External Data Sources Research Articles

Immune cell deconvolution of bulk DNA methylation data reveals an association with methylation class, key somatic alterations, and cell state in glial/glioneuronal tumors

It is recognized that the tumor microenvironment (TME) plays a critical role in the biology of cancer. To better understand the role of immune cell components in CNS tumors, we applied a deconvolution approach to bulk DNA methylation array data in a large set of newly profiled samples (n = 741) as well as samples from external data sources (n = 3311) of methylation-defined glial and glioneuronal tumors. Using the cell-type proportion data as input, we used dimensionality reduction to visualize sample-wise patterns that emerge from the cell type proportion estimations. In IDH-wildtype glioblastomas (n = 2,072), we identified distinct tumor clusters based on immune cell proportion and demonstrated an association with oncogenic alterations such as EGFR amplification and CDKN2A/B homozygous deletion. We also investigated the immune cluster-specific distribution of four malignant cellular states (AC-like, OPC-like, MES-like and NPC-like) in the IDH-wildtype cohort. We identified two major immune-based subgroups of IDH-mutant gliomas, which largely aligned with 1p/19q co-deletion status. Non-codeleted gliomas showed distinct proportions of a key genomic aberration (CDKN2A/B loss) among immune cell-based groups. We also observed significant positive correlations between monocyte proportion and expression of PD-L1 and PD-L2 (R = 0.54 and 0.68, respectively). Overall, the findings highlight specific roles of the TME in biology and classification of CNS tumors, where specific immune cell admixtures correlate with tumor types and genomic alterations.

Data-Driven Virtual Replication of Thermostatically Controlled Domestic Heating Systems

Thermostatic load control systems are widespread in many countries. Since they provide heat for domestic hot water and space heating on a massive scale in the residential sector, the assessment of their energy performance and the effect of different control strategies requires simplified modeling techniques demanding a small number of inputs and low computational resources. Data-driven techniques are envisaged as one of the best options to meet these constraints. This paper presents a novel methodology consisting of the combination of an optimization algorithm, two auto-regressive models and a control loop algorithm able to virtually replicate the control of thermostatically driven systems. This combined strategy includes all the thermostatically controlled modes governed by the set point temperature and enables automatic assessment of the energy consumption impact of multiple scenarios. The required inputs are limited to available historical readings from smart thermostats and external climate data sources. The methodology has been trained and validated with data sets coming from a selection of 11 smart thermostats, connected to gas boilers, placed in several households located in north-eastern Spain. Important conclusions of the research are that these techniques can estimate the temperature decay of households when the space heating is off as well as the energy consumption needed to reach the comfort conditions. The results of the research also show that estimated median energy savings of 18.1% and 36.5% can be achieved if the usual set point temperature schedule is lowered by 1 °C and 2 °C, respectively.

Comparative analysis of machine learning approaches to classify tumor mutation burden in lung adenocarcinoma using histopathology images

Both histologic subtypes and tumor mutation burden (TMB) represent important biomarkers in lung cancer, with implications for patient prognosis and treatment decisions. Typically, TMB is evaluated by comprehensive genomic profiling but this requires use of finite tissue specimens and costly, time-consuming laboratory processes. Histologic subtype classification represents an established component of lung adenocarcinoma histopathology, but can be challenging and is associated with substantial inter-pathologist variability. Here we developed a deep learning system to both classify histologic patterns in lung adenocarcinoma and predict TMB status using de-identified Hematoxylin and Eosin (H&E) stained whole slide images. We first trained a convolutional neural network to map histologic features across whole slide images of lung cancer resection specimens. On evaluation using an external data source, this model achieved patch-level area under the receiver operating characteristic curve (AUC) of 0.78–0.98 across nine histologic features. We then integrated the output of this model with clinico-demographic data to develop an interpretable model for TMB classification. The resulting end-to-end system was evaluated on 172 held out cases from TCGA, achieving an AUC of 0.71 (95% CI 0.63–0.80). The benefit of using histologic features in predicting TMB is highlighted by the significant improvement this approach offers over using the clinical features alone (AUC of 0.63 [95% CI 0.53–0.72], p = 0.002). Furthermore, we found that our histologic subtype-based approach achieved performance similar to that of a weakly supervised approach (AUC of 0.72 [95% CI 0.64–0.80]). Together these results underscore that incorporating histologic patterns in biomarker prediction for lung cancer provides informative signals, and that interpretable approaches utilizing these patterns perform comparably with less interpretable, weakly supervised approaches.

Big Data’s Disruptive Effect on Job Profiles: Management Accountants’ Case Study

The abundance of new innovative data sources creates opportunities and challenges for all professions and professionals working with information. One of these professionals is the management accountant (MA). Although their tasks have expanded over time and especially recently, MAs have not fully employed all the available internal and external data sources to describe, diagnose, visualize, predict and prescribe possible solutions that enable smart decisions with positive effects on businesses. Thus, the paper investigates the impact of Big Data, including Data Analytics, on MA’s job profile. Through a review of the most recent academic and professional publications, the paper contributes to the debate surrounding the redefinition of the role of MAs in organizations in a novel informational perspective of Abbott’s theory. The results could serve as a research agenda and incentive for further studies, as well as provide MAs with a guide on the topic of the enlargement of their role(s), respectively, the augmentation of their tasks and responsibilities regarding the analysis of Big Data. Furthermore, the research may provide both a rich and flexible framework to help practitioners in their analysis of potential risks, opportunities and challenges when handling Big Data, and a lens for professional accounting associations and bodies by helping them to prioritize the holding and seizing of jurisdictions as an imperative part of safety and security.

Multi_Scale_Tools: A Python Library to Exploit Multi-Scale Whole Slide Images

Algorithms proposed in computational pathology can allow to automatically analyze digitized tissue samples of histopathological images to help diagnosing diseases. Tissue samples are scanned at a high-resolution and usually saved as images with several magnification levels, namely whole slide images (WSIs). Convolutional neural networks (CNNs) represent the state-of-the-art computer vision methods targeting the analysis of histopathology images, aiming for detection, classification and segmentation. However, the development of CNNs that work with multi-scale images such as WSIs is still an open challenge. The image characteristics and the CNN properties impose architecture designs that are not trivial. Therefore, single scale CNN architectures are still often used. This paper presents Multi_Scale_Tools, a library aiming to facilitate exploiting the multi-scale structure of WSIs. Multi_Scale_Tools currently include four components: a pre-processing component, a scale detector, a multi-scale CNN for classification and a multi-scale CNN for segmentation of the images. The pre-processing component includes methods to extract patches at several magnification levels. The scale detector allows to identify the magnification level of images that do not contain this information, such as images from the scientific literature. The multi-scale CNNs are trained combining features and predictions that originate from different magnification levels. The components are developed using private datasets, including colon and breast cancer tissue samples. They are tested on private and public external data sources, such as The Cancer Genome Atlas (TCGA). The results of the library demonstrate its effectiveness and applicability. The scale detector accurately predicts multiple levels of image magnification and generalizes well to independent external data. The multi-scale CNNs outperform the single-magnification CNN for both classification and segmentation tasks. The code is developed in Python and it will be made publicly available upon publication. It aims to be easy to use and easy to be improved with additional functions.

Using Administrative Records and Survey Data to Construct Samples of Tweeters and Tweets

Abstract Social media data can provide new insights into political phenomena, but users do not always represent people, posts and accounts are not typically linked to demographic variables for use as statistical controls or in subgroup comparisons, and activities on social media can be difficult to interpret. For data scientists, adding demographic variables and comparisons to closed-ended survey responses have the potential to improve interpretations of inferences drawn from social media—for example, through comparisons of online expressions and survey responses, and by assessing associations with offline outcomes like voting. For survey methodologists, adding social media data to surveys allows for rich behavioral measurements, including comparisons of public expressions with attitudes elicited in a structured survey. Here, we evaluate two popular forms of linkages—administrative and survey—focusing on two questions: How does the method of creating a sample of Twitter users affect its behavioral and demographic profile? What are the relative advantages of each of these methods? Our analyses illustrate where and to what extent the sample based on administrative data diverges in demographic and partisan composition from surveyed Twitter users who report being registered to vote. Despite demographic differences, each linkage method results in behaviorally similar samples, especially in activity levels; however, conventionally sized surveys are likely to lack the statistical power to study subgroups and heterogeneity (e.g., comparing conversations of Democrats and Republicans) within even highly salient political topics. We conclude by developing general recommendations for researchers looking to study social media by linking accounts with external benchmark data sources.

Extracting Semantic Information from Graphic Schemes

The problem of extracting semantic information from an electronic document specified in the vector graphics format and containing a graphic model (diagram) built using a graphic editor is considered. The problem is to program retrieving certain structural properties and parametric circuit and entering them into a database for later use. Based on the analysis of the capabilities of graphic editors, a conclusion has made about the relevance of this task for universal editors that are not tied to specific graphic notations and use open graphic document formats, which allows program processing. The proposed approach considers graphic documents at three levels of abstraction: conceptual (semantic properties of a schema), logical (presentation of semantic properties at the internal level of the document) and physical (internal organization of a graphic document). The solution to the problem is based on the construction of a conceptual-logical mapping, i.e., mapping a conceptual model of a circuit to a logical model of a graphic document, according to its physical model. Within the framework of the approach, an algorithm for constructing the indicated mapping is developed, presented in the form of an object-oriented pseudocode. The study of internal markup in open graphic formats made it possible to build models for identifying circuit elements and their connections to each other, which is necessary for a specific application of the algorithm. Expressions for addressing schema elements and accessing their properties are obtained. The proposed approach is implemented on the base of a situation-oriented paradigm, within which the extraction process is driven by a hierarchical situational model. The processed data is specified in the situational model in the form of virtual documents displayed on heterogeneous external data sources. For the problem being solved, we consider the mapping to two variants of vector graphics formats: to a "flat" markup file and to a set of such files in an electronic archive. The practical use of the results is illustrated by the example of extracting semantic information from graphical models developed at various stages of database design.

Introducing GloRiSe – a global database on river sediment composition

Abstract. Rivers transport dissolved and solid loads from terrestrial realms to the oceans and between inland reservoirs, representing major mass fluxes on Earth's surface. The composition of river water and sediment provides clues to a plethora of Earth and environmental processes, including weathering, erosion, nutrient and carbon cycling, environmental pollution, reservoir exchange, and tectonic cycles. While there are documented, publicly available databases for riverine dissolved and suspended nutrients, there is no openly accessible, georeferenced database for riverine suspended sediment composition. Here, we present a globally representative set of 2828 suspended and bed sediment compositional measurements from 1683 locations around the globe. This database, named Global River Sediments (GloRiSe) version 1.1, includes major, minor and trace elements, along with mineralogical data, and provides time series for some sites. Each observation is complemented by metadata describing geographic location, sampling date and time, sample treatment, and measurement details, which allows for grouping and selection of observations, as well as for interoperability with external data sources, and improves interpretability. Information on references, unit conversion and references makes the database comprehensible. Notably, the close to globe-spanning extent of this compilation allows the derivation of data-driven, spatially resolved global-scale conclusions about the role of rivers and processes related to them within the Earth system. GloRiSe version 1.1 can be downloaded from Zenodo (https://doi.org/10.5281/zenodo.4485795, Müller et al., 2021) and GitHub (https://github.com/GerritMuller/GloRiSe, last access: 26 May 2021), where updates with adapted version numbers will become available, along with a technical documentation and an example calculation in the form of MATLAB scripts, which calculate the sediment-flux-weighted major element composition of the annual riverine suspended sediment export to the ocean and related uncertainties.

A comparison of associations with childhood lung function between air pollution exposure assessment methods with and without accounting for time-activity patterns

BackgroundTo investigate associations between annual average air pollution exposures and health, most epidemiological studies rely on estimated residential exposures because information on actual time-activity patterns can only be collected for small populations and short periods of time due to costs and logistic constraints. In the current study, we aim to compare exposure assessment methodologies that use data on time-activity patterns of children with residence-based exposure assessment. We compare estimated exposures and associations with lung function for residential exposures and exposures accounting for time activity patterns. MethodsWe compared four annual average air pollution exposure assessment methodologies; two rely on residential exposures only, the other two incorporate estimated time activity patterns. The time-activity patterns were based on assumptions about the activity space and make use of available external data sources for the duration of each activity. Mapping of multiple air pollutants (NO2, NOX, PM2.5, PM2.5absorbance, PM10) at a fine resolution as input to exposure assessment was based on land use regression modelling. First, we assessed the correlations between the exposures from the four exposure methods. Second, we compared estimates of the cross-sectional associations between air pollution exposures and lung function at age 8 within the PIAMA birth cohort study for the four exposure assessment methodologies. ResultsThe exposures derived from the four exposure assessment methodologies were highly correlated (R > 0.95) for all air pollutants. Similar statistically significant decreases in lung function were found for all four methods. For example, for NO2 the decrease in FEV1 was −1.40% (CI; −2.54, −0.24%) per IQR (9.14 μg/m3) for front door exposure, and −1.50% (CI; −2.68, −0.30%) for the methodology which incorporates time activity pattern and actual school addresses. ConclusionsExposure estimates from methods based on the residential location only and methods including time activity patterns were highly correlated and associated with similar decreases in lung function. Our study illustrates that the annual average exposure to air pollution for 8-year-old children in the Netherlands is sufficiently captured by residential exposures.

Too stringent or too Lenient: Antecedents and consequences of perceived stringency of COVID-19 policies in the United States

In the United States, federal and local governments have attempted to contain the spread of Coronavirus Disease 2019 (COVID-19) by implementing a variety of policies such as stay-at-home orders and mask mandates. Perceptions can influence behaviors; therefore, it is important to understand how people perceive the stringency of COVID-19 policies, what factors shape perceived policy stringency, and whether and how policy perceptions impact the practice of prevention behaviors. With rolling-cross sectional survey data collected in the US from June to October 2020 and other external sources of data, the study examines the impact of objective risk of the pandemic, information seeking, and political ideology at the individual and the state levels on perceived policy stringency, and the impact of perceived policy stringency on prevention behaviors such as mask wearing and social distancing. The findings reveal that objective risk and political ideology are significantly associated with perceived policy stringency. The perceived policy stringency has negative associations with prevention behaviors. The findings provide important implications for the development process of compulsory public health policies during the pandemic.

Value of incorporating geospatial information into the prediction of on-street parking occupancy – A case study

ABSTRACT In light of growing urban traffic, car parking becomes increasingly critical for cities to manage. As a result, the prediction of parking occupancy has sparked significant research interest in recent years. While many external data sources have been considered in the prediction models, the underlying geographic context has mostly been ignored. Thus, in order to study the contribution of geospatial information to parking occupancy prediction models, road network centrality, land use, and Point of Interest (POI) data were incorporated in Random Forest (RF) and Artificial Neural Network (ANN, specifically Feedforward Neural Network FFNN) prediction models in this work. Model performances were compared to a baseline, which only considers historical and temporal input data. Moreover, the influence of the amount of training data, the prediction horizon, and the spatial variation of the prediction were explored. The results show that the inclusion of geospatial information led to a performance improvement of up to 25% compared to the baseline. Besides, as the prediction horizon expanded, predictions became less reliable, while the relevance of geospatial data increased. In general, land use and POI data proved to be more beneficial than road network centrality. The amount of training data did not have a significant influence on the performance of the RF model. The ANN model, conversely, achieved optimal results on a training input of 5 days. Likely attributable to varying occupancy patterns, prediction performance disparities could be identified for different parking districts and street segments. Generally, the RF model outperformed the ANN model on all predictions.

Multilevel modeling of county-level excessive alcohol use, rurality, and COVID-19 case fatality rates in the US.

Reports of disparities in COVID-19 mortality rates are emerging in the public health literature as the pandemic continues to unfold. Alcohol misuse varies across the US and is related to poorer health and comorbidities that likely affect the severity of COVID-19 infection. High levels of pre-pandemic alcohol misuse in some counties may have set the stage for worse COVID-19 outcomes. Furthermore, this relationship may depend on how rural a county is, as access to healthcare in rural communities has lagged behind more urban areas. The objective of this study was to test for associations between county-level COVID-19 mortality, pre-pandemic county-level excessive drinking, and county rurality. We used national COVID-19 data from the New York Times to calculate county-level case fatality rates (n = 3,039 counties and county equivalents; October 1 -December 31, 2020) and other external county-level data sources for indicators of rurality and health. We used beta regression to model case fatality rates, adjusted for several county-level population characteristics. We included a multilevel component to our model and defined state as a random intercept. Our focal predictor was a single variable representing nine possible combinations of low/mid/high alcohol misuse and low/mid/high rurality. The median county-level COVID-19 case fatality rate was 1.57%. Compared to counties with low alcohol misuse and low rurality (referent), counties with high levels of alcohol and mid (β = -0.17, p = 0.008) or high levels of rurality (β = -0.24, p<0.001) demonstrated significantly lower case fatality rates. Our findings highlight the intersecting roles of county-level alcohol consumption, rurality, and COVID-19 mortality.

Feature engineering and machine learning for causality assessment in pharmacovigilance: Lessons learned from application to the FDA Adverse Event Reporting System

BackgroundOur objective was to support the automated classification of Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) reports for their usefulness in assessing the possibility of a causal relationship between a drug product and an adverse event. MethodWe used a data set of 326 redacted FAERS reports that was previously annotated using a modified version of the World Health Organization–Uppsala Monitoring Centre criteria for drug causality assessment by a group of SEs at the FDA and supported a similar study on the classification of reports using supervised machine learning and text engineering methods. We explored many potential features, including the incorporation of natural language processing on report text and information from external data sources, for supervised learning and developed models for predicting the classification status of reports. We then evaluated the models on a larger data set of previously unseen reports. ResultsThe best-performing models achieved recall and F1 scores on both data sets above 0.80 for the identification of assessable reports (i.e. those containing enough information to make an informed causality assessment) and above 0.75 for the identification of reports meeting at least a Possible causality threshold. ConclusionsCausal inference from FAERS reports depends on many components with complex logical relationships that are yet to be made fully computable. Efforts focused on readily addressable tasks, such as quickly eliminating unassessable reports, fit naturally in SE's thought processes to provide real enhancements for FDA workflows.

Planning for the Next Pandemic: Ethics and Innovation Today for Improved Clinical Trials Tomorrow

The coronavirus pandemic has brought public attention to the steps required to produce valid scientific clinical research in drug development. Traditional ethical principles that guide clinical research remain the guiding compass for physicians, patients, public health officials, investigators, drug developers and the public. Accelerating the process of delivering safe and effective treatments and vaccines against COVID-19 is a moral imperative. The apparent clash between the regulated system of phased randomized clinical trials and urgent public health need requires leveraging innovation with ethical scientific rigor. We reflect on the Belmont principles of autonomy, beneficence and justice as the pandemic unfolds, and illustrate the role of innovative clinical trial designs in alleviating pandemic challenges. Our discussion highlights selected types of innovative trial design and correlates them with ethical parameters and public health benefits. Details are provided for platform trials and other innovative designs such as basket and umbrella trials, designs leveraging external data sources, multi-stage seamless trials, preplanned control arm data sharing between larger trials, and higher order systems of linked trials coordinated more broadly between individual trials and phases of development, recently introduced conceptually as “PIPELINEs.”

Linking national immigration data to provincial repositories: The case of Canada.

BackgroundCanadian health data repositories link datasets at the provincial level, based on their residents’ registrations to provincial health insurance plans. Linking national datasets with provincial health care registries poses several challenges that may result in misclassification and impact the estimation of linkage rates. A recent linkage of a federal immigration database in the province of Manitoba illustrates these challenges.Objectivesa) To describe the linkage of the federal Immigration, Refugees and Citizenship Canada Permanent Resident (IRCC-PR) database with the Manitoba healthcare registry and b) compare data linkage methods and rates between four Canadian provinces accounting for interprovincial mobility of immigrants.MethodsWe compared linkage rates by immigrant’s province of intended destination (province vs. rest of Canada). We used external nationwide immigrant tax filing records to approximate actual settlement and obtain linkage rates corrected for interprovincial mobility.ResultsThe immigrant linkage rates in Manitoba before and after accounting for interprovincial mobility were 84.8% and 96.1, respectively. Linkage rates did not substantially differ according to immigrants’ characteristics, with a few exceptions. Observed linkage rates across the four provinces ranged from 74.0% to 86.7%. After correction for interprovincial mobility, the estimated linkage rates increased > 10 percentage points for the provinces that stratified by intended destination (British Columbia and Manitoba) and decreased up to 18 percentage points for provinces that could not use immigration records of those who did not intend to settle in the province (New Brunswick and Ontario).ConclusionsDespite variations in methodology, provincial linkage rates were relatively high. The use of a national immigration dataset for linkage to provincial repositories allows a more comprehensive linkage than that of province-specific subsets. Observed linkage rates can be biased downwards by interprovincial migration, and methods that use external data sources can contribute to assessing potential selection bias and misclassification.

Racial disparities in access to prostate cancer clinical trials: A county-level analysis.

6553 Background: African American men (AAs) have a higher burden of prostate cancer compared to other populations. We sought to determine if they experience disparities in access to prostate cancer clinical trials. Methods: We created a county-level database of all U.S. counties by linking together prostate cancer clinical trial data from the Aggregate Analysis of ClincalTrials.gov (AACT) database with county-level socioeconomic, demographic and healthcare facility data derived from several external data sources. Using this data linkage, we examined two specific potential access barriers. First, we investigated the relationship between %AAs in the county and access to NCI designated cancer facilities, adjusting for county population size and other characteristics. Then, among counties with cancer facilities, we investigated the relationship between the %AAs in the county and number of available prostate cancer treatment trials per capita per year. We used logistic and negative binomial regression models, respectively, to address these questions. Results: Between 2008 and 2015, 613 prostate cancer trial sites were found among 3,145 U.S. counties. Counties with higher %AAs were less likely to have cancer facilities (adjusted odds ratio = 0.85, 95% CI (0.78, 0.92)). Among counties with cancer facilities, those with higher %AAs had significantly fewer prostate cancer trials per capita per year (rate ratio per 10% increase in %AAs: 0.90, 95% CI (0.83,0.96)), after adjusting for county-level sociodemographic and healthcare system factors. Conclusions: Counties with higher proportions of AAs appear to be less likely to have access to NCI designated cancer facilities. Among counties with cancer facilities, those with higher proportions of AAs appear to have fewer available prostate cancer treatment trials per capita per year. Clinical trials in prostate cancer therapy should ensure adequate availability of enrollment sites in regions with high concentrations of AAs.

A Decade of Drinking: Temporal Trends in Apparent Household Beer Intake and Standard Drink Consumption in the United States

Beer remains the greatest source of per capita alcohol consumption in the United States, and increasing market availability and consumer demand for higher alcohol has meaningful public health consequences. Objectives: To determine whether apparent alcohol intake from beer changed among households over time, we used nationally-representative US Nielsen Consumer Panel purchasing data from 2004 to 2014, and incorporated information on percent alcohol by volume (ABV) to compute the number of standard drinks of alcohol consumed from beer as a result. Methods: We queried external data sources (e.g. official manufacture, consumer beer-related websites) to obtain beer-specific ABVs, merged this information with Nielsen consumer-level data, and calculated the average rate of beer and standard drink consumption per household per year. We used joinpoint regression to estimate annual percentage changes and annual absolute changes in intake over time, with separate piecewise linear segments fit between years if a significant deviation in trend was detected. Results: Higher alcohol content beer consumption increased steadily across the decade, accounting for 9.6% of total intake in 2004 compared to 21.6% of total intake by 2014. Standard drink intake from beer declined sharply post-2011 by 3.04% annually (95% CI: −5.93, −0.06) or by 4.52 standard drinks (95% CI: −8.69, −0.35) yearly – coinciding with several beer industry transitions, market share fluctuations, and consumer preference changes for beer occurring around that time. Conclusions: Despite consistent increases in higher alcohol content beer intake across the decade, households do not appear to be consuming more standard drinks of alcohol from beer as a result. Supplemental data for this article is available online at https://doi.org/10.1080/10826084.2021.1928208 .

Correlating Immune Cell Infiltration Patterns with Recurrent Somatic Mutations in Advanced Clear Cell Renal Cell Carcinoma

BackgroundClear cell renal cell carcinoma (ccRCC) tumors have low frequencies of genetic alterations compared with other malignancies, but very high levels of immune cell infiltration and favorable response rates to immunotherapy. Currently, the interplay between specific ccRCC somatic mutations and immune infiltration pattern is unclear. ObjectiveTo analyze the associations between common ccRCC somatic mutations and immune cell infiltration patterns within the tumor immune microenvironment (TIME). Design, setting, and participantsThe study included tumor samples (24 primary and 24 metastatic) from 48 patients with stage IV ccRCC. Targeted sequencing was performed for well-characterized recurrent somatic mutations in ccRCC, with the analysis focusing on the six most common ones: VHL, BAP1, PBRM1, SETD2, TP53, and KDM5C. For each sample, multiplex immunofluorescence (IF) was performed in lymphoid and myeloid panels, for seven regions of interest in three zones (tumor core, stroma, and tumor-stroma interface). IF-derived cellular densities were compared across patients, stratified by their somatic mutation status, using a linear mixed-model analysis. External validation was pursued using RNA-seq enrichment scoring from three large external data sources. Results and limitationsTumors with SETD2 mutations demonstrated significantly decreased levels of FOXP3+ T cells in the tumor core, stroma, and tumor-stroma interface. PBRM1 mutations were associated with decreased FOXP3+ T cells in the tumor core. Primary KDM5C mutations were associated with significantly increased CD206+ macrophage tumor infiltration in the tumor core. A computational method estimating immune cell types in the TIME using bulk RNA-seq data, xCell scoring, failed to validate associations from the IF analysis in large external data sets. A major limitation of the study is the relatively small patient population studied. ConclusionsThis study provides evidence that common somatic mutations in ccRCC, such as SETD2, PBRM1, and KDM5C, are associated with distinct immune infiltration patterns within the TIME. Patient summaryIn this study, we analyzed tumor samples from patients with metastatic kidney cancer to determine whether common genetic mutations that arise from the cancer cells are associated with the density of immune cells found within those tumors. We found several distinct immune cell patterns that were associated with specific genetic mutations. These findings provide insight into the interaction between cancer genetics and the immune system in kidney cancer.

Intra-database validation of case-identifying algorithms using reconstituted electronic health records from healthcare claims data

BackgroundDiagnosis performances of case-identifying algorithms developed in healthcare database are usually assessed by comparing identified cases with an external data source. When this is not feasible, intra-database validation can present an appropriate alternative.ObjectivesTo illustrate through two practical examples how to perform intra-database validations of case-identifying algorithms using reconstituted Electronic Health Records (rEHRs).MethodsPatients with 1) multiple sclerosis (MS) relapses and 2) metastatic castration-resistant prostate cancer (mCRPC) were identified in the French nationwide healthcare database (SNDS) using two case-identifying algorithms. A validation study was then conducted to estimate diagnostic performances of these algorithms through the calculation of their positive predictive value (PPV) and negative predictive value (NPV). To that end, anonymized rEHRs were generated based on the overall information captured in the SNDS over time (e.g. procedure, hospital stays, drug dispensing, medical visits) for a random selection of patients identified as cases or non-cases according to the predefined algorithms. For each disease, an independent validation committee reviewed the rEHRs of 100 cases and 100 non-cases in order to adjudicate on the status of the selected patients (true case/ true non-case), blinded with respect to the result of the corresponding algorithm.ResultsAlgorithm for relapses identification in MS showed a 95% PPV and 100% NPV. Algorithm for mCRPC identification showed a 97% PPV and 99% NPV.ConclusionThe use of rEHRs to conduct an intra-database validation appears to be a valuable tool to estimate the performances of a case-identifying algorithm and assess its validity, in the absence of alternative.

LADEA: A Software Infrastructure for Audio Delivery and Analytics

The LoRa technology enables long distance links with reduced power consumption at low cost, the main limitation being the low bandwidth that it offers. With LoRa, remote locations, like rural areas, can benefit from connectivity based services that would otherwise be impossible. In this work, we describe a LoRa architecture that can include generic external data sources using an MQTT-based interface. We particularly focus on audio sources aiming to two basic services: a voice messaging system that allows users who cannot read or write to send voice notes, and an audio compression service to extract the main features from the audio input to use it for developing intelligent ML-based audio analytics.