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Background: Stage I epithelial ovarian cancer (EOC) encompasses five histologically different subtypes of tumors confined to the ovaries with a generally favorable prognosis. Despite the intrinsic heterogeneity, all stage I EOCs are treated with complete resection and adjuvant therapy in most of the cases. Owing to the lack of robust prognostic markers, this often leads to overtreatment. Therefore, a better molecular characterization of stage I EOCs could improve the assessment of the risk of relapse and the refinement of optimal treatment options. Materials and methods: 205 stage I EOCs tumor biopsies with a median follow-up of eight years were gathered from two independent Italian tumor tissue collections, and the genome distribution of somatic copy number alterations (SCNAs) was investigated by shallow whole genome sequencing (sWGS) approach. Results: Despite the variability in SCNAs distribution both across and within the histotypes, we were able to define three common genomic instability patterns, namely stable, unstable, and highly unstable. These patterns were based on the percentage of the genome affected by SCNAs and on their length. The genomic instability pattern was strongly predictive of patients’ prognosis also with multivariate models including currently used clinico-pathological variables. Conclusions: The results obtained in this study support the idea that novel molecular markers, in this case genomic instability patterns, can anticipate the behavior of stage I EOC regardless of tumor subtype and provide valuable prognostic information. Thus, it might be propitious to extend the study of these genomic instability patterns to improve rational management of this disease.

isomiRs, the sequence-variants of microRNA, are known to be tissue and cell type specific but their physiological role is largely unknown. In this study we explored for the first time the expression of isomiRs across different stage I epithelial ovarian cancer (EOC) histological subtypes, in order to shed new light on their biological role in tumor growth and progression. In a multi-centric retrospective cohort of tumor biopsies (n=215) we sequenced small RNAs finding 971 expressed miRNAs, 64% of which are isomiRs. Among them, 42 isomiRs showed a clear histotype specific pattern, confirming our previously identified miRNA markers (miR192/194 and miR30a-3p/5p for mucinous and clear cell subtypes respectively) and uncovering new biomarkers for all the five subtypes. Using integrative models, we found that the 38% of these miRNA expression alterations is the result of copy number variations while the 17% of differential transcriptional activities. Our work represents the first attempt to characterize isomiRs expression in stage I EOC within and across subtypes and to contextualize their alterations in the framework of the large genomic heterogeneity of this tumor.

Stage III/IV epithelial ovarian cancer (EOC) is a systemic disease. The clonal relationship among different tumor lesions at diagnosis (spatial heterogeneity) and how tumor clonal architecture evolves over time (temporal heterogeneity) have not yet been defined. Such knowledge would help to develop new target-based strategies, as biomarkers which can adjudge the success of therapeutic intervention should be independent of spatial and temporal heterogeneity. The work described in this paper addresses spatial and temporal heterogeneity in a cohort of 71 tumor biopsies using targeted NGS technology. These samples were taken from twelve high grade serous (HGS) and seven non HSG-EOC, both at the time of primary surgery when the tumor was naïve to chemotherapy and after chemotherapy. Matched tumor lesions growing in the ovary or at other anatomical sites show very different mutational landscapes with branched tumor evolution. Mutations in ATM, ATR,TGFB3,VCAM1 and COL3A1 genes were shared across all lesions. BRCA1 and BRCA2 genes were frequently mutated in synchronous lesions of non HGS-EOC. Relapsed disease seems to originate from resistant clones originally present at the time of primary surgery rather than from resistance acquired de novo during platinum based therapy. Overall the work suggests that EOC continues to evolve. More detailed mapping of genetic lesions is necessary to improve therapeutic strategies.

High-grade serous ovarian cancer (HGS-EOCs) is generally sensitive to front-line platinum (Pt)-based chemotherapy although most patients at an advanced stage relapse with progressive resistant disease. Clinical or molecular data to identify primary resistant cases at diagnosis are not yet available. HGS-EOC biopsies from 105 Pt-sensitive (Pt-s) and 89 Pt-resistant (Pt-r) patients were retrospectively selected from two independent tumor tissue collections. Pathway analysis was done integrating miRNA and mRNA expression profiles. Signatures were further validated in silico on a cohort of 838 HGS-EOC cases from a published dataset. In all, 131 mRNAs and 5 miRNAs belonging to different functionally related molecular pathways distinguish Pt-s from Pt-r cases. Then, 17 out of 23 selected elements were validated by orthogonal approaches (SI signature). As resistance to Pt is associated with a short progression-free survival (PFS) and overall survival (OS), the prognostic role of the SI signature was assessed, and 14 genes associated with PFS and OS, in multivariate analyses (SII signature). The prognostic value of the SII signature was validated in a third extensive cohort. The expression profiles of SDF2L1, PPP1R12A and PRKG1 genes (SIII signature) served as independent prognostic biomarkers of Pt-response and survival. The study identified a prognostic molecular signature based on the combined expression profile of three genes which had never been associated with the clinical outcome of HGS-EOC. This may lead to early identification, at the time of diagnosis, of patients who would not greatly benefit from standard chemotherapy and are thus eligible for novel investigational approaches.

Stage I epithelial ovarian cancer (EOC) represents about 10% of all EOCs. It is characterized by a complex histopathological and molecular heterogeneity, and it is composed of five main histological subtypes (mucinous, endometrioid, clear cell and high, and low grade serous), which have peculiar genetic, molecular, and clinical characteristics. As it occurs less frequently than advanced-stage EOC, its molecular features have not been thoroughly investigated. In this study, using in silico approaches and gene expression data, on a multicentric cohort composed of 208 snap-frozen tumor biopsies, we explored the subtype-specific molecular alterations that regulate tumor aggressiveness in stage I EOC. We found that single genes rather than pathways are responsible for histotype specificities and that a cAMP-PKA-CREB1 signaling axis seems to play a central role in histotype differentiation. Moreover, our results indicate that immune response seems to be, at least in part, involved in histotype differences, as a higher immune-reactive behavior of serous and mucinous samples was observed with respect to other histotypes.

Purpose: Stage I epithelial ovarian cancer (EOC) represents about 10% of all EOCs and is characterized by good prognosis with fewer than 20% of patients relapsing. As it occurs less frequently than advanced-stage EOC, its molecular features have not been thoroughly investigated. We have demonstrated that in stage I EOC miR-200c-3p can predict patients' outcome. In the present study, we analyzed the expression of long non-coding RNAs (lncRNA) to enable potential definition of a non-coding transcriptional signature with prognostic relevance for stage I EOC. Experimental Design: 202 snap-frozen stage I EOC tumor biopsies, 47 of which relapsed, were gathered together from three independent tumor tissue collections and subdivided into a training set (n = 73) and a validation set (n = 129). Median follow up was 9 years. LncRNAs' expression profiles were correlated in univariate and multivariate analysis with overall survival (OS) and progression-free survival (PFS). Results: The expression of lnc-SERTAD2-3, lnc-SOX4-1, lnc- HRCT1-1, and PVT1 was associated in univariate and multivariate analyses with relapse and poor outcome in both training and validation sets (P < 0.001). Using the expression profiles of PVT1, lnc-SERTAD2-3, and miR-200c-3p simultaneously, it was possible to stratify patients into high and low risk. The OS for high- and low-risk individuals are 36 and 123 months, respectively (OR, 15.55; 95% confidence interval, 3.81-63.36). Conclusions: We have identified a non-coding transcriptional signature predictor of survival and biomarker of relapse for stage I EOC. Clin Cancer Res; 23(9); 2356-66.

© 2017 Sales et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Antarctic krill (Euphausia superba) is a key species in the Southern Ocean with an estimated biomass between 100 and 500 million tonnes. Changes in krill population viability would have catastrophic effect on the Antarctic ecosystem. One looming threat due to elevated levels of anthropogenic atmospheric carbon dioxide (CO2) is ocean acidification (lowering of sea water pH by CO2 dissolving into the oceans). The genetics of Antarctic krill has long been of scientific interest for both for the analysis of population structure and analysis of functional genetics. However, the genetic resources available for the species are relatively modest. We have developed the most advanced genetic database on Euphausia superba, KrillDB, which includes comprehensive data sets of former and present transcriptome projects. In particular, we have built a de novo transcriptome assembly using more than 360 million Illumina sequence reads generated from larval krill including individuals subjected to different CO2levels. The database gives access to: 1) the full list of assembled genes and transcripts; 2) their level of similarity to transcripts and proteins from other species; 3) the predicted protein domains contained within each transcript; 4) their predicted GO terms; 5) the level of expression of each transcript in the different larval stages and CO2treatments. All references to external entities (sequences, domains, GO terms) are equipped with a link to the appropriate source database. Moreover, the software implements a full-text search engine that makes it possible to submit free-form queries. KrillDB represents the first largescale attempt at classifying and annotating the full krill transcriptome. For this reason, we believe it

Stage III/IV epithelial ovarian cancer (EOC) is a systemic disease. The clonal relationship among different tumor lesions at diagnosis (spatial heterogeneity) and how tumor clonal architecture evolves over time (temporal heterogeneity) have not yet been defined. Such knowledge would help to develop new target-based strategies, as biomarkers which can adjudge the success of therapeutic intervention should be independent of spatial and temporal heterogeneity. The work described in this paper addresses spatial and temporal heterogeneity in a cohort of 71 tumor biopsies using targeted NGS technology. These samples were taken from twelve high grade serous (HGS) and seven non HSG-EOC, both at the time of primary surgery when the tumor was naïve to chemotherapy and after chemotherapy. Matched tumor lesions growing in the ovary or at other anatomical sites show very different mutational landscapes with branched tumor evolution. Mutations in ATM, ATR, TGFB3, VCAM1 and COL3A1 genes were shared across all lesions. BRCA1 and BRCA2 genes were frequently mutated in synchronous lesions of non HGS-EOC. Relapsed disease seems to originate from resistant clones originally present at the time of primary surgery rather than from resistance acquired de novo during platinum based therapy. Overall the work suggests that EOC continues to evolve. More detailed mapping of genetic lesions is necessary to improve therapeutic strategies.

Background: Clinical and pathological parameters of patients with epithelial ovarian cancer (EOC) do not thoroughly predict patients' outcome. Despite the good outcome of stage I EOC compared with that of stages III and IV, the risk assessment and treatments are almost the same. However, only 20% of stage I EOC cases relapse and die, meaning that only a proportion of patients need intensive treatment and closer follow-up. Thus, the identification of cell mechanisms that could improve outcome prediction and rationalize therapeutic options is an urgent need in the clinical practice. Patients and methods: We have gathered together 203 patients with stage I EOC diagnosis, from whom snap-frozen tumor biopsies were available at the time of primary surgery before any treatment. Patients, with a median follow-up of 7 years, were stratified into a training set and a validation set. Results and conclusions: Integrated analysis of miRNA and gene expression profiles allowed to identify a prognostic cell pathway, composed of 16 miRNAs and 10 genes, wiring the cell cycle, 'Activins/Inhibins' and 'Hedgehog' signaling pathways. Once validated by an independent technique, all the elements of the circuit resulted associated with overall survival (OS) and progression-free survival (PFS), in both univariate and multivariate models. For each patient, the circuit expressions have been translated into an activation state index (integrated signature classifier, ISC), used to stratify patients into classes of risk. This prediction reaches the 89.7% of sensitivity and 96.6% of specificity for the detection of PFS events. The prognostic value was then confirmed in the external independent validation set in which the PFS events are predicted with 75% sensitivity and 94.7% specificity. Moreover, the ISC shows higher classification performance than conventional clinical classifiers. Thus, the identified circuit enhances the understanding of the molecular mechanisms lagging behind stage I EOC and

Background: The majority of patients with stage III-IV epithelial ovarian cancer (EOC) relapse after initially responding to platinum-based chemotherapy, and develop resistance. The genomic features involved in drug resistance are unknown. To unravel some of these features, we investigated the mutational profile of genes involved in pathways related to drug sensitivity in a cohort of matched tumors obtained at first surgery (Ft-S) and second surgery (Sd-S). Patients and methods: Matched biopsies (33) taken at Ft-S and Sd-S were selected from the 'Pandora' tumor tissue collection. DNA libraries for 65 genes were generated using the TruSeq Custom Amplicon kit and sequenced on MiSeq (Illumina). Data were analyzed using a high-performance cluster computing platform (Cloud4CARE project) and independently validated. Results: A total of 2270 somatic mutations were identified (89.85% base substitutions 8.19% indels, and 1.92% unknown). Homologous recombination (HR) genes and TP53 were mutated in the majority of Ft-S, while ATM, ATR, TOP2A and TOP2B were mutated in the entire dataset. Only 2% of mutations were conserved between matched Ft-S and Sd-S. Mutations detected at second surgery clustered patients in two groups characterized by different mutational profiles in genes associated with HR, PI3K, miRNA biogenesis and signal transduction. Conclusions: There was a low level of concordance between Ft-S and Sd-S in terms of mutations in genes involved in key processes of tumor growth and drug resistance. This result suggests the importance of future longitudinal analyses to improve the clinical management of relapsed EOC.

Epithelial ovarian cancer (EOC) is one of the most lethal gynecologic diseases, with survival rate virtually unchanged for the past 30 years. EOC comprises different histotypes with molecular and clinical heterogeneity, but up till now the present gold standard platinum-based treatment has been conducted without any patient stratification. The aim of the present study is to generate microRNA (miRNA) profiles characteristic of each stage I EOC histotype, to identify subtype-specific biomarkers to improve our understanding underlying the tumor mechanisms.

Background: International Federation of Gynecology and Obstetrics stage I epithelial ovarian cancer (EOC) has a significantly better prognosis than stage III/IV EOC, with about 80% of patients surviving at 5 years (compared with about 20% of those with stage III/IV EOC). However, 20% of patients with stage I EOC relapse within 5 years. It is therefore crucial that the biological properties of stage I EOCs are further elucidated. MicroRNAs (miRNAs) have shown diagnostic and prognostic potential in stage III and IV EOCs, but the small number of patients diagnosed with stage I EOC has so far prevented an investigation of its molecular features. We profiled miRNA expression in stage I EOC tumours to assess whether there is a miRNA signature associated with overall and progression-free survival (PFS) in stage I EOC. Methods: We analysed tumour samples from 144 patients (29 of whom relapsed) with stage I EOC gathered from two independent tumour tissue collections (A and B), both with a median follow-up of 9 years. 89 samples from tumour tissue collection A were stratified into a training set (51 samples, 15 of which were from patients who relapsed) for miRNA signature generation, and into a validation set (38 samples, seven of which were from patients who relapsed) for signature validation. Tumour tissue collection B (55 samples, seven of which were from patients who relapsed) was used as an independent test set. The Cox proportional hazards model and the log-rank test were used to assess the correlation of quantitative reverse transcription PCR (qRT-PCR)-validated miRNAs with overall survival and PFS. Findings: A signature of 34 miRNAs associated with survival was generated by microarray analysis in the training set. In both the training set and validation set, qRT-PCR analysis confirmed that 11 miRNAs (miR-214, miR-199a-3p, miR-199a-5p, miR-145, miR-200b, miR-30a, miR-30a*, miR-30d, miR-200c, miR-20a, and miR-143) were expressed differently in relapsers compared with no

Contains fulltext : 88001.pdf (publisher's version ) (Closed access), BACKGROUND: The advent of Systems Biology has been accompanied by the blooming of pathway databases. Currently pathways are defined generically with respect to the organ or cell type where a reaction takes place. The cell type specificity of the reactions is the foundation of immunological research, and capturing this specificity is of paramount importance when using pathway-based analyses to decipher complex immunological datasets. Here, we present DC-ATLAS, a novel and versatile resource for the interpretation of high-throughput data generated perturbing the signaling network of dendritic cells (DCs). RESULTS: Pathways are annotated using a novel data model, the Biological Connection Markup Language (BCML), a SBGN-compliant data format developed to store the large amount of information collected. The application of DC-ATLAS to pathway-based analysis of the transcriptional program of DCs stimulated with agonists of the toll-like receptor family allows an integrated description of the flow of information from the cellular sensors to the functional outcome, capturing the temporal series of activation events by grouping sets of reactions that occur at different time points in well-defined functional modules. CONCLUSIONS: The initiative significantly improves our understanding of DC biology and regulatory networks. Developing a systems biology approach for immune system holds the promise of translating knowledge on the immune system into more successful immunotherapy strategies.

Background: The advent of Systems Biology has been accompanied by the blooming of pathway databases. Currently pathways are defined generically with respect to the organ or cell type where a reaction takes place. The cell type specificity of the reactions is the foundation of immunological research, and capturing this specificity is of paramount importance when using pathway-based analyses to decipher complex immunological datasets. Here, we present DC-ATLAS, a novel and versatile resource for the interpretation of high-throughput data generated perturbing the signaling network of dendritic cells (DCs). Results: Pathways are annotated using a novel data model, the Biological Connection Markup Language (BCML), a SBGN-compliant data format developed to store the large amount of information collected. The application of DC-ATLAS to pathway-based analysis of the transcriptional program of DCs stimulated with agonists of the toll-like receptor family allows an integrated description of the flow of information from the cellular sensors to the functional outcome, capturing the temporal series of activation events by grouping sets of reactions that occur at different time points in well-defined functional modules. Conclusions: The initiative significantly improves our understanding of DC biology and regulatory networks. Developing a systems biology approach for immune system holds the promise of translating knowledge on the immune system into more successful immunotherapy strategies. © 2010 Cavalieri et al; licensee BioMed Central Ltd.

Background: The application of high-throughput genomic tools in nutrition research is a widespread practice. However, it is becoming increasingly clear that the outcome of individual expression studies is insufficient for the comprehensive understanding of such a complex field. Currently, the availability of the large amounts of expression data in public repositories has opened up new challenges on microarray data analyses. We have focused on PPAR alpha, a ligand-activated transcription factor functioning as fatty acid sensor controlling the gene expression regulation of a large set of genes in various metabolic organs such as liver, small intestine or heart. The function of PPAR alpha is strictly connected to the function of its target genes and, although many of these have already been identified, major elements of its physiological function remain to be uncovered. To further investigate the function of PPAR alpha, we have applied a cross-species meta-analysis approach to integrate sixteen microarray datasets studying high fat diet and PPAR alpha signal perturbations in different organisms. Results: We identified 164 genes (MDEGs) that were differentially expressed in a constant way in response to a high fat diet or to perturbations in PPARs signalling. In particular, we found five genes in yeast which were highly conserved and homologous of PPAR alpha targets in mammals, potential candidates to be used as models for the equivalent mammalian genes. Moreover, a screening of the MDEGs for all known transcription factor binding sites and the comparison with a human genome-wide screening of Peroxisome Proliferating Response Elements (PPRE), enabled us to identify, 20 new potential candidate genes that show, both binding site, both change in expression in the condition studied. Lastly, we found a non random localization of the differentially expressed genes in the genome. Conclusion: The results presented are potentially of great interest to resume the currently availa