Publications by authors named "Marco Chierici"

25 Publications

  • Page 1 of 1

A verified genomic reference sample for assessing performance of cancer panels detecting small variants of low allele frequency.

Genome Biol 2021 04 16;22(1):111. Epub 2021 Apr 16.

Marketing, Integrated DNA Technologies, Inc., 1710 Commercial Park, Coralville, IA, 52241, USA.

Background: Oncopanel genomic testing, which identifies important somatic variants, is increasingly common in medical practice and especially in clinical trials. Currently, there is a paucity of reliable genomic reference samples having a suitably large number of pre-identified variants for properly assessing oncopanel assay analytical quality and performance. The FDA-led Sequencing and Quality Control Phase 2 (SEQC2) consortium analyze ten diverse cancer cell lines individually and their pool, termed Sample A, to develop a reference sample with suitably large numbers of coding positions with known (variant) positives and negatives for properly evaluating oncopanel analytical performance.

Results: In reference Sample A, we identify more than 40,000 variants down to 1% allele frequency with more than 25,000 variants having less than 20% allele frequency with 1653 variants in COSMIC-related genes. This is 5-100× more than existing commercially available samples. We also identify an unprecedented number of negative positions in coding regions, allowing statistical rigor in assessing limit-of-detection, sensitivity, and precision. Over 300 loci are randomly selected and independently verified via droplet digital PCR with 100% concordance. Agilent normal reference Sample B can be admixed with Sample A to create new samples with a similar number of known variants at much lower allele frequency than what exists in Sample A natively, including known variants having allele frequency of 0.02%, a range suitable for assessing liquid biopsy panels.

Conclusion: These new reference samples and their admixtures provide superior capability for performing oncopanel quality control, analytical accuracy, and validation for small to large oncopanels and liquid biopsy assays.
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http://dx.doi.org/10.1186/s13059-021-02316-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8051128PMC
April 2021

Cross-oncopanel study reveals high sensitivity and accuracy with overall analytical performance depending on genomic regions.

Genome Biol 2021 04 16;22(1):109. Epub 2021 Apr 16.

Greenwood Genetic Center, 106 Gregor Mendel Circle, Greenwood, SC, 29646, USA.

Background: Targeted sequencing using oncopanels requires comprehensive assessments of accuracy and detection sensitivity to ensure analytical validity. By employing reference materials characterized by the U.S. Food and Drug Administration-led SEquence Quality Control project phase2 (SEQC2) effort, we perform a cross-platform multi-lab evaluation of eight Pan-Cancer panels to assess best practices for oncopanel sequencing.

Results: All panels demonstrate high sensitivity across targeted high-confidence coding regions and variant types for the variants previously verified to have variant allele frequency (VAF) in the 5-20% range. Sensitivity is reduced by utilizing VAF thresholds due to inherent variability in VAF measurements. Enforcing a VAF threshold for reporting has a positive impact on reducing false positive calls. Importantly, the false positive rate is found to be significantly higher outside the high-confidence coding regions, resulting in lower reproducibility. Thus, region restriction and VAF thresholds lead to low relative technical variability in estimating promising biomarkers and tumor mutational burden.

Conclusion: This comprehensive study provides actionable guidelines for oncopanel sequencing and clear evidence that supports a simplified approach to assess the analytical performance of oncopanels. It will facilitate the rapid implementation, validation, and quality control of oncopanels in clinical use.
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http://dx.doi.org/10.1186/s13059-021-02315-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8051090PMC
April 2021

Cellular and gene signatures of tumor-infiltrating dendritic cells and natural-killer cells predict prognosis of neuroblastoma.

Nat Commun 2020 11 25;11(1):5992. Epub 2020 Nov 25.

Department of Paediatric Haematology/Oncology and of Cell and Gene Therapy, Ospedale Pediatrico Bambino Gesù, IRCCS, 00146, Rome, Italy.

Tumor-infiltrating lymphocytes play an essential role in improving clinical outcome of neuroblastoma (NB) patients, but their relationship with other tumor-infiltrating immune cells in the T cell-inflamed tumors remains poorly investigated. Here we show that dendritic cells (DCs) and natural killer (NK) cells are positively correlated with T-cell infiltration in human NB, both at transcriptional and protein levels, and associate with a favorable prognosis. Multiplex imaging displays DC/NK/T cell conjugates in the tumor microenvironment of low-risk NB. Remarkably, this connection is further strengthened by the identification of gene signatures related to DCs and NK cells able to predict survival of NB patients and strongly correlate with the expression of PD-1 and PD-L1. In summary, our findings unveil a key prognostic role of DCs and NK cells and indicate their related gene signatures as promising tools for the identification of clinical biomarkers to better define risk stratification and survival of NB patients.
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http://dx.doi.org/10.1038/s41467-020-19781-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7689423PMC
November 2020

Integrative Network Fusion: A Multi-Omics Approach in Molecular Profiling.

Front Oncol 2020 30;10:1065. Epub 2020 Jun 30.

Fondazione Bruno Kessler, Trento, Italy.

Recent technological advances and international efforts, such as The Cancer Genome Atlas (TCGA), have made available several pan-cancer datasets encompassing multiple omics layers with detailed clinical information in large collection of samples. The need has thus arisen for the development of computational methods aimed at improving cancer subtyping and biomarker identification from multi-modal data. Here we apply the Integrative Network Fusion (INF) pipeline, which combines multiple omics layers exploiting Similarity Network Fusion (SNF) within a machine learning predictive framework. INF includes a feature ranking scheme (rSNF) on SNF-integrated features, used by a classifier over juxtaposed multi-omics features (juXT). In particular, we show instances of INF implementing Random Forest (RF) and linear Support Vector Machine (LSVM) as the classifier, and two baseline RF and LSVM models are also trained on juXT. A compact RF model, called rSNFi, trained on the intersection of top-ranked biomarkers from the two approaches juXT and rSNF is finally derived. All the classifiers are run in a 10x5-fold cross-validation schema to warrant reproducibility, following the guidelines for an unbiased Data Analysis Plan by the US FDA-led initiatives MAQC/SEQC. INF is demonstrated on four classification tasks on three multi-modal TCGA oncogenomics datasets. Gene expression, protein expression and copy number variants are used to predict estrogen receptor status (BRCA-ER, = 381) and breast invasive carcinoma subtypes (BRCA-subtypes, = 305), while gene expression, miRNA expression and methylation data is used as predictor layers for acute myeloid leukemia and renal clear cell carcinoma survival (AML-OS, = 157; KIRC-OS, = 181). In test, INF achieved similar Matthews Correlation Coefficient (MCC) values and 97% to 83% smaller feature sizes (FS), compared with juXT for BRCA-ER (MCC: 0.83 vs. 0.80; FS: 56 vs. 1801) and BRCA-subtypes (0.84 vs. 0.80; 302 vs. 1801), improving KIRC-OS performance (0.38 vs. 0.31; 111 vs. 2319). INF predictions are generally more accurate in test than one-dimensional omics models, with smaller signatures too, where transcriptomics consistently play the leading role. Overall, the INF framework effectively integrates multiple data levels in oncogenomics classification tasks, improving over the performance of single layers alone and naive juxtaposition, and provides compact signature sizes.
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http://dx.doi.org/10.3389/fonc.2020.01065DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7340129PMC
June 2020

Predictability of drug-induced liver injury by machine learning.

Biol Direct 2020 02 13;15(1). Epub 2020 Feb 13.

Fondazione Bruno Kessler, Via Sommarive 18, Trento, 38123, Italy.

Background: Drug-induced liver injury (DILI) is a major concern in drug development, as hepatotoxicity may not be apparent at early stages but can lead to life threatening consequences. The ability to predict DILI from in vitro data would be a crucial advantage. In 2018, the Critical Assessment Massive Data Analysis group proposed the CMap Drug Safety challenge focusing on DILI prediction.

Methods And Results: The challenge data included Affymetrix GeneChip expression profiles for the two cancer cell lines MCF7 and PC3 treated with 276 drug compounds and empty vehicles. Binary DILI labeling and a recommended train/test split for the development of predictive classification approaches were also provided. We devised three deep learning architectures for DILI prediction on the challenge data and compared them to random forest and multi-layer perceptron classifiers. On a subset of the data and for some of the models we additionally tested several strategies for balancing the two DILI classes and to identify alternative informative train/test splits. All the models were trained with the MAQC data analysis protocol (DAP), i.e., 10x5 cross-validation over the training set. In all the experiments, the classification performance in both cross-validation and external validation gave Matthews correlation coefficient (MCC) values below 0.2. We observed minimal differences between the two cell lines. Notably, deep learning approaches did not give an advantage on the classification performance.

Discussion: We extensively tested multiple machine learning approaches for the DILI classification task obtaining poor to mediocre performance. The results suggest that the CMap expression data on the two cell lines MCF7 and PC3 are not sufficient for accurate DILI label prediction.

Reviewers: This article was reviewed by Maciej Kandula and Paweł P. Labaj.
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http://dx.doi.org/10.1186/s13062-020-0259-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7020573PMC
February 2020

A Pan-Cancer Approach to Predict Responsiveness to Immune Checkpoint Inhibitors by Machine Learning.

Cancers (Basel) 2019 Oct 15;11(10). Epub 2019 Oct 15.

Experimental and Clinical Pharmacology Unit, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, 33081 Aviano, Italy.

Immunotherapy by using immune checkpoint inhibitors (ICI) has dramatically improved the treatment options in various cancers, increasing survival rates for treated patients. Nevertheless, there are heterogeneous response rates to ICI among different cancer types, and even in the context of patients affected by a specific cancer. Thus, it becomes crucial to identify factors that predict the response to immunotherapeutic approaches. A comprehensive investigation of the mutational and immunological aspects of the tumor can be useful to obtain a robust prediction. By performing a pan-cancer analysis on gene expression data from the Cancer Genome Atlas (TCGA, 8055 cases and 29 cancer types), we set up and validated a machine learning approach to predict the potential for positive response to ICI. Support vector machines (SVM) and extreme gradient boosting (XGboost) models were developed with a 10×5-fold cross-validation schema on 80% of TCGA cases to predict ICI responsiveness defined by a score combining tumor mutational burden and TGF- β signaling. On the remaining 20% validation subset, our SVM model scored 0.88 accuracy and 0.27 Matthews Correlation Coefficient. The proposed machine learning approach could be useful to predict the putative response to ICI treatment by expression data of primary tumors.
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http://dx.doi.org/10.3390/cancers11101562DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6827166PMC
October 2019

Evaluating reproducibility of AI algorithms in digital pathology with DAPPER.

PLoS Comput Biol 2019 03 27;15(3):e1006269. Epub 2019 Mar 27.

Fondazione Bruno Kessler, Trento, Italy.

Artificial Intelligence is exponentially increasing its impact on healthcare. As deep learning is mastering computer vision tasks, its application to digital pathology is natural, with the promise of aiding in routine reporting and standardizing results across trials. Deep learning features inferred from digital pathology scans can improve validity and robustness of current clinico-pathological features, up to identifying novel histological patterns, e.g., from tumor infiltrating lymphocytes. In this study, we examine the issue of evaluating accuracy of predictive models from deep learning features in digital pathology, as an hallmark of reproducibility. We introduce the DAPPER framework for validation based on a rigorous Data Analysis Plan derived from the FDA's MAQC project, designed to analyze causes of variability in predictive biomarkers. We apply the framework on models that identify tissue of origin on 787 Whole Slide Images from the Genotype-Tissue Expression (GTEx) project. We test three different deep learning architectures (VGG, ResNet, Inception) as feature extractors and three classifiers (a fully connected multilayer, Support Vector Machine and Random Forests) and work with four datasets (5, 10, 20 or 30 classes), for a total of 53, 000 tiles at 512 × 512 resolution. We analyze accuracy and feature stability of the machine learning classifiers, also demonstrating the need for diagnostic tests (e.g., random labels) to identify selection bias and risks for reproducibility. Further, we use the deep features from the VGG model from GTEx on the KIMIA24 dataset for identification of slide of origin (24 classes) to train a classifier on 1, 060 annotated tiles and validated on 265 unseen ones. The DAPPER software, including its deep learning pipeline and the Histological Imaging-Newsy Tiles (HINT) benchmark dataset derived from GTEx, is released as a basis for standardization and validation initiatives in AI for digital pathology.
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http://dx.doi.org/10.1371/journal.pcbi.1006269DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6467397PMC
March 2019

Tumor-infiltrating T cells and PD-L1 expression in childhood malignant extracranial germ-cell tumors.

Oncoimmunology 2019;8(2):e1542245. Epub 2018 Dec 13.

Department of Oncohaematology, IRCCS, Ospedale Pediatrico Bambino Gesù, Rome, Italy.

Although pediatric malignant extracranial germ-cell tumors (meGCTs) are among the most chemosensitive solid tumors, a group of patients relapse and die of disease. To identify new markers predicting clinical outcome, we examined the prognostic relevance of tumor-infiltrating T lymphocytes (TILs) and the expression of PD-1 and PD-L1 in a cohort of pediatric meGCTs by immunohistochemistry. MeGCTs were variously infiltrated by T cell-subtypes according to the tumor subtype, tumor location and age at diagnosis. We distinguished three different phenotypes: i) tumors not infiltrated by T cells (immature teratomas and half of the yolk sac tumors), ii) tumors highly infiltrated by CD8 T cells expressing PD-1, which identifies activated tumor-reactive T cells (seminomas and dysgerminomas), iii) tumors highly infiltrated by CD8 T cells within an immunosuppressive tumor microenvironment characterized by CD4FOXP3 Treg cells and PD-L1-expressing tumor cells (embryonal carcinomas, choriocarcinomas and the remaining yolk sac tumors). Tumor subtypes belonging mixed meGCTs were variously infiltrated, suggesting the coexistence of multiple immune microenvironments either facilitating or precluding the entry of T cells. These findings support the hypothesis that TILs influence the development of meGCTs and might be of clinical relevance to improve risk stratification and the treatment of pediatric patients.
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http://dx.doi.org/10.1080/2162402X.2018.1542245DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6343784PMC
December 2018

Machine learning models for predicting endocrine disruption potential of environmental chemicals.

J Environ Sci Health C Environ Carcinog Ecotoxicol Rev 2018 10;36(4):237-251. Epub 2019 Jan 10.

a Fondazione Bruno Kessler , Trento , Italy.

We introduce here ML4Tox, a framework offering Deep Learning and Support Vector Machine models to predict agonist, antagonist, and binding activities of chemical compounds, in this case for the estrogen receptor ligand-binding domain. The ML4Tox models have been developed with a 10 × 5-fold cross-validation schema on the training portion of the CERAPP ToxCast dataset, formed by 1677 chemicals, each described by 777 molecular features. On the CERAPP "All Literature" evaluation set (agonist: 6319 compounds; antagonist 6539; binding 7283), ML4Tox significantly improved sensitivity over published results on all three tasks, with agonist: 0.78 vs 0.56; antagonist: 0.69 vs 0.11; binding: 0.66 vs 0.26.
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http://dx.doi.org/10.1080/10590501.2018.1537155DOI Listing
April 2019

Distillation of the clinical algorithm improves prognosis by multi-task deep learning in high-risk Neuroblastoma.

PLoS One 2018 7;13(12):e0208924. Epub 2018 Dec 7.

Fondazione Bruno Kessler, Trento, Italy.

We introduce the CDRP (Concatenated Diagnostic-Relapse Prognostic) architecture for multi-task deep learning that incorporates a clinical algorithm, e.g., a risk stratification schema to improve prognostic profiling. We present the first application to survival prediction in High-Risk (HR) Neuroblastoma from transcriptomics data, a task that studies from the MAQC consortium have shown to remain the hardest among multiple diagnostic and prognostic endpoints predictable from the same dataset. To obtain a more accurate risk stratification needed for appropriate treatment strategies, CDRP combines a first component (CDRP-A) synthesizing a diagnostic task and a second component (CDRP-N) dedicated to one or more prognostic tasks. The approach leverages the advent of semi-supervised deep learning structures that can flexibly integrate multimodal data or internally create multiple processing paths. CDRP-A is an autoencoder trained on gene expression on the HR/non-HR risk stratification by the Children's Oncology Group, obtaining a 64-node representation in the bottleneck layer. CDRP-N is a multi-task classifier for two prognostic endpoints, i.e., Event-Free Survival (EFS) and Overall Survival (OS). CDRP-A provides the HR embedding input to the CDRP-N shared layer, from which two branches depart to model EFS and OS, respectively. To control for selection bias, CDRP is trained and evaluated using a Data Analysis Protocol (DAP) developed within the MAQC initiative. CDRP was applied on Illumina RNA-Seq of 498 Neuroblastoma patients (HR: 176) from the SEQC study (12,464 Entrez genes) and on Affymetrix Human Exon Array expression profiles (17,450 genes) of 247 primary diagnostic Neuroblastoma of the TARGET NBL cohort. On the SEQC HR patients, CDRP achieves Matthews Correlation Coefficient (MCC) 0.38 for EFS and MCC = 0.19 for OS in external validation, improving over published SEQC models. We show that a CDRP-N embedding is indeed parametrically associated to increasing severity and the embedding can be used to better stratify patients' survival.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0208924PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6285384PMC
May 2019

Multi-omics integration for neuroblastoma clinical endpoint prediction.

Biol Direct 2018 04 3;13(1). Epub 2018 Apr 3.

Fondazione Bruno Kessler, Via Sommarive 18, Trento, 38123, Italy.

Background: High-throughput methodologies such as microarrays and next-generation sequencing are routinely used in cancer research, generating complex data at different omics layers. The effective integration of omics data could provide a broader insight into the mechanisms of cancer biology, helping researchers and clinicians to develop personalized therapies.

Results: In the context of CAMDA 2017 Neuroblastoma Data Integration challenge, we explore the use of Integrative Network Fusion (INF), a bioinformatics framework combining a similarity network fusion with machine learning for the integration of multiple omics data. We apply the INF framework for the prediction of neuroblastoma patient outcome, integrating RNA-Seq, microarray and array comparative genomic hybridization data. We additionally explore the use of autoencoders as a method to integrate microarray expression and copy number data.

Conclusions: The INF method is effective for the integration of multiple data sources providing compact feature signatures for patient classification with performances comparable to other methods. Latent space representation of the integrated data provided by the autoencoder approach gives promising results, both by improving classification on survival endpoints and by providing means to discover two groups of patients characterized by distinct overall survival (OS) curves.

Reviewers: This article was reviewed by Djork-Arné Clevert and Tieliu Shi.
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http://dx.doi.org/10.1186/s13062-018-0207-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5907722PMC
April 2018

Phylogenetic convolutional neural networks in metagenomics.

BMC Bioinformatics 2018 03 8;19(Suppl 2):49. Epub 2018 Mar 8.

Fondazione Bruno Kessler (FBK), Via Sommarive 18 Povo, Trento, I-38123, Italy.

Background: Convolutional Neural Networks can be effectively used only when data are endowed with an intrinsic concept of neighbourhood in the input space, as is the case of pixels in images. We introduce here Ph-CNN, a novel deep learning architecture for the classification of metagenomics data based on the Convolutional Neural Networks, with the patristic distance defined on the phylogenetic tree being used as the proximity measure. The patristic distance between variables is used together with a sparsified version of MultiDimensional Scaling to embed the phylogenetic tree in a Euclidean space.

Results: Ph-CNN is tested with a domain adaptation approach on synthetic data and on a metagenomics collection of gut microbiota of 38 healthy subjects and 222 Inflammatory Bowel Disease patients, divided in 6 subclasses. Classification performance is promising when compared to classical algorithms like Support Vector Machines and Random Forest and a baseline fully connected neural network, e.g. the Multi-Layer Perceptron.

Conclusion: Ph-CNN represents a novel deep learning approach for the classification of metagenomics data. Operatively, the algorithm has been implemented as a custom Keras layer taking care of passing to the following convolutional layer not only the data but also the ranked list of neighbourhood of each sample, thus mimicking the case of image data, transparently to the user.
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http://dx.doi.org/10.1186/s12859-018-2033-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5850953PMC
March 2018

Focal adhesion kinase depletion reduces human hepatocellular carcinoma growth by repressing enhancer of zeste homolog 2.

Cell Death Differ 2017 05 24;24(5):889-902. Epub 2017 Mar 24.

Liver Research Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy.

Hepatocellular carcinoma (HCC) is the most common type of liver cancer in humans. The focal adhesion tyrosine kinase (FAK) is often over-expressed in human HCC and FAK inhibition may reduce HCC cell invasiveness. However, the anti-oncogenic effect of FAK knockdown in HCC cells remains to be clarified. We found that FAK depletion in HCC cells reduced in vitro and in vivo tumorigenicity, by inducing G2/M arrest and apoptosis, decreasing anchorage-independent growth, and modulating the expression of several cancer-related genes. Among these genes, we showed that FAK silencing decreased transcription and nuclear localization of enhancer of zeste homolog 2 (EZH2) and its tri-methylation activity on lysine 27 of histone H3 (H3K27me3). Accordingly, FAK, EZH2 and H3K27me3 were concomitantly upregulated in human HCCs compared to non-tumor livers. In vitro experiments demonstrated that FAK affected EZH2 expression and function by modulating, at least in part, p53 and E2F2/3 transcriptional activity. Moreover, FAK silencing downregulated both EZH2 binding and histone H3K27me3 levels at the promoter of its target gene NOTCH2. Finally, we found that pharmacological inhibition of FAK activity resembled these effects although milder. In summary, we demonstrate that FAK depletion reduces HCC cell growth by affecting cancer-promoting genes including the pro-oncogene EZH2. Furthermore, we unveil a novel unprecedented FAK/EZH2 crosstalk in HCC cells, thus identifying a targetable network paving the way for new anticancer therapies.
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http://dx.doi.org/10.1038/cdd.2017.34DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5423113PMC
May 2017

PD-L1 Is a Therapeutic Target of the Bromodomain Inhibitor JQ1 and, Combined with HLA Class I, a Promising Prognostic Biomarker in Neuroblastoma.

Clin Cancer Res 2017 Aug 7;23(15):4462-4472. Epub 2017 Mar 7.

Immuno-Oncology Laboratory, Oncohaematology Department, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy.

This study sought to evaluate the expression of programmed cell death-ligand-1 (PD-L1) and HLA class I on neuroblastoma cells and programmed cell death-1 (PD-1) and lymphocyte activation gene 3 (LAG3) on tumor-infiltrating lymphocytes to better define patient risk stratification and understand whether this tumor may benefit from therapies targeting immune checkpoint molecules. IHC staining for PD-L1, HLA class I, PD-1, and LAG3 was assessed in 77 neuroblastoma specimens, previously characterized for tumor-infiltrating T-cell density and correlated with clinical outcome. Surface expression of PD-L1 was evaluated by flow cytometry and IHC in neuroblastoma cell lines and tumors genetically and/or pharmacologically inhibited for MYC and MYCN. A dataset of 477 human primary neuroblastomas from GEO and ArrayExpress databases was explored for PD-L1, MYC, and MYCN correlation. Multivariate Cox regression analysis demonstrated that the combination of PD-L1 and HLA class I tumor cell density is a prognostic biomarker for predicting overall survival in neuroblastoma patients ( = 0.0448). MYC and MYCN control the expression of PD-L1 in neuroblastoma cells both and Consistently, abundance of PD-L1 transcript correlates with MYC expression in primary neuroblastoma. The combination of PD-L1 and HLA class I represents a novel prognostic biomarker for neuroblastoma. Pharmacologic inhibition of MYCN and MYC may be exploited to target PD-L1 and restore an efficient antitumor immunity in high-risk neuroblastoma. .
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http://dx.doi.org/10.1158/1078-0432.CCR-16-2601DOI Listing
August 2017

Monitoring Perinatal Gut Microbiota in Mouse Models by Mass Spectrometry Approaches: Parental Genetic Background and Breastfeeding Effects.

Front Microbiol 2016 26;7:1523. Epub 2016 Sep 26.

Human Microbiome Unit, Area of Genetic and Rare Diseases, Bambino Gesù Children's Hospital, IRCCSRome, Italy; Unit of Parasitology, Department of Laboratory, Bambino Gesù Children's Hospital, IRCCSRome, Italy.

At birth, contact with external stimuli, such as nutrients derived from food, is necessary to modulate the symbiotic balance between commensal and pathogenic bacteria, protect against bacterial dysbiosis, and initiate the development of the mucosal immune response. Among a variety of different feeding patterns, breastfeeding represents the best modality. In fact, the capacity of breast milk to modulate the composition of infants' gut microbiota leads to beneficial effects on their health. In this study, we used newborn mice as a model to evaluate the effect of parental genetic background (i.e., IgA-producing mice and IgA-deficient mice) and feeding modulation (i.e., maternal feeding and cross-feeding) on the onset and shaping of gut microbiota after birth. To investigate these topics, we used either a culturomic approach that employed Matrix Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry (MS), or bottom-up Liquid Chromatography, with subsequent MSMS shotgun metaproteomic analysis that compared and assembled results of the two techniques. We found that the microbial community was enriched by lactic acid bacteria when pups were breastfed by wild-type (WT) mothers, while IgA-deficient milk led to an increase in the opportunistic bacterial pathogen (OBP) population. Cross-feeding results suggested that IgA supplementation promoted the exclusion of some OBPs and the temporary appearance of beneficial species in pups fed by WT foster mothers. Our results show that both techniques yield a picture of microbiota from different angles and with varying depths. In particular, our metaproteomic pipeline was found to be a reliable tool in the description of microbiota. Data from these studies are available via ProteomeXchange, with identifier PXD004033.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5036385PMC
http://dx.doi.org/10.3389/fmicb.2016.01523DOI Listing
September 2016

Identification of GALNT14 as a novel neuroblastoma predisposition gene.

Oncotarget 2015 Sep;6(28):26335-46

U.O.C. Bioterapie, IRCCS A.O.U. San Martino-IST, Istituto Nazionale per la Ricerca sul Cancro, Genoa, Italy.

Although several genes have been associated to neuroblastoma (NB) predisposition and aggressiveness, further genes are likely involved in the overall risk of developing this pediatric cancer. We thus carried out whole-exome sequencing on germline DNA from two affected second cousins and two unlinked healthy relatives from a large family with hereditary NB. Bioinformatics analysis revealed 6999 variations that were exclusively shared by the two familial NB cases. We then considered for further analysis all unknown or rare missense mutations, which involved 30 genes. Validation and analysis of these variants led to identify a GALNT14 mutation (c.802C > T) that properly segregated in the family and was predicted as functionally damaging by PolyPhen2 and SIFT. Screening of 8 additional NB families and 167 sporadic cases revealed this GALNT14 mutation in the tumors of two twins and in the germline of one sporadic NB patient. Moreover, a significant association between MYCN amplification and GALNT14 expression was observed in both NB patients and cell lines. Also, GALNT14 higher expression is associated with a worse OS in a public dataset of 88 NB samples (http://r2.amc.nl). GALNT14 is a member of the polypeptide N-acetylgalactosaminyl-transferase family and maps closely to ALK on 2p23.1, a region we previously discovered in linkage with NB in the family here considered. The aberrant function of GALNTs can result in altered glycoproteins that have been associated to the promotion of tumor aggressiveness in various cancers. Although rare, the recurrence of this mutation suggests GALNT14 as a novel gene potentially involved in NB predisposition.
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http://dx.doi.org/10.18632/oncotarget.4501DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4694905PMC
September 2015

Comparison of RNA-seq and microarray-based models for clinical endpoint prediction.

Genome Biol 2015 Jun 25;16:133. Epub 2015 Jun 25.

Marshfield Clinic Research Foundation, Center of Human Genetics, 1000 N Oak Avenue, Marshfield, WI, 54449, USA.

Background: Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model.

Results: We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models.

Conclusions: We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.
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http://dx.doi.org/10.1186/s13059-015-0694-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4506430PMC
June 2015

The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance.

Nat Biotechnol 2014 Sep 24;32(9):926-32. Epub 2014 Aug 24.

Fondazione Bruno Kessler, Trento, Italy.

The concordance of RNA-sequencing (RNA-seq) with microarrays for genome-wide analysis of differential gene expression has not been rigorously assessed using a range of chemical treatment conditions. Here we use a comprehensive study design to generate Illumina RNA-seq and Affymetrix microarray data from the same liver samples of rats exposed in triplicate to varying degrees of perturbation by 27 chemicals representing multiple modes of action (MOAs). The cross-platform concordance in terms of differentially expressed genes (DEGs) or enriched pathways is linearly correlated with treatment effect size (R(2)0.8). Furthermore, the concordance is also affected by transcript abundance and biological complexity of the MOA. RNA-seq outperforms microarray (93% versus 75%) in DEG verification as assessed by quantitative PCR, with the gain mainly due to its improved accuracy for low-abundance transcripts. Nonetheless, classifiers to predict MOAs perform similarly when developed using data from either platform. Therefore, the endpoint studied and its biological complexity, transcript abundance and the genomic application are important factors in transcriptomic research and for clinical and regulatory decision making.
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http://dx.doi.org/10.1038/nbt.3001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4243706PMC
September 2014

A promoter-level mammalian expression atlas.

Nature 2014 Mar;507(7493):462-70

Regulated transcription controls the diversity, developmental pathways and spatial organization of the hundreds of cell types that make up a mammal. Using single-molecule cDNA sequencing, we mapped transcription start sites (TSSs) and their usage in human and mouse primary cells, cell lines and tissues to produce a comprehensive overview of mammalian gene expression across the human body. We find that few genes are truly 'housekeeping', whereas many mammalian promoters are composite entities composed of several closely separated TSSs, with independent cell-type-specific expression profiles. TSSs specific to different cell types evolve at different rates, whereas promoters of broadly expressed genes are the most conserved. Promoter-based expression analysis reveals key transcription factors defining cell states and links them to binding-site motifs. The functions of identified novel transcripts can be predicted by coexpression and sample ontology enrichment analyses. The functional annotation of the mammalian genome 5 (FANTOM5) project provides comprehensive expression profiles and functional annotation of mammalian cell-type-specific transcriptomes with wide applications in biomedical research.
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http://dx.doi.org/10.1038/nature13182DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4529748PMC
March 2014

Microbial community structure in vineyard soils across altitudinal gradients and in different seasons.

FEMS Microbiol Ecol 2013 Jun 27;84(3):588-602. Epub 2013 Feb 27.

Department of Sustainable Agro-ecosystems and Bioresources, Research and Innovation Centre, Fondazione Edmund Mach, San Michele all'Adige, Italy.

Microbial communities living in nine vineyards distributed over three altitudinal transects were studied over 2 years. Fungal and bacterial community dynamics were explored using automated ribosomal intergenic spacer analysis (ARISA) and by determining bacterial cells and fungal colony-forming units (CFUs). Moreover, extensive chemical and physical analyses of the soils were carried out. Multivariate analyses demonstrated that bacterial and fungal communities are affected by altitude, which acts as a complex physicochemical gradient. In fact, soil moisture, Al, Mg, Mn and clay content are changing with altitude and influencing the bacterial genetic structure, while in the case of fungi, soil moisture, B and clay content are found to be the main drivers of the community. Moreover, other exchangeable cations and heavy metals, not correlating with altitude, are involved in the ordination of the sites, especially Cu. Qualitative ARISA revealed the presence of a stable core microbiome of operational taxonomic units (OTUs) within each transect, which ranged between 57% and 68% of total OTUs in the case of fungi and between 63% and 72% for bacteria. No seasonal effect on the composition of microbial communities was found, demonstrating that bacterial and fungal communities in vineyards are mostly stable over the considered seasons.
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http://dx.doi.org/10.1111/1574-6941.12087DOI Listing
June 2013

TOFwave: reproducibility in biomarker discovery from time-of-flight mass spectrometry data.

Mol Biosyst 2012 Nov 9;8(11):2845-9. Epub 2012 Aug 9.

Fondazione Bruno Kessler, Trento, Italy.

Many are the sources of variability that can affect reproducibility of disease biomarkers from time-of-flight (TOF) Mass Spectrometry (MS) data. Here we present TOFwave, a complete software pipeline for TOF-MS biomarker identification, that limits the impact of parameter tuning along the whole chain of preprocessing and model selection modules. Peak profiles are obtained by a preprocessing based on Continuous Wavelet Transform (CWT), coupled with a machine learning protocol aimed at avoiding selection bias effects. Only two parameters (minimum peak width and a signal to noise cutoff) have to be explicitly set. The TOFwave pipeline is built on top of the mlpy Python package. Examples on Matrix-Assisted Laser Desorption and Ionization (MALDI) TOF datasets are presented. Software prototype, datasets and details to replicate results in this paper can be found at http://mlpy.sf.net/tofwave/.
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http://dx.doi.org/10.1039/c2mb25223fDOI Listing
November 2012

Dexamethasone therapy in preterm infants developing bronchopulmonary dysplasia: effect on pulmonary surfactant disaturated-phosphatidylcholine kinetics.

Pediatr Res 2008 Apr;63(4):433-7

Departments of Pediatrics, University of Padova, 35128 Padova, Italy.

The role of corticosteroid in severe bronchopulmonary dyplasia (BPD) is still debated. Scanty data are available on the corticosteroids effect on surfactant metabolism. Our objective was to compare surfactant kinetics in preterm infants with developing BPD, before and after dexamethasone (DEXA) treatment. Twenty-eight studies were performed in 14 preterm infants (birth weight 786 +/- 192 g, gestational age 26 +/- 1 wk) on high ventilatory setting, before (age 22 +/- 11 d) and after (age 33 +/- 11 d) DEXA. C-labeled dipalmitoyl-phosphatidylcholine (DPPC) was administered endotrachelly to trace pulmonary surfactant. Surfactant disaturated-phosphatidylcholine (DSPC) kinetics and pools were calculated from DSPC C-enrichment curves of serial tracheal aspirates and bi-compartmental analysis. Total protein and myeloperoxidase (MPO) activity in tracheal aspirates were also measured and expressed per ml of Epithelial Lining Fluid (ELF). After DEXA, DSPC alveolar pool increased significantly from 8.2 +/- 7.6 to 10.6 +/- 11.3 mg/kg (p = 0.039), total proteins and MPO were reduced from 8.8 +/- 8.6 to 3.1 +/- 2.1 mg/ml ELF (p = 0.046) and from 1822 +/- 1224 to 1261 +/- 987 mU/mlELF (p = 0.028) respectively. In conclusion, DEXA treatment in mechanically ventilated preterm infants with severe respiratory failure and at high risk of developing BPD, significantly reduced inflammatory markers and increased alveolar surfactant DSPC pool.
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http://dx.doi.org/10.1203/PDR.0b013e3181659759DOI Listing
April 2008

Machine learning methods for predictive proteomics.

Brief Bioinform 2008 Mar 29;9(2):119-28. Epub 2008 Feb 29.

FBK, via Sommarive 18, I-38100 Povo (Trento), Italy.

The search for predictive biomarkers of disease from high-throughput mass spectrometry (MS) data requires a complex analysis path. Preprocessing and machine-learning modules are pipelined, starting from raw spectra, to set up a predictive classifier based on a shortlist of candidate features. As a machine-learning problem, proteomic profiling on MS data needs caution like the microarray case. The risk of overfitting and of selection bias effects is pervasive: not only potential features easily outnumber samples by 10(3) times, but it is easy to neglect information-leakage effects during preprocessing from spectra to peaks. The aim of this review is to explain how to build a general purpose design analysis protocol (DAP) for predictive proteomic profiling: we show how to limit leakage due to parameter tuning and how to organize classification and ranking on large numbers of replicate versions of the original data to avoid selection bias. The DAP can be used with alternative components, i.e. with different preprocessing methods (peak clustering or wavelet based), classifiers e.g. Support Vector Machine (SVM) or feature ranking methods (recursive feature elimination or I-Relief). A procedure for assessing stability and predictive value of the resulting biomarkers' list is also provided. The approach is exemplified with experiments on synthetic datasets (from the Cromwell MS simulator) and with publicly available datasets from cancer studies.
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http://dx.doi.org/10.1093/bib/bbn008DOI Listing
March 2008

Glucose production by deconvolution in intravenous and oral glucose tolerance tests: role of output variable.

Conf Proc IEEE Eng Med Biol Soc 2006;2006:5045-8

Department of Information Engineering, University of Padova, Padova, Italy.

Endogenous glucose production (EGP) after a glucose stimulus can be estimated by deconvolution of the endogenous component of glucose concentration, which is computed from noisy measurements. This study analyzes how measurement errors propagate to endogenous glucose and affect EGP reconstruction during intravenous (IVGTT) and oral (MEAL) glucose tolerance tests. Monte Carlo simulations show that the effect of errors on endogenous glucose and thus on EGP is more critical during IVGTT than during MEAL. A two regularization-parameter deconvolution technique for IVGTT is proposed, which successfully handles this added difficulty.
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http://dx.doi.org/10.1109/IEMBS.2006.259961DOI Listing
March 2008

Surfactant disaturated-phosphatidylcholine kinetics in acute respiratory distress syndrome by stable isotopes and a two compartment model.

Respir Res 2007 Feb 21;8:13. Epub 2007 Feb 21.

Department of Pediatrics, University of Padova, Padova, Italy.

Background: In patients with acute respiratory distress syndrome (ARDS), it is well known that only part of the lungs is aerated and surfactant function is impaired, but the extent of lung damage and changes in surfactant turnover remain unclear. The objective of the study was to evaluate surfactant disaturated-phosphatidylcholine turnover in patients with ARDS using stable isotopes.

Methods: We studied 12 patients with ARDS and 7 subjects with normal lungs. After the tracheal instillation of a trace dose of 13C-dipalmitoyl-phosphatidylcholine, we measured the 13C enrichment over time of palmitate residues of disaturated-phosphatidylcholine isolated from tracheal aspirates. Data were interpreted using a model with two compartments, alveoli and lung tissue, and kinetic parameters were derived assuming that, in controls, alveolar macrophages may degrade between 5 and 50% of disaturated-phosphatidylcholine, the rest being lost from tissue. In ARDS we assumed that 5-100% of disaturated-phosphatidylcholine is degraded in the alveolar space, due to release of hydrolytic enzymes. Some of the kinetic parameters were uniquely determined, while others were identified as lower and upper bounds.

Results: In ARDS, the alveolar pool of disaturated-phosphatidylcholine was significantly lower than in controls (0.16 +/- 0.04 vs. 1.31 +/- 0.40 mg/kg, p < 0.05). Fluxes between tissue and alveoli and de novo synthesis of disaturated-phosphatidylcholine were also significantly lower, while mean resident time in lung tissue was significantly higher in ARDS than in controls. Recycling was 16.2 +/- 3.5 in ARDS and 31.9 +/- 7.3 in controls (p = 0.08).

Conclusion: In ARDS the alveolar pool of surfactant is reduced and disaturated-phosphatidylcholine turnover is altered.
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http://dx.doi.org/10.1186/1465-9921-8-13DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1819376PMC
February 2007
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