An unsupervised fuzzy ensemble algorithmic scheme for gene expression data analysis

Background: In recent years unsupervised ensemble clustering methods have been successfully applied to DNA microarray data analysis to improve the accuracy and the reliability of clustering results. Nevertheless, a major problem is represented by the fact that classes of functionally correlated examples (e.g. subclasses of diseases characterized at bio-molecular level) are not in general clearly separable, and in many cases the same gene may belong to different functional classes (e.g. may participate to different biological processes). Results: We propose an ensemble clustering algorithm scheme, based on a fuzzy approach, that directly permit to deal with overlapping classes or with genes or samples that may belong to more clusters at the same time. From our algorithmic scheme several fuzzy ensemble clustering algorithms may be derived, according to the way the multiple clusterings are combined and the consensus clustering is generated. We test some of the proposed ensemble algorithms with two DNA microarray data sets available on the web, comparing the results with other single and ensemble clustering methods. Conclusions: Our proposed fuzzy ensemble approach may be applied to discover classes of co-expressed genes or subclasses of functionally related examples, and in principle it may be applied for the unsupervised analysis of different types of complex bio-molecular data. Fuzzy ensemble algorithms can assign each gene/sample to multiple classes and can estimate and improve the accuracy and the reliability of the discovered clusterings, as shown by our experimental results

Paradigm of tunable clustering using binarization of consensus partition matrices (Bi-CoPaM) for gene discovery

Copyright @ 2013 Abu-Jamous et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Clustering analysis has a growing role in the study of co-expressed genes for gene discovery. Conventional binary and fuzzy clustering do not embrace the biological reality that some genes may be irrelevant for a problem and not be assigned to a cluster, while other genes may participate in several biological functions and should simultaneously belong to multiple clusters. Also, these algorithms cannot generate tight clusters that focus on their cores or wide clusters that overlap and contain all possibly relevant genes. In this paper, a new clustering paradigm is proposed. In this paradigm, all three eventualities of a gene being exclusively assigned to a single cluster, being assigned to multiple clusters, and being not assigned to any cluster are possible. These possibilities are realised through the primary novelty of the introduction of tunable binarization techniques. Results from multiple clustering experiments are aggregated to generate one fuzzy consensus partition matrix (CoPaM), which is then binarized to obtain the final binary partitions. This is referred to as Binarization of Consensus Partition Matrices (Bi-CoPaM). The method has been tested with a set of synthetic datasets and a set of five real yeast cell-cycle datasets. The results demonstrate its validity in generating relevant tight, wide, and complementary clusters that can meet requirements of different gene discovery studies.National Institute for Health Researc

Targeting tumour re-wiring by triple blockade of mTORC1, epidermal growth factor, and oestrogen receptor signalling pathways in endocrine-resistant breast cancer

Background Endocrine therapies are the mainstay of treatment for oestrogen receptor (ER)-positive (ER+) breast cancer (BC). However, resistance remains problematic largely due to enhanced cross-talk between ER and growth factor pathways, circumventing the need for steroid hormones. Previously, we reported the anti-proliferative effect of everolimus (RAD001-mTORC1 inhibitor) with endocrine therapy in resistance models; however, potential routes of escape from treatment via ERBB2/3 signalling were observed. We hypothesised that combined targeting of three cellular nodes (ER, ERBB, and mTORC1) may provide enhanced long-term clinical utility. Methods A panel of ER+ BC cell lines adapted to long-term oestrogen deprivation (LTED) and expressing ESR1wt or ESR1Y537S, modelling acquired resistance to an aromatase-inhibitor (AI), were treated in vitro with a combination of RAD001 and neratinib (pan-ERBB inhibitor) in the presence or absence of oestradiol (E2), tamoxifen (4-OHT), or fulvestrant (ICI182780). End points included proliferation, cell signalling, cell cycle, and effect on ER-mediated transactivation. An in-vivo model of AI resistance was treated with monotherapies and combinations to assess the efficacy in delaying tumour progression. RNA-seq analysis was performed to identify changes in global gene expression as a result of the indicated therapies. Results Here, we show RAD001 and neratinib (pan-ERBB inhibitor) caused a concentration-dependent decrease in proliferation, irrespective of the ESR1 mutation status. The combination of either agent with endocrine therapy further reduced proliferation but the maximum effect was observed with a triple combination of RAD001, neratinib, and endocrine therapy. In the absence of oestrogen, RAD001 caused a reduction in ER-mediated transcription in the majority of the cell lines, which associated with a decrease in recruitment of ER to an oestrogen-response element on the TFF1 promoter. Contrastingly, neratinib increased both ER-mediated transactivation and ER recruitment, an effect reduced by the addition of RAD001. In-vivo analysis of an LTED model showed the triple combination of RAD001, neratinib, and fulvestrant was most effective at reducing tumour volume. Gene set enrichment analysis revealed that the addition of neratinib negated the epidermal growth factor (EGF)/EGF receptor feedback loops associated with RAD001. Conclusions Our data support the combination of therapies targeting ERBB2/3 and mTORC1 signalling, together with fulvestrant, in patients who relapse on endocrine therapy and retain a functional ER

The Italian Network for Tumor Biotherapy (NIBIT): Getting together to push the field forward

As for a consolidated tradition, the 5th annual meeting of the Italian Network for Cancer Biotherapy took place in the Certosa of Pontignano, a Tuscan monastery, on September 20–22, 2007. The congress gathered more than 40 Italian leading groups representing academia, biotechnology and pharmaceutical industry. Aim of the meeting was to share new advances in cancer bio-immunotherapy and to promote their swift translation from pre-clinical research to clinical applications. Several topics were covered including: a) molecular and cellular mechanisms of tumor escape; b) therapeutic antibodies and recombinant constructs; c) clinical trials up-date and new programs; d) National Cooperative Networks and their potential interactions; e) old and new times in cancer immunology, an "amarcord". Here, we report the main issues discussed during the meeting

Tumor Invasion of Salmonella enterica Serovar Typhimurium Is Accompanied by Strong Hemorrhage Promoted by TNF-α

BACKGROUND:Several facultative anaerobic bacteria with potential therapeutic abilities are known to preferentially colonize solid tumors after systemic administration. How they efficiently find and invade the tumors is still unclear. However, this is an important issue to be clarified when bacteria should be tailored for application in cancer therapy. METHODOLOGY/PRINCIPAL FINDINGS:We describe the initial events of colonization of an ectopic transplantable tumor by Salmonella enterica serovar Typhimurium. Initially, after intravenous administration, bacteria were found in blood, spleen, and liver. Low numbers were also detected in tumors associated with blood vessels as could be observed by immunohistochemistry. A rapid increase of TNF-alpha in blood was observed at that time, in addition to other pro-inflammatory cytokines. This induced a tremendous influx of blood into the tumors by vascular disruption that could be visualized in H&E stainings and quantified by hemoglobin measurements of tumor homogenate. Most likely, together with the blood, bacteria were flushed into the tumor. In addition, blood influx was followed by necrosis formation, bacterial growth, and infiltration of neutrophilic granulocytes. Depletion of TNF-alpha retarded blood influx and delayed bacterial tumor-colonization. CONCLUSION:Our findings emphasize similarities between Gram-negative tumor-colonizing bacteria and tumor vascular disrupting agents and show the involvement of TNF-alpha in the initial phase of tumor-colonization by bacteria

Ensemble Clustering with a Fuzzy Approach

Ensemble clustering is a novel research field that extends to unsupervised learning the approach originally developed for classification and supervised learning problems. In particular ensemble clustering methods have been developed to improve the robustness and accuracy of clustering algorithms, as well as the ability to capture the structure of complex data. In many clustering applications an example may belong to multiple clusters, and the introduction of fuzzy set theory concepts can improve the level of flexibility needed to model the uncertainty underlying real data in several application domains. In this paper, we propose an unsupervised fuzzy ensemble clustering approach that permit to dispose both of the flexibility of the fuzzy sets and the robustness of the ensemble methods. Our algorithmic scheme can generate different ensemble clustering algorithms that allow to obtain the final consensus clustering both in crisp and fuzzy formats

An algorithm to assess the reliability of hierarchical clusters in gene expression data

The validation of clusters discovered in bio-molecular data is a central issue in bioinformatics. Recently, stability-based methods have been successfully applied to the analysis of the reliability of clusterings characterized by a relatively low number of examples and clusters. Nevertheless, several problems in functional genomics are characterized by a very large number of examples and clusters. We present a stability-based algorithm to discover significant clusters in hierarchical clusterings with a large number of examples and clusters. Preliminary results on gene expression data of patients affected by Human Myeloid Leukemia, show how to apply the proposed method when thousands of gene clusters are involved

Cyclophenil, a non-steroidal compound with a higher central than peripheral oestrogenic activity: study of its effects on uterine growth and on some central parameters in castrated female rats

The oestrogenic activity of cyclophenil, a non-steroidal compound which has structural analogies with both stilbene and triphenylethylene, has been reevaluated utilizing both central and peripheral parameters. The central parameters considered were LH, FSH, prolactin secretion and two enzymatic systems known to be oestrogen-sensitive: hypophyseal 5alpha-reductase and hypothalamic aromatase. The uterine growth test was used to determine oestrogenic peripheral activity. The compound was administered at various doses in comparison with oestradiol benzoate (EB) to long-term castrated female rats. Cyclophenil has an activity 1/8110 times that of EB on uterine growth, and 1/1660 and 1/550 times that of EB in inhibiting LH and FSH, respectively. The hypophyseal 5 alpha-reductase (expressed as DHT formation) was inhibited 1710 times less by cyclophenil than by EB. The other parameters considered were unsuitable to provide a statistically reliable estimate of the potency ratios between the two compounds. The data show that cyclophenil is an oestrogenic compound with peculiar characteristics. This substance is more effective in expressing its oestrogenic activity in central structures than in the peripheral ones

A stability-based algorithm to validate hierarchical clusters of genes

Stability-based methods have been successfully applied in functional genomics to the analysis of the reliability of clusterings characterised by a relatively low number of examples and clusters. The application of these methods to the validation of gene clusters discovered in biomolecular data may lead to computational problems due to the large amount of possible clusters involved. To address this problem, we present a stability-based algorithm to discover significant clusters in hierarchical clusterings with a large number of examples and clusters. The reliability of clusters of genes discovered in gene expression data of patients affected by human myeloid leukaemia is analysed through the proposed algorithm, and their relationships with specific biological processes are tested by means of Gene Ontology-based functional enrichment methods

Intra-tumoral Salmonella typhimurium induces a systemic anti-tumor immune response that is directed by low-dose radiation to treat distal disease

Salmonella typhimurium is a facultative anaerobic bacterium able to multiply preferentially in tumors and inhibit their growth. The mechanisms through which Salmonella exerts its anti-cancer properties are not fully understood. We recently showed that intra-tumoral Salmonella injection results not only in the regression of even bulky tumor masses, but also impacts on the growth of distant untreated lesions. Here we describe how Salmonella exerts its systemic anti-cancer effects and means to potentiate them. The outburst of an early inflammatory reaction in the treated tumor promotes the development of an immunostimulatory cytokine environment both locally and in the draining lymph node. Within the next 10 days, an efficient cross-presentation of endogenous tumor antigens by dendritic cells at the tumor-draining lymph node leads to the priming of effective anti-tumor CD8(+) T cell responses. This potentially broadly reactive T cell repertoire can be directed to other pre-established melanomas by low-dose radiotherapy enhancing the Salmonella anticancer effect. We demonstrate that Salmonella-based therapy coupled to low-dose radiotherapy dampens tumor immune escape mechanisms at different levels and allows controlling systemic disease in a CD8(+) T cell-dependent manner