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One of the main objectives of high-throughput genomics studies is to obtain a low-dimensional set of observables-a signature-for sample classification purposes (diagnosis, prognosis, stratification). Biological data, such as gene or protein expression, are commonly characterized by an up/down regulation behavior, for which discriminant-based methods could perform with high accuracy and easy interpretability. To obtain the most out of these methods features selection is even more critical, but it is known to be a NP-hard problem, and thus most feature selection approaches focuses on one feature at the time (k-best, Sequential Feature Selection, recursive feature elimination). We propose DNetPRO, Discriminant Analysis with Network PROcessing, a supervised network-based signature identification method. This method implements a network-based heuristic to generate one or more signatures out of the best performing feature pairs. The algorithm is easily scalable, allowing efficient computing for high number of observables ([Formula: see text]-[Formula: see text]). We show applications on real high-throughput genomic datasets in which our method outperforms existing results, or is compatible with them but with a smaller number of selected features. Moreover, the geometrical simplicity of the resulting class-separation surfaces allows a clearer interpretation of the obtained signatures in comparison to nonlinear classification models., (© 2022. The Author(s).)

Purpose: Recurrently mutated genes and chromosomal abnormalities have been identified in myelodysplastic syndromes (MDS). We aim to integrate these genomic features into disease classification and prognostication., Methods: We retrospectively enrolled 2,043 patients. Using Bayesian networks and Dirichlet processes, we combined mutations in 47 genes with cytogenetic abnormalities to identify genetic associations and subgroups. Random-effects Cox proportional hazards multistate modeling was used for developing prognostic models. An independent validation on 318 cases was performed., Results: We identify eight MDS groups (clusters) according to specific genomic features. In five groups, dominant genomic features include splicing gene mutations ( SF3B1 , SRSF2 , and U2AF1 ) that occur early in disease history, determine specific phenotypes, and drive disease evolution. These groups display different prognosis (groups with SF3B1 mutations being associated with better survival). Specific co-mutation patterns account for clinical heterogeneity within SF3B1 - and SRSF2 -related MDS. MDS with complex karyotype and/or TP53 gene abnormalities and MDS with acute leukemia-like mutations show poorest prognosis. MDS with 5q deletion are clustered into two distinct groups according to the number of mutated genes and/or presence of TP53 mutations. By integrating 63 clinical and genomic variables, we define a novel prognostic model that generates personally tailored predictions of survival. The predicted and observed outcomes correlate well in internal cross-validation and in an independent external cohort. This model substantially improves predictive accuracy of currently available prognostic tools. We have created a Web portal that allows outcome predictions to be generated for user-defined constellations of genomic and clinical features., Conclusion: Genomic landscape in MDS reveals distinct subgroups associated with specific clinical features and discrete patterns of evolution, providing a proof of concept for next-generation disease classification and prognosis., Competing Interests: Manja MeggendorferEmployment: MLL Munich Leukemia Laboratory Marianna RossiConsulting or Advisory Role: Pfizer, Celgene, IQvia, Janssen Emanuele AngelucciHonoraria: Celgene, Vertex Pharmaceuticals Incorporated (MA) and CRISPR Therapeutics AG (CH)Consulting or Advisory Role: Novartis, Bluebird BioTravel, Accommodations, Expenses: Janssen-Cilag Massimo BernardiHonoraria: CelgeneConsulting or Advisory Role: PfizerTravel, Accommodations, Expenses: Medac, Amgen, Sanofi, Jazz Pharmaceuticals, BioTest, Abbvie, Takeda Lorenza BorinLeadership: CelgeneSpeakers' Bureau: GenzymeTravel, Accommodations, Expenses: Genzyme Benedetto BrunoHonoraria: Jazz Pharmaceuticals, Novartis, AmgenResearch Funding: Amgen Valeria SantiniHonoraria: Celgene/Bristol-Myers Squibb, Novartis, Janssen-CilagConsulting or Advisory Role: Celgene/Bristol-Myers Squibb, Novartis, Menarini, Takeda, PfizerResearch Funding: CelgeneTravel, Accommodations, Expenses: Janssen-Cilag, Celgene Andrea BacigalupoHonoraria: Pfizer, Therakos, Novartis, Sanofi, Jazz Pharmaceuticals, Riemser, Merck Sharp & Dohme, Janssen-Cilag, Gilead Sciences, Kiadis Pharma, Astellas PharmaConsulting or Advisory Role: Novartis, Kiadis Pharma, Gilead Sciences, Astellas PharmaSpeakers' Bureau: Pfizer, Therakos, Novartis, Sanofi, Riemser, Merck Sharp & Dohme, Adienne, Jazz PharmaceuticalsTravel, Accommodations, Expenses: Sanofi, Therakos, Jazz Pharmaceuticals Maria Teresa VosoHonoraria: Celgene/Jazz, AbbvieConsulting or Advisory Role: Celgene/JazzSpeakers' Bureau: CelgeneResearch Funding: Celgene Esther OlivaHonoraria: Celgene, Novartis, Amgen, Alexion PharmaceuticalsConsulting or Advisory Role: Amgen, Celgene, NovartisSpeakers' Bureau: Celgene, NovartisPatents, Royalties, Other Intellectual Property: Royalties for QOL-E instrument Francesco PassamontiSpeakers' Bureau: Novartis, AOP Orphan Pharmaceuticals Niccolò BolliConsulting or Advisory Role: JanssenSpeakers' Bureau: Celgene, Amgen Alessandro RambaldiHonoraria: Amgen, OmerosConsulting or Advisory Role: Amgen, Omeros, Novartis, Astellas Pharma, Jazz PharmaceuticalsTravel, Accommodations, Expenses: Celgene Wolfgang KernEmployment: MLL Munich Leukemia LaboratoryLeadership: MLL Munich Leukemia LaboratoryStock and Other Ownership Interests: MLL Munich Leukemia Laboratory Shahram KordastiHonoraria: Beckman Coulter, GWT-TUD, Alexion PharmaceuticalsConsulting or Advisory Role: Syneos HealthResearch Funding: Celgene, Novartis Guillermo SanzHonoraria: CelgeneConsulting or Advisory Role: Abbvie, Celgene, Helsinn Healthcare, Janssen, Roche, Amgen, Boehringer Ingelheim, Novartis, TakedaSpeakers' Bureau: TakedaResearch Funding: CelgeneTravel, Accommodations, Expenses: Celgene, Takeda, Gilead Sciences, Roche Pharma AG Armando SantoroConsulting or Advisory Role: Bristol-Myers Squibb, Servier, Gilead Sciences, Pfizer, Eisai, Bayer AG, MSD, Sanofi, ArQuleSpeakers' Bureau: Takeda, Roche, Abbvie, Amgen, Celgene, AstraZeneca, ArQule, Lilly, Sandoz, Novartis, Bristol-Myers Squibb, Servier, Gilead Sciences, Pfizer, Eisai, Bayer AG, MSD Uwe PlatzbeckerHonoraria: Celgene/JazzConsulting or Advisory Role: Celgene/JazzResearch Funding: Amgen, Janssen, Novartis, BerGenBio, CelgenePatents, Royalties, Other Intellectual Property: part of a patent for a TFR-2 antibody (Rauner et al. Nature Metabolics 2019)Travel, Accommodations, Expenses: Celgene Pierre FenauxHonoraria: CelgeneResearch Funding: Celgene Torsten HaferlachEmployment: MLL Munich Leukemia LaboratoryLeadership: MLL Munich Leukemia LaboratoryConsulting or Advisory Role: IlluminaNo other potential conflicts of interest were reported.

Background: Aneuploidy occurs in more than 20% of acute myeloid leukemia (AML) cases and correlates with an adverse prognosis., Methods: To understand the molecular bases of aneuploid acute myeloid leukemia (A-AML), this study examined the genomic profile in 42 A-AML cases and 35 euploid acute myeloid leukemia (E-AML) cases., Results: A-AML was characterized by increased genomic complexity based on exonic variants (an average of 26 somatic mutations per sample vs 15 for E-AML). The integration of exome, copy number, and gene expression data revealed alterations in genes involved in DNA repair (eg, SLX4IP, RINT1, HINT1, and ATR) and the cell cycle (eg, MCM2, MCM4, MCM5, MCM7, MCM8, MCM10, UBE2C, USP37, CK2, CK3, CK4, BUB1B, NUSAP1, and E2F) in A-AML, which was associated with a 3-gene signature defined by PLK1 and CDC20 upregulation and RAD50 downregulation and with structural or functional silencing of the p53 transcriptional program. Moreover, A-AML was enriched for alterations in the protein ubiquitination and degradation pathway (eg, increased levels of UHRF1 and UBE2C and decreased UBA3 expression), response to reactive oxygen species, energy metabolism, and biosynthetic processes, which may help in facing the unbalanced protein load. E-AML was associated with BCOR/BCORL1 mutations and HOX gene overexpression., Conclusions: These findings indicate that aneuploidy-related and leukemia-specific alterations cooperate to tolerate an abnormal chromosome number in AML, and they point to the mitotic and protein degradation machineries as potential therapeutic targets., (© 2018 The Authors. Cancer published by Wiley Periodicals, Inc. on behalf of American Cancer Society.)

We characterize different tumour types in search for multi-tumour drug targets, in particular aiming for drug repurposing and novel drug combinations. Starting from 11 tumour types from The Cancer Genome Atlas, we obtain three clusters based on transcriptomic correlation profiles. A network-based analysis, integrating gene expression profiles and protein interactions of cancer-related genes, allows us to define three cluster-specific signatures, with genes belonging to NF-κB signaling, chromosomal instability, ubiquitin-proteasome system, DNA metabolism, and apoptosis biological processes. These signatures have been characterized by different approaches based on mutational, pharmacological and clinical evidences, demonstrating the validity of our selection. Moreover, we define new pharmacological strategies validated by in vitro experiments that show inhibition of cell growth in two tumour cell lines, with significant synergistic effect. Our study thus provides a list of genes and pathways that could possibly be used, singularly or in combination, for the design of novel treatment strategies.

Inflammaging is a recent theory of aging originally proposed in 2000 where data and conceptualizations regarding the aging of the immune system (immunosenescence) and the evolution of immune responses from invertebrates to mammals converged. This theory has received an increasing number of citations and experimental confirmations. Here we present an updated version of inflammaging focused on omics data - particularly on glycomics - collected on centenarians, semi-supercentenarians and their offspring. Accordingly, we arrived to the following conclusions: i) inflammaging has a structure where specific combinations of pro- and anti-inflammatory mediators are involved; ii) inflammaging is systemic and more complex than we previously thought, as many organs, tissues and cell types participate in producing pro- and anti-inflammatory stimuli defined "molecular garbage"; iii) inflammaging is dynamic, can be propagated locally to neighboring cells and systemically from organ to organ by circulating products and microvesicles, and amplified by chronic age-related diseases constituting a "local fire", which in turn produces additional inflammatory stimuli and molecular garbage; iv) an integrated Systems Medicine approach is urgently needed to let emerge a robust and highly informative set/combination of omics markers able to better grasp the complex molecular core of inflammaging in elderly and centenarians., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)

Background: Detecting somatic mutations in whole exome sequencing data of cancer samples has become a popular approach for profiling cancer development, progression and chemotherapy resistance. Several studies have proposed software packages, filters and parametrizations. However, many research groups reported low concordance among different methods. We aimed to develop a pipeline which detects a wide range of single nucleotide mutations with high validation rates. We combined two standard tools - Genome Analysis Toolkit (GATK) and MuTect - to create the GATK-LOD N method. As proof of principle, we applied our pipeline to exome sequencing data of hematological (Acute Myeloid and Acute Lymphoblastic Leukemias) and solid (Gastrointestinal Stromal Tumor and Lung Adenocarcinoma) tumors. We performed experiments on simulated data to test the sensitivity and specificity of our pipeline., Results: The software MuTect presented the highest validation rate (90 %) for mutation detection, but limited number of somatic mutations detected. The GATK detected a high number of mutations but with low specificity. The GATK-LOD N increased the performance of the GATK variant detection (from 5 of 14 to 3 of 4 confirmed variants), while preserving mutations not detected by MuTect. However, GATK-LOD N filtered more variants in the hematological samples than in the solid tumors. Experiments in simulated data demonstrated that GATK-LOD N increased both specificity and sensitivity of GATK results., Conclusion: We presented a pipeline that detects a wide range of somatic single nucleotide variants, with good validation rates, from exome sequencing data of cancer samples. We also showed the advantage of combining standard algorithms to create the GATK-LOD N method, that increased specificity and sensitivity of GATK results. This pipeline can be helpful in discovery studies aimed to profile the somatic mutational landscape of cancer genomes.

Background: Methods for the integrative analysis of multi-omics data are required to draw a more complete and accurate picture of the dynamics of molecular systems. The complexity of biological systems, the technological limits, the large number of biological variables and the relatively low number of biological samples make the analysis of multi-omics datasets a non-trivial problem., Results and Conclusions: We review the most advanced strategies for integrating multi-omics datasets, focusing on mathematical and methodological aspects.

Background and objectives Epigenetic estimators based on DNA methylation levels have emerged as promising biomarkers of human aging. These estimators exhibit natural variations across human groups, but data about indigenous populations remain underrepresented in research. This study aims to investigate differences in epigenetic estimators between two distinct human populations, both residing in the Gran Chaco region of Argentina, the Native-American Wichí, and admixed Criollos who are descendants of intermarriages between Native Americans and the first European colonizers, using a population genetic approach. Methodology We analyzed 24 Wichí (mean age: 39.2 ± 12.9 yo) and 24 Criollos (mean age: 41.1 ± 14.0 yo) for DNA methylation levels using the Infinium MethylationEPIC (Illumina) to calculate 16 epigenetic estimators. Additionally, we examined genome-wide genetic variation using the HumanOmniExpress BeadChip (Illumina) to gain insights into the genetic history of these populations. Results Our results indicate that Native-American Wichí are epigenetically older compared to Criollos according to five epigenetic estimators. Analyses within the Criollos population reveal that global ancestry does not influence the differences observed, while local (chromosomal) ancestry shows positive associations between specific SNPs located in genomic regions over-represented by Native-American ancestry and measures of epigenetic age acceleration (AgeAccelHannum). Furthermore, we demonstrate that differences in population ecologies also contribute to observed epigenetic differences. Conclusions and implications Overall, our study suggests that while the genomic history may partially account for the observed epigenetic differences, non-genetic factors, such as lifestyle and ecological factors, play a substantial role in the variability of epigenetic estimators, thereby contributing to variations in human epigenetic aging. [ABSTRACT FROM AUTHOR]

Contemporary biomedicine is producing large amount of data, especially within the fields of "omic" sciences. Nevertheless, other fields, such as neuroscience, are producing similar amount of data by using non-invasive techniques such as imaging, functional magnetic resonance and electroencephalography. Nowadays a big challenge and a new research horizon for Systems Biology is to develop methods to integrate and model this data in an unifying framework capable to disentangle this amazing complexity. In this paper we show how methods from genomic data analysis can be applied to brain data. In particular the concept of pathways, networks and multiplex are discussed. These methods can lead to a clear distinction of various regimes of brain activity. Moreover, this method could be the basis for a Systems Biology analysis of brain data and for the integration of these data in a multivariate and multidimensional framework. The feasibility of this integration is strongly dependent from the feature extraction method used. In our case we used an "alphabet" derived from a multi-resolution analysis that is capable to capture the most relevant information from these complex signals. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the impact of TP53 (tumor protein 53, p53) on genomic stability of osteosarcoma (OS). Materials and methods: In first instance, we expressed in OS cell line SAOS-2 (lacking p53) a wild type (wt) p53 construct, whose protein undergoes nuclear import and activation in response to ionizing radiations (IR). Thereafter, we investigated genomic imbalances (amplifications and deletions at genes or DNA regions most frequently altered in human cancers) associated with radio-resistance relative to p53 expression by mean of an array-based comparative genomic hybridization (aCGH) strategy. Finally we investigated a putative marker of radio-induced oxidative stress, a 4,977 bp deletion at mitochondrial (mt) DNA usually referred to as 'common' deletion, by mean of a polimerase chain reaction (PCR) strategy. Results: In radio-resistant subclones generated from wt p53-transfected SAOS-2 cells DNA deletions were remarkably reduced and the accumulation of 'common' deletion at mtDNA (that may let the persistence of oxidative damage by precluding detoxification from reactive oxygen species [ROS]) completely abrogated. Conclusions: The results of our study confirm that wt p53 has a role in protection of OS cell DNA integrity. Multiple mechanisms involved in p53 safeguard of genomic integrity and prevention of deletion outcome are discussed. [ABSTRACT FROM AUTHOR]