FC1000: normalized gene expression changes of systematically perturbed human cells.

The systematic study of transcriptional responses to genetic and chemical perturbations in human cells is still in its early stages. The largest available dataset to date is the newly released L1000 compendium. With its 1.3 million gene expression profiles of treated human cells it offers many opportunities for biomedical data mining, but also data normalization challenges of new dimensions. We developed a novel and practical approach to obtain accurate estimates of fold change response profiles from L1000, based on the RUV (Remove Unwanted Variation) statistical framework. Extending RUV to a big data setting, we propose an estimation procedure, in which an underlying RUV model is tuned by feedback through dataset specific statistical measures, reflecting p-value distributions and internal gene knockdown controls. Applying these metrics - termed evaluation endpoints - to disjoint data splits and integrating the results to select an optimal normalization, the procedure reduces bias and noise in the L1000 data, which in turn broadens the potential of this resource for pharmacological and functional genomic analyses. Our pipeline and normalization results are distributed as an R package (nelanderlab.org/FC1000.html).

Stem cell plasticity, acetylation of H3K14, and de novo gene activation rely on KAT7.

In the conventional model of transcriptional activation, transcription factors bind to response elements and recruit co-factors, including histone acetyltransferases. Contrary to this model, we show that the histone acetyltransferase KAT7 (HBO1/MYST2) is required genome wide for histone H3 lysine 14 acetylation (H3K14ac). Examining neural stem cells, we find that KAT7 and H3K14ac are present not only at transcribed genes but also at inactive genes, intergenic regions, and in heterochromatin. KAT7 and H3K14ac were not required for the continued transcription of genes that were actively transcribed at the time of loss of KAT7 but indispensable for the activation of repressed genes. The absence of KAT7 abrogates neural stem cell plasticity, diverse differentiation pathways, and cerebral cortex development. Re-expression of KAT7 restored stem cell developmental potential. Overexpression of KAT7 enhanced neuron and oligodendrocyte differentiation. Our data suggest that KAT7 prepares chromatin for transcriptional activation and is a prerequisite for gene activation.

The Use of Subgroup Disproportionality Analyses to Explore the Sensitivity of a Global Database of Individual Case Safety Reports to Known Pharmacogenomic Risk Variants Common in Japan.

INTRODUCTION: Genetic variations of enzymes that affect the pharmacokinetics and hence effects of medications differ between ethnicities, resulting in variation in the risk of adverse drug reactions (ADR) between different populations. Previous work has demonstrated that risk-group considerations can be incorporated into approaches of statistical signal detection. It is unknown whether databases of individual case safety reports (ICSRs) are sensitive to pharmacogenomic differences between populations. OBJECTIVE: The aim of this study was to explore the sensitivity of a global database of ICSRs to known pharmacogenomic risk variants common in Japan. METHODS: The data source was VigiBase, the global database of ICSRs, including all reports entered in the version frozen on 5 January 2020. Subgroup disproportionality analysis was used to compare ICSRs of two subgroups, Japan and rest of world (RoW). Reports for UGT1A1-metabolized irinotecan and the CYP2C19-metabolized drugs voriconazole, escitalopram and clopidogrel were selected for comparison between the subgroups based upon known genetic polymorphisms with high prevalence in Japan. Contrast between the subgroups was quantified by IC delta [Formula: see text]), a robust shrinkage observed-to-expected (OE) ratio on a log scale. Harmonic mean p values (HMP) were calculated for each drug to evaluate whether a list of pre-specified ADRs were collectively significantly over- (or under-)reported as hypothesized. Daily drug dosages were calculated for ICSRs with sufficient information, and dose distributions were compared between Japan and RoW and related to differences in regionally approved doses. RESULTS: The predictions of over-reporting patterns for specific ADRs were observed and confirmed in bootstrap HMP analyses (p = 0.004 for irinotecan and p < 0.001 for each of voriconazole, escitalopram and clopidogrel) and compared with similar drugs with different metabolic pathways. The impact of proactive regulatory action, such as recommended dosing and therapeutic drug monitoring (TDM), was also observable within the global database. For irinotecan and escitalopram, there was evidence of use of lower dosages as recommended in the Japanese labels; for voriconazole, there was evidence of use of TDM with an over-reporting of terms related to drug level measurements and an under-reporting of liver toxicity. CONCLUSIONS: Pharmaco-ethnic vulnerabilities caused by pharmacogenomic differences between populations may contribute to differences in ADR reporting between countries in a global database of ICSRs. Regional analyses within a global database can inform on the effectiveness of local risk minimization measures and should be leveraged to catalyse the conversion of real-world usage into safer use of drugs in ethnically tailored ways.

Deciphering clonality in aneuploid breast tumors using SNP array and sequencing data.

Intra-tumor heterogeneity concerns the existence of genetically different subclones within the same tumor. Single sample quantification of heterogeneity relies on precise determination of chromosomal copy numbers throughout the genome, and an assessment of whether identified mutation variant allele fractions match clonal or subclonal copy numbers. We discuss these issues using data from SNP arrays, whole exome sequencing and pathologist purity estimates on several breast cancers characterized by ERBB2 amplification. We show that chromosomal copy numbers can only be estimated from SNP array signals or sequencing depths for subclonal tumor samples with simple subclonal architectures under certain assumptions.