Publications by authors named "Luigi Cerulo"

24 Publications

  • Page 1 of 1

Centrosome Dynamics and Its Role in Inflammatory Response and Metastatic Process.

Biomolecules 2021 Apr 23;11(5). Epub 2021 Apr 23.

Department of Biochemistry and Molecular Biology, Faculty of Pharmacy, Complutense University Madrid, 28040 Madrid, Spain.

Metastasis is a process by which cancer cells escape from the location of the primary tumor invading normal tissues at distant organs. Chromosomal instability (CIN) is a hallmark of human cancer, associated with metastasis and therapeutic resistance. The centrosome plays a major role in organizing the microtubule cytoskeleton in animal cells regulating cellular architecture and cell division. Loss of centrosome integrity activates the p38-p53-p21 pathway, which results in cell-cycle arrest or senescence and acts as a cell-cycle checkpoint pathway. Structural and numerical centrosome abnormalities can lead to aneuploidy and CIN. New findings derived from studies on cancer and rare genetic disorders suggest that centrosome dysfunction alters the cellular microenvironment through Rho GTPases, p38, and JNK (c-Jun N-terminal Kinase)-dependent signaling in a way that is favorable for pro-invasive secretory phenotypes and aneuploidy tolerance. We here review recent data on how centrosomes act as complex molecular platforms for Rho GTPases and p38 MAPK (Mitogen activated kinase) signaling at the crossroads of CIN, cytoskeleton remodeling, and immune evasion via both cell-autonomous and non-autonomous mechanisms.
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http://dx.doi.org/10.3390/biom11050629DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8146599PMC
April 2021

A review of COVID-19 biomarkers and drug targets: resources and tools.

Brief Bioinform 2021 03;22(2):701-713

Computer Science and Engineering at University of Naples 'Federico II'.

The stratification of patients at risk of progression of COVID-19 and their molecular characterization is of extreme importance to optimize treatment and to identify therapeutic options. The bioinformatics community has responded to the outbreak emergency with a set of tools and resource to identify biomarkers and drug targets that we review here. Starting from a consolidated corpus of 27 570 papers, we adopt latent Dirichlet analysis to extract relevant topics and select those associated with computational methods for biomarker identification and drug repurposing. The selected topics span from machine learning and artificial intelligence for disease characterization to vaccine development and to therapeutic target identification. Although the way to go for the ultimate defeat of the pandemic is still long, the amount of knowledge, data and tools generated so far constitutes an unprecedented example of global cooperation to this threat.
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http://dx.doi.org/10.1093/bib/bbaa328DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7799271PMC
March 2021

Deep learning predicts short non-coding RNA functions from only raw sequence data.

PLoS Comput Biol 2020 11 11;16(11):e1008415. Epub 2020 Nov 11.

Biogem Scarl, Istituto di Ricerche Genetiche "Gaetano Salvatore", Ariano Irpino, Italy.

Small non-coding RNAs (ncRNAs) are short non-coding sequences involved in gene regulation in many biological processes and diseases. The lack of a complete comprehension of their biological functionality, especially in a genome-wide scenario, has demanded new computational approaches to annotate their roles. It is widely known that secondary structure is determinant to know RNA function and machine learning based approaches have been successfully proven to predict RNA function from secondary structure information. Here we show that RNA function can be predicted with good accuracy from a lightweight representation of sequence information without the necessity of computing secondary structure features which is computationally expensive. This finding appears to go against the dogma of secondary structure being a key determinant of function in RNA. Compared to recent secondary structure based methods, the proposed solution is more robust to sequence boundary noise and reduces drastically the computational cost allowing for large data volume annotations. Scripts and datasets to reproduce the results of experiments proposed in this study are available at: https://github.com/bioinformatics-sannio/ncrna-deep.
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http://dx.doi.org/10.1371/journal.pcbi.1008415DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7682815PMC
November 2020

Adaptive one-class Gaussian processes allow accurate prioritization of oncology drug targets.

Bioinformatics 2021 06;37(10):1420-1427

BIOGEM Istituto di Ricerche Genetiche "G. Salvatore", 83031 Ariano Irpino, Italy.

Motivation: The cost of drug development has dramatically increased in the last decades, with the number new drugs approved per billion US dollars spent on R&D halving every year or less. The selection and prioritization of targets is one the most influential decisions in drug discovery. Here we present a Gaussian Process model for the prioritization of drug targets cast as a problem of learning with only positive and unlabeled examples.

Results: Since the absence of negative samples does not allow standard methods for automatic selection of hyperparameters, we propose a novel approach for hyperparameter selection of the kernel in One Class Gaussian Processes. We compare our methods with state-of-the-art approaches on benchmark datasets and then show its application to druggability prediction of oncology drugs. Our score reaches an AUC 0.90 on a set of clinical trial targets starting from a small training set of 102 validated oncology targets. Our score recovers the majority of known drug targets and can be used to identify novel set of proteins as drug target candidates.

Availability And Implementation: The matrix of features for each protein is available at: https://bit.ly/3iLgZTa. Source code implemented in Python is freely available for download at https://github.com/AntonioDeFalco/Adaptive-OCGP.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btaa968DOI Listing
June 2021

A map of tumor-host interactions in glioma at single-cell resolution.

Gigascience 2020 10;9(10)

Department of Electrical Engineering and Information Technology (DIETI), University of Naples "Federico II", Via Claudio 21, 80128 Naples, Italy.

Background: Single-cell RNA sequencing is the reference technique for characterizing the heterogeneity of the tumor microenvironment. The composition of the various cell types making up the microenvironment can significantly affect the way in which the immune system activates cancer rejection mechanisms. Understanding the cross-talk signals between immune cells and cancer cells is of fundamental importance for the identification of immuno-oncology therapeutic targets.

Results: We present a novel method, single-cell Tumor-Host Interaction tool (scTHI), to identify significantly activated ligand-receptor interactions across clusters of cells from single-cell RNA sequencing data. We apply our approach to uncover the ligand-receptor interactions in glioma using 6 publicly available human glioma datasets encompassing 57,060 gene expression profiles from 71 patients. By leveraging this large-scale collection we show that unexpected cross-talk partners are highly conserved across different datasets in the majority of the tumor samples. This suggests that shared cross-talk mechanisms exist in glioma.

Conclusions: Our results provide a complete map of the active tumor-host interaction pairs in glioma that can be therapeutically exploited to reduce the immunosuppressive action of the microenvironment in brain tumor.
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http://dx.doi.org/10.1093/gigascience/giaa109DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7645027PMC
October 2020

Transcriptional regulatory networks of tumor-associated macrophages that drive malignancy in mesenchymal glioblastoma.

Genome Biol 2020 08 26;21(1):216. Epub 2020 Aug 26.

Department of Neurosurgery, University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Background: Glioblastoma (GBM) is a complex disease with extensive molecular and transcriptional heterogeneity. GBM can be subcategorized into four distinct subtypes; tumors that shift towards the mesenchymal phenotype upon recurrence are generally associated with treatment resistance, unfavorable prognosis, and the infiltration of pro-tumorigenic macrophages.

Results: We explore the transcriptional regulatory networks of mesenchymal-associated tumor-associated macrophages (MA-TAMs), which drive the malignant phenotypic state of GBM, and identify macrophage receptor with collagenous structure (MARCO) as the most highly differentially expressed gene. MARCO TAMs induce a phenotypic shift towards mesenchymal cellular state of glioma stem cells, promoting both invasive and proliferative activities, as well as therapeutic resistance to irradiation. MARCO TAMs also significantly accelerate tumor engraftment and growth in vivo. Moreover, both MA-TAM master regulators and their target genes are significantly correlated with poor clinical outcomes and are often associated with genomic aberrations in neurofibromin 1 (NF1) and phosphoinositide 3-kinases/mammalian target of rapamycin/Akt pathway (PI3K-mTOR-AKT)-related genes. We further demonstrate the origination of MA-TAMs from peripheral blood, as well as their potential association with tumor-induced polarization states and immunosuppressive environments.

Conclusions: Collectively, our study characterizes the global transcriptional profile of TAMs driving mesenchymal GBM pathogenesis, providing potential therapeutic targets for improving the effectiveness of GBM immunotherapy.
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http://dx.doi.org/10.1186/s13059-020-02140-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7448990PMC
August 2020

Retinoic Acid Induces Embryonic Stem Cells (ESCs) Transition to 2 Cell-Like State Through a Coordinated Expression of and .

Front Cell Dev Biol 2019 17;7:385. Epub 2020 Jan 17.

Biogem Scarl, Istituto di Ricerche Genetiche "Gaetano Salvatore," Ariano Irpino, Italy.

Embryonic stem cells (ESCs) are derived from inner cell mass (ICM) of the blastocyst. In serum/LIF culture condition, they show variable expression of pluripotency genes that mark cell fluctuation between pluripotency and differentiation metastate. The ESCs subpopulation marked by zygotic genome activation gene (ZGA) signature, including , retains a wider differentiation potency than epiblast-derived ESCs. We have recently shown that retinoic acid (RA) significantly enhances Zscan4 cell population. However, it remains unexplored how RA initiates the ESCs to 2-cell like reprogramming. Here we found that RA is decisive for ESCs to 2C-like cell transition, and reconstructed the gene network surrounding . We revealed that RA regulates 2C-like population co-activating and . We provided novel evidence that RA dependent ESCs to 2C-like cell transition is regulated by , and antagonized by . Our suggested mechanism could shed light on the role of RA on ESC reprogramming.
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http://dx.doi.org/10.3389/fcell.2019.00385DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6979039PMC
January 2020

JAK/Stat5-mediated subtype-specific lymphocyte antigen 6 complex, locus G6D (LY6G6D) expression drives mismatch repair proficient colorectal cancer.

J Exp Clin Cancer Res 2019 Jan 22;38(1):28. Epub 2019 Jan 22.

Department of Sciences and Technologies, University of Sannio, Via Port'Arsa, 1182100, Benevento, Italy.

Background: Human microsatellite-stable (MSS) colorectal cancers (CRCs) are immunologically "cold" tumour subtypes characterized by reduced immune cytotoxicity. The molecular linkages between immune-resistance and human MSS CRC is not clear.

Methods: We used transcriptome profiling, in silico analysis, immunohistochemistry, western blot, RT-qPCR and immunofluorescence staining to characterize novel CRC immune biomarkers. The effects of selective antagonists were tested by in vitro assays of long term viability and analysis of kinase active forms using anti-phospho antibodies.

Results: We identified the lymphocyte antigen 6 complex, locus G6D (LY6G6D) as significantly overexpressed (around 15-fold) in CRC when compared with its relatively low expression in other human solid tumours. LY6G6D up-regulation was predominant in MSS CRCs characterized by an enrichment of immune suppressive regulatory T-cells and a limited repertoire of PD-1/PD-L1 immune checkpoint receptors. Coexpression of LY6G6D and CD15 increases the risk of metastatic relapse in response to therapy. Both JAK-STAT5 and RAS-MEK-ERK cascades act in concert as key regulators of LY6G6D and Fucosyltransferase 4 (FUT4), which direct CD15-mediated immune-resistance. Momelotinib, an inhibitor of JAK1/JAK2, consistently abrogated the STAT5/LY6G6D axis in vitro, sensitizing MSS cancer cells with an intact JAK-STAT signaling, to efficiently respond to trametinib, a MEK inhibitor used in clinical setting. Notably, colon cancer cells can evade JAK2/JAK1-targeted therapy by a reversible shift of the RAS-MEK-ERK pathway activity, which explains the treatment failure of JAK1/2 inhibitors in refractory CRC.

Conclusions: Combined targeting of STAT5 and MAPK pathways has superior therapeutic effects on immune resistance. In addition, the new identified LY6G6D antigen is a promising molecular target for human MSS CRC.
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http://dx.doi.org/10.1186/s13046-018-1019-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6343337PMC
January 2019

Detection of long non-coding RNA homology, a comparative study on alignment and alignment-free metrics.

BMC Bioinformatics 2018 Nov 6;19(1):407. Epub 2018 Nov 6.

Dep. of Science and Technology, University of Sannio, via Port'Arsa, 11, Benevento, 82100, Italy.

Background: Long non-coding RNAs (lncRNAs) represent a novel class of non-coding RNAs having a crucial role in many biological processes. The identification of long non-coding homologs among different species is essential to investigate such roles in model organisms as homologous genes tend to retain similar molecular and biological functions. Alignment-based metrics are able to effectively capture the conservation of transcribed coding sequences and then the homology of protein coding genes. However, unlike protein coding genes the poor sequence conservation of long non-coding genes makes the identification of their homologs a challenging task.

Results: In this study we compare alignment-based and alignment-free string similarity metrics and look at promoter regions as a possible source of conserved information. We show that promoter regions encode relevant information for the conservation of long non-coding genes across species and that such information is better captured by alignment-free metrics. We perform a genome wide test of this hypothesis in human, mouse, and zebrafish.

Conclusions: The obtained results persuaded us to postulate the new hypothesis that, unlike protein coding genes, long non-coding genes tend to preserve their regulatory machinery rather than their transcribed sequence. All datasets, scripts, and the prediction tools adopted in this study are available at https://github.com/bioinformatics-sannio/lncrna-homologs .
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http://dx.doi.org/10.1186/s12859-018-2441-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6220562PMC
November 2018

Circulating microRNAs expression profile in newly diagnosed and imatinib treated chronic phase - chronic myeloid leukemia.

Leuk Lymphoma 2019 03 6;60(3):805-811. Epub 2018 Sep 6.

a Department of Internal Medical , São Paulo State University (UNESP-FMB) , Botucatu , SP , Brazil.

Chronic myeloid leukemia (CML) is a stem cell derived malignant disorder result of translocation t(9;22)(q34;q11) called Philadelphia chromosome (Ph). microRNAS (miRNAs) are involved in several biological processes, altering the progression of various pathologies, including CML. This study evaluated whether circulating miRNAs display differential expression profiles in peripheral blood of CML-Chronic Phase (CML-CP) patients newly diagnosed in comparison with CML-CP treated with imatinib. We obtained peripheral blood samples from CML-CP Ph patients divided among group 1 (untreated newly diagnosed) and group 2 (treated with imatinib). A pool of total leukocytes from healthy donors was considered as control group. Expression analyses were performed for 768 miRNAs by RT-qPCR array. Bioinformatic tools were used to identify significant pathways and interaction networks. We found 80 deregulated miRNAs between the groups and, according to bioinformatic analysis, they are involved in different pathways, including molecular mechanisms of cancer. The study allows better understanding of disease molecular behavior, and it is useful for possible monitoring CML treatment and prognostic biomarkers identification.
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http://dx.doi.org/10.1080/10428194.2018.1499905DOI Listing
March 2019

RGBM: regularized gradient boosting machines for identification of the transcriptional regulators of discrete glioma subtypes.

Nucleic Acids Res 2018 04;46(7):e39

Department of Science and Technology, University of Sannio, Benevento, Italy.

We propose a generic framework for gene regulatory network (GRN) inference approached as a feature selection problem. GRNs obtained using Machine Learning techniques are often dense, whereas real GRNs are rather sparse. We use a Tikonov regularization inspired optimal L-curve criterion that utilizes the edge weight distribution for a given target gene to determine the optimal set of TFs associated with it. Our proposed framework allows to incorporate a mechanistic active biding network based on cis-regulatory motif analysis. We evaluate our regularization framework in conjunction with two non-linear ML techniques, namely gradient boosting machines (GBM) and random-forests (GENIE), resulting in a regularized feature selection based method specifically called RGBM and RGENIE respectively. RGBM has been used to identify the main transcription factors that are causally involved as master regulators of the gene expression signature activated in the FGFR3-TACC3-positive glioblastoma. Here, we illustrate that RGBM identifies the main regulators of the molecular subtypes of brain tumors. Our analysis reveals the identity and corresponding biological activities of the master regulators characterizing the difference between G-CIMP-high and G-CIMP-low subtypes and between PA-like and LGm6-GBM, thus providing a clue to the yet undetermined nature of the transcriptional events among these subtypes.
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http://dx.doi.org/10.1093/nar/gky015DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6283452PMC
April 2018

A metabolic function of FGFR3-TACC3 gene fusions in cancer.

Nature 2018 01 3;553(7687):222-227. Epub 2018 Jan 3.

Institute for Cancer Genetics, Columbia University Medical Center, New York, New York 10032, USA.

Chromosomal translocations that generate in-frame oncogenic gene fusions are notable examples of the success of targeted cancer therapies. We have previously described gene fusions of FGFR3-TACC3 (F3-T3) in 3% of human glioblastoma cases. Subsequent studies have reported similar frequencies of F3-T3 in many other cancers, indicating that F3-T3 is a commonly occuring fusion across all tumour types. F3-T3 fusions are potent oncogenes that confer sensitivity to FGFR inhibitors, but the downstream oncogenic signalling pathways remain unknown. Here we show that human tumours with F3-T3 fusions cluster within transcriptional subgroups that are characterized by the activation of mitochondrial functions. F3-T3 activates oxidative phosphorylation and mitochondrial biogenesis and induces sensitivity to inhibitors of oxidative metabolism. Phosphorylation of the phosphopeptide PIN4 is an intermediate step in the signalling pathway of the activation of mitochondrial metabolism. The F3-T3-PIN4 axis triggers the biogenesis of peroxisomes and the synthesis of new proteins. The anabolic response converges on the PGC1α coactivator through the production of intracellular reactive oxygen species, which enables mitochondrial respiration and tumour growth. These data illustrate the oncogenic circuit engaged by F3-T3 and show that F3-T3-positive tumours rely on mitochondrial respiration, highlighting this pathway as a therapeutic opportunity for the treatment of tumours with F3-T3 fusions. We also provide insights into the genetic alterations that initiate the chain of metabolic responses that drive mitochondrial metabolism in cancer.
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http://dx.doi.org/10.1038/nature25171DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5771419PMC
January 2018

Identification of genetic determinants of breast cancer immune phenotypes by integrative genome-scale analysis.

Oncoimmunology 2017;6(2):e1253654. Epub 2017 Feb 6.

Tumor Biology, Immunology, and Therapy Section, Division of Translational Medicine, Research Branch, Sidra Medical and Research Center , Doha, Qatar.

Cancer immunotherapy is revolutionizing the clinical management of several tumors, but has demonstrated limited activity in breast cancer. The development of more effective treatments is hindered by incomplete knowledge of the genetic determinant of immune responsiveness. To fill this gap, we mined copy number alteration, somatic mutation, and expression data from The Cancer Genome Atlas (TCGA). By using RNA-sequencing data from 1,004 breast cancers, we defined distinct immune phenotypes characterized by progressive expression of transcripts previously associated with immune-mediated rejection. The T helper 1 (Th-1) phenotype (ICR4), which also displays upregulation of immune-regulatory transcripts such as , and , was associated with prolonged patients' survival. We validated these findings in an independent meta-cohort of 1,954 breast cancer gene expression data. Chromosome segment 4q21, which includes genes encoding for the Th-1 chemokines CXCL9-11, was significantly amplified only in the immune favorable phenotype (ICR4). The mutation and neoantigen load progressively decreased from ICR4 to ICR1 but could not fully explain immune phenotypic differences. Mutations of were enriched in the immune favorable phenotype (ICR4). Conversely, the presence of and mutations were tightly associated with an immune-unfavorable phenotype (ICR1). Using both the TCGA and the validation dataset, the degree of MAPK deregulation segregates breast tumors according to their immune disposition. These findings suggest that mutation-driven perturbations of MAPK pathways are linked to the negative regulation of intratumoral immune response in breast cancer. Modulations of MAPK pathways could be experimentally tested to enhance breast cancer immune sensitivity.
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http://dx.doi.org/10.1080/2162402X.2016.1253654DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5353940PMC
February 2017

Identification of long non-coding transcripts with feature selection: a comparative study.

BMC Bioinformatics 2017 Mar 23;18(1):187. Epub 2017 Mar 23.

Department of Science and Technology, University of Sannio, via Port'Arsa, 11, Benevento, 82100, Italy.

Background: The unveiling of long non-coding RNAs as important gene regulators in many biological contexts has increased the demand for efficient and robust computational methods to identify novel long non-coding RNAs from transcripts assembled with high throughput RNA-seq data. Several classes of sequence-based features have been proposed to distinguish between coding and non-coding transcripts. Among them, open reading frame, conservation scores, nucleotide arrangements, and RNA secondary structure have been used with success in literature to recognize intergenic long non-coding RNAs, a particular subclass of non-coding RNAs.

Results: In this paper we perform a systematic assessment of a wide collection of features extracted from sequence data. We use most of the features proposed in the literature, and we include, as a novel set of features, the occurrence of repeats contained in transposable elements. The aim is to detect signatures (groups of features) able to distinguish long non-coding transcripts from other classes, both protein-coding and non-coding. We evaluate different feature selection algorithms, test for signature stability, and evaluate the prediction ability of a signature with a machine learning algorithm. The study reveals different signatures in human, mouse, and zebrafish, highlighting that some features are shared among species, while others tend to be species-specific. Compared to coding potential tools and similar supervised approaches, including novel signatures, such as those identified here, in a machine learning algorithm improves the prediction performance, in terms of area under precision and recall curve, by 1 to 24%, depending on the species and on the signature.

Conclusions: Understanding which features are best suited for the prediction of long non-coding RNAs allows for the development of more effective automatic annotation pipelines especially relevant for poorly annotated genomes, such as zebrafish. We provide a web tool that recognizes novel long non-coding RNAs with the obtained signatures from fasta and gtf formats. The tool is available at the following url: http://www.bioinformatics-sannio.org/software/ .
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http://dx.doi.org/10.1186/s12859-017-1594-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5364679PMC
March 2017

Detection of statistically significant network changes in complex biological networks.

BMC Syst Biol 2017 03 4;11(1):32. Epub 2017 Mar 4.

QCRI - Qatar Computing Research Institute, HBKU, Doha, Qatar.

Background: Biological networks contribute effectively to unveil the complex structure of molecular interactions and to discover driver genes especially in cancer context. It can happen that due to gene mutations, as for example when cancer progresses, the gene expression network undergoes some amount of localized re-wiring. The ability to detect statistical relevant changes in the interaction patterns induced by the progression of the disease can lead to the discovery of novel relevant signatures. Several procedures have been recently proposed to detect sub-network differences in pairwise labeled weighted networks.

Methods: In this paper, we propose an improvement over the state-of-the-art based on the Generalized Hamming Distance adopted for evaluating the topological difference between two networks and estimating its statistical significance. The proposed procedure exploits a more effective model selection criteria to generate p-values for statistical significance and is more efficient in terms of computational time and prediction accuracy than literature methods. Moreover, the structure of the proposed algorithm allows for a faster parallelized implementation.

Results: In the case of dense random geometric networks the proposed approach is 10-15x faster and achieves 5-10% higher AUC, Precision/Recall, and Kappa value than the state-of-the-art. We also report the application of the method to dissect the difference between the regulatory networks of IDH-mutant versus IDH-wild-type glioma cancer. In such a case our method is able to identify some recently reported master regulators as well as novel important candidates.

Conclusions: We show that our network differencing procedure can effectively and efficiently detect statistical significant network re-wirings in different conditions. When applied to detect the main differences between the networks of IDH-mutant and IDH-wild-type glioma tumors, it correctly selects sub-networks centered on important key regulators of these two different subtypes. In addition, its application highlights several novel candidates that cannot be detected by standard single network-based approaches.
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http://dx.doi.org/10.1186/s12918-017-0412-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336651PMC
March 2017

Emerging Insight into MAPK Inhibitors and Immunotherapy in Colorectal Cancer.

Curr Med Chem 2017 ;24(14):1383-1402

Department of Biochemistry and Molecular Biology II, Faculty of Pharmacy, Complutense University, Madrid. Spain.

Our understanding of the genetic and non-genetic molecular alterations associated with colorectal cancer (CRC) progression and therapy resistance has markedly expanded in the recent years. In addition to their effects on tumor biology, targeted therapies can have effects on host immune responses. However, the mechanisms by which immune cells organize tumor microenvironments to regulate T-cell activity need to be comprehensively defined. There is good evidence in the literature that alterations in different members of the MAPK superfamily (mainly ERKs and p38 MAPKs) modify the inflammatory response and antitumor immunity, enhancing metastatic features of the tumors. In addition, a plethora of alterations that emerge at relapse often converge on the activation of MAPKs, particularly, ERKs, which act in concert with other oncogenic signals to modulate cellular homeostasis and clonal evolution during targeted therapies. Herein, we discuss how this knowledge can be translated into drug development strategies aimed at increasing tumor antigenicity and antitumor immune responses. Insights from these studies could provide a framework for considering additional combinations of targeted therapies and immunotherapies for the treatment of CRC.
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http://dx.doi.org/10.2174/0929867324666170227114356DOI Listing
August 2017

Cancer-related CD15/FUT4 overexpression decreases benefit to agents targeting EGFR or VEGF acting as a novel RAF-MEK-ERK kinase downstream regulator in metastatic colorectal cancer.

J Exp Clin Cancer Res 2015 Oct 1;34:108. Epub 2015 Oct 1.

Department of Sciences and Technologies, University of Sannio, Via Port'Arsa, 1182100, Benevento, Italy.

Background: Cancer-related immune antigens in the tumor microenvironment could represent an obstacle to agents targeting EGFR "cetuximab" or VEGF "bevacizumab" in metastatic colorectal cancer (mCRC) patients.

Methods: Infiltrating immune cells into tumor tissues, cancer-related expression of immune antigens (CD3, CD8, CD68, CD73, MPO, CD15/FUT4) from 102 mCRC patients receiving first-line Cetuximab or Bevacizumab plus chemotherapy were assessed by immunohistochemistry and validated in an independent tissue microarrays of 140 patients. Genome-wide expression profiles from 436 patients and 60 colon cancer cell lines were investigated using bioinformatics analysis. In vitro kinase assays of target genes activated by chemokines or growth factors were performed.

Results: Here, we report that cancer-related CD15/FUT4 is overexpressed in most of mCRCs patients (43 %) and associates with lower intratumoral CD3+ and CD8+ T cells, higher systemic inflammation (NLR at diagnosis >5) and poorer outcomes, in terms of response and progression-free survival than those CD15/FUT4-low or negative ones (adjusted hazard ratio (HR) = 2.92; 95 % CI = 1.86-4.41; P < 0.001). Overexpression of CD15/FUT4 is induced through RAF-MEK-ERK kinase cascade, suppressed by MEK inhibitors and exhibits a close connection with constitutive oncogenic signalling pathways that respond to ERBB3 or FGFR4 activation (P < 0.001). CD15/FUT4-high expressing colon cancer cells with primary resistance to cetuximab or bevacizumab are significantly more sensitive to MEK inhibitors than CD15/FUT4-low counterparts.

Conclusion: Cancer-related CD15/FUT4 overexpression participates in cetuximab or bevacizumab mechanisms of resistance in mCRC patients. CD15/FUT4 as a potential target of the antitumor immune response requires further evaluation in clinical studies.
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http://dx.doi.org/10.1186/s13046-015-0225-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4590269PMC
October 2015

Systems biology analysis reveals NFAT5 as a novel biomarker and master regulator of inflammatory breast cancer.

J Transl Med 2015 May 1;13:138. Epub 2015 May 1.

Department of Science and Technology, University of Sannio, Benevento, Italy.

Background: Inflammatory breast cancer (IBC) is the most rare and aggressive variant of breast cancer (BC); however, only a limited number of specific gene signatures with low generalization abilities are available and few reliable biomarkers are helpful to improve IBC classification into a molecularly distinct phenotype. We applied a network-based strategy to gain insight into master regulators (MRs) linked to IBC pathogenesis.

Methods: In-silico modeling and Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) on IBC/non-IBC (nIBC) gene expression data (n = 197) was employed to identify novel master regulators connected to the IBC phenotype. Pathway enrichment analysis was used to characterize predicted targets of candidate genes. The expression pattern of the most significant MRs was then evaluated by immunohistochemistry (IHC) in two independent cohorts of IBCs (n = 39) and nIBCs (n = 82) and normal breast tissues (n = 15) spotted on tissue microarrays. The staining pattern of non-neoplastic mammary epithelial cells was used as a normal control.

Results: Using in-silico modeling of network-based strategy, we identified three top enriched MRs (NFAT5, CTNNB1 or β-catenin, and MGA) strongly linked to the IBC phenotype. By IHC assays, we found that IBC patients displayed a higher number of NFAT5-positive cases than nIBC (69.2% vs. 19.5%; p-value = 2.79 10(-7)). Accordingly, the majority of NFAT5-positive IBC samples revealed an aberrant nuclear expression in comparison with nIBC samples (70% vs. 12.5%; p-value = 0.000797). NFAT5 nuclear accumulation occurs regardless of WNT/β-catenin activated signaling in a substantial portion of IBCs, suggesting that NFAT5 pathway activation may have a relevant role in IBC pathogenesis. Accordingly, cytoplasmic NFAT5 and membranous β-catenin expression were preferentially linked to nIBC, accounting for the better prognosis of this phenotype.

Conclusions: We provide evidence that NFAT-signaling pathway activation could help to identify aggressive forms of BC and potentially be a guide to assignment of phenotype-specific therapeutic agents. The NFAT5 transcription factor might be developed into routine clinical practice as a putative biomarker of IBC phenotype.
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http://dx.doi.org/10.1186/s12967-015-0492-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4438533PMC
May 2015

De novo reconstruction of gene regulatory networks from time series data, an approach based on formal methods.

Methods 2014 Oct 21;69(3):298-305. Epub 2014 Jun 21.

Dept. of Engineering, University of Sannio, Benevento, Italy.

Reverse engineering of gene regulatory relationships from genomics data is a crucial task to dissect the complex underlying regulatory mechanism occurring in a cell. From a computational point of view the reconstruction of gene regulatory networks is an undetermined problem as the large number of possible solutions is typically high in contrast to the number of available independent data points. Many possible solutions can fit the available data, explaining the data equally well, but only one of them can be the biologically true solution. Several strategies have been proposed in literature to reduce the search space and/or extend the amount of independent information. In this paper we propose a novel algorithm based on formal methods, mathematically rigorous techniques widely adopted in engineering to specify and verify complex software and hardware systems. Starting with a formal specification of gene regulatory hypotheses we are able to mathematically prove whether a time course experiment belongs or not to the formal specification, determining in fact whether a gene regulation exists or not. The method is able to detect both direction and sign (inhibition/activation) of regulations whereas most of literature methods are limited to undirected and/or unsigned relationships. We empirically evaluated the approach on experimental and synthetic datasets in terms of precision and recall. In most cases we observed high levels of accuracy outperforming the current state of art, despite the computational cost increases exponentially with the size of the network. We made available the tool implementing the algorithm at the following url: http://www.bioinformatics.unisannio.it.
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http://dx.doi.org/10.1016/j.ymeth.2014.06.005DOI Listing
October 2014

Identification of a novel gene signature of ES cells self-renewal fluctuation through system-wide analysis.

PLoS One 2014 2;9(1):e83235. Epub 2014 Jan 2.

Department of Stem Cell and Development, Istituto di Ricerche Genetiche Gaetano Salvatore Biogem scarl, Ariano Irpino, Italy ; Department of Science, Università degli Studi del Sannio, Benevento, Italy.

Embryonic Stem cells (ESCs) can be differentiated into ectoderm, endoderm, and mesoderm derivatives, producing the majority of cell types. In regular culture conditions, ESCs' self-renewal is maintained through molecules that inhibit spontaneous differentiation enabling long-term cellular expansion. This undifferentiating condition is characterized by multiple metastable states that fluctuate between self-renewal and differentiation balance. Here, we aim to characterize the high-pluripotent ESC metastate marked by the expression of Zscan4 through a supervised machine learning framework based on an ensemble of support vector machine (SVM) classifiers. Our study revealed a leukaemia inhibitor factor (Lif) dependent not-canonical pluripotency signature (AF067063, BC061212, Dub1, Eif1a, Gm12794, Gm13871, Gm4340, Gm4850, Tcstv1/3, and Zfp352), that specifically marks Zscan4 ESCs' fluctuation. This novel ESC metastate is enhanced by high-pluripotency culture conditions obtained through Extracellular signal Regulated-Kinase (ERK) and Glycogen synthase kinase-3 (Gsk-3) signaling inhibition (2i). Significantly, we reported that the conditional ablation of the novel ESC metastate marked by the expression of Gm12794 is required for ESCs self-renewal maintenance. In conclusion, we extend the comprehension of ESCs biology through the identification of a novel molecular signature associated to pluripotency programming.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0083235PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3879232PMC
November 2014

Ensemble of gene signatures identifies novel biomarkers in colorectal cancer activated through PPARγ and TNFα signaling.

PLoS One 2013 19;8(8):e72638. Epub 2013 Aug 19.

Department of Sciences and Technologies, University of Sannio, Benevento, Italy.

We describe a novel bioinformatic and translational pathology approach, gene Signature Finder Algorithm (gSFA) to identify biomarkers associated with Colorectal Cancer (CRC) survival. Here a robust set of CRC markers is selected by an ensemble method. By using a dataset of 232 gene expression profiles, gSFA discovers 16 highly significant small gene signatures. Analysis of dichotomies generated by the signatures results in a set of 133 samples stably classified in good prognosis group and 56 samples in poor prognosis group, whereas 43 remain unreliably classified. AKAP12, DCBLD2, NT5E and SPON1 are particularly represented in the signatures and selected for validation in vivo on two independent patients cohorts comprising 140 tumor tissues and 60 matched normal tissues. Their expression and regulatory programs are investigated in vitro. We show that the coupled expression of NT5E and DCBLD2 robustly stratifies our patients in two groups (one of which with 100% survival at five years). We show that NT5E is a target of the TNF-α signaling in vitro; the tumor suppressor PPARγ acts as a novel NT5E antagonist that positively and concomitantly regulates DCBLD2 in a cancer cell context-dependent manner.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0072638PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3795784PMC
July 2014

A negative selection heuristic to predict new transcriptional targets.

BMC Bioinformatics 2013 14;14 Suppl 1:S3. Epub 2013 Jan 14.

Department of Science, University of Sannio, Benevento, Italy.

Background: Supervised machine learning approaches have been recently adopted in the inference of transcriptional targets from high throughput trascriptomic and proteomic data showing major improvements from with respect to the state of the art of reverse gene regulatory network methods. Beside traditional unsupervised techniques, a supervised classifier learns, from known examples, a function that is able to recognize new relationships for new data. In the context of gene regulatory inference a supervised classifier is coerced to learn from positive and unlabeled examples, as the counter negative examples are unavailable or hard to collect. Such a condition could limit the performance of the classifier especially when the amount of training examples is low.

Results: In this paper we improve the supervised identification of transcriptional targets by selecting reliable counter negative examples from the unlabeled set. We introduce an heuristic based on the known topology of transcriptional networks that in fact restores the conventional positive/negative training condition and shows a significant improvement of the classification performance. We empirically evaluate the proposed heuristic with the experimental datasets of Escherichia coli and show an example of application in the prediction of BCL6 direct core targets in normal germinal center human B cells obtaining a precision of 60%.

Conclusions: The availability of only positive examples in learning transcriptional relationships negatively affects the performance of supervised classifiers. We show that the selection of reliable negative examples, a practice adopted in text mining approaches, improves the performance of such classifiers opening new perspectives in the identification of new transcriptional targets.
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http://dx.doi.org/10.1186/1471-2105-14-S1-S3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3548675PMC
August 2013

VEGA: variational segmentation for copy number detection.

Bioinformatics 2010 Dec 19;26(24):3020-7. Epub 2010 Oct 19.

Department of Biological and Environmental Studies, University of Sannio, Benevento, Italy.

Motivation: Genomic copy number (CN) information is useful to study genetic traits of many diseases. Using array comparative genomic hybridization (aCGH), researchers are able to measure the copy number of thousands of DNA loci at the same time. Therefore, a current challenge in bioinformatics is the development of efficient algorithms to detect the map of aberrant chromosomal regions.

Methods: We describe an approach for the segmentation of copy number aCGH data. Variational estimator for genomic aberrations (VEGA) adopt a variational model used in image segmentation. The optimal segmentation is modeled as the minimum of an energy functional encompassing both the quality of interpolation of the data and the complexity of the solution measured by the length of the boundaries between segmented regions. This solution is obtained by a region growing process where the stop condition is completely data driven.

Results: VEGA is compared with three algorithms that represent the state of the art in CN segmentation. Performance assessment is made both on synthetic and real data. Synthetic data simulate different noise conditions. Results on these data show the robustness with respect to noise of variational models and the accuracy of VEGA in terms of recall and precision. Eight mantle cell lymphoma cell lines and two samples of glioblastoma multiforme are used to evaluate the behavior of VEGA on real biological data. Comparison between results and current biological knowledge shows the ability of the proposed method in detecting known chromosomal aberrations.

Availability: VEGA has been implemented in R and is available at the address http://www.dsba.unisannio.it/Members/ceccarelli/vega in the section Download.
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http://dx.doi.org/10.1093/bioinformatics/btq586DOI Listing
December 2010

Learning gene regulatory networks from only positive and unlabeled data.

BMC Bioinformatics 2010 May 5;11:228. Epub 2010 May 5.

Department of Biological and Environmental Studies, University of Sannio, Benevento, Italy.

Background: Recently, supervised learning methods have been exploited to reconstruct gene regulatory networks from gene expression data. The reconstruction of a network is modeled as a binary classification problem for each pair of genes. A statistical classifier is trained to recognize the relationships between the activation profiles of gene pairs. This approach has been proven to outperform previous unsupervised methods. However, the supervised approach raises open questions. In particular, although known regulatory connections can safely be assumed to be positive training examples, obtaining negative examples is not straightforward, because definite knowledge is typically not available that a given pair of genes do not interact.

Results: A recent advance in research on data mining is a method capable of learning a classifier from only positive and unlabeled examples, that does not need labeled negative examples. Applied to the reconstruction of gene regulatory networks, we show that this method significantly outperforms the current state of the art of machine learning methods. We assess the new method using both simulated and experimental data, and obtain major performance improvement.

Conclusions: Compared to unsupervised methods for gene network inference, supervised methods are potentially more accurate, but for training they need a complete set of known regulatory connections. A supervised method that can be trained using only positive and unlabeled data, as presented in this paper, is especially beneficial for the task of inferring gene regulatory networks, because only an incomplete set of known regulatory connections is available in public databases such as RegulonDB, TRRD, KEGG, Transfac, and IPA.
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http://dx.doi.org/10.1186/1471-2105-11-228DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2887423PMC
May 2010
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