Validation of the Clinical Use of GIScar, an Academic-developed Genomic Instability Score Predicting Sensitivity to Maintenance Olaparib for Ovarian Cancer.

PURPOSE: The optimal application of maintenance PARP inhibitor therapy for ovarian cancer requires accessible, robust, and rapid testing of homologous recombination deficiency (HRD). However, in many countries, access to HRD testing is problematic and the failure rate is high. We developed an academic HRD test to support treatment decision-making. EXPERIMENTAL DESIGN: Genomic Instability Scar (GIScar) was developed through targeted sequencing of a 127-gene panel to determine HRD status. GIScar was trained from a noninterventional study with 250 prospectively collected ovarian tumor samples. GIScar was validated on 469 DNA tumor samples from the PAOLA-1 trial evaluating maintenance olaparib for newly diagnosed ovarian cancer, and its predictive value was compared with Myriad Genetics MyChoice (MGMC). RESULTS: GIScar showed significant correlation with MGMC HRD classification (kappa statistics: 0.780). From PAOLA-1 samples, more HRD-positive tumors were identified by GIScar (258) than MGMC (242), with a lower proportion of inconclusive results (1% vs. 9%, respectively). The HRs for progression-free survival (PFS) with olaparib versus placebo were 0.45 [95% confidence interval (CI), 0.33-0.62] in GIScar-identified HRD-positive BRCA-mutated tumors, 0.50 (95% CI, 0.31-0.80) in HRD-positive BRCA-wild-type tumors, and 1.02 (95% CI, 0.74-1.40) in HRD-negative tumors. Tumors identified as HRD positive by GIScar but HRD negative by MGMC had better PFS with olaparib (HR, 0.23; 95% CI, 0.07-0.72). CONCLUSIONS: GIScar is a valuable diagnostic tool, reliably detecting HRD and predicting sensitivity to olaparib for ovarian cancer. GIScar showed high analytic concordance with MGMC test and fewer inconclusive results. GIScar is easily implemented into diagnostic laboratories with a rapid turnaround.

IMGT® databases, related tools and web resources through three main axes of research and development.

IMGT®, the international ImMunoGeneTics information system®, http://www.imgt.org/, is at the forefront of the immunogenetics and immunoinformatics fields with more than 30 years of experience. IMGT® makes available databases and tools to the scientific community pertaining to the adaptive immune response, based on the IMGT-ONTOLOGY. We focus on the recent features of the IMGT® databases, tools, reference directories and web resources, within the three main axes of IMGT® research and development. Axis I consists in understanding the adaptive immune response, by deciphering the identification and characterization of the immunoglobulin (IG) and T cell receptor (TR) genes in jawed vertebrates. It is the starting point of the two other axes, namely the analysis and exploration of the expressed IG and TR repertoires based on comparison with IMGT reference directories in normal and pathological situations (Axis II) and the analysis of amino acid changes and functions of 2D and 3D structures of antibody and TR engineering (Axis III).

IMGT® Biocuration and Comparative Study of the T Cell Receptor Beta Locus of Veterinary Species Based on Homo sapiens TRB.

IMGT®, the international ImMunoGeneTics information system® is the global reference in immunogenetics and immunoinformatics. By its creation in 1989 by Marie-Paule Lefranc (Université de Montpellier and CNRS), IMGT® marked the advent of immunoinformatics, which emerged at the interface between immunogenetics and bioinformatics. IMGT® is specialized in the immunoglobulins (IG) or antibodies, T cell receptors (TR), major histocompatibility (MH), and proteins of the IgSF and MhSF superfamilies. T cell receptors are divided into two groups, αß and γδ TR, which express distinct TR containing either α and ß, or γ and δ chains, respectively. The TRß locus (TRB) was recently described and annotated by IMGT® biocurators for several veterinary species, i.e., cat (Felis catus), dog (Canis lupus familiaris), ferret (Mustela putorius furo), pig (Sus scrofa), rabbit (Oryctolagus cuniculus), rhesus monkey (Macaca mulatta), and sheep (Ovis aries). The aim of the present study is to compare the genes of the TRB locus among these different veterinary species based on Homo sapiens. The results reveal that there are similarities but also differences including the number of genes by subgroup which may demonstrate duplications and/or deletions during evolution.

Agronomic Linked Data (AgroLD): A knowledge-based system to enable integrative biology in agronomy.

Recent advances in high-throughput technologies have resulted in a tremendous increase in the amount of omics data produced in plant science. This increase, in conjunction with the heterogeneity and variability of the data, presents a major challenge to adopt an integrative research approach. We are facing an urgent need to effectively integrate and assimilate complementary datasets to understand the biological system as a whole. The Semantic Web offers technologies for the integration of heterogeneous data and their transformation into explicit knowledge thanks to ontologies. We have developed the Agronomic Linked Data (AgroLD- www.agrold.org), a knowledge-based system relying on Semantic Web technologies and exploiting standard domain ontologies, to integrate data about plant species of high interest for the plant science community e.g., rice, wheat, arabidopsis. We present some integration results of the project, which initially focused on genomics, proteomics and phenomics. AgroLD is now an RDF (Resource Description Format) knowledge base of 100M triples created by annotating and integrating more than 50 datasets coming from 10 data sources-such as Gramene.org and TropGeneDB-with 10 ontologies-such as the Gene Ontology and Plant Trait Ontology. Our evaluation results show users appreciate the multiple query modes which support different use cases. AgroLD's objective is to offer a domain specific knowledge platform to solve complex biological and agronomical questions related to the implication of genes/proteins in, for instances, plant disease resistance or high yield traits. We expect the resolution of these questions to facilitate the formulation of new scientific hypotheses to be validated with a knowledge-oriented approach.