics/publishedVersio

    Proteomic identification of putative biomarkers of neo-adjuvant chemotherapy resistance in luminal (ER+) breast cancer

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    Background: Neoadjuvant chemotherapy is a standard treatment for locally advanced breast cancer however chemoresistance can be a major obstacle in ER+ cancers. Using comparative proteomic approaches (antibody microarray/AbMA and 2D-PAGE with MALDI-TOF/TOF MS) to investigate a pilot series of breast cancer samples our research group recently identified 14-3-3 theta/tau, tBID and BcL-XL as putative biomarkers of response to neoadjuvant chemotherapy (Hodgkinson et al J Prot 2012, 75:1276-1283 and 75:2745-2752). Here we aimed to analyse further samples using the AbMA approach and to re-analyse the combined data. Methods: Samples from chemoresistant and chemosensitive breast cancers were selected following anthracycline-taxane chemotherapy and 4 experiments were performed using ductal ER+ tumours. Differential protein expression was compared between chemoresistant and chemosensitive samples using the Panorama XPRESS Profiler725 AbMA kit. The combined data from 9 AbMA assays and 3 2D-PAGE/MS experiments was then analysed using Ingenuity Pathway Analysis (IPA; Ingenuity Systems). A pilot series of archival samples was used for clinical validation of putative predictive biomarkers. Results: 89 differentially expressed proteins (DEPs) were seen in the 4 further AbMA experiments. In the combined dataset (12 experiments from 2 proteomic platforms), 8 DEPs were seen in at least 3 experiments. These were 14-3-3 theta, 14-3-3 epsilon, 14-3-3 gamma, Bcl-xl, Bid, Phosphokinase B, Vimentin and FAK. 121 DEPs from the combined data were analysed using IPA; 13 DEPs were mapped onto the PI3K/AKT pathway. Clinical validation in a pilot series of archival samples revealed AkT-1 Ser473 and FAKY397 alongside the previously identified and validated 14-3-3 theta/tau, and tBID to be significantly associated with chemotherapy resistance. Conclusion: We have now identified at least 8 proteins which could play a role in breast chemoresistance. We propose a potential role for AkT-1, FAK, 14-3-3 theta/tau and tBID as predictive biomarkers of neoadjuvant chemotherapy resistance in breast cancer. Further validation in a larger sample series is now required

    Proteomic identification of putative biomarkers of neo-adjuvant chemotherapy resistance in luminal (ER+) breast cancer

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    Background:Neoadjuvant chemotherapy is a standard treatment for locally advanced breast cancer however chemoresistance can be a major obstacle in ER+ cancers. Using comparative proteomic approaches (antibody microarray/AbMA and 2D-PAGE with MALDI-TOF/TOF MS) to investigate a pilot series of breast cancer samples our research group recently identified 14-3-3 theta/tau, tBID and BcL-XL as putative biomarkers of response to neoadjuvant chemotherapy (Hodgkinson et al J Prot 2012, 75:1276-1283 and 75:2745-2752). Here we aimed to analyse further samples using the AbMA approach and to re-analyse the combined data.Methods:Samples from chemoresistant and chemosensitive breast cancers were selected following anthracycline-taxane chemotherapy and 4 experiments were performed using ductal ER+ tumours. Differential protein expression was compared between chemoresistant and chemosensitive samples using the Panorama XPRESS Profiler725 AbMA kit. The combined data from 9 AbMA assays and 3 2D-PAGE/MS experiments was then analysed using Ingenuity Pathway Analysis (IPA; Ingenuity Systems). A pilot series of archival samples was used for clinical validation of putative predictive biomarkers.Results:89 differentially expressed proteins (DEPs) were seen in the 4 further AbMA experiments. In the combined dataset (12 experiments from 2 proteomic platforms), 8 DEPs were seen in at least 3 experiments. These were 14-3-3 theta, 14-3-3 epsilon, 14-3-3 gamma, Bcl-xl, Bid, Phosphokinase B, Vimentin and FAK. 121 DEPs from the combined data were analysed using IPA; 13 DEPs were mapped onto the PI3K/AKT pathway. Clinical validation in a pilot series of archival samples revealed AkT-1 Ser473 and FAKY397 alongside the previously identified and validated 14-3-3 theta/tau, and tBID to be significantly associated with chemotherapy resistance.Conclusion: We have now identified at least 8 proteins which could play a role in breast chemoresistance. We propose a potential role for AkT-1, FAK, 14-3-3 theta/tau and tBID as predictive biomarkers of neoadjuvant chemotherapy resistance in breast cancer. Further validation in a larger sample series is now required

    Data mining of gene arrays for biomarkers of survival in ovarian cancer

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    The expected five-year survival rate from a stage III ovarian cancer diagnosis is a mere 22%; this applies to the 7000 new cases diagnosed yearly in the UK. Stratification of patients with this heterogeneous disease, based on active molecular pathways, would aid a targeted treatment improving the prognosis for many cases. While hundreds of genes have been associated with ovarian cancer, few have yet been verified by peer research for clinical significance. Here, a meta-analysis approach was applied to two care fully selected gene expression microarray datasets. Artificial neural networks, Cox univariate survival analyses and T-tests identified genes whose expression was consistently and significantly associated with patient survival. The rigor of this experimental design increases confidence in the genes found to be of interest. A list of 56 genes were distilled from a potential 37,000 to be significantly related to survival in both datasets with a FDR of 1.39859 × 10−11, the identities of which both verify genes already implicated with this disease and provide novel genes and pathways to pursue. Further investigation and validation of these may lead to clinical insights and have potential to predict a patient’s response to treatment or be used as a novel target for therapy

    The role of peroxiredoxins in cancer

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    Peroxiredoxins (PRDXs) are a ubiquitously expressed family of small (22-27 kDa) non-seleno peroxidases that catalyze the peroxide reduction of H2O2, organic hydroperoxides and peroxynitrite. They are highly involved in the control of various physiological functions, including cell growth, differentiation, apoptosis, embryonic development, lipid metabolism, the immune response, as well as cellular homeostasis. Although the protective role of PRDXs in cardiovascular and neurological diseases is well established, their role in cancer remains controversial. Increasing evidence suggests the involvement of PRDXs in carcinogenesis and in the development of drug resistance. Numerous types of cancer cells, in fact, are characterized by an increase in reactive oxygen species (ROS) production, and often exhibit an altered redox environment compared with normal cells. The present review focuses on the complex association between oxidant balance and cancer, and it provides a brief account of the involvement of PRDXs in tumorigenesis and in the development of chemoresistance

    Transcriptional Shift Identifies a Set of Genes Driving Breast Cancer Chemoresistance

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    Background Distant recurrences after antineoplastic treatment remain a serious problem for breast cancer clinical management, which threats patients’ life. Systemic therapy is administered to eradicate cancer cells from the organism, both at the site of the primary tumor and at any other potential location. Despite this intervention, a significant proportion of breast cancer patients relapse even many years after their primary tumor has been successfully treated according to current clinical standards, evidencing the existence of a chemoresistant cell subpopulation originating from the primary tumor.Methods/Findings To identify key molecules and signaling pathways which drive breast cancer chemoresistance we performed gene expression analysis before and after anthracycline and taxane-based chemotherapy and compared the results between different histopathological response groups (good-, mid- and bad-response), established according to the Miller & Payne grading system. Two cohorts of 33 and 73 breast cancer patients receiving neoadjuvant chemotherapy were recruited for whole-genome expression analysis and validation assay, respectively. Identified genes were subjected to a bioinformatic analysis in order to ascertain the molecular function of the proteins they encode and the signaling in which they participate. High throughput technologies identified 65 gene sequences which were over-expressed in all groups (P ≤ 0·05 Bonferroni test). Notably we found that, after chemotherapy, a significant proportion of these genes were over-expressed in the good responders group, making their tumors indistinguishable from those of the bad responders in their expression profile (P ≤ 0.05 Benjamini-Hochgerg`s method).Conclusions These data identify a set of key molecular pathways selectively up-regulated in post-chemotherapy cancer cells, which may become appropriate targets for the development of future directed therapies against breast cancer.Thanks are due to the Consejería de Economia, Innovación y Ciencia (CEIC) from the Junta de Andalucía and Fondo Europeo de Desarrollo Regional (FEDER)/Fondo de Cohesión Europeo (FSE) to financial support through the Programa Operativo FEDER/FSE de Andalucía 2007-2013 and the research project CTS-5350. The authors also acknowledge financial support by the PN de I+D+i 2006-2009/ISCIII/Ministerio de Sanidad, Servicios Sociales e Igualdad (Spain) and Fondo Europeo de Desarrollo Regional (FEDER) from the European Union, through the research project PI06/90388

    The current status and future prospects for therapeutic targeting of KEAP1-NRF2 and β-TrCP-NRF2 interactions in cancer chemoresistance

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    Drug resistance is one of the biggest challenges in cancer treatment and limits the potential to cure patients. In many tumors, sustained activation of the protein NRF2 makes tumor cells resistant to chemo- and radiotherapy. Thus, blocking inappropriate NRF2 activity in cancers has been shown to reduce resistance in models of the disease. There is a growing scientific interest in NRF2 inhibitors. However, the compounds developed so far are not target-specific and are associated with a high degree of toxicity, hampering clinical applications. Compounds that can enhance the binding of NRF2 to its ubiquitination-facilitating regulator proteins, either KEAP1 or β-TrCP, have the potential to increase NRF2 degradation and may be of value as potential chemosensitising agents in cancer treatment. Approaches based on molecular glue-type mechanisms, in which ligands stabilise a ternary complex between a protein and its binding partner have shown to enhance β-catenin degradation by stabilising its interaction with β-TrCP. This strategy could be applied to rationally discover degradative β-TrCP-NRF2 and KEAP1-NRF2 protein-protein interaction enhancers. We are proposing a novel approach to selectively suppress NRF2 activity in tumors. It is based on recent methodology and has the potential to be a promising new addition to the arsenal of anticancer agents

    APE1 controls DICER1 expression in NSCLC through miR-33a and miR-130b

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    Increasing evidence suggests different, not completely understood roles of microRNA biogenesis in the development and progression of lung cancer. The overexpression of the DNA repair protein apurinic/apyrimidinic endodeoxyribonuclease 1 (APE1) is an important cause of poor chemotherapeutic response in lung cancer and its involvement in onco-miRNAs biogenesis has been recently described. Whether APE1 regulates miRNAs acting as prognostic biomarkers of lung cancer has not been investigated, yet. In this study, we analyzed miRNAs differential expression upon APE1 depletion in the A549 lung cancer cell line using high-throughput methods. We defined a signature of 13 miRNAs that strongly correlate with APE1 expression in human lung cancer: miR-1246, miR-4488, miR-24, miR-183, miR-660, miR-130b, miR-543, miR-200c, miR-376c, miR-218, miR-146a, miR-92b and miR-33a. Functional enrichment analysis of this signature revealed its biological relevance in cancer cell proliferation and survival. We validated DICER1 as a direct functional target of the APE1-regulated miRNA-33a-5p and miR-130b-3p. Importantly, IHC analyses of different human tumors confirmed a negative correlation existing between APE1 and Dicer1 protein levels. DICER1 downregulation represents a prognostic marker of cancer development but the mechanisms at the basis of this phenomenon are still completely unknown. Our findings, suggesting that APE1 modulates DICER1 expression via miR-33a and miR-130b, reveal new mechanistic insights on DICER1 regulation, which are of relevance in lung cancer chemoresistance and cancer invasiveness

    Biomarker Discovery with Quantum Neural Networks: A Case-study in CTLA4-Activation Pathways

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    Biomarker discovery is a challenging task due to the massive search space. Quantum computing and quantum Artificial Intelligence (quantum AI) can be used to address the computational problem of biomarker discovery tasks. We propose a Quantum Neural Networks (QNNs) architecture to discover biomarkers for input activation pathways. The Maximum Relevance, Minimum Redundancy (mRMR) criteria is used to score biomarker candidate sets. Our proposed model is economical since the neural solution can be delivered on constrained hardware. We demonstrate the proof of concept on four activation pathways associated with CTLA4, including (1) CTLA4-activation stand-alone, (2) CTLA4-CD8A-CD8B co-activation, (3) CTLA4-CD2 co-activation, and (4) CTLA4-CD2-CD48-CD53-CD58-CD84 co-activation. The model indicates new biomarkers associated with the mutational activation of CLTA4-associated pathways, including 20 genes: CLIC4, CPE, ETS2, FAM107A, GPR116, HYOU1, LCN2, MACF1, MT1G, NAPA, NDUFS5, PAK1, PFN1, PGAP3, PPM1G, PSMD8, RNF213, SLC25A3, UBA1, and WLS. We open source the implementation at: https://github.com/namnguyen0510/Biomarker-Discovery-with-Quantum-Neural-Networks
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