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Inflammation is a cancer hallmark. NSAIDs improve overall survival (OS) in certain cancers. Real-world studies explored here whether 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 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 vs. 13.1% for nonusers) 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. Significance: NLP and real-world studies conducted in large cohorts explored whether NSAIDs improved survival across NSCLC stages, histopathology, gender, smoking history, or demographic groups. A statistically significant association between NSAID use and NSCLC survival was found. This provides a rationale for future NSAID randomized NSCLC trials.

Supplementary Table 6: The MD Anderson cohort 5-year survival rate and difference of 5-year restricted mean survival time in months between NSAID users and non-users by gender, race, and smoking status corresponding to Figure 2. Comparisons are made to the Georgetown cohort.

Supplemental Figure 2. The Kaplan-Meier analysis of overall survival and NSAID use in lung cancer cases within the MedStar-Georgetown University database (Georgetown cohort).

Inflammation is a cancer hallmark. NSAIDs improve overall survival (OS) in certain cancers. Real-world studies explored here whether 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 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 vs. 13.1% for nonusers) 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.Significance:NLP and real-world studies conducted in large cohorts explored whether NSAIDs improved survival across NSCLC stages, histopathology, gender, smoking history, or demographic groups. A statistically significant association between NSAID use and NSCLC survival was found. This provides a rationale for future NSAID randomized NSCLC trials.

Supplementary Figure S1. Sequence homology to human and mouse reference genomes. Supplementary Figure S2. No SNU-5 tumors establish upon continuous treatment with emibetuzumab Supplementary Figure S3. MET level in 7333 and with or without emibetuzumab treatment. Supplementary Figure S4. Log2 ratios for MYC, PVT1, and ERBB3 copy number gains in the 4626 tumor. Supplementary Figure S5. HER3 level in SNU-5 cell lines. Supplementary Figure S6. Both emibetuzumab-resistant cell lines are resistant to Sym015 in vitro. Supplementary Figure S7. SNU-5 are equally sensitive to Sym015 and Sym015 LALA in vivo.

Supplementary Table S4. Sensitivity of emibetuzumab-resistant cell lines to various TKIs compared with SNU-5 cells.

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.

PDF file - 16K, The correlation of the (BIV) measured from PLCO and SPA.

PDF file - 13K, The plot illustrates the distributions of the technical CV's (metabolite levels on a log scale), a measure of laboratory variability. The x-axis represents the metabolite quantile ranking (e.g. 0.5 represents the median), the y-axis represents the actual CV, and the curves (black for SPA and dashed red for PLCO) show the CV for the specified metabolite quantile ranking.

PDF file - 15K, The correlation between the ICCs measured for the metabolites in both PLCO and SPA.

PDF file - 94K, Metabolite Classification

PDF file - 65K

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. Cancer Epidemiol Biomarkers Prev; 22(4); 631–40. ©2013 AACR.

PDF file - 46K, A)The proportion of metabolites in each of 10 categories that have an ICC exceeding 0.2, 0.5, and 0.8 in SPA. N is the total number of metabolites within the specified category. B) The proportion of metabolites in each of 10 categories that have an ICC exceeding 0.2, 0.5, and 0.8 in PLCO. N is the total number of metabolites within the specified category.

PDF file - 22K, Each metabolite level was normalized by three steps: a) log transforming b) subtracting the mean of the log(metabolite) level c) dividing by the standard deviation of the log(metabolite) level. This figure shows the histogram of all (number of subjects x number of metabolites) normalized levels. The absence of longer tails (e.g. little kurtosis) suggests that taking the log of metabolite levels produced relatively normally distributed measurements.

PDF file - 59K, A list of the identified metabolites with the lowest values of between-subject variability, (e.g. the highest within-subject variability), among all metabolites with an ICC > 0.8. Rows include metabolite name, , the equivalent value from the age and gender adjusted (A.G.A) model, the equivalent from a female-only model, p-value for the metabolite's association with age, and p-value for the metabolite's association with gender.

The COVID-19 pandemic catalyzed the rapid dissemination of papers and preprints investigating the disease and its associated virus, SARS-CoV-2. The multifaceted nature of COVID-19 demands a multidisciplinary approach, but the urgency of the crisis combined with the need for social distancing measures present unique challenges to collaborative science. We applied a massive online open publishing approach to this problem using Manubot. Through GitHub, collaborators summarized and critiqued COVID-19 literature, creating a review manuscript. Manubot automatically compiled citation information for referenced preprints, journal publications, websites, and clinical trials. Continuous integration workflows retrieved up-to-date data from online sources nightly, regenerating some of the manuscript's figures and statistics. Manubot rendered the manuscript into PDF, HTML, LaTeX, and DOCX outputs, immediately updating the version available online upon the integration of new content. Through this effort, we organized over 50 scientists from a range of backgrounds who evaluated over 1,500 sources and developed seven literature reviews. While many efforts from the computational community have focused on mining COVID-19 literature, our project illustrates the power of open publishing to organize both technical and non-technical scientists to aggregate and disseminate information in response to an evolving crisis.

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.

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.

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 STATEMENTThe 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.

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.

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).

Summary Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with poor patient survival. Toward understanding the underlying molecular alterations that drive PDAC oncogenesis, we conducted comprehensive proteogenomic analysis of 140 pancreatic cancers, 67 normal adjacent tissues, and 9 normal pancreatic ductal tissues. Proteomic, phosphoproteomic, and glycoproteomic analyses were used to characterize proteins and their modifications. In addition, whole-genome sequencing, whole-exome sequencing, methylation, RNA sequencing (RNA-seq), and microRNA sequencing (miRNA-seq) were performed on the same tissues to facilitate an integrated proteogenomic analysis and determine the impact of genomic alterations on protein expression, signaling pathways, and post-translational modifications. To ensure robust downstream analyses, tumor neoplastic cellularity was assessed via multiple orthogonal strategies using molecular features and verified via pathological estimation of tumor cellularity based on histological review. This integrated proteogenomic characterization of PDAC will serve as a valuable resource for the community, paving the way for early detection and identification of novel therapeutic targets.

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.

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.

F ST is a statistic that is frequently used to analyze population structure. Recent work has shown that FST depends strongly on the underlying genetic diversity of a locus from which it is computed... The population-genetic statistic FST is used widely to describe allele frequency distributions in subdivided populations. The increasing availability of DNA sequence data has recently enabled computations of FST from sequence-based “haplotype loci.” At the same time, theoretical work has revealed that FST has a strong dependence on the underlying genetic diversity of a locus from which it is computed, with high diversity constraining values of FST 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 FST 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 FST, and usually decreases it. We compute FST 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 FST calculated on haplotypic data.

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.

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. Wang et al. perform integrated proteogenomic analysis of adult glioblastoma (GBM), including metabolomics, lipidomics, and single nuclei RNA-Seq, revealing insights into the immune landscape of GBM, cell-specific nature of EMT signatures, histone acetylation in classical GBM, and the existence of signaling hubs which could provide therapeutic vulnerabilities.

BackgroundLife expectancy can be estimated accurately from a cohort of individuals born in the same year and followed from birth to death. Due to the difficult and time-consuming nature of following a cohort prospectively, life expectancy is often assessed based on death data, which may lead to potentially biased estimates. This is more likely to be a problem in rare diseases such as Morquio syndrome A.MethodTo investigate how accurate the estimation of life expectancy is using death data, we simulate the survival of individuals with Morquio syndrome A under four different survival scenarios. In each scenario, we estimate the mean and median survival times within a defined period and compare them with the true life expectancy.ResultsWhen life expectancy is constant during the entire period, using death data does not result in a biased estimate of life expectancy. However, when life expectancy increases during the follow-up period, using only death data leads to a substantial underestimation of life expectancy.ConclusionLife expectancy can change over time, along with changes in the environment and/or biomedical innovation. When the life expectancy is increasing — as is often expected to be the case in rare diseases — estimating it based on contemporary death data will result in a downward bias. Therefore, it is crucial to understand how estimates of life expectancy are obtained and to interpret them in an appropriate context, and to assess estimation methods within a sensitivity analysis framework, similar to the simulations performed herein.

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.

Background Cardiotoxicity is a serious adverse event associated with some of the most effective breast cancer therapies. Currently, it is difficult to predict which patients will develop cardiotoxicity due to the multiplicity of clinical, behavioral, and biological factors involved. MethodsHere we describe an effort to apply biomedical informatics approaches to patient data from MedStar Health’s EHR systems to discover and characterize factors that contribute to cardiotoxicity in a real world breast cancer population.ResultsData wrangling techniques including merging data from disparate clinical systems, data transformation, and de-identification of personal health information (PHI)were appliedto the raw clinical data to produce a structured integrated dataset for predictive analysis and hypothesis generation. Using this dataset as input, weshowed howpredictive models can be developed to identify patients at high risk for cardiotoxicity. ConclusionsWe demonstrate how suchmodels can be used for hypothesis generation and data exploration with the ultimate goal of developing applications for precision medicine.

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. 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 StatementThe external pallidum is a key, yet an understudied component of the basal ganglia. Neural activity in the pallidum goes awry in neurological 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 (dys)function. This was in part due to 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.

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.

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.

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.