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Inflammation is a cancer hallmark. Nonsteroidal anti-inflammatory drugs (NSAIDs) improve overall survival (OS) in certain cancers. Real-world studies explored here if NSAIDs improve non-small cell lung cancer (NSCLC) OS. Analyses independently interrogated clinical databases from The University of Texas MD Anderson Cancer Center (MDACC cohort, 1987 to 2015; 33,162 NSCLCs and 3,033 NSAID users) and Georgetown-MedStar health system (Georgetown cohort, 2000 to 2019; 4,497 NSCLCs and 1,993 NSAID users). Structured and unstructured clinical data were extracted from electronic health records (EHRs) using natural language processing (NLP). Associations were made between NSAID use and NSCLC prognostic features (tobacco use, gender, race, and body mass index, BMI). NSAIDs were statistically-significantly (P < 0.0001) associated with increased NSCLC survival (5-year OS 29.7% for NSAID users versus 13.1% for non-users) in the MDACC cohort. NSAID users gained 11.6 months over nonusers in 5-year restricted mean survival time. Stratified analysis by stage, histopathology and multicovariable assessment substantiated benefits. NSAID users were pooled independent of NSAID type and by NSAID type. Landmark analysis excluded immortal time bias. Survival improvements (P < 0.0001) were confirmed in the Georgetown cohort. Thus, real-world NSAID usage was independently associated with increased NSCLC survival in the MDACC and Georgetown cohorts. Findings were confirmed by landmark analyses and NSAID type. The OS benefits persisted despite tobacco use and did not depend on gender, race, or BMI (MDACC cohort, P < 0.0001). These real-world findings could guide future NSAID lung cancer randomized trials., Competing Interests: Conflicts of Interest The authors do not report any conflicts of interest to disclose other than SMB and SM are current employees of AstraZeneca, Gaithersburg, MD. However, this study was done before they joined AstraZeneca.

Competing Interests: I have read the journal’s policy and have the following conflicts: AG filed a patent application with the Wisconsin Alumni Research Foundation related to classifying activated T cells. KHY is the inventor of a quantitative pathology analytical system (U.S. Patent 10,832,406). This invention is not related to this work. EJF is a Scientific Advisory Board member at Viosera Therapeutics. AAK is a co-inventor on 4 patent applications related to machine learning applications in biology.

Objective: Life expectancy can be estimated accurately from a cohort of individuals born in the same year and followed from birth to death. However, due to the resource-consuming nature of following a cohort prospectively, life expectancy is often assessed based upon retrospective death record reviews. This conventional approach may lead to potentially biased estimates, in particular when estimating life expectancy of rare diseases such as Morquio syndrome A. We investigated the accuracy of life expectancy estimation using death records by simulating the survival of individuals with Morquio syndrome A under four different scenarios., Results: When life expectancy was constant during the entire period, using death data did not result in a biased estimate. However, when life expectancy increased over time, as is often expected to be the case in rare diseases, using only death data led to a substantial underestimation of life expectancy. We emphasize that it is therefore crucial to understand how estimates of life expectancy are obtained, to interpret them in an appropriate context, and to assess estimation methods within a sensitivity analysis framework, similar to the simulations performed herein., (© 2022. The Author(s).)

After emerging in China in late 2019, the novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread worldwide, and as of mid-2021, it remains a significant threat globally. Only a few coronaviruses are known to infect humans, and only two cause infections similar in severity to SARS-CoV-2: Severe acute respiratory syndrome-related coronavirus , a species closely related to SARS-CoV-2 that emerged in 2002, and Middle East respiratory syndrome-related coronavirus , which emerged in 2012. Unlike the current pandemic, previous epidemics were controlled rapidly through public health measures, but the body of research investigating severe acute respiratory syndrome and Middle East respiratory syndrome has proven valuable for identifying approaches to treating and preventing novel coronavirus disease 2019 (COVID-19). Building on this research, the medical and scientific communities have responded rapidly to the COVID-19 crisis and identified many candidate therapeutics. The approaches used to identify candidates fall into four main categories: adaptation of clinical approaches to diseases with related pathologies, adaptation based on virological properties, adaptation based on host response, and data-driven identification (ID) of candidates based on physical properties or on pharmacological compendia. To date, a small number of therapeutics have already been authorized by regulatory agencies such as the Food and Drug Administration (FDA), while most remain under investigation. The scale of the COVID-19 crisis offers a rare opportunity to collect data on the effects of candidate therapeutics. This information provides insight not only into the management of coronavirus diseases but also into the relative success of different approaches to identifying candidate therapeutics against an emerging disease. IMPORTANCE The COVID-19 pandemic is a rapidly evolving crisis. With the worldwide scientific community shifting focus onto the SARS-CoV-2 virus and COVID-19, a large number of possible pharmaceutical approaches for treatment and prevention have been proposed. What was known about each of these potential interventions evolved rapidly throughout 2020 and 2021. This fast-paced area of research provides important insight into how the ongoing pandemic can be managed and also demonstrates the power of interdisciplinary collaboration to rapidly understand a virus and match its characteristics with existing or novel pharmaceuticals. As illustrated by the continued threat of viral epidemics during the current millennium, a rapid and strategic response to emerging viral threats can save lives. In this review, we explore how different modes of identifying candidate therapeutics have borne out during COVID-19.

The novel coronavirus SARS-CoV-2, which emerged in late 2019, has since spread around the world and infected hundreds of millions of people with coronavirus disease 2019 (COVID-19). While this viral species was unknown prior to January 2020, its similarity to other coronaviruses that infect humans has allowed for rapid insight into the mechanisms that it uses to infect human hosts, as well as the ways in which the human immune system can respond. Here, we contextualize SARS-CoV-2 among other coronaviruses and identify what is known and what can be inferred about its behavior once inside a human host. Because the genomic content of coronaviruses, which specifies the virus's structure, is highly conserved, early genomic analysis provided a significant head start in predicting viral pathogenesis and in understanding potential differences among variants. The pathogenesis of the virus offers insights into symptomatology, transmission, and individual susceptibility. Additionally, prior research into interactions between the human immune system and coronaviruses has identified how these viruses can evade the immune system's protective mechanisms. We also explore systems-level research into the regulatory and proteomic effects of SARS-CoV-2 infection and the immune response. Understanding the structure and behavior of the virus serves to contextualize the many facets of the COVID-19 pandemic and can influence efforts to control the virus and treat the disease. IMPORTANCE COVID-19 involves a number of organ systems and can present with a wide range of symptoms. From how the virus infects cells to how it spreads between people, the available research suggests that these patterns are very similar to those seen in the closely related viruses SARS-CoV-1 and possibly Middle East respiratory syndrome-related CoV (MERS-CoV). Understanding the pathogenesis of the SARS-CoV-2 virus also contextualizes how the different biological systems affected by COVID-19 connect. Exploring the structure, phylogeny, and pathogenesis of the virus therefore helps to guide interpretation of the broader impacts of the virus on the human body and on human populations. For this reason, an in-depth exploration of viral mechanisms is critical to a robust understanding of SARS-CoV-2 and, potentially, future emergent human CoVs (HCoVs).

We have previously established that PV + neurons and Npas1 + neurons are distinct neuron classes in the external globus pallidus (GPe): they have different topographical, electrophysiological, circuit, and functional properties. Aside from Foxp2 + neurons, which are a unique subclass within the Npas1 + class, we lack driver lines that effectively capture other GPe neuron subclasses. In this study, we examined the utility of Kcng4-Cre, Npr3-Cre, and Npy2r-Cre mouse lines (both males and females) for the delineation of GPe neuron subtypes. By using these novel driver lines, we have provided the most exhaustive investigation of electrophysiological studies of GPe neuron subtypes to date. Corroborating our prior studies, GPe neurons can be divided into two statistically distinct clusters that map onto PV + and Npas1 + classes. By combining optogenetics and machine learning-based tracking, we showed that optogenetic perturbation of GPe neuron subtypes generated unique behavioral structures. Our findings further highlighted the dissociable roles of GPe neurons in regulating movement and anxiety-like behavior. We concluded that Npr3 + neurons and Kcng4 + neurons are distinct subclasses of Npas1 + neurons and PV + neurons, respectively. Finally, by examining local collateral connectivity, we inferred the circuit mechanisms involved in the motor patterns observed with optogenetic perturbations. In summary, by identifying mouse lines that allow for manipulations of GPe neuron subtypes, we created new opportunities for interrogations of cellular and circuit substrates that can be important for motor function and dysfunction. SIGNIFICANCE STATEMENT Within the basal ganglia, the external globus pallidus (GPe) has long been recognized for its involvement in motor control. However, we lacked an understanding of precisely how movement is controlled at the GPe level as a result of its cellular complexity. In this study, by using transgenic and cell-specific approaches, we showed that genetically-defined GPe neuron subtypes have distinct roles in regulating motor patterns. In addition, the in vivo contributions of these neuron subtypes are in part shaped by the local, inhibitory connections within the GPe. In sum, we have established the foundation for future investigations of motor function and disease pathophysiology., (Copyright © 2021 the authors.)

Glioblastoma (GBM) is the most aggressive nervous system cancer. Understanding its molecular pathogenesis is crucial to improving diagnosis and treatment. Integrated analysis of genomic, proteomic, post-translational modification and metabolomic data on 99 treatment-naive GBMs provides insights to GBM biology. We identify key phosphorylation events (e.g., phosphorylated PTPN11 and PLCG1) as potential switches mediating oncogenic pathway activation, as well as potential targets for EGFR-, TP53-, and RB1-altered tumors. Immune subtypes with distinct immune cell types are discovered using bulk omics methodologies, validated by snRNA-seq, and correlated with specific expression and histone acetylation patterns. Histone H2B acetylation in classical-like and immune-low GBM is driven largely by BRDs, CREBBP, and EP300. Integrated metabolomic and proteomic data identify specific lipid distributions across subtypes and distinct global metabolic changes in IDH-mutated tumors. This work highlights biological relationships that could contribute to stratification of GBM patients for more effective treatment., Competing Interests: Declaration of interests S.Y. is employed by Sema4. A.H.K. consults for Monteris Medical. P.W. is a statistical consultant for Sema4. M.G.C. receives research support from Orbus Therapeutics and NeoimmuneTech Inc, and royalties from UpToDate., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

In this study, we consider admixed populations through their expected heterozygosity, a measure of genetic diversity. A population is termed admixed if its members possess recent ancestry from two or more separate sources. As a result of the fusion of source populations with different genetic variants, admixed populations can exhibit high levels of genetic diversity, reflecting contributions of their multiple ancestral groups. For a model of an admixed population derived from K source populations, we obtain a relationship between its heterozygosity and its proportions of admixture from the various source populations. We show that the heterozygosity of the admixed population is at least as great as that of the least heterozygous source population, and that it potentially exceeds the heterozygosities of all of the source populations. The admixture proportions that maximize the heterozygosity possible for an admixed population formed from a specified set of source populations are also obtained under specific conditions. We examine the special case of [Formula: see text] source populations in detail, characterizing the maximal admixture in terms of the heterozygosities of the two source populations and the value of [Formula: see text] between them. In this case, the heterozygosity of the admixed population exceeds the maximal heterozygosity of the source groups if the divergence between them, measured by [Formula: see text], is large enough, namely above a certain bound that is a function of the heterozygosities of the source groups. We present applications to simulated data as well as to data from human admixture scenarios, providing results useful for interpreting the properties of genetic variability in admixed populations.

The external globus pallidus (GPe) is a critical node within the basal ganglia circuit. Phasic changes in the activity of GPe neurons during movement and their alterations in Parkinson's disease (PD) argue that the GPe is important in motor control. Parvalbumin-positive (PV + ) neurons and Npas1 + neurons are the two principal neuron classes in the GPe. The distinct electrophysiological properties and axonal projection patterns argue that these two neuron classes serve different roles in regulating motor output. However, the causal relationship between GPe neuron classes and movement remains to be established. Here, by using optogenetic approaches in mice (both males and females), we showed that PV + neurons and Npas1 + neurons promoted and suppressed locomotion, respectively. Moreover, PV + neurons and Npas1 + neurons are under different synaptic influences from the subthalamic nucleus (STN). Additionally, we found a selective weakening of STN inputs to PV + neurons in the chronic 6-hydroxydopamine lesion model of PD. This finding reinforces the idea that the reciprocally connected GPe-STN network plays a key role in disease symptomatology and thus provides the basis for future circuit-based therapies. SIGNIFICANCE STATEMENT The external pallidum is a key, yet an understudied component of the basal ganglia. Neural activity in the pallidum goes awry in neurologic diseases, such as Parkinson's disease. While this strongly argues that the pallidum plays a critical role in motor control, it has been difficult to establish the causal relationship between pallidal activity and motor function/dysfunction. This was in part because of the cellular complexity of the pallidum. Here, we showed that the two principal neuron types in the pallidum have opposing roles in motor control. In addition, we described the differences in their synaptic influence. Importantly, our research provides new insights into the cellular and circuit mechanisms that explain the hypokinetic features of Parkinson's disease., (Copyright © 2020 the authors.)

We consider the scenario where there is an exposure, multiple biologically defined sets of biomarkers, and an outcome. We propose a new two-step procedure that tests if any of the sets of biomarkers mediate the exposure/outcome relationship, while maintaining a prespecified familywise error rate. The first step of the proposed procedure is a screening step that removes all groups that are unlikely to be strongly associated with both the exposure and the outcome. The second step adapts recent advances in postselection inference to test if there are true mediators in each of the remaining candidate sets. We use simulation to show that this simple two-step procedure has higher statistical power to detect true mediating sets when compared with existing procedures. We then use our two-step procedure to identify a set of Lysine-related metabolites that potentially mediate the known relationship between increased body mass index and the increased risk of estrogen-receptor positive breast cancer in postmenopausal women., (© 2020 John Wiley & Sons, Ltd.)

Purpose: The cancer research community is constantly evolving to better understand tumor biology, disease etiology, risk stratification, and pathways to novel treatments. Yet the clinical cancer genomics field has been hindered by redundant efforts to meaningfully collect and interpret disparate data types from multiple high-throughput modalities and integrate into clinical care processes. Bespoke data models, knowledgebases, and one-off customized resources for data analysis often lack adequate governance and quality control needed for these resources to be clinical grade. Many informatics efforts focused on genomic interpretation resources for neoplasms are underway to support data collection, deposition, curation, harmonization, integration, and analytics to support case review and treatment planning., Methods: In this review, we evaluate and summarize the landscape of available tools, resources, and evidence used in the evaluation of somatic and germline tumor variants within the context of molecular tumor boards., Results: Molecular tumor boards (MTBs) are collaborative efforts of multidisciplinary cancer experts equipped with genomic interpretation resources to aid in the delivery of accurate and timely clinical interpretations of complex genomic results for each patient, within an institution or hospital network. Virtual MTBs (VMTBs) provide an online forum for collaborative governance, provenance, and information sharing between experts outside a given hospital network with the potential to enhance MTB discussions. Knowledge sharing in VMTBs and communication with guideline-developing organizations can lead to progress evidenced by data harmonization across resources, crowd-sourced and expert-curated genomic assertions, and a more informed and explainable usage of artificial intelligence., Conclusion: Advances in cancer genomics interpretation aid in better patient and disease classification, more streamlined identification of relevant literature, and a more thorough review of available treatments and predicted patient outcomes.

Within the basal ganglia circuit, the external globus pallidus (GPe) is critically involved in motor control. Aside from Foxp2 + neurons and ChAT + neurons that have been established as unique neuron types, there is little consensus on the classification of GPe neurons. Properties of the remaining neuron types are poorly defined. In this study, we leverage new mouse lines, viral tools, and molecular markers to better define GPe neuron subtypes. We found that Sox6 represents a novel, defining marker for GPe neuron subtypes. Lhx6 + neurons that lack the expression of Sox6 were devoid of both parvalbumin and Npas1. This result confirms previous assertions of the existence of a unique Lhx6 + population. Neurons that arise from the Dbx1 + lineage were similarly abundant in the GPe and displayed a heterogeneous makeup. Importantly, tracing experiments revealed that Npas1 + -Nkx2.1 + neurons represent the principal noncholinergic, cortically-projecting neurons. In other words, they form the pallido-cortical arm of the cortico-pallido-cortical loop. Our data further show that pyramidal-tract neurons in the cortex collateralized within the GPe, forming a closed-loop system between the two brain structures. Overall, our findings reconcile some of the discrepancies that arose from differences in techniques or the reliance on preexisting tools. Although spatial distribution and electrophysiological properties of GPe neurons reaffirm the diversification of GPe subtypes, statistical analyses strongly support the notion that these neuron subtypes can be categorized under the two principal neuron classes: PV + neurons and Npas1 + neurons. SIGNIFICANCE STATEMENT The poor understanding of the neuronal composition in the external globus pallidus (GPe) undermines our ability to interrogate its precise behavioral and disease involvements. In this study, 12 different genetic crosses were used, hundreds of neurons were electrophysiologically characterized, and >100,000 neurons were histologically- and/or anatomically-profiled. Our current study further establishes the segregation of GPe neuron classes and illustrates the complexity of GPe neurons in adult mice. Our results support the idea that Npas1 + -Nkx2.1 + neurons are a distinct GPe neuron subclass. By providing a detailed analysis of the organization of the cortico-pallidal-cortical projection, our findings establish the cellular and circuit substrates that can be important for motor function and dysfunction., (Copyright © 2020 the authors.)

Purpose: In this work, we introduce CDGnet (Cancer-Drug-Gene Network), an evidence-based network approach for recommending targeted cancer therapies. CDGnet represents a user-friendly informatics tool that expands the range of targeted therapy options for patients with cancer who undergo molecular profiling by including the biologic context via pathway information., Methods: CDGnet considers biologic pathway information specifically by looking at targets or biomarkers downstream of oncogenes and is personalized for individual patients via user-inputted molecular alterations and cancer type. It integrates a number of different sources of knowledge: patient-specific inputs (molecular alterations and cancer type), US Food and Drug Administration-approved therapies and biomarkers (curated from DailyMed), pathways for specific cancer types (from Kyoto Encyclopedia of Genes and Genomes [KEGG]), gene-drug connections (from DrugBank), and oncogene information (from KEGG). We consider 4 different evidence-based categories for therapy recommendations. Our tool is delivered via an R/Shiny Web application. For the 2 categories that use pathway information, we include an interactive Sankey visualization built on top of d3.js that also provides links to PubChem., Results: We present a scenario for a patient who has estrogen receptor (ER)-positive breast cancer with FGFR1 amplification. Although many therapies exist for patients with ER-positive breast cancer, FGFR1 amplifications may confer resistance to such treatments. CDGnet provides therapy recommendations, including PIK3CA, MAPK, and RAF inhibitors, by considering targets or biomarkers downstream of FGFR1., Conclusion: CDGnet provides results in a number of easily accessible and usable forms, separating targeted cancer therapies into categories in an evidence-based manner that incorporates biologic pathway information.

Triple negative breast cancer (TNBC), a clinically aggressive breast cancer subtype, affects 15-35% of women from Latin America. Using an approach of direct integration of copy number and global miRNA profiling data, performed simultaneously in the same tumor specimens, we identified a panel of 17 miRNAs specifically associated with TNBC of ancestrally characterized patients from Latin America, Brazil. This panel was differentially expressed between the TNBC and non-TNBC subtypes studied ( p ≤ 0.05, FDR ≤ 0.25), with their expression levels concordant with the patterns of copy number alterations (CNAs), present mostly frequent at 8q21.3-q24.3, 3q24-29, 6p25.3-p12.2, 1q21.1-q44, 5q11.1-q22.1, 11p13-p11.2, 13q12.11-q14.3, 17q24.2-q25.3 and Xp22.33-p11.21. The combined 17 miRNAs presented a high power (AUC = 0.953 (0.78-0.99);95% CI) in discriminating between the TNBC and non-TNBC subtypes of the patients studied. In addition, the expression of 14 and 15 of the 17miRNAs was significantly associated with tumor subtype when adjusted for tumor stage and grade, respectively. In conclusion, the panel of miRNAs identified demonstrated the impact of CNAs in miRNA expression levels and identified miRNA target genes potentially affected by both CNAs and miRNA deregulation. These targets, involved in critical signaling pathways and biological functions associated specifically with the TNBC transcriptome of Latina patients, can provide biological insights into the observed differences in the TNBC clinical outcome among racial/ethnic groups, taking into consideration their genetic ancestry., Competing Interests: CONFLICTS OF INTEREST All the authors declare no conflicts of interest., (Copyright: © 2019 Sugita et al.)

Objectives: Scalable informatics solutions that provide molecularly tailored treatment recommendations to clinicians are needed to streamline precision oncology in care settings., Materials and Methods: We developed a cloud-based virtual molecular tumor board (VMTB) platform that included a knowledgebase, scoring model, rules engine, an asynchronous virtual chat room and a reporting tool that generated a treatment plan for each of the 1725 patients based on their molecular profile, previous treatment history, structured trial eligibility criteria, clinically relevant cancer gene-variant assertions, biomarker-treatment associations, and current treatment guidelines. The VMTB systematically allows clinician users to combine expert-curated data and structured data from clinical charts along with molecular testing data to develop consensus on treatments, especially those that require off-label and clinical trial considerations., Results: The VMTB was used as part of the cancer care process for a focused subset of 1725 patients referred by advocacy organizations wherein resultant personalized reports were successfully delivered to treating oncologists. Median turnaround time from data receipt to report delivery decreased from 14 days to 4 days over 4 years while the volume of cases increased nearly 2-fold each year. Using a novel scoring model for ranking therapy options, oncologists chose to implement the VMTB-derived therapies over others, except when pursuing immunotherapy options without molecular support., Discussion: VMTBs will play an increasingly critical role in precision oncology as the compendium of biomarkers and associated therapy options available to a patient continues to expand., Conclusion: Further development of such clinical augmentation tools that systematically combine patient-derived molecular data, real-world evidence from electronic health records and expert curated knowledgebases on biomarkers with computational tools for ranking best treatments can support care pathways at point of care., (© The Author(s) 2019. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

The population-genetic statistic [Formula: see text] is used widely to describe allele frequency distributions in subdivided populations. The increasing availability of DNA sequence data has recently enabled computations of [Formula: see text] from sequence-based "haplotype loci." At the same time, theoretical work has revealed that [Formula: see text] has a strong dependence on the underlying genetic diversity of a locus from which it is computed, with high diversity constraining values of [Formula: see text] to be low. In the case of haplotype loci, for which two haplotypes that are distinct over a specified length along a chromosome are treated as distinct alleles, genetic diversity is influenced by haplotype length: longer haplotype loci have the potential for greater genetic diversity. Here, we study the dependence of [Formula: see text] on haplotype length. Using a model in which a haplotype locus is sequentially incremented by one biallelic locus at a time, we show that increasing the length of the haplotype locus can either increase or decrease the value of [Formula: see text], and usually decreases it. We compute [Formula: see text] on haplotype loci in human populations, finding a close correspondence between the observed values and our theoretical predictions. We conclude that effects of haplotype length are valuable to consider when interpreting [Formula: see text] calculated on haplotypic data., (Copyright © 2019 by the Genetics Society of America.)

Objective: The KRAS gene is the most frequently mutated gene in pancreatic cancer, and no successful anti-Ras therapy has been developed. Gastrin has been shown to stimulate pancreatic cancer in an autocrine fashion. We hypothesized that reactivation of the peptide gastrin collaborates with KRAS during pancreatic carcinogenesis., Methods: LSL-Kras; P48-Cre (KC) mutant KRAS transgenic mice were crossed with gastrin-KO (GKO) mice to develop GKO/KC mice. Pancreata were examined for 8 months for stage of pancreatic intraepithelial neoplasia lesions, inflammation, fibrosis, gastrin peptide, and microRNA expression. Pancreatic intraepithelial neoplasias from mice were collected by laser capture microdissection and subjected to reverse-phase protein microarray, for gastrin and protein kinases associated with signal transduction. Gastrin mRNA was measured by RNAseq in human pancreatic cancer tissues and compared to that in normal pancreas., Results: In the absence of gastrin, PanIN progression, inflammation, and fibrosis were significantly decreased and signal transduction was reversed to the canonical pathway with decreased KRAS. Gastrin re-expression in the PanINs was mediated by miR-27a. Gastrin mRNA expression was significantly increased in human pancreatic cancer samples compared to normal human pancreas controls., Conclusions: This study supports the mitogenic role of gastrin in activation of KRAS during pancreatic carcinogenesis.

Urine is increasingly being considered as a source of biomarker development in Duchenne Muscular Dystrophy (DMD), a severe, life-limiting disorder that affects approximately 1 in 4500 boys. In this study, we considered the mdx mice-a murine model of DMD-to discover biomarkers of disease, as well as pharmacodynamic biomarkers responsive to prednisolone, a corticosteroid commonly used to treat DMD. Longitudinal urine samples were analyzed from male age-matched mdx and wild-type mice randomized to prednisolone or vehicle control via liquid chromatography tandem mass spectrometry. A large number of metabolites (869 out of 6,334) were found to be significantly different between mdx and wild-type mice at baseline (Bonferroni-adjusted p-value < 0.05), thus being associated with disease status. These included a metabolite with m/z = 357 and creatine, which were also reported in a previous human study looking at serum. Novel observations in this study included peaks identified as biliverdin and hypusine. These four metabolites were significantly higher at baseline in the urine of mdx mice compared to wild-type, and significantly changed their levels over time after baseline. Creatine and biliverdin levels were also different between treated and control groups, but for creatine this may have been driven by an imbalance at baseline. In conclusion, our study reports a number of biomarkers, both known and novel, which may be related to either the mechanisms of muscle injury in DMD or prednisolone treatment., Competing Interests: The authors have declared that no competing interests exist.

Modern scientific studies from many diverse areas of research abound with multiple hypothesis testing concerns. The false discovery rate (FDR) is one of the most commonly used approaches for measuring and controlling error rates when performing multiple tests. Adaptive FDRs rely on an estimate of the proportion of null hypotheses among all the hypotheses being tested. This proportion is typically estimated once for each collection of hypotheses. Here, we propose a regression framework to estimate the proportion of null hypotheses conditional on observed covariates. This may then be used as a multiplication factor with the Benjamini-Hochberg adjusted p -values, leading to a plug-in FDR estimator. We apply our method to a genome-wise association meta-analysis for body mass index. In our framework, we are able to use the sample sizes for the individual genomic loci and the minor allele frequencies as covariates. We further evaluate our approach via a number of simulation scenarios. We provide an implementation of this novel method for estimating the proportion of null hypotheses in a regression framework as part of the Bioconductor package swfdr., Competing Interests: The authors declare that they have no competing interests.

Harmonization of cancer variant representation, efficient communication, and free distribution of clinical variant-associated knowledge are central problems that arise with increased usage of clinical next-generation sequencing. The Clinical Genome Resource (ClinGen) Somatic Working Group (WG) developed a minimal variant level data (MVLD) representation of cancer variants, and has an ongoing collaboration with Clinical Interpretations of Variants in Cancer (CIViC), an open-source platform supporting crowdsourced and expert-moderated cancer variant curation. Harmonization between MVLD and CIViC variant formats was assessed by formal field-by-field analysis. Adjustments to the CIViC format were made to harmonize with MVLD and support ClinGen Somatic WG curation activities, including four new features in CIViC: (1) introduction of an assertions feature for clinical variant assessment following the Association of Molecular Pathologists (AMP) guidelines, (2) group-level curation tracking for organizations, enabling member transparency, and curation effort summaries, (3) introduction of ClinGen Allele Registry IDs to CIViC, and (4) mapping of CIViC assertions into ClinVar submission with automated submissions. A generalizable workflow utilizing MVLD and new CIViC features is outlined for use by ClinGen Somatic WG task teams for curation and submission to ClinVar, and provides a model for promoting harmonization of cancer variant representation and efficient distribution of this information., (© 2018 The Authors. Human Mutation published by Wiley Periodicals, Inc.)

Purpose: We conducted usability studies on commercially available molecular diagnostic (MDX) test reports to identify strengths and weaknesses in content and form that drive clinical decision making. Given routine genomic testing in cancer medicine, oncologists must interpret MDX reports as well as evidence concerning clinical utility of biomarkers accurately for treatment or trial selection. This work aims to evaluate effectiveness of MDX reports in facilitating cancer treatment planning., Methods: Fourteen clinicians at an academic tertiary care medical facility, with a wide range of experience in oncology and in the use of molecular testing, participated in this study. Three commercially available, widely used, Clinical Laboratory Improvement Amendments (CLIA)-certified, College of American Pathologists (CAP)-accredited test reports (labeled Laboratories A, B, and C) were used. Eye tracking, surveys, and think-aloud protocols were used to collect usability data for these MDX reports focusing on ease of comprehension and actionability., Results: Clinicians found two primary areas in molecular diagnostic reports most useful for patient care: therapy options with benefit or lack of benefit to patients, including enrolling clinical trials; and pathogenic tumor molecular anomalies detected. Therapeutic implications and therapy classes such as US Food and Drug Administration-approved off-label, on-label, clinical trials were critical for decision making. However, all reports had usability and comprehension issues in these areas and could be improved., Conclusion: Focused usability studies can help drive our understanding of the clinical workflow for use of molecular diagnostic tests in cancer care. This in turn can have major effects on quality of care, outcomes, costs, and patient satisfaction. This study demonstrates the use of specific usability techniques (eye tracking and think-aloud protocols) to help clinical laboratories improve MDX report design in a precision oncology treatment setting.

Motivation: The biological pathways linking exposures and disease risk are often poorly understood. To gain insight into these pathways, studies may try to identify biomarkers that mediate the exposure/disease relationship. Such studies often simultaneously test hundreds or thousands of biomarkers., Results: We consider a set of m biomarkers and a corresponding set of null hypotheses, where the jth null hypothesis states that biomarker j does not mediate the exposure/disease relationship. We propose a Multiple Comparison Procedure (MCP) that rejects a set of null hypotheses or, equivalently, identifies a set of mediators, while asymptotically controlling the Family-Wise Error Rate (FWER) or False Discovery Rate (FDR). We use simulations to show that, compared to currently available methods, our proposed method has higher statistical power to detect true mediators. We then apply our method to a breast cancer study and identify nine metabolites that may mediate the known relationship between an increased BMI and an increased risk of breast cancer., Availability and Implementation: R package MultiMed on https://github.com/SiminaB/MultiMed., Supplementary Information: Supplementary data are available at Bioinformatics online.

Failure of clinical trials due to development of resistance to MET-targeting therapeutic agents is an emerging problem. Mechanisms of acquired resistance to MET tyrosine kinase inhibitors are well described, whereas characterization of mechanisms of resistance toward MET-targeting antibodies is limited. This study investigated mechanisms underlying in vivo resistance to two antibody therapeutics currently in clinical development: an analogue of the MET-targeting antibody emibetuzumab and Sym015, a mixture of two antibodies targeting nonoverlapping epitopes of MET. Upon long-term in vivo treatment of a MET -amplified gastric cancer xenograft model (SNU-5), emibetuzumab-resistant, but not Sym015-resistant, tumors emerged. Resistant tumors were isolated and used to establish resistant cell lines. Characterization of both tumors and cell lines using extensive protein and signaling pathway activation mapping along with next-generation sequencing revealed two distinct resistance profiles, one involving PTEN loss and the other involving activation of the PI3K pathway, likely via MYC and ERBB3 copy number gains. PTEN loss left one model unaffected by PI3K/AKT targeting but sensitive to mTOR targeting, while the PI3K pathway-activated model was partly sensitive to targeting of multiple PI3K pathway proteins. Importantly, both resistant models were sensitive to treatment with Sym015 in vivo due to antibody-dependent cellular cytotoxicity-mediated tumor growth inhibition, MET degradation, and signaling inhibition. Taken together, our data provide key insights into potential mechanisms of resistance to a single MET-targeting antibody, demonstrate superiority of Sym015 in preventing acquired resistance, and confirm Sym015 antitumor activity in tumors resistant to a single MET antibody. Mol Cancer Ther; 17(6); 1259-70. ©2018 AACR ., (©2018 American Association for Cancer Research.)

Statistical approaches for population structure estimation have been predominantly driven by a particular data type, single-nucleotide polymorphisms (SNPs). However, in the presence of weak identifiability in SNPs, population structure estimation can suffer from undesirable accuracy loss. Copy number variations (CNVs) are genomic structural variants with loci that are commonly shared within a specific population and thus provide valuable information for estimation of the ancestry of sampled populations. We develop a Bayesian joint modeling framework of SNPs and CNVs, called POPSTR, to better understand population structure than approaches that use SNPs solely. To deal with the increased data volume, we use the Metropolis Adjusted Langevin algorithm (MALA) that guides the target distribution in a computationally efficient way. We illustrate applications of our approach using the HapMap 2005 project data. We carry out simulation studies and show that the performance of our approach is comparable or better than that of popular benchmarks, STRUCTURE and ADMIXTURE. We also observe that using only CNVs can be remarkably efficient if SNP data are not available.

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine., (© 2018 The Authors.)

Competing Interests: AUTHORS’ DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO’s conflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/po/author-center. Simina M. Boca Research Funding: Symphogen (Inst) Orestis A. Panagiotou No relationship to disclose Shruti Rao Research Funding: Symphogen (Inst) Peter B. McGarvey No relationship to disclose Subha Madhavan Leadership: Perthera Stock and Other Ownership Interests: Perthera Consulting or Advisory Role: Perthera

Predictive biomarkers have the potential to facilitate cancer precision medicine by guiding the optimal choice of therapies for patients. However, clinicians are faced with an enormous volume of often-contradictory evidence regarding the therapeutic context of chemopredictive biomarkers.We extensively surveyed public literature to systematically review the predictive effect of 7 biomarkers claimed to predict response to various chemotherapy drugs: ERCC1-platinums, RRM1-gemcitabine, TYMS-5-fluorouracil/Capecitabine, TUBB3-taxanes, MGMT-temozolomide, TOP1-irinotecan/topotecan, and TOP2A-anthracyclines. We focused on studies that investigated changes in gene or protein expression as predictors of drug sensitivity or resistance. We considered an evidence framework that ranked studies from high level I evidence for randomized controlled trials to low level IV evidence for pre-clinical studies and patient case studies.We found that further in-depth analysis will be required to explore methodological issues, inconsistencies between studies, and tumor specific effects present even within high evidence level studies. Some of these nuances will lend themselves to automation, others will require manual curation. However, the comprehensive cataloging and analysis of dispersed public data utilizing an evidence framework provides a high level perspective on clinical actionability of these protein biomarkers. This framework and perspective will ultimately facilitate clinical trial design as well as therapeutic decision-making for individual patients.

About 70% of all breast cancers are estrogen receptor alpha positive (ER+; ESR1). Many are treated with antiestrogens. Unfortunately, de novo and acquired resistance to antiestrogens is common but the underlying mechanisms remain unclear. Since growth of cancer cells is dependent on adequate energy and metabolites, the metabolomic profile of endocrine resistant breast cancers likely contains features that are deterministic of cell fate. Thus, we integrated data from metabolomic and transcriptomic analyses of ER+ MCF7-derived breast cancer cells that are antiestrogen sensitive (LCC1) or resistant (LCC9) that resulted in a gene-metabolite network associated with EGR1 (early growth response 1). In human ER+ breast tumors treated with endocrine therapy, higher EGR1 expression was associated with a more favorable prognosis. Mechanistic studies showed that knockdown of EGR1 inhibited cell growth in both cells and EGR1 overexpression did not affect antiestrogen sensitivity. Comparing metabolite profiles in LCC9 cells following perturbation of EGR1 showed interruption of lipid metabolism. Tolfenamic acid, an anti-inflammatory drug, decreased EGR1 protein levels and synergized with antiestrogens in inhibiting cell proliferation in LCC9 cells. Collectively, these findings indicate that EGR1 is an important regulator of breast cancer cell metabolism and is a promising target to prevent or reverse endocrine resistance., Competing Interests: CONFLICTS OF INTEREST The authors have no conflicts of interest to disclose.

Meta-analysis can average estimates of multiple parameters, such as a treatment's effect on multiple outcomes, across studies. Univariate meta-analysis (UVMA) considers each parameter individually, while multivariate meta-analysis (MVMA) considers the parameters jointly and accounts for the correlation between their estimates. The performance of MVMA and UVMA has been extensively compared in scenarios with two parameters. Our objective is to compare the performance of MVMA and UVMA as the number of parameters, p, increases. Specifically, we show that (i) for fixed-effect (FE) meta-analysis, the benefit from using MVMA can substantially increase as p increases; (ii) for random effects (RE) meta-analysis, the benefit from MVMA can increase as p increases, but the potential improvement is modest in the presence of high between-study variability and the actual improvement is further reduced by the need to estimate an increasingly large between study covariance matrix; and (iii) when there is little to no between-study variability, the loss of efficiency due to choosing RE MVMA over FE MVMA increases as p increases. We demonstrate these three features through theory, simulation, and a meta-analysis of risk factors for non-Hodgkin lymphoma., (© Published 2017. This article is a U.S. Government work and is in the public domain in the USA.)

Domains are folded structures and evolutionary building blocks of protein molecules. Their three-dimensional atomic conformations, which define biological functions, can be coarse-grained into levels of a hierarchy. Here we build global dynamical models for the evolution of domains at fold and fold superfamily (FSF) levels. We fit the models with data from phylogenomic trees of domain structures and evaluate the distributions of the resulting parameters and their implications. The trees were inferred from a census of domain structures in hundreds of genomes from all three superkingdoms of life. The models used birth-death differential equations with the global abundances of structures as state variables, with one set of equations for folds and another for FSFs. Only the transitions present in the tree are assumed possible. Each fold or FSF diversifies in variants, eventually producing a new fold or FSF. The parameters specify rates of generation of variants and of new folds or FSFs. The equations were solved for the parameters by simplifying the trees to a comb-like topology, treating branches as emerging directly from a trunk. We found that the rate constants for folds and FSFs evolved similarly. These parameters showed a sharp transient change at about 1.5 Gyrs ago. This time coincides with a period in which domains massively combined in proteins and their arrangements distributed in novel lineages during the rise of organismal diversification. Our simulations suggest that exploration of protein structure space occurs through coarse-grained discoveries that undergo fine-grained elaboration.

Serum metabolite profiling in Duchenne muscular dystrophy (DMD) may enable discovery of valuable molecular markers for disease progression and treatment response. Serum samples from 51 DMD patients from a natural history study and 22 age-matched healthy volunteers were profiled using liquid chromatography coupled to mass spectrometry (LC-MS) for discovery of novel circulating serum metabolites associated with DMD. Fourteen metabolites were found significantly altered (1% false discovery rate) in their levels between DMD patients and healthy controls while adjusting for age and study site and allowing for an interaction between disease status and age. Increased metabolites included arginine, creatine and unknown compounds at m/z of 357 and 312 while decreased metabolites included creatinine, androgen derivatives and other unknown yet to be identified compounds. Furthermore, the creatine to creatinine ratio is significantly associated with disease progression in DMD patients. This ratio sharply increased with age in DMD patients while it decreased with age in healthy controls. Overall, this study yielded promising metabolic signatures that could prove useful to monitor DMD disease progression and response to therapies in the future.

Background: Studies of related individuals have consistently demonstrated notable familial aggregation of cancer. We aim to estimate the heritability and genetic correlation attributable to the additive effects of common single-nucleotide polymorphisms (SNPs) for cancer at 13 anatomical sites., Methods: Between 2007 and 2014, the US National Cancer Institute has generated data from genome-wide association studies (GWAS) for 49 492 cancer case patients and 34 131 control patients. We apply novel mixed model methodology (GCTA) to this GWAS data to estimate the heritability of individual cancers, as well as the proportion of heritability attributable to cigarette smoking in smoking-related cancers, and the genetic correlation between pairs of cancers., Results: GWAS heritability was statistically significant at nearly all sites, with the estimates of array-based heritability, hl (2), on the liability threshold (LT) scale ranging from 0.05 to 0.38. Estimating the combined heritability of multiple smoking characteristics, we calculate that at least 24% (95% confidence interval [CI] = 14% to 37%) and 7% (95% CI = 4% to 11%) of the heritability for lung and bladder cancer, respectively, can be attributed to genetic determinants of smoking. Most pairs of cancers studied did not show evidence of strong genetic correlation. We found only four pairs of cancers with marginally statistically significant correlations, specifically kidney and testes (ρ = 0.73, SE = 0.28), diffuse large B-cell lymphoma (DLBCL) and pediatric osteosarcoma (ρ = 0.53, SE = 0.21), DLBCL and chronic lymphocytic leukemia (CLL) (ρ = 0.51, SE =0.18), and bladder and lung (ρ = 0.35, SE = 0.14). Correlation analysis also indicates that the genetic architecture of lung cancer differs between a smoking population of European ancestry and a nonsmoking Asian population, allowing for the possibility that the genetic etiology for the same disease can vary by population and environmental exposures., Conclusion: Our results provide important insights into the genetic architecture of cancers and suggest new avenues for investigation., (Published by Oxford University Press 2015. This work is written by (a) US Government employee(s) and is in the public domain in the US.)

Background: Coffee intake may be inversely associated with colorectal cancer; however, previous studies have been inconsistent. Serum coffee metabolites are integrated exposure measures that may clarify associations with cancer and elucidate underlying mechanisms., Objectives: Our aims were 2-fold as follows: 1) to identify serum metabolites associated with coffee intake and 2) to examine these metabolites in relation to colorectal cancer., Design: In a nested case-control study of 251 colorectal cancer cases and 247 matched control subjects from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial, we conducted untargeted metabolomics analyses of baseline serum by using ultrahigh-performance liquid-phase chromatography-tandem mass spectrometry and gas chromatography-mass spectrometry. Usual coffee intake was self-reported in a food-frequency questionnaire. We used partial Pearson correlations and linear regression to identify serum metabolites associated with coffee intake and conditional logistic regression to evaluate associations between coffee metabolites and colorectal cancer., Results: After Bonferroni correction for multiple comparisons (P = 0.05 ÷ 657 metabolites), 29 serum metabolites were positively correlated with coffee intake (partial correlation coefficients: 0.18-0.61; P < 7.61 × 10(-5)); serum metabolites most highly correlated with coffee intake (partial correlation coefficients >0.40) included trigonelline (N'-methylnicotinate), quinate, and 7 unknown metabolites. Of 29 serum metabolites, 8 metabolites were directly related to caffeine metabolism, and 3 of these metabolites, theophylline (OR for 90th compared with 10th percentiles: 0.44; 95% CI: 0.25, 0.79; P-linear trend = 0.006), caffeine (OR for 90th compared with 10th percentiles: 0.56; 95% CI: 0.35, 0.89; P-linear trend = 0.015), and paraxanthine (OR for 90th compared with 10th percentiles: 0.58; 95% CI: 0.36, 0.94; P-linear trend = 0.027), were inversely associated with colorectal cancer., Conclusions: Serum metabolites can distinguish coffee drinkers from nondrinkers; some caffeine-related metabolites were inversely associated with colorectal cancer and should be studied further to clarify the role of coffee in the cause of colorectal cancer. The Prostate, Lung, Colorectal, and Ovarian trial was registered at clinicaltrials.gov as NCT00002540., (© 2015 American Society for Nutrition.)

Ionizing radiation (IR) is an integral component of our lives due to highly prevalent sources such as medical, environmental, and/or accidental. Thus, understanding of the mechanisms by which radiation toxicity develops is crucial to address acute and chronic health problems that occur following IR exposure. Immediate formation of IR-induced free radicals as well as their persistent effects on metabolism through subsequent alterations in redox mediated inter- and intracellular processes are globally accepted as significant contributors to early and late effects of IR exposure. This includes but is not limited to cytotoxicity, genomic instability, fibrosis and inflammation. Damage to the critical biomolecules leading to detrimental long-term alterations in metabolic redox homeostasis following IR exposure has been the focus of various independent investigations over last several decades. The growth of the "omics" technologies during the past decade has enabled integration of "data from traditional radiobiology research", with data from metabolomics studies. This review will focus on the role of tetrahydrobiopterin (BH4), an understudied redox-sensitive metabolite, plays in the pathogenesis of post-irradiation normal tissue injury as well as how the metabolomic readout of BH4 metabolism fits in the overall picture of disrupted oxidative metabolism following IR exposure.

Background: The application of metabolomics in epidemiological studies would potentially allow researchers to identify biomarkers associated with exposures and diseases. However, within-individual variability of metabolite levels caused by temporal variation of metabolites, together with technical variability introduced by laboratory procedures, may reduce the study power to detect such associations. We assessed the sources of variability of metabolites from urine samples and the implications for designing epidemiologic studies., Methods: We measured 539 metabolites in urine samples from the Navy Colon Adenoma Study using liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectroscopy (GC-MS). The study collected 2-3 samples per person from 17 male subjects (age 38-70) over 2-10 days. We estimated between-individual, within-individual, and technical variability and calculated expected study power with a specific focus on large case-control and nested case-control studies., Results: Overall technical reliability was high (median intraclass correlation = 0.92), and for 72% of the metabolites, the majority of total variance can be attributed to between-individual variability. Age, gender and body mass index explained only a small proportion of the total metabolite variability. For a relative risk (comparing upper and lower quartiles of "usual" levels) of 1.5, we estimated that a study with 500, 1,000, and 5,000 individuals could detect 1.0%, 4.5% and 75% of the metabolite associations., Conclusions: The use of metabolomics in urine samples from epidemiological studies would require large sample sizes to detect associations with moderate effect sizes.

Background: A high body mass index (BMI) is a major risk factor for several chronic diseases, but the biology underlying these associations is not well-understood. Dyslipidemia, inflammation, and elevated levels of growth factors and sex steroid hormones explain some of the increased disease risk, but other metabolic factors not yet identified may also play a role., Design: In order to discover novel metabolic biomarkers of BMI, we used non-targeted metabolomics to assay 317 metabolites in blood samples from 947 participants and examined the cross-sectional associations between metabolite levels and BMI. Participants were from three studies in the United States and China. Height, weight, and potential confounders were ascertained by questionnaire (US studies) or direct measurement (Chinese study). Metabolite levels were measured using liquid-phase chromatography and gas chromatography coupled with mass spectrometry. We evaluated study-specific associations using linear regression, adjusted for age, gender, and smoking, and we estimated combined associations using random effects meta-analysis., Results: The meta-analysis revealed 37 metabolites significantly associated with BMI, including 19 lipids, 12 amino acids, and 6 others, at the Bonferroni significance threshold (p<0.00016). Eighteen of these associations had not been previously reported, including histidine, an amino acid neurotransmitter, and butyrylcarnitine, a lipid marker of whole-body fatty acid oxidation. Heterogeneity by study was minimal (all P heterogeneity >0.05). In total, 110 metabolites were associated with BMI at the p<0.05 level., Conclusion: These findings establish a baseline for the BMI metabolome, and suggest new targets for researchers attempting to clarify mechanistic links between high BMIs and disease risk.

Motivation: Modern biomedical and epidemiological studies often measure hundreds or thousands of biomarkers, such as gene expression or metabolite levels. Although there is an extensive statistical literature on adjusting for 'multiple comparisons' when testing whether these biomarkers are directly associated with a disease, testing whether they are biological mediators between a known risk factor and a disease requires a more complex null hypothesis, thus offering additional methodological challenges., Results: We propose a permutation approach that tests multiple putative mediators and controls the family wise error rate. We demonstrate that, unlike when testing direct associations, replacing the Bonferroni correction with a permutation approach that focuses on the maximum of the test statistics can significantly improve the power to detect mediators even when all biomarkers are independent. Through simulations, we show the power of our method is 2-5× larger than the power achieved by Bonferroni correction. Finally, we apply our permutation test to a case-control study of dietary risk factors and colorectal adenoma to show that, of 149 test metabolites, docosahexaenoate is a possible mediator between fish consumption and decreased colorectal adenoma risk., Availability and Implementation: R-package included in online Supplementary Material.

A key problem in high-dimensional significance analysis is to find pre-defined sets that show enrichment for a statistical signal of interest; the classic example is the enrichment of gene sets for differentially expressed genes. Here, we propose a new decision-theory approach to the analysis of gene sets which focuses on estimating the fraction of non-null variables in a set. We introduce the idea of "atoms," non-overlapping sets based on the original pre-defined set annotations. Our approach focuses on finding the union of atoms that minimizes a weighted average of the number of false discoveries and missed discoveries. We introduce a new false discovery rate for sets, called the atomic false discovery rate (afdr), and prove that the optimal estimator in our decision-theory framework is to threshold the afdr. These results provide a coherent and interpretable framework for the analysis of sets that addresses the key issues of overlapping annotations and difficulty in interpreting p values in both competitive and self-contained tests. We illustrate our method and compare it to a popular existing method using simulated examples, as well as gene-set and brain ROI data analyses., (© 2013, The International Biometric Society.)

Background: Metabolite levels within an individual vary over time. This within-individual variability, coupled with technical variability, reduces the power for epidemiologic studies to detect associations with disease. Here, the authors assess the variability of a large subset of metabolites and evaluate the implications for epidemiologic studies., Methods: Using liquid chromatography/mass spectrometry (LC/MS) and gas chromatography-mass spectroscopy (GC/MS) platforms, 385 metabolites were measured in 60 women at baseline and year-one of the Shanghai Physical Activity Study, and observed patterns were confirmed in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening study., Results: Although the authors found high technical reliability (median intraclass correlation = 0.8), reliability over time within an individual was low. Taken together, variability in the assay and variability within the individual accounted for the majority of variability for 64% of metabolites. Given this, a metabolite would need, on average, a relative risk of 3 (comparing upper and lower quartiles of "usual" levels) or 2 (comparing quartiles of observed levels) to be detected in 38%, 74%, and 97% of studies including 500, 1,000, and 5,000 individuals. Age, gender, and fasting status factors, which are often of less interest in epidemiologic studies, were associated with 30%, 67%, and 34% of metabolites, respectively, but the associations were weak and explained only a small proportion of the total metabolite variability., Conclusion: Metabolomics will require large, but feasible, sample sizes to detect the moderate effect sizes typical for epidemiologic studies., Impact: We offer guidelines for determining the sample sizes needed to conduct metabolomic studies in epidemiology.

We consider the properties of the F(st) measure of genetic divergence between an admixed population and its parental source populations. Among all possible populations admixed among an arbitrary set of parental populations, we show that the value of F(st) between an admixed population and a specific source population is maximized when the admixed population is simply the most distant of the other source populations. For the case with only two parental populations, as a function of the admixture fraction, we further demonstrate that this F(st) value is monotonic and convex, so that F(st) is informative about the admixture fraction. We illustrate our results using example human population-genetic data, showing how they provide a framework in which to interpret the features of F(st) in admixed populations., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Medulloblastoma (MB) is the most common malignant brain tumor of children. To identify the genetic alterations in this tumor type, we searched for copy number alterations using high-density microarrays and sequenced all known protein-coding genes and microRNA genes using Sanger sequencing in a set of 22 MBs. We found that, on average, each tumor had 11 gene alterations, fewer by a factor of 5 to 10 than in the adult solid tumors that have been sequenced to date. In addition to alterations in the Hedgehog and Wnt pathways, our analysis led to the discovery of genes not previously known to be altered in MBs. Most notably, inactivating mutations of the histone-lysine N-methyltransferase genes MLL2 or MLL3 were identified in 16% of MB patients. These results demonstrate key differences between the genetic landscapes of adult and childhood cancers, highlight dysregulation of developmental pathways as an important mechanism underlying MBs, and identify a role for a specific type of histone methylation in human tumorigenesis.

Recent research has revealed complex heterogeneous genomic landscapes in human cancers. However, mutations tend to occur within a core group of pathways and biological processes that can be grouped into gene sets. To better understand the significance of these pathways, we have developed an approach that initially scores each gene set at the patient rather than the gene level. In mutation analysis, these patient-oriented methods are more transparent, interpretable, and statistically powerful than traditional gene-oriented methods., (© 2010 Boca et al.; licensee BioMed Central Ltd.)

Recently, the observation of a high-frequency private allele, the 9-repeat allele at microsatellite D9S1120, in all sampled Native American and Western Beringian populations has been interpreted as evidence that all modern Native Americans descend primarily from a single founding population. However, this inference assumed that all copies of the 9-repeat allele were identical by descent and that the geographic distribution of this allele had not been influenced by natural selection. To investigate whether these assumptions are satisfied, we genotyped 34 single nucleotide polymorphisms across approximately 500 kilobases (kb) around D9S1120 in 21 Native American and Western Beringian populations and 54 other worldwide populations. All chromosomes with the 9-repeat allele share the same haplotypic background in the vicinity of D9S1120, suggesting that all sampled copies of the 9-repeat allele are identical by descent. Ninety-one percent of these chromosomes share the same 76.26 kb haplotype, which we call the "American Modal Haplotype" (AMH). Three observations lead us to conclude that the high frequency and widespread distribution of the 9-repeat allele are unlikely to be the result of positive selection: 1) aside from its association with the 9-repeat allele, the AMH does not have a high frequency in the Americas, 2) the AMH is not unusually long for its frequency compared with other haplotypes in the Americas, and 3) in Latin American mestizo populations, the proportion of Native American ancestry at D9S1120 is not unusual compared with that observed at other genomewide microsatellites. Using a new method for estimating the time to the most recent common ancestor (MRCA) of all sampled copies of an allele on the basis of an estimate of the length of the genealogy descended from the MRCA, we calculate the mean time to the MRCA of the 9-repeat allele to be between 7,325 and 39,900 years, depending on the demographic model used. The results support the hypothesis that all modern Native Americans and Western Beringians trace a large portion of their ancestry to a single founding population that may have been isolated from other Asian populations prior to expanding into the Americas.

Human cancer is caused by the accumulation of mutations in oncogenes and tumor suppressor genes. To catalog the genetic changes that occur during tumorigenesis, we isolated DNA from 11 breast and 11 colorectal tumors and determined the sequences of the genes in the Reference Sequence database in these samples. Based on analysis of exons representing 20,857 transcripts from 18,191 genes, we conclude that the genomic landscapes of breast and colorectal cancers are composed of a handful of commonly mutated gene "mountains" and a much larger number of gene "hills" that are mutated at low frequency. We describe statistical and bioinformatic tools that may help identify mutations with a role in tumorigenesis. These results have implications for understanding the nature and heterogeneity of human cancers and for using personal genomics for tumor diagnosis and therapy.