Interaction of the oncoprotein transcription factor MYC with its chromatin cofactor WDR5 is essential for tumor maintenance.

The oncoprotein transcription factor MYC is overexpressed in the majority of cancers. Key to its oncogenic activity is the ability of MYC to regulate gene expression patterns that drive and maintain the malignant state. MYC is also considered a validated anticancer target, but efforts to pharmacologically inhibit MYC have failed. The dependence of MYC on cofactors creates opportunities for therapeutic intervention, but for any cofactor this requires structural understanding of how the cofactor interacts with MYC, knowledge of the role it plays in MYC function, and demonstration that disrupting the cofactor interaction will cause existing cancers to regress. One cofactor for which structural information is available is WDR5, which interacts with MYC to facilitate its recruitment to chromatin. To explore whether disruption of the MYC-WDR5 interaction could potentially become a viable anticancer strategy, we developed a Burkitt\u27s lymphoma system that allows replacement of wild-type MYC for mutants that are defective for WDR5 binding or all known nuclear MYC functions. Using this system, we show that WDR5 recruits MYC to chromatin to control the expression of genes linked to biomass accumulation. We further show that disrupting the MYC-WDR5 interaction within the context of an existing cancer promotes rapid and comprehensive tumor regression in vivo. These observations connect WDR5 to a core tumorigenic function of MYC and establish that, if a therapeutic window can be established, MYC-WDR5 inhibitors could be developed as anticancer agents

Mixture models for undiagnosed prevalent disease and interval-censored incident disease: applications to a cohort assembled from electronic health records.

For cost-effectiveness and efficiency, many large-scale general-purpose cohort studies are being assembled within large health-care providers who use electronic health records. Two key features of such data are that incident disease is interval-censored between irregular visits and there can be pre-existing (prevalent) disease. Because prevalent disease is not always immediately diagnosed, some disease diagnosed at later visits are actually undiagnosed prevalent disease. We consider prevalent disease as a point mass at time zero for clinical applications where there is no interest in time of prevalent disease onset. We demonstrate that the naive Kaplan-Meier cumulative risk estimator underestimates risks at early time points and overestimates later risks. We propose a general family of mixture models for undiagnosed prevalent disease and interval-censored incident disease that we call prevalence-incidence models. Parameters for parametric prevalence-incidence models, such as the logistic regression and Weibull survival (logistic-Weibull) model, are estimated by direct likelihood maximization or by EM algorithm. Non-parametric methods are proposed to calculate cumulative risks for cases without covariates. We compare naive Kaplan-Meier, logistic-Weibull, and non-parametric estimates of cumulative risk in the cervical cancer screening program at Kaiser Permanente Northern California. Kaplan-Meier provided poor estimates while the logistic-Weibull model was a close fit to the non-parametric. Our findings support our use of logistic-Weibull models to develop the risk estimates that underlie current US risk-based cervical cancer screening guidelines. Published 2017. This article has been contributed to by US Government employees and their work is in the public domain in the USA

Adherence Patterns to Extended Cervical Screening Intervals in Women Undergoing HPV and Cytology Cotesting

Although guidelines have recommended extended interval cervical screening using concurrent human papillomavirus (HPV) and cytology (“cotesting”) for over a decade, little is known about its adoption into routine care. Using longitudinal medical record data (2003-2015) from Kaiser Permanente Northern California (KPNC), which adopted triennial cotesting in 2003, we examined adherence to extended interval screening. We analyzed predictors of screening intervals among 504,202 women undergoing routine screening, categorizing interval length into early

Viral coinfection analysis using a MinHash toolkit

Abstract: Background: Human papillomavirus (HPV) is a common sexually transmitted infection associated with cervical cancer that frequently occurs as a coinfection of types and subtypes. Highly similar sublineages that show over 100-fold differences in cancer risk are not distinguishable in coinfections with current typing methods. Results: We describe an efficient set of computational tools, rkmh, for analyzing complex mixed infections of related viruses based on sequence data. rkmh makes extensive use of MinHash similarity measures, and includes utilities for removing host DNA and classifying reads by type, lineage, and sublineage. We show that rkmh is capable of assigning reads to their HPV type as well as HPV16 lineage and sublineages. Conclusions: Accurate read classification enables estimates of percent composition when there are multiple infecting lineages or sublineages. While we demonstrate rkmh for HPV with multiple sequencing technologies, it is also applicable to other mixtures of related sequences

MYC regulates ribosome biogenesis and mitochondrial gene expression programs through its interaction with host cell factor-1.

The oncoprotein transcription factor MYC is a major driver of malignancy and a highly validated but challenging target for the development of anticancer therapies. Novel strategies to inhibit MYC may come from understanding the co-factors it uses to drive pro-tumorigenic gene expression programs, providing their role in MYC activity is understood. Here we interrogate how one MYC co-factor, host cell factor (HCF)-1, contributes to MYC activity in a human Burkitt lymphoma setting. We identify genes connected to mitochondrial function and ribosome biogenesis as direct MYC/HCF-1 targets and demonstrate how modulation of the MYC-HCF-1 interaction influences cell growth, metabolite profiles, global gene expression patterns, and tumor growth in vivo. This work defines HCF-1 as a critical MYC co-factor, places the MYC-HCF-1 interaction in biological context, and highlights HCF-1 as a focal point for development of novel anti-MYC therapies

Geschlechterstereotype und Geschlechterrollen

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Viral coinfection analysis using a MinHash toolkit

Abstract: Background: Human papillomavirus (HPV) is a common sexually transmitted infection associated with cervical cancer that frequently occurs as a coinfection of types and subtypes. Highly similar sublineages that show over 100-fold differences in cancer risk are not distinguishable in coinfections with current typing methods. Results: We describe an efficient set of computational tools, rkmh, for analyzing complex mixed infections of related viruses based on sequence data. rkmh makes extensive use of MinHash similarity measures, and includes utilities for removing host DNA and classifying reads by type, lineage, and sublineage. We show that rkmh is capable of assigning reads to their HPV type as well as HPV16 lineage and sublineages. Conclusions: Accurate read classification enables estimates of percent composition when there are multiple infecting lineages or sublineages. While we demonstrate rkmh for HPV with multiple sequencing technologies, it is also applicable to other mixtures of related sequences

Mass-Tag Enhanced Immuno-Laser Desorption/Ionization Mass Spectrometry for Sensitive Detection of Intact Protein Antigens

A new read-out method for antibody arrays using laser desorption/ionization-mass spectrometry (LDI-MS) is presented. Small, photocleavable reporter molecules with a defined mass called "mass-tags" are used for detection of immunocaptured proteins from human plasma. Using prostate specific antigen (PSA), a biomarker for prostate cancer, as a model antigen, a high sensitivity generic detection methodology based immunocapture with a primary antibody and with a biotin labeled secondary antibody coupled to mass-tagged avidin is demonstrated. As each secondary antibody can bind several avidin molecules, each having a large number of mass-tags, signal amplification can be achieved. The developed PSA sandwich mass-tag analysis method provided a limit of detection below 200 pg/mL (6 pM) for a 10 mu L plasma sample, Well below the clinically relevant cutoff value of 3-4 ng/mL. This brings the limit of detection (LOD) for detection of intact antigens with matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-MS) down to levels comparable to capture by anti-peptide antibodies selected reaction monitoring (SISCAPA SRM) and enzyme linked immunosorbent assay (ELISA), as 6 pM corresponds to a maximal amount of 60 amol PSA captured on-spot. We :propose the potential use Of LDI (laser desorption/ionization) with mass-tag read-out implemented in a sandwich assay format for low abundant and/or early disease biomarker detection