Outcome in Advanced Ovarian Cancer following an Appropriate and Comprehensive Effort at Upfront Cytoreduction: A Twenty-Year Experience in a Single Cancer Institute

Objectives. The purpose of this retrospective evaluation of advanced-stage ovarian cancer patients was to compare outcome with published findings from other centers and to discuss future options for the management of advanced ovarian carcinoma patients. Methods. A retrospective series of 340 patients with a mean age of 58 years (range: 17–88) treated for FIGO stage III and IV ovarian cancer between January 1985 and January 2005 was reviewed. All patients had primary cytoreductive surgery, without extensive bowel, peritoneal, or systematic lymph node resection, thereby allowing initiation of chemotherapy without delay. Chemotherapy consisted of cisplatin-based chemotherapy in combination with alkylating agents before 2000, whereas carboplatin and paclitaxel regimes were generally used after 1999-2000. Overall survival and disease-free survival were analyzed by the Kaplan-Meier method and the log-rank test. Results. With a mean followup of 101 months (range: 5 to 203), 280 events (recurrence or death) were observed and 245 patients (72%) had died. The mortality and morbidity related to surgery were low. The main prognostic factor for overall survival was postoperative residual disease (P < .0002), while the main prognostic factor for disease-free survival was histological tumor type (P < .0007). Multivariate analysis identified three significant risk factors: optimal surgery (RR = 2.2 for suboptimal surgery), menopausal status (RR = 1.47 for postmenopausal women), and presence of a taxane in the chemotherapy combination (RR = 0.72). Conclusion. These results confirm that optimal surgery defined by an appropriate and comprehensive effort at upfront cytoreduction limits morbidity related to the surgical procedure and allows initiation of chemotherapy without any negative impact on survival. The impact of neoadjuvant chemotherapy to improve resectability while lowering the morbidity of the surgical procedure is discussed

Reconstruction of ancestral chromosome architecture and gene repertoire reveals principles of genome evolution in a model yeast genus

International audienceReconstructing genome history is complex but necessary to reveal quantitative principles governing genome evolution. Such reconstruction requires recapitulating into a single evolutionary framework the evolution of genome architecture and gene repertoire. Here, we reconstructed the genome history of the genus Lachancea that appeared to cover a continuous evolutionary range from closely related to more diverged yeast species. Our approach integrated the generation of a high-quality genome data set; the development of AnChro, a new algorithm for reconstructing ancestral genome architecture; and a comprehensive analysis of gene repertoire evolution. We found that the ancestral genome of the genus Lachancea contained eight chromosomes and about 5173 protein-coding genes. Moreover, we characterized 24 horizontal gene transfers and 159 putative gene creation events that punctuated species diversification. We retraced all chromosomal rearrangements, including gene losses, gene duplications, chromosomal inversions and translocations at single gene resolution. Gene duplications outnumbered losses and balanced rearrangements with 1503, 929, and 423 events, respectively. Gene content variations between extant species are mainly driven by differential gene losses, while gene duplications remained globally constant in all lineages. Remarkably, we discovered that balanced chromosomal rearrangements could be responsible for up to 14% of all gene losses by disrupting genes at their breakpoints. Finally, we found that nonsynonymous substitutions reached fixation at a coordinated pace with chromosomal inversions, translocations, and duplications, but not deletions. Overall, we provide a granular view of genome evolution within an entire eukaryotic genus, linking gene content, chromosome rearrangements , and protein divergence into a single evolutionary framework

A genomic and transcriptomic approach for a differential diagnosis between primary and secondary ovarian carcinomas in patients with a previous history of breast cancer

<p>Abstract</p> <p>Background</p> <p>The distinction between primary and secondary ovarian tumors may be challenging for pathologists. The purpose of the present work was to develop genomic and transcriptomic tools to further refine the pathological diagnosis of ovarian tumors after a previous history of breast cancer.</p> <p>Methods</p> <p>Sixteen paired breast-ovary tumors from patients with a former diagnosis of breast cancer were collected. The genomic profiles of paired tumors were analyzed using the Affymetrix GeneChip^®Mapping 50 K Xba Array or Genome-Wide Human SNP Array 6.0 (for one pair), and the data were normalized with ITALICS (ITerative and Alternative normaLIzation and Copy number calling for affymetrix Snp arrays) algorithm or Partek Genomic Suite, respectively. The transcriptome of paired samples was analyzed using Affymetrix GeneChip^®Human Genome U133 Plus 2.0 Arrays, and the data were normalized with gc-Robust Multi-array Average (gcRMA) algorithm. A hierarchical clustering of these samples was performed, combined with a dataset of well-identified primary and secondary ovarian tumors.</p> <p>Results</p> <p>In 12 of the 16 paired tumors analyzed, the comparison of genomic profiles confirmed the pathological diagnosis of primary ovarian tumor (n = 5) or metastasis of breast cancer (n = 7). Among four cases with uncertain pathological diagnosis, genomic profiles were clearly distinct between the ovarian and breast tumors in two pairs, thus indicating primary ovarian carcinomas, and showed common patterns in the two others, indicating metastases from breast cancer. In all pairs, the result of the transcriptomic analysis was concordant with that of the genomic analysis.</p> <p>Conclusions</p> <p>In patients with ovarian carcinoma and a previous history of breast cancer, SNP array analysis can be used to distinguish primary and secondary ovarian tumors. Transcriptomic analysis may be used when primary breast tissue specimen is not available.</p

ITALICS: an algorithm for normalization and DNA copy number calling for Affymetrix SNP arrays

International audienceMotivation: Affymetrix SNP arrays can be used to determine the DNA copy number measurement of 11 000–500 000 SNPs along the genome. Their high density facilitates the precise localization of genomic alterations and makes them a powerful tool for studies of cancers and copy number polymorphism. Like other microarray technologies it is influenced by non-relevant sources of variation, requiring correction. Moreover, the amplitude of variation induced by non-relevant effects is similar or greater than the biologically relevant effect (i.e. true copy number), making it difficult to estimate non-relevant effects accurately without including the biologically relevant effect. Results: We addressed this problem by developing ITALICS, a normalization method that estimates both biological and nonrelevant effects in an alternate, iterative manner, accurately eliminating irrelevant effects. We compared our normalization method with other existing and available methods, and found that ITALICS outperformed these methods for several in-house datasets and one public dataset. These results were validated biologically by quantitative PCR. Availability: The R package ITALICS (ITerative and Alternative normaLIzation and Copy number calling for affymetrix Snp arrays) has been submitted to Bioconductor. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

Trends in IT Innovation to Build a Next Generation Bioinformatics Solution to Manage and Analyse Biological Big Data Produced by NGS Technologies

Sequencing the human genome began in 1994, and 10 years of work were necessary in order to provide a nearly complete sequence. Nowadays, NGS technologies allow sequencing of a whole human genome in a few days. This deluge of data challenges scientists in many ways, as they are faced with data management issues and analysis and visualization drawbacks due to the limitations of current bioinformatics tools. In this paper, we describe how the NGS Big Data revolution changes the way of managing and analysing data. We present how biologists are confronted with abundance of methods, tools, and data formats. To overcome these problems, focus on Big Data Information Technology innovations from web and business intelligence. We underline the interest of NoSQL databases, which are much more efficient than relational databases. Since Big Data leads to the loss of interactivity with data during analysis due to high processing time, we describe solutions from the Business Intelligence that allow one to regain interactivity whatever the volume of data is. We illustrate this point with a focus on the Amadea platform. Finally, we discuss visualization challenges posed by Big Data and present the latest innovations with JavaScript graphic libraries

Discrete analysis of camelid variable domains: sequences, structures, and in-silico structure prediction: Sequence-structure characteristics of VHH domains

International audienceAntigen binding by antibodies requires precise orientation of the complementarity- determining region (CDR) loops in the variable domain to establish the correct contact surface. Members of the family Camelidae have a modified form of immunoglobulin gamma (IgG) with only heavy chains, called Heavy Chain only Antibodies (HCAb). Antigen binding in HCAbs is mediated by only 3 CDR loops from the single variable domain (VHH) at the N-terminus of each heavy chain. This feature of the VHH, along with other important features, e.g. easy expression, small size, thermo-stability and hydrophilicity, made them promising candidates for therapeutics and diagnostics. Thus, to design better VHH domains, it is important to thoroughly understand their sequence and structure characteristics and relationships. In this study sequence, characteristics of VHH have been analysed in depth, along with their structural features using innovative approaches, namely a structural alphabet. An elaborate summary of various studies proposing structural models of VHHs showed diversity in the algorithms used. Finally, a case study to elucidate the differences in structural models from single and multiple templates is presented. In this case study, along with the above-mentioned aspects of VHH, an exciting view of various factors in structure prediction of VHH, like template framework selection, is also discussed

Linear epitope mapping of the humoral response against SARS-CoV-2 in two independent African cohorts

Abstract Profiling of the antibody responses to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) proteins in African populations is scarce. Here, we performed a detailed IgM and IgG epitope mapping study against 487 peptides covering SARS-CoV-2 wild-type structural proteins. A panel of 41 pre-pandemic and 82 COVID-19 RT-PCR confirmed sera from Madagascar and Senegal were used. We found that the main 36 immunodominant linear epitopes identified were (i) similar in both countries, (ii) distributed mainly in the Spike and the Nucleocapsid proteins, (iii) located outside the RBD and NTD regions where most of the reported SARS-CoV-2 variant mutations occur, and (iv) identical to those reported in European, North American, and Asian studies. Within the severe group, antibody levels were inversely correlated with the viral load. This first antibody epitope mapping study performed in patients from two African countries may be helpful to guide rational peptide-based diagnostic assays or vaccine development

Viruses traverse the human proteome through peptide interfaces that can be biomimetically leveraged for drug discovery

International audienceSignificance Viruses have designed small protein interfaces to interact with human proteins. These viral peptides are original molecules to modulate the activity of host targets and an inspiration to create original drugs. Here, the wealth of virus-host protein interactions existing in the literature is integrated in an substantial database. A sample peptide library is screened against several pathogens, highlighting peptides modulators of replication. From one of them, a drug discovery program identifies highly potent antiviral molecules interacting with human metabolic targets. These molecules are proven to be active for treatment of mouse model of nonalcoholic steatohepatitis with chronic kidney disease. Our approach validates an original biomimetic framework to address cellular functions for fundamental applications and drug discovery