Drug and xenobiotic metabolizing enzymes (DXME) play important roles in drug responses and carcinogenesis. Recent studies have found that expression of DXME in cancer cells significantly affects drug clearance and the onset of drug resistance. In this study we compared the expression of DXME in breast tumor tissue samples from patients representing three ethnic groups: Caucasian Americans (CA), African Americans (AA), and Asian Americans (AS). We further combined DXME gene expression data with eQTL data from the GTEx project and with allele frequency data from the 1000 Genomes project to identify SNPs that may be associated with differential expression of DXME genes. We identified substantial differences among CA, AA, and AS populations in the expression of DXME genes and in activation of pathways involved in drug metabolism, including those involved in metabolizing chemotherapy drugs that are commonly used in the treatment of breast cancer. These data suggest that differential expression of DXME may associate with health disparities in breast cancer outcomes observed among these three ethnic groups. Our study suggests that development of personalized treatment strategies for breast cancer patients could be improved by considering both germline genotypes and tumor specific mutations and expression profiles related to DXME genes.

Asian Americans (AS) have significantly lower incidence and mortality rates of breast cancer (BRCA) than Caucasian Americans (CA). While this racial disparity has been documented the underlying pathogenetic factors explaining it are obscure. We addressed this issue by an integrative genomics approach to compare mRNA expression between AS and CA cases of BRCA. RNA-seq data from the Cancer Genome Atlas showed that mRNA expression revealed significant differences at gene and pathway levels. Increased susceptibility and severity in CA patients were likely the result of synergistic environmental and genetic risk factors, with arachidonic acid metabolism and PPAR signaling pathways implicated in linking environmental and genetic factors. An analysis that also added eQTL data from the Genotype-Tissue Expression Project and single nucleotide polymorphism (SNP) data from the 1000 Genomes Project identified several SNPs associated with differentially expressed genes. Overall, the associations we identified may enable a more focused study of genotypic differences that may help explain the disparity in BRCA incidence and mortality rates in CA and AS populations and inform precision medicine.

The Precision Medicine Initiative is a multicenter effort aiming at formulating personalized treatments leveraging on individual patient data (clinical, genome sequence and functional genomic data) together with the information in large knowledge bases (KBs) that integrate genome annotation, disease association studies, electronic health records and other data types. The biomedical literature provides a rich foundation for populating these KBs, reporting genetic and molecular interactions that provide the scaffold for the cellular regulatory systems and detailing the influence of genetic variants in these interactions. The goal of BioCreative VI Precision Medicine Track was to extract this particular type of information and was organized in two tasks: (i) document triage task, focused on identifying scientific literature containing experimentally verified protein–protein interactions (PPIs) affected by genetic mutations and (ii) relation extraction task, focused on extracting the affected interactions (protein pairs). To assist system developers and task participants, a large-scale corpus of PubMed documents was manually annotated for this task. Ten teams worldwide contributed 22 distinct text-mining models for the document triage task, and six teams worldwide contributed 14 different text-mining systems for the relation extraction task. When comparing the text-mining system predictions with human annotations, for the triage task, the best F-score was 69.06%, the best precision was 62.89%, the best recall was 98.0% and the best average precision was 72.5%. For the relation extraction task, when taking homologous genes into account, the best F-score was 37.73%, the best precision was 46.5% and the best recall was 54.1%. Submitted systems explored a wide range of methods, from traditional rule-based, statistical and machine learning systems to state-of-the-art deep learning methods. Given the level of participation and the individual team results we find the precision medicine track to be successful in engaging the text-mining research community. In the meantime, the track produced a manually annotated corpus of 5509 PubMed documents developed by BioGRID curators and relevant for precision medicine. The data set is freely available to the community, and the specific interactions have been integrated into the BioGRID data set. In addition, this challenge provided the first results of automatically identifying PubMed articles that describe PPI affected by mutations, as well as extracting the affected relations from those articles. Still, much progress is needed for computer-assisted precision medicine text mining to become mainstream. Future work should focus on addressing the remaining technical challenges and incorporating the practical benefits of text-mining tools into real-world precision medicine information-related curation.

ABSTRACTThe functions of protein kinases have been widely studied and many kinase inhibitors have been developed into FDA-approved therapeutics. A substantial fraction of the human kinome is nonetheless understudied. In this perspective, members of the NIH Understudied Kinome Consortium mine publicly available databases to assess the functionality of these understudied kinases as well as their potential to be therapeutic targets for drug discovery campaigns. We start with a re-analysis of the kinome as a whole and describe criteria for creating an inclusive set of 710 kinase domains as well as a curated set of 557 protein kinase like (PKL) domains. We define an understudied (‘dark’) kinome by quantifying the public knowledge on each kinase with a PKL domain using an automatic reading machine. We find a substantial number are essential in the Cancer Dependency Map and differentially expressed or mutated in disease databases such as The Cancer Genome Atlas. Based on this and other data, it seems likely that the dark kinome contains biologically important genes, a subset of which may be viable drug targets.