The pharmacogenetics and pharmacogenomics knowledge base: accentuating the knowledge

PharmGKB is a knowledge base that captures the relationships between drugs, diseases/phenotypes and genes involved in pharmacokinetics (PK) and pharmacodynamics (PD). This information includes literature annotations, primary data sets, PK and PD pathways, and expert-generated summaries of PK/PD relationships between drugs, diseases/phenotypes and genes. PharmGKB's website is designed to effectively disseminate knowledge to meet the needs of our users. PharmGKB currently has literature annotations documenting the relationship of over 500 drugs, 450 diseases and 600 variant genes. In order to meet the needs of whole genome studies, PharmGKB has added new functionalities, including browsing the variant display by chromosome and cytogenetic locations, allowing the user to view variants not located within a gene. We have developed new infrastructure for handling whole genome data, including increased methods for quality control and tools for comparison across other data sources, such as dbSNP, JSNP and HapMap data. PharmGKB has also added functionality to accept, store, display and query high throughput SNP array data. These changes allow us to capture more structured information on phenotypes for better cataloging and comparison of data. PharmGKB is available at www.pharmgkb.or

Mapping gene associations in human mitochondria using clinical disease phenotypes

Nuclear genes encode most mitochondrial proteins, and their mutations cause diverse and debilitating clinical disorders. To date, 1,200 of these mitochondrial genes have been recorded, while no standardized catalog exists of the associated clinical phenotypes. Such a catalog would be useful to develop methods to analyze human phenotypic data, to determine genotype-phenotype relations among many genes and diseases, and to support the clinical diagnosis of mitochondrial disorders. Here we establish a clinical phenotype catalog of 174 mitochondrial disease genes and study associations of diseases and genes. Phenotypic features such as clinical signs and symptoms were manually annotated from full-text medical articles and classified based on the hierarchical MeSH ontology. This classification of phenotypic features of each gene allowed for the comparison of diseases between different genes. In turn, we were then able to measure the phenotypic associations of disease genes for which we calculated a quantitative value that is based on their shared phenotypic features. The results showed that genes sharing more similar phenotypes have a stronger tendency for functional interactions, proving the usefulness of phenotype similarity values in disease gene network analysis. We then constructed a functional network of mitochondrial genes and discovered a higher connectivity for non-disease than for disease genes, and a tendency of disease genes to interact with each other. Utilizing these differences, we propose 168 candidate genes that resemble the characteristic interaction patterns of mitochondrial disease genes. Through their network associations, the candidates are further prioritized for the study of specific disorders such as optic neuropathies and Parkinson disease. Most mitochondrial disease phenotypes involve several clinical categories including neurologic, metabolic, and gastrointestinal disorders, which might indicate the effects of gene defects within the mitochondrial system. The accompanying knowledgebase (http://www.mitophenome.org/) supports the study of clinical diseases and associated genes

p53 as a potential predictive factor of response to chemotherapy: feasibility of p53 assessment using a functional test in yeast from trucut biopsies in breast cancer patients

Assessment of the predictive value of p53 requires the testing of large numbers of samples from patients enrolled in prospective phase III clinical trials. The goal of this study was to determine whether p53 status can be determined by p53 yeast functional assay using the limiting amounts of material that can typically be obtained in prospective phase III trials (particularly when chemotherapy is given before surgery). All patients presenting with a clinically palpable tumour which could be considered large enough to perform a trucut biopsy (⩾2 cm breast tumour) were eligible for this study. Two trucut biopsies and one incisional biopsy were performed on the surgical specimens (mastectomy or tumourectomy). Samples were snap frozen and cryostat sections were taken for histology and p53 testing. Thirty patients were included. Three samples out of 90 failed to give any p53 PCR products, probably because these samples contained almost entirely fibrous tissue. Of the 87 samples that could be tested, the incisional and trucut biopsies results were fully concordant in every case. p53 could be defined in 97% of patients by double trucut biopsy. Eight out of 30 tumours tested were mutant for p53 (27%). p53 status can be reliably determined by yeast assay from single frozen sections of trucut biopsies. Histological examination before p53 testing is essential to exclude cases where the p53 result may reflect only the status of the normal cells in the biopsy

Genomic Profiling Identifies GATA6 as a Candidate Oncogene Amplified in Pancreatobiliary Cancer

Pancreatobiliary cancers have among the highest mortality rates of any cancer type. Discovering the full spectrum of molecular genetic alterations may suggest new avenues for therapy. To catalogue genomic alterations, we carried out array-based genomic profiling of 31 exocrine pancreatic cancers and 6 distal bile duct cancers, expanded as xenografts to enrich the tumor cell fraction. We identified numerous focal DNA amplifications and deletions, including in 19% of pancreatobiliary cases gain at cytoband 18q11.2, a locus uncommonly amplified in other tumor types. The smallest shared amplification at 18q11.2 included GATA6, a transcriptional regulator previously linked to normal pancreas development. When amplified, GATA6 was overexpressed at both the mRNA and protein levels, and strong immunostaining was observed in 25 of 54 (46%) primary pancreatic cancers compared to 0 of 33 normal pancreas specimens surveyed. GATA6 expression in xenografts was associated with specific microarray gene-expression patterns, enriched for GATA binding sites and mitochondrial oxidative phosphorylation activity. siRNA mediated knockdown of GATA6 in pancreatic cancer cell lines with amplification led to reduced cell proliferation, cell cycle progression, and colony formation. Our findings indicate that GATA6 amplification and overexpression contribute to the oncogenic phenotypes of pancreatic cancer cells, and identify GATA6 as a candidate lineage-specific oncogene in pancreatobiliary cancer, with implications for novel treatment strategies

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An Electronic Health Record Text Mining Tool to Collect Real‐World Drug Treatment Outcomes: A Validation Study in Patients With Metastatic Renal Cell Carcinoma

Real-world evidence can close the inferential gap between marketing authorization studies and clinical practice. However, the current standard for real-world data extraction from electronic health records (EHRs) for treatment evaluation is manual review (MR), which is time-consuming and laborious. Clinical Data Collector (CDC) is a novel natural language processing and text mining software tool for both structured and unstructured EHR data and only shows relevant EHR sections improving efficiency. We investigated CDC as a real-world data (RWD) collection method, through application of CDC queries for patient inclusion and information extraction on a cohort of patients with metastatic renal cell carcinoma (RCC) receiving systemic drug treatment. Baseline patient characteristics, disease characteristics, and treatment outcomes were extracted and these were compared with MR for validation. One hundred patients receiving 175 treatments were included using CDC, which corresponded to 99% with MR. Calculated median overall survival was 21.7 months (95% confidence interval (CI) 18.7-24.8) vs. 21.7 months (95% CI 18.6-24.8) and progression-free survival 8.9 months (95% CI 5.4-12.4) vs. 7.6 months (95% CI 5.7-9.4) for CDC vs. MR, respectively. Highest F1-score was found for cancer-related variables (88.1-100), followed by comorbidities (71.5-90.4) and adverse drug events (53.3-74.5), with most diverse scores on international metastatic RCC database criteria (51.4-100). Mean data collection time was 12 minutes (CDC) vs. 86 minutes (MR). In conclusion, CDC is a promising tool for retrieving RWD from EHRs because the correct patient population can be identified as well as relevant outcome data, such as overall survival and progression-free survival.Clinical Pharmacy and Toxicolog