Assessment of Polygenic Architecture and Risk Prediction based on Common Variants Across Fourteen Cancers, bioRxiv, 2019-08-10

AbstractWe analyzed summary-level data from genome-wide association studies (GWAS) of European ancestry across fourteen cancer sites to estimate the number of common susceptibility variants (polygenicity) contributing to risk, as well as the distribution of their associated effect sizes. All cancers evaluated showed polygenicity, involving at a minimum thousands of independent susceptibility variants. For some malignancies, particularly chronic lymphoid leukemia (CLL) and testicular cancer, there are a larger proportion of variants with larger effect sizes than those for other cancers. In contrast, most variants for lung and breast cancers have very small associated effect sizes. For different cancer sites, we estimate a wide range of GWAS sample sizes, required to explain 80% of GWAS heritability, varying from 60,000 cases for CLL to over 1,000,000 cases for lung cancer. The maximum relative risk achievable for subjects at the 99th risk percentile of underlying polygenic risk scores, compared to average risk, ranges from 12 for testicular to 2.5 for ovarian cancer. We show that polygenic risk scores have substantial potential for risk stratification for relatively common cancers such as breast, prostate and colon, but limited potential for other cancer sites because of modest heritability and lower disease incidence.

biorxiv genetics 0-100-users 2019

WAPL maintains dynamic cohesin to preserve lineage specific distal gene regulation, bioRxiv, 2019-08-10

SUMMARYThe cohesin complex plays essential roles in sister chromatin cohesin, chromosome organization and gene expression. The role of cohesin in gene regulation is incompletely understood. Here, we report that the cohesin release factor WAPL is crucial for maintaining a pool of dynamic cohesin bound to regions that are associated with lineage specific genes in mouse embryonic stem cells. These regulatory regions are enriched for active enhancer marks and transcription factor binding sites, but largely devoid of CTCF binding sites. Stabilization of cohesin, which leads to a loss of dynamic cohesin from these regions, does not affect transcription factor binding or active enhancer marks, but does result in changes in promoter-enhancer interactions and downregulation of genes. Acute cohesin depletion can phenocopy the effect of WAPL depletion, showing that cohesin plays a crucial role in maintaining expression of lineage specific genes. The binding of dynamic cohesin to chromatin is dependent on the pluripotency transcription factor OCT4, but not NANOG. Finally, dynamic cohesin binding sites are also found in differentiated cells, suggesting that they represent a general regulatory principle. We propose that cohesin dynamically binding to regulatory sites creates a favorable spatial environment in which promoters and enhancers can communicate to ensure proper gene expression.HIGHLIGHTS<jatslist list-type=order><jatslist-item>The cohesin release factor WAPL is crucial for maintaining a pluripotency-specific phenotype.<jatslist-item><jatslist-item>Dynamic cohesin is enriched at lineage specific loci and overlaps with binding sites of pluripotency transcription factors.<jatslist-item><jatslist-item>Expression of lineage specific genes is maintained by dynamic cohesin binding through the formation of promoter-enhancer associated self-interaction domains.<jatslist-item><jatslist-item>CTCF-independent cohesin binding to chromatin is controlled by the pioneer factor OCT4.<jatslist-item>

biorxiv genomics 0-100-users 2019

Non-oncology drugs are a source of previously unappreciated anti-cancer activity, bioRxiv, 2019-08-09

ABSTRACTAnti-cancer uses of non-oncology drugs have been found on occasion, but such discoveries have been serendipitous and rare. We sought to create a public resource containing the growth inhibitory activity of 4,518 drugs tested across 578 human cancer cell lines. To accomplish this, we used PRISM, which involves drug treatment of molecularly barcoded cell lines in pools. Relative barcode abundance following treatment thus reflects cell line viability. We found that an unexpectedly large number of non-oncology drugs selectively inhibited subsets of cancer cell lines. Moreover, the killing activity of the majority of these drugs was predictable based on the molecular features of the cell lines. Follow-up of several of these compounds revealed novel mechanisms. For example, compounds that kill by inducing PDE3A-SLFN12 complex formation; vanadium-containing compounds whose killing is dependent on the sulfate transporter SLC26A2; the alcohol dependence drug disulfiram, which kills cells with low expression of metallothioneins; and the anti-inflammatory drug tepoxalin, whose killing is dependent on high expression of the multi-drug resistance gene ABCB1. These results illustrate the potential of the PRISM drug repurposing resource as a starting point for new oncology therapeutic development. The resource is available at <jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httpsdepmap.org>httpsdepmap.org<jatsext-link>.

biorxiv cancer-biology 100-200-users 2019

Serosolver an open source tool to infer epidemiological and immunological dynamics from serological data, bioRxiv, 2019-08-09

AbstractWe present a flexible, open source R package designed to obtain additional biological and epidemiological insights from commonly available serological datasets. Analysis of serological responses against pathogens with multiple strains such as influenza pose a specific statistical challenge because observed antibody responses measured in serological assays depend both on unobserved prior infections and the resulting cross-reactive antibody dynamics that these infections generate. We provide a general modelling framework to jointly infer these two typically confounded biological processes using antibody titres against current and historical strains. We do this by linking latent infection dynamics with a mechanistic model of antibody dynamics that generates expected antibody titres over time. This makes it possible to use observations of antibodies in serological assays to infer an individual’s infection history as well as the parameters of the antibody process model. Our aim is to provide a flexible inference package that can be applied to a range of datasets studying different viruses over different timescales. We present two case studies to illustrate how our model can infer key immunological parameters, such as antibody titre boosting, waning and cross-reaction, and well as latent epidemiological processes such as attack rates and age-stratified infection risk.

biorxiv immunology 0-100-users 2019

 

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