The Pharmacogenomics Knowledgebase (PharmGKB) is a resource that collects, curates, and disseminates information about the impact of human genetic variation on drug responses. It provides clinically relevant information, including dosing guidelines, annotated drug labels, and potentially actionable gene–drug associations and genotype–phenotype relationships. Curators assign levels of evidence to variant–drug associations using well-defined criteria based on careful literature review. Thus, PharmGKB is a useful source of high-quality information supporting personalized medicine–implementation projects.

The discovery of rare genetic variants is accelerating, and clear guidelines for distinguishing disease-causing sequence variants from the many potentially functional variants present in any human genome are urgently needed. Without rigorous standards we risk an acceleration of false-positive reports of causality, which would impede the translation of genomic research findings into the clinical diagnostic setting and hinder biological understanding of disease. Here we discuss the key challenges of assessing sequence variants in human disease, integrating both gene-level and variant-level support for causality. We propose guidelines for summarizing confidence in variant pathogenicity and highlight several areas that require further resource development.

Lung cancer is the most prevalent cancer worldwide, and histopathological assessment is indispensable for its diagnosis. However, human evaluation of pathology slides cannot accurately predict patients' prognoses. In this study, we obtain 2,186 haematoxylin and eosin stained histopathology whole-slide images of lung adenocarcinoma and squamous cell carcinoma patients from The Cancer Genome Atlas (TCGA), and 294 additional images from Stanford Tissue Microarray (TMA) Database. We extract 9,879 quantitative image features and use regularized machine-learning methods to select the top features and to distinguish shorter-term survivors from longer-term survivors with stage I adenocarcinoma (P<0.003) or squamous cell carcinoma (P=0.023) in the TCGA data set. We validate the survival prediction framework with the TMA cohort (P<0.036 for both tumour types). Our results suggest that automatically derived image features can predict the prognosis of lung cancer patients and thereby contribute to precision oncology. Our methods are extensible to histopathology images of other organs.

Chemoinformatics is an established discipline focusing on extracting, processing and extrapolating meaningful data from chemical structures. With the rapid explosion of chemical 'big' data from HTS and combinatorial synthesis, machine learning has become an indispensable tool for drug designers to mine chemical information from large compound databases to design drugs with important biological properties. To process the chemical data, we first reviewed multiple processing layers in the chemoinformatics pipeline followed by the introduction of commonly used machine learning models in drug discovery and QSAR analysis. Here, we present basic principles and recent case studies to demonstrate the utility of machine learning techniques in chemoinformatics analyses; and we discuss limitations and future directions to guide further development in this evolving field.

Doxorubicin (Adriamycin) is an anthracycline chemotherapy agent effective in treating a wide range of malignancies with a well–established dose–response cardiotoxic side effect that can lead to heart failure. At present, it is not possible to predict which patients will be affected by doxorubicin-induced cardiotoxicity (DIC). Here we demonstrate that patient–specific human induced pluripotent stem cell–derived cardiomyocytes (hiPSC–CMs) can recapitulate individual patients’ predilection to DIC at the single cell level. hiPSC–CMs derived from breast cancer patients who suffered clinical DIC are consistently more sensitive to doxorubicin toxicity, demonstrating decreased cell viability, mitochondrial and metabolic function, calcium handling, and antioxidant pathway activity, along with increased reactive oxygen species (ROS) production compared to hiPSC–CMs from patients who did not experience DIC. Together, our data indicate that hiPSC–CMs are a suitable platform for identifying and verifying the genetic basis and molecular mechanisms of DIC.

IMPORTANCE Whole-genome sequencing (WGS) is increasingly applied in clinical medicine and is expected to uncover clinically significant findings regardless of sequencing indication. OBJECTIVES To examine coverage and concordance of clinically relevant genetic variation provided by WGS technologies; to quantitate inherited disease risk and pharmacogenomic findings in WGS data and resources required for their discovery and interpretation; and to evaluate clinical action prompted by WGS findings. DESIGN, SETTING, AND PARTICIPANTS An exploratory study of 12 adult participants recruited at Stanford University Medical Center who underwent WGS between November 2011 and March 2012. A multidisciplinary team reviewed all potentially reportable genetic findings. Five physicians proposed initial clinical follow-up based on the genetic findings. MAIN OUTCOMES AND MEASURES Genome coverage and sequencing platform concordance in different categories of genetic disease risk, person-hours spent curating candidate disease-risk variants, interpretation agreement between trained curators and disease genetics databases, burden of inherited disease risk and pharmacogenomic findings, and burden and interrater agreement of proposed clinical follow-up. RESULTS Depending on sequencing platform, 10% to 19% of inherited disease genes were not covered to accepted standards for single nucleotide variant discovery. Genotype concordance was high for previously described single nucleotide genetic variants (99%-100%) but low for small insertion/deletion variants (53%-59%). Curation of 90 to 127 genetic variants in each participant required a median of 54 minutes (range, 5-223 minutes) per genetic variant, resulted in moderate classification agreement between professionals (Gross κ, 0.52; 95%CI, 0.40-0.64), and reclassified 69%of genetic variants cataloged as disease causing in mutation databases to variants of uncertain or lesser significance. Two to 6 personal disease-risk findings were discovered in each participant, including 1 frameshift deletion in the BRCA1 gene implicated in hereditary breast and ovarian cancer. Physician review of sequencing findings prompted consideration of a median of 1 to 3 initial diagnostic tests and referrals per participant, with fair interrater agreement about the suitability of WGS findings for clinical follow-up (Fleiss κ, 0.24; P < 001). CONCLUSIONS AND RELEVANCE In this exploratory study of 12 volunteer adults, the use of WGS was associated with incomplete coverage of inherited disease genes, low reproducibility of detection of genetic variation with the highest potential clinical effects, and uncertainty about clinically reportable findings. In certain cases, WGS will identify clinically actionable genetic variants warranting early medical intervention. These issues should be considered when determining the role of WGS in clinical medicine.

Directional translocation of the ribosome through the messenger RNA open reading frame is a critical determinant of translational fidelity. This process entails a complex interplay of large-scale conformational changes within the actively translating particle, which together coordinate the movement of transfer and messenger RNA substrates with respect to the large and small ribosomal subunits. Using pre-steady state, single-molecule fluorescence resonance energy transfer imaging, we have tracked the nature and timing of these conformational events within the Escherichia coli ribosome from five structural perspectives. Our investigations reveal direct evidence of structurally and kinetically distinct, late intermediates during substrate movement, whose resolution is rate-determining to the translocation mechanism. These steps involve intra-molecular events within the EFG(GDP)-bound ribosome, including exaggerated, reversible fluctuations of the small subunit head domain, which ultimately facilitate peptidyl-tRNA’s movement into its final post-translocation position.

Protein synthesis by the ribosome is highly dependent on the ionic conditions in the cellular environment, but the roles of ribosome solvation remain poorly understood. Moreover, the function of modifications to ribosomal RNA and ribosomal proteins are unclear. Here we present the structure of the Escherichia coli 70S ribosome to 2.4 Å resolution. The structure reveals details of the ribosomal subunit interface that are conserved in all domains of life, and suggest how solvation contributes to ribosome integrity and function. The structure also suggests how the conformation of ribosomal protein uS12 likely impacts its contribution to messenger RNA decoding. This structure helps to explain the phylogenetic conservation of key elements of the ribosome, including posttranscriptional and posttranslational modifications and should serve as a basis for future antibiotic development.