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Silvio Bicciato

Publications and source records attributed to Silvio Bicciato.

18 recordsLinked to original sources
PubMed2026

FIERCE: reconstructing dynamic trajectories from the differentiation potency of single cells.

MOTIVATION: Since the introduction of single-cell RNA sequencing (scRNA-seq), numerous computational approaches have been developed to reconstruct dynamic cellular processes from static transcriptional profiles. These methods order cells along continuous trajectories by assessing their similarity in the gene-expression space. However, they rely on several assumptions, such as prior knowledge of the structure and directionality of the expected genealogy. These assumptions can limit their application to complex cellular systems with poorly understood developmental paths. RESULTS: To address this challenge, we introduce FIERCE (Framework for InfERence of the veloCity of Entropy), a novel computational pipeline designed to predict the changes in the differentiation potency of single cells during dynamic processes. Through a fully unsupervised approach, FIERCE enables the inference of cell lineages directly on the differentiation landscape of the biological system, thus eliminating the need for prior specification of developmental parameters. We demonstrate the efficacy of FIERCE by reconstructing three well-known mouse differentiation systems and by quantifying its accuracy on simulated data. AVAILABILITY AND IMPLEMENTATION: The FIERCE R package is available on GitHub at https://github.com/bicciatolab/FIERCE.

Cell Differentiation↗
PubMed2007

Molecular characterization of human multiple myeloma cell lines by integrative genomics: insights into the biology of the disease.

To investigate the patterns of genetic lesions in a panel of 23 human multiple myeloma cell lines (HMCLs), we made a genomic integrative analysis involving FISH, and both gene expression and genome-wide profiling approaches. The expression profiles of the genes targeted by the main IGH translocations showed that the WHSC1/MMSET gene involved in t(4;14)(p16;q32) was expressed at different levels in all of the HMCLs, and that the expression of the MAF gene was not restricted to the HMCLs carrying t(14;16)(q32;q23). Supervised analyses identified a limited number of genes specifically associated with t(4;14) and involved in different biological processes. The signature related to MAF/MAFB expression included the known MAF target genes CCND2 and ITGB7, as well as genes controlling cell shape and cell adhesion. Genome-wide DNA profiling allowed the identification of a gain on chromosome arm 1q in 88% of the analyzed cell lines, together with recurrent gains on 8q, 18q, 7q, and 20q; the most frequent deletions affected 1p, 13q, 17p, and 14q; and almost all of the cell lines presented LOH on chromosome 13. Two hundred and twenty-two genes were found to be simultaneously overexpressed and amplified in our panel, including the BCL2 locus at 18q21.33. Our data further support the evidence of the genomic complexity of multiple myeloma and reinforce the role of an integrated genomic approach in improving our understanding of the molecular pathogenesis of the disease. This article contains Supplementary Material available at http://www.interscience.wiley.com/jpages/1045-2257/suppmat.

Cell Line, Tumor↗
PubMed2007

Integrative genomic analysis reveals distinct transcriptional and genetic features associated with chromosome 13 deletion in multiple myeloma.

BACKGROUND AND OBJECTIVES: The chromosome 13 deletion (Delta13) is one of the most frequent chromosomal alterations in multiple myeloma (MM). Delta13 is associated with an unfavorable prognosis, although there is increasing agreement that its prognostic relevance must be related to the ploidy status and the presence of different chromosomal translocations. The aim of this study was to provide a comprehensive analysis of the transcriptional features of Delta13 in MM. DESIGN AND METHODS: Highly purified plasma cells from 80 newly diagnosed MM patients were characterized by means of fluorescence in situ hybridization (FISH) and high-density oligonucleotide microarray for gene expression profiling and chromosomal alterations. RESULTS: We identified 67 differentially expressed genes in the patients with and without the chromosome 13 deletion, all of which were downregulated in the cases with Delta13: 44 mapped along the whole chromosome 13, seven on chromosome 11 and three on chromosome 19. Functional analyses of the selected genes indicated their involvement in protein biosynthesis, ubiquitination and transcriptional regulation. An integrative genomic approach based on regional analyses of the gene expression data identified distinct chromosomal regions whose global expression modulation could differentiate Delta13-positive cases, in particular the upregulation of 1q21-1q42 and the downregulation of 19p and almost the entire chromosome 11. FISH analyses confirmed the close relationship between Delta13-positivity and the presence of extra copies of 1q21-1q42 (p=6 x 10(-4)) or the absence of chromosome 11 and 19 trisomy (p=5 x 10(-4)). INTERPRETATION AND CONCLUSIONS: Our results indicate that distinct types of chromosomal aberrations are closely related to the transcriptional profiles of Delta13-positive cases, suggesting that the contribution of Delta13 to the malignancy should be considered together with associated abnormalities.

Adult↗
PubMed2006

A gene expression signature associated with survival in metastatic melanoma.

BACKGROUND: Current clinical and histopathological criteria used to define the prognosis of melanoma patients are inadequate for accurate prediction of clinical outcome. We investigated whether genome screening by means of high-throughput gene microarray might provide clinically useful information on patient survival. METHODS: Forty-three tumor tissues from 38 patients with stage III and stage IV melanoma were profiled with a 17,500 element cDNA microarray. Expression data were analyzed using significance analysis of microarrays (SAM) to identify genes associated with patient survival, and supervised principal components (SPC) to determine survival prediction. RESULTS: SAM analysis revealed a set of 80 probes, corresponding to 70 genes, associated with survival, i.e. 45 probes characterizing longer and 35 shorter survival times, respectively. These transcripts were included in a survival prediction model designed using SPC and cross-validation which allowed identifying 30 predicting probes out of the 80 associated with survival. CONCLUSION: The longer-survival group of genes included those expressed in immune cells, both innate and acquired, confirming the interplay between immunological mechanisms and the natural history of melanoma. Genes linked to immune cells were totally lacking in the poor-survival group, which was instead associated with a number of genes related to highly proliferative and invasive tumor cells.

Journal Article↗
PubMed2006

Strategies for comparing gene expression profiles from different microarray platforms: application to a case-control experiment.

Meta-analysis of microarray data is increasingly important, considering both the availability of multiple platforms using disparate technologies and the accumulation in public repositories of data sets from different laboratories. We addressed the issue of comparing gene expression profiles from two microarray platforms by devising a standardized investigative strategy. We tested this procedure by studying MDA-MB-231 cells, which undergo apoptosis on treatment with resveratrol. Gene expression profiles were obtained using high-density, short-oligonucleotide, single-color microarray platforms: GeneChip (Affymetrix) and CodeLink (Amersham). Interplatform analyses were carried out on 8414 common transcripts represented on both platforms, as identified by LocusLink ID, representing 70.8% and 88.6% of annotated GeneChip and CodeLink features, respectively. We identified 105 differentially expressed genes (DEGs) on CodeLink and 42 DEGs on GeneChip. Among them, only 9 DEGs were commonly identified by both platforms. Multiple analyses (BLAST alignment of probes with target sequences, gene ontology, literature mining, and quantitative real-time PCR) permitted us to investigate the factors contributing to the generation of platform-dependent results in single-color microarray experiments. An effective approach to cross-platform comparison involves microarrays of similar technologies, samples prepared by identical methods, and a standardized battery of bioinformatic and statistical analyses.

Breast Neoplasms↗
PubMed2006

Tumors induce a subset of inflammatory monocytes with immunosuppressive activity on CD8+ T cells.

Active suppression of tumor-specific T lymphocytes can limit the efficacy of immune surveillance and immunotherapy. While tumor-recruited CD11b+ myeloid cells are known mediators of tumor-associated immune dysfunction, the true nature of these suppressive cells and the fine biochemical pathways governing their immunosuppressive activity remain elusive. Here we describe a population of circulating CD11b+IL-4 receptor alpha+ (CD11b+IL-4Ralpha+), inflammatory-type monocytes that is elicited by growing tumors and activated by IFN-gamma released from T lymphocytes. CD11b+IL-4Ralpha+ cells produced IL-13 and IFN-gamma and integrated the downstream signals of these cytokines to trigger the molecular pathways suppressing antigen-activated CD8+ T lymphocytes. Analogous immunosuppressive circuits were active in CD11b+ cells present within the tumor microenvironment. These suppressor cells challenge the current idea that tumor-conditioned immunosuppressive monocytes/macrophages are alternatively activated. Moreover, our data show how the inflammatory response elicited by tumors had detrimental effects on the adaptive immune system and suggest novel approaches for the treatment of tumor-induced immune dysfunctions.

Animals↗
PubMed2006

Validation by RQ-PCR and flow cytometry of alpha-defensin1-3 (DEFA1-3) overexpression in relapsed and refractory acute lymphoblastic leukemia.

In spite of high cure rates and improved overall survival, 25% of pediatric patients with acute lymphoblastic leukemia (ALL) relapse after obtaining complete remission. Additionally a small proportion of patients are refractory and do not attain remission. Microarray expression analysis of matched diagnosis-relapse B-lineage ALL sample pairs identified DEFA1-3 as a potential marker of relapse. Here, validation of DEFA1-3 as a marker for therapy resistance is explored. DEFA1-3 expression was analysed by RQ-PCR in patient paired samples at diagnosis and relapse of 6 early-relapse (within 18 months) and 8 late-relapse (beyond 18 months) B-lineage ALL. Diagnostic samples of 19 patients with ALL who are in continuous complete remission (median time from diagnosis 47 month) and diagnostic samples of 5 refractory patients who had not achieved remission at day 35 of therapy were also analyzed. In addition, overexpression of alpha-defensin1-3 proteins in blast cells at relapse was analysed by flow cytometry. DEFA1-3 was overexpressed at relapse as compared to diagnosis in 12 of 14 samples. At diagnosis, the expression of DEFA1-3 was significantly higher in samples from refractory patients as compared to those of patients who are in CR and to those of patients who experienced late relapse. At diagnosis, patients who relapsed early after diagnosis could not be distinguished from refractory patients based on DEFA1-3 expression levels. Results suggest that high levels of DEFA1-3 mRNA and alpha-defensin1-3 protein expression are correlated with disease progression and failure of adequate response to conventional chemotherapy.

Adolescent↗
PubMed2005

Toward the identification of a tolerogenic signature in IDO-competent dendritic cells.

Although much is known about the transcriptional profiles of dendritic cells (DCs) during maturation, the molecular switches critical for the induction of a tolerogenic program in DC subsets are still obscure. We examined the gene-expression profiles of murine splenic CD8+ DCs rendered highly tolerogenic by interferon-gamma (IFN-gamma), which activates the enzyme indoleamine 2,3-dioxygenase (IDO, encoded by Indo) and thus initiates the immunosuppressive pathway of tryptophan catabolism. By examining the expression of a series of relevant genes in IDO+ compared with IDO- DCs, we found consistent and selective association of the IDO-competent phenotype with down-modulation of the Tyrobp gene, encoding the signaling adapter DAP12, which typically associates with activating receptors. Down-modulation of Tyrobp involved IFN consensus sequence binding protein (ICSBP), a transcription factor also known as IRF-8. In murine and human monocyte-derived DCs, silencing DAP12 expression imparted IDO functional competence to IDO- cells, whereas silencing IRF-8 in IDO+ counterparts abolished IDO expression and function. Thus, IRF-8 is required in tolerogenic DCs for the positive regulation of Indo and the negative regulation of Tyrobp. Overall, these studies reveal the occurrence of a simple and evolutionarily conserved code in the control of tolerance by an ancestral metabolic enzyme.

Adaptor Proteins, Signal Transducing↗
PubMed2005

Molecular classification of multiple myeloma: a distinct transcriptional profile characterizes patients expressing CCND1 and negative for 14q32 translocations.

PURPOSE: The deregulation of CCND1, CCND2 and CCND3 genes represents a common event in multiple myeloma (MM). A recently proposed classification grouped MM patients into five classes on the basis of their cyclin D expression profiles and the presence of the main translocations involving the immunoglobulin heavy chain locus (IGH) at 14q32. In this study, we provide a molecular characterization of the identified translocations/cyclins (TC) groups. MATERIALS AND METHODS: The gene expression profiles of purified plasma cells from 50 MM cases were used to stratify the samples into the five TC classes and identify their transcriptional fingerprints. The cyclin D expression data were validated by means of real-time quantitative polymerase chain reaction analysis; fluorescence in situ hybridization was used to investigate the cyclin D loci arrangements, and to detect the main IGH translocations and the chromosome 13q deletion. RESULTS: Class-prediction analysis identified 112 probe sets as characterizing the TC1, TC2, TC4 and TC5 groups, whereas the TC3 samples showed heterogeneous phenotypes and no marker genes. The TC2 group, which showed extra copies of the CCND1 locus and no IGH translocations or the chromosome 13q deletion, was characterized by the overexpression of genes involved in protein biosynthesis at the translational level. A meta-analysis of published data sets validated the identified gene expression signatures. CONCLUSION: Our data contribute to the understanding of the molecular and biologic features of distinct MM subtypes. The identification of a distinctive gene expression pattern in TC2 patients may improve risk stratification and indicate novel therapeutic targets.

Adult↗
PubMed2005

Thalidomide downregulates angiogenic genes in bone marrow endothelial cells of patients with active multiple myeloma.

PURPOSE: To study the antiangiogenic effect of thalidomide. PATIENTS AND METHODS: The expression of key angiogenic genes was studied in bone marrow endothelial cells (ECs) of patients with active and nonactive multiple myeloma (MM), monoclonal gammopathies unattributed/unassociated (MG[u]), diffuse large B-cell non-Hodgkin's lymphoma, in a Kaposi's sarcoma (KS) cell line, and in healthy human umbilical vein ECs (HUVECs) following exposure to therapeutic doses of thalidomide. RESULTS: Thalidomide markedly downregulates the genes in a dose-dependent fashion in active MMECs and KS cell line, but upregulates them or is ineffective in nonactive MMECs, MG(u)ECs, NHL-ECs, and in HUVECs. Secretion of vascular endothelial growth factor (VEGF), basic fibroblast growth factor (bFGF) and hepatocyte growth factor also diminishes according to the dose in culture conditioned media (CM) of active MMECs and KS, whereas it does not change in the other CM. CONCLUSION: Inhibition by thalidomide is probably confined to the genes of active MMECs and KS. This would account for its higher efficacy in these diseases.

Adult↗
PubMed2005

Gene expression profiling of plasma cell dyscrasias reveals molecular patterns associated with distinct IGH translocations in multiple myeloma.

Multiple myeloma (MM) is the most common form of plasma cell dyscrasia, characterized by a marked heterogeneity of genetic lesions and clinical course. It may develop from a premalignant condition (monoclonal gammopathy of undetermined significance, MGUS) or progress from intramedullary to extramedullary forms (plasma cell leukemia, PCL). To provide insights into the molecular characterization of plasma cell dyscrasias and to investigate the contribution of specific genetic lesions to the biological and clinical heterogeneity of MM, we analysed the gene expression profiles of plasma cells isolated from seven MGUS, 39 MM and six PCL patients by means of DNA microarrays. MMs resulted highly heterogeneous at transcriptional level, whereas the differential expression of genes mainly involved in DNA metabolism and proliferation distinguished MGUS from PCLs and the majority of MM cases. The clustering of MM patients was mainly driven by the presence of the most recurrent translocations involving the immunoglobulin heavy-chain locus. Distinct gene expression patterns have been found to be associated with different lesions: the overexpression of CCND2 and genes involved in cell adhesion pathways was observed in cases with deregulated MAF and MAFB, whereas genes upregulated in cases with the t(4;14) showed apoptosis-related functions. The peculiar finding in patients with the t(11;14) was the downregulation of the alpha-subunit of the IL-6 receptor. In addition, we identified a set of cancer germline antigens specifically expressed in a subgroup of MM patients characterized by an aggressive clinical evolution, a finding that could have implications for patient classification and immunotherapy.

Adult↗
PubMed2004

Acute leukemia subclassification: a marker protein expression perspective.

Improved leukemia classification and tailoring of therapy have greatly improved patient outcome particularly for children with acute leukemia (AL). Using immunophenotyping, molecular genetics and cytogenetics the low hanging fruits of biomedical research have been successfully incorporated in routine diagnosis of leukemia subclasses. Future improvements in the classification and understanding of leukemia biology will very likely be more slow and laborious. Recently, gene expression profiling has provided a framework for the global molecular analysis of hematological cancers, and high throughput proteomic analysis of leukemia samples is on the way. Here we consider classification of acute leukemia samples by flow cytometry using the marker proteins of immunophenotyping as a component of the proteome. Marker protein expressions are converted into quantitative expression values and subjected to computational analysis. Quantitative multivariate analysis from panels of marker proteins has demonstrated that marker protein expression profiles can distinguish MLLre from non-MLLre ALL cases and also allow to specifically distinguish MLL/AF4 cases. Potentially, these quantitative expression analyses can be used in clinical diagnosis. Immunophenotypic data collection using flow cytometry is a fast and relatively easily accessible technology that has already been implemented in most centers for leukemia diagnosis and the translation into quantitative expression data sets is a matter of flow cytometer settings and output calibration. However, before application in clinical diagnostics can occur it is crucial that quantitative immunophenotypic data set analysis is validated in independent experiments and in large data sets.

Acute Disease↗
PubMed2004

Artificial neural network technologies to identify biomarkers for therapeutic intervention.

High-throughput technologies such as DNA/RNA microarrays, mass spectrometry and protein chips are creating unprecedented opportunities to accelerate towards the understanding of living systems and the identification of target genes and pathways for drug development and therapeutic intervention. However, the increasing volumes of data generated by molecular profiling experiments pose formidable challenges to investigate an overwhelming mass of information and turn it into predictive, deployable markers. Advanced biostatistics and machine learning methods from computer science have been applied to analyze and correlate numerical values of profiling intensities to physiological states. This article reviews the application of artificial neural networks, an information-processing tool, to the identification of sets of diagnostic/prognostic biomarkers from high-throughput profiling data.

Animals↗
PubMed2003

Pattern identification and classification in gene expression data using an autoassociative neural network model.

The application of DNA microarray technology for analysis of gene expression creates enormous opportunities to accelerate the pace in understanding living systems and identification of target genes and pathways for drug development and therapeutic intervention. Parallel monitoring of the expression profiles of thousands of genes seems particularly promising for a deeper understanding of cancer biology and the identification of molecular signatures supporting the histological classification schemes of neoplastic specimens. However, the increasing volume of data generated by microarray experiments poses the challenge of developing equally efficient methods and analysis procedures to extract, interpret, and upgrade the information content of these databases. Herein, a computational procedure for pattern identification, feature extraction, and classification of gene expression data through the analysis of an autoassociative neural network model is described. The identified patterns and features contain critical information about gene-phenotype relationships observed during changes in cell physiology. They represent a rational and dimensionally reduced base for understanding the basic biology of the onset of diseases, defining targets of therapeutic intervention, and developing diagnostic tools for the identification and classification of pathological states. The proposed method has been tested on two different microarray datasets-Golub's analysis of acute human leukemia [Golub et al. (1999) Science 286:531-537], and the human colon adenocarcinoma study presented by Alon et al. [1999; Proc Natl Acad Sci USA 97:10101-10106]. The analysis of the neural network internal structure allows the identification of specific phenotype markers and the extraction of peculiar associations among genes and physiological states. At the same time, the neural network outputs provide assignment to multiple classes, such as different pathological conditions or tissue samples, for previously unseen instances.

Adenocarcinoma↗
PubMed2003

Plasminogen activator inhibitor-2: a molecular biomarker for head and neck cancer progression.

Head and neck squamous cell carcinoma (HNSCC) is an aggressive epithelial malignancy in which the early diagnosis of premalignant lesions is known to directly correlate with increased survival. However, only a portion of biopsies showing dysplasia will progress to cancer, and there are no currently accepted criteria for predicting which lesions will progress. Therefore, diagnostic protocols that can identify the lesions that are likely to become HNSCC are required. RNA was isolated from normal keratinocytes, the immortalized but nontumorigenic HaCat cell line, and the tumor cell lines SCC-4, SCC-9, SCC-25, and OSCC-3. The RNA was then labeled and used to probe nylon microarray filters that contained a total of 9184 genes (5295 named and 3889 Expression Sequence Tags). Genes whose expression demonstrated a 3-fold or greater change were considered significant. Comparison of expression profiles from normal, HaCat, and four tumor cell lines revealed changes in gene expression in a total of 508 genes. Of these, 16 genes showed a consistent loss of expression when comparing normal to immortalized keratinocytes. In addition, 10 genes demonstrated a consistent loss of expression in the tumor cell lines only. In this latter group of genes, plasminogen activator inhibitor-2 (PAI-2), a gene whose expression has been linked to cell invasion, was additionally investigated. Altered expression of PAI-2 in the different cultured cells was validated via real-time quantitative-PCR. In addition, immunohistochemical evaluation of biopsy samples revealed a high expression of PAI-2 in both normal and dysplastic epithelium with a marked decrease of expression in areas of the biopsies containing HNSCC. These data demonstrate that genomic profiling can then be used to identify potential genotypic/phenotypic biomarkers that may predict which dysplastic lesions are most likely to progress to HNSCC.

Biomarkers, Tumor↗
PubMed2002

Prediction of in vivo synergistic activity of antiangiogenic compounds by gene expression profiling.

Angiogenesis, an essential phenotype for tumor formation, requires the interaction of many cells within the tumor microenvironment. Therefore, successful antiangiogenic therapies must be able to block all of the different mechanisms tumors use to induce neovascularization. A major challenge for developing such protocols is determining which agents are likely to have the highest degree of synergistic activity in vivo. We treated human microvascular endothelial cells with six inhibitors of angiogenesis and used microarrays to seek divergent patterns of gene expression suggestive of potential synergies. The expression profiles of a thrombospondin-mimetic peptide (DI-TSPa) and TNP-470 (TNP) were very similar, whereas endostatin had a dramatically different profile. In vitro, endostatin was synergistically antiangiogenic with either TNP-470 or DI-TSPa. In vivo, mice bearing Lewis lung carcinoma cells treated with a combination of endostatin and either DI-TSPa or TNP-470, at doses that were ineffective when used alone, resulted in a marked inhibition of tumor growth and decreased tumor angiogenesis. Conversely, animals treated with both DI-TSPa and TNP-470 demonstrated a modest effect on both tumor growth and angiogenesis. These results suggest that even in the absence of a complete mechanistic understanding of how these inhibitors work, gene expression profiling may be used to predict synergistic antiangiogenic activity and thus maximize their antitumor efficacy.

Angiogenesis Inhibitors↗
PubMedDate not supplied

Fermentation diagnosis by multivariate statistical analysis.

During the course of fermentation, online measuring procedures able to estimate the performance of the current operation are highly desired. Unfortunately, the poor mechanistic understanding of most biologic systems hampers attempts at direct online evaluation of the bioprocess, which is further complicated by the lack of appropriate online sensors and the long lag time associated with offline assays. Quite often available data lack sufficient detail to be directly used, and after a cursory evaluation are stored away. However, these historic databases of process measurements may still retain some useful information. A multivariate statistical procedure has been applied for analyzing the measurement profiles acquired during the monitoring of several fed-batch fermentations for the production of erythromycin. Multivariate principal component analysis has been used to extract information from the multivariate historic database by projecting the process variables onto a low-dimensional space defined by the principal components. Thus, each fermentation is identified by a temporal profile in the principal component plane. The projections represent monitoring charts, consistent with the concept of statistical process control, which are useful for tracking the progress of each fermentation batch and identifying anomalous behaviors (process diagnosis and fault detection).

Biotechnology↗