The Monarch Initiative in 2019: an integrative data and analytic platform connecting phenotypes to genotypes across species.

In biology and biomedicine, relating phenotypic outcomes with genetic variation and environmental factors remains a challenge: patient phenotypes may not match known diseases, candidate variants may be in genes that haven’t been characterized, research organisms may not recapitulate human or veterinary diseases, environmental factors affecting disease outcomes are unknown or undocumented, and many resources must be queried to find potentially significant phenotypic associations. The Monarch Initiative (https://monarchinitiative.org) integrates information on genes, variants, genotypes, phenotypes and diseases in a variety of species, and allows powerful ontology-based search. We develop many widely adopted ontologies that together enable sophisticated computational analysis, mechanistic discovery and diagnostics of Mendelian diseases. Our algorithms and tools are widely used to identify animal models of human disease through phenotypic similarity, for differential diagnostics and to facilitate translational research. Launched in 2015, Monarch has grown with regards to data (new organisms, more sources, better modeling); new API and standards; ontologies (new Mondo unified disease ontology, improvements to ontologies such as HPO and uPheno); user interface (a redesigned website); and community development. Monarch data, algorithms and tools are being used and extended by resources such as GA4GH and NCATS Translator, among others, to aid mechanistic discovery and diagnostics

BRCAness Profile of Sporadic Ovarian Cancer Predicts Disease Recurrence

BACKGROUND:The consequences of defective homologous recombination (HR) are not understood in sporadic ovarian cancer, nor have the potential role of HR proteins other than BRCA1 and BRCA2 been clearly defined. However, it is clear that defects in HR and other DNA repair pathways are important to the effectiveness of current therapies. We hypothesize that a subset of sporadic ovarian carcinomas may harbor anomalies in HR pathways, and that a BRCAness profile (defects in HR or other DNA repair pathways) could influence response rate and survival after treatment with platinum drugs. Clinical availability of a BRCAness profile in patients and/or tumors should improve treatment outcomes. OBJECTIVE:To define the BRCAness profile of sporadic ovarian carcinoma and determine whether BRCA1, PARP, FANCD2, PTEN, H2AX, ATM, and P53 protein expression correlates with response to treatment, disease recurrence, and recurrence-free survival. MATERIALS AND METHODS:Protein microarray analysis of ovarian cancer tissue was used to determine protein expression levels for defined DNA repair proteins. Correlation with clinical and pathologic parameters in 186 patients with advanced stage III-IV and grade 3 ovarian cancer was analyzed using Chi square, Kaplan-Meier method, Cox proportional hazard model, and cumulative incidence function. RESULTS:High PARP, FANCD2 and BRCA1 expressions were significantly correlated with each other; however, elevated p53 expression was associated only with high PARP and FANCD2. Of all patients, 9% recurred within the first year. Among early recurring patients, 41% had high levels of PARP, FANCD2 and P53, compared to 19.5% of patients without early recurrence (p = 0.04). Women with high levels of PARP, FANCD2 and/or P53 had first year cumulative cancer incidence of 17% compared with 7% for the other groups (P = 0.03). CONCLUSIONS:Patients with concomitantly high levels of PARP, FANCD2 and P53 protein expression are at increased risk of early ovarian cancer recurrence and platinum resistance

The Monarch Initiative in 2019: an integrative data and analytic platform connecting phenotypes to genotypes across species.

In biology and biomedicine, relating phenotypic outcomes with genetic variation and environmental factors remains a challenge: patient phenotypes may not match known diseases, candidate variants may be in genes that haven\u27t been characterized, research organisms may not recapitulate human or veterinary diseases, environmental factors affecting disease outcomes are unknown or undocumented, and many resources must be queried to find potentially significant phenotypic associations. The Monarch Initiative (https://monarchinitiative.org) integrates information on genes, variants, genotypes, phenotypes and diseases in a variety of species, and allows powerful ontology-based search. We develop many widely adopted ontologies that together enable sophisticated computational analysis, mechanistic discovery and diagnostics of Mendelian diseases. Our algorithms and tools are widely used to identify animal models of human disease through phenotypic similarity, for differential diagnostics and to facilitate translational research. Launched in 2015, Monarch has grown with regards to data (new organisms, more sources, better modeling); new API and standards; ontologies (new Mondo unified disease ontology, improvements to ontologies such as HPO and uPheno); user interface (a redesigned website); and community development. Monarch data, algorithms and tools are being used and extended by resources such as GA4GH and NCATS Translator, among others, to aid mechanistic discovery and diagnostics

The fanconi anemia DNA repair pathway:structural and functional insights into a complex disorder

Mutations in any of at least sixteen FANC genes (FANCA–Q) cause Fanconi anemia, a disorder characterized by sensitivity to DNA interstrand crosslinking agents. The clinical features of cytopenia, developmental defects, and tumor predisposition are similar in each group, suggesting that the gene products participate in a common pathway. The Fanconi anemia DNA repair pathway consists of an anchor complex that recognizes damage caused by interstrand crosslinks, a multisubunit ubiquitin ligase that monoubiquitinates two substrates, and several downstream repair proteins including nucleases and homologous recombination enzymes. We review progress in the use of structural and biochemical approaches to understanding how each FANC protein functions in this pathway