Crowdsourcing Effort Research Articles

Teaching transposon classification as a means to crowd source the curation of repeat annotation – a tardigrade perspective

BackgroundThe advancement of sequencing technologies results in the rapid release of hundreds of new genome assemblies a year providing unprecedented resources for the study of genome evolution. Within this context, the significance of in-depth analyses of repetitive elements, transposable elements (TEs) in particular, is increasingly recognized in understanding genome evolution. Despite the plethora of available bioinformatic tools for identifying and annotating TEs, the phylogenetic distance of the target species from a curated and classified database of repetitive element sequences constrains any automated annotation effort. Moreover, manual curation of raw repeat libraries is deemed essential due to the frequent incompleteness of automatically generated consensus sequences.ResultsHere, we present an example of a crowd-sourcing effort aimed at curating and annotating TE libraries of two non-model species built around a collaborative, peer-reviewed teaching process. Manual curation and classification are time-consuming processes that offer limited short-term academic rewards and are typically confined to a few research groups where methods are taught through hands-on experience. Crowd-sourcing efforts could therefore offer a significant opportunity to bridge the gap between learning the methods of curation effectively and empowering the scientific community with high-quality, reusable repeat libraries.ConclusionsThe collaborative manual curation of TEs from two tardigrade species, for which there were no TE libraries available, resulted in the successful characterization of hundreds of new and diverse TEs in a reasonable time frame. Our crowd-sourcing setting can be used as a teaching reference guide for similar projects: A hidden treasure awaits discovery within non-model organisms.

A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer

BackgroundPredictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti–PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC.MethodsParticipants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials.ResultsA total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression–based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1.ConclusionsThis DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICI efficacy.Trial registration: CheckMate 026; NCT02041533, registered January 22, 2014.CheckMate 227; NCT02477826, registered June 23, 2015.

A crowdsourcing database for the copy-number variation of the Spanish population

BackgroundDespite being a very common type of genetic variation, the distribution of copy-number variations (CNVs) in the population is still poorly understood. The knowledge of the genetic variability, especially at the level of the local population, is a critical factor for distinguishing pathogenic from non-pathogenic variation in the discovery of new disease variants.ResultsHere, we present the SPAnish Copy Number Alterations Collaborative Server (SPACNACS), which currently contains copy number variation profiles obtained from more than 400 genomes and exomes of unrelated Spanish individuals. By means of a collaborative crowdsourcing effort whole genome and whole exome sequencing data, produced by local genomic projects and for other purposes, is continuously collected. Once checked both, the Spanish ancestry and the lack of kinship with other individuals in the SPACNACS, the CNVs are inferred for these sequences and they are used to populate the database. A web interface allows querying the database with different filters that include ICD10 upper categories. This allows discarding samples from the disease under study and obtaining pseudo-control CNV profiles from the local population. We also show here additional studies on the local impact of CNVs in some phenotypes and on pharmacogenomic variants. SPACNACS can be accessed at: http://csvs.clinbioinfosspa.es/spacnacs/.ConclusionSPACNACS facilitates disease gene discovery by providing detailed information of the local variability of the population and exemplifies how to reuse genomic data produced for other purposes to build a local reference database.

Radio source-component association for the LOFAR Two-metre Sky Survey with region-based convolutional neural networks

Context. Radio loud active galactic nuclei (RLAGNs) are often morphologically complex objects that can consist of multiple, spatially separated, components. Only when the spatially separated radio components are correctly grouped together can we start to look for the corresponding optical host galaxy and infer physical parameters such as the size and luminosity of the radio object. Existing radio detection software to group these spatially separated components together is either experimental or based on assumptions that do not hold for current generation surveys, such that, in practice, astronomers often rely on visual inspection to resolve radio component association. However, applying visual inspection to all the hundreds of thousands of well-resolved RLAGNs that appear in the images from the Low Frequency Array (LOFAR) Two-metre Sky Survey (LoTSS) at 144 MHz, is a daunting, time-consuming process, even with extensive manpower. Aims. Using a machine learning approach, we aim to automate the radio component association of large (>15 arcsec) radio components. Methods. We turned the association problem into a classification problem and trained an adapted Fast region-based convolutional neural network to mimic the expert annotations from the first LoTSS data release. We implemented a rotation data augmentation to reduce overfitting and simplify the component association by removing unresolved radio sources that are likely unrelated to the large and bright radio components that we consider using predictions from an existing gradient boosting classifier. Results. For large (>15 arcsec) and bright (>10 mJy) radio components in the LoTSS first data release, our model provides the same associations for 85.3% ± 0.6 of the cases as those derived when astronomers perform the association manually. When the association is done through public crowd-sourced efforts, a result similar to that of our model is attained. Conclusions. Our method is able to efficiently carry out manual radio-component association for huge radio surveys and can serve as a basis for either automated radio morphology classification or automated optical host identification. This opens up an avenue to study the completeness and reliability of samples of radio sources with extended, complex morphologies.

Redesigning Undergraduate Laboratories: From Recipes to Research

In an effort to introduce discovery-based learning in our undergraduate laboratory curriculum, we joined two collaborative, crowd-sourcing efforts to engage early career undergraduates in searching for novel antibiotics or bacteriophages as antimicrobial therapies. This was the first step in transforming the curriculum into one focused on providing research experiences to all our undergraduates. Our goal was to engage students in open-ended research projects that foster a sense of ownership, advance knowledge, and introduce relevant skills. We also developed courses built on faculty research programs and biotechnology industry practices. In one, students design cell culture models of tissue regeneration. Another connects to faculty research investigating stress responses in a model of mycobacterial tuberculosis. In an immunotherapies course, students design cell culture and protein purification strategies to maximize generation of a monoclonal antibody. In our newest course, students examine number and diversity of soil nematodes in urban locations. This course has evolved to include interdisciplinary research into the environmental history of the sites. Assessments demonstrated gains in concrete skills and related concepts, and in aspects such self-efficacy. All are measures shown to increase retention and student future success.

Call for a Public Open Database of All Chemical Reactions.

Today there exists no public, freely downloadable, comprehensive database of all known chemical reactions and associated information. Such a database not only would serve chemical sciences and technologies around the world but also would enable the power of modern AI and machine learning methods to be unleashed on a host of fundamental problems. In time, this could lead to important scientific discoveries and economic developments for the benefit of humanity. While ideally such a repository ought to be created and maintained by an international consortium, in the near future, it may be easier to begin the process through governmental agencies such as the National Science Foundation or the National Institutes of Health. Working together, we could use a multipronged approach that could combine negotiations with commercial stakeholders, crowd-sourcing efforts, automated extraction methods, and legislative actions.

A community challenge to evaluate RNA-seq, fusion detection, and isoform quantification methods for cancer discovery.

The accurate identification and quantitation of RNA isoforms present in the cancer transcriptome is key for analyses ranging from the inference of the impacts of somatic variants to pathway analysis to biomarker development and subtype discovery. The ICGC-TCGA DREAM Somatic Mutation Calling in RNA (SMC-RNA) challenge was a crowd-sourced effort to benchmark methods for RNA isoform quantification and fusion detection from bulk cancer RNA sequencing (RNA-seq) data. It concluded in 2018 with a comparison of 77 fusion detection entries and 65 isoform quantification entries on 51 synthetic tumors and 32 cell lines with spiked-in fusion constructs. We report the entries used to build this benchmark, the leaderboard results, and the experimental features associated with the accurate prediction of RNA species. This challenge required submissions to be in the form of containerized workflows, meaning each of the entries described is easily reusable through CWL and Docker containers at https://github.com/SMC-RNA-challenge. A record of this paper's transparent peer review process is included in the supplemental information.

Mapathons versus automated feature extraction: a comparative analysis for strengthening immunization microplanning

BackgroundSocial instability and logistical factors like the displacement of vulnerable populations, the difficulty of accessing these populations, and the lack of geographic information for hard-to-reach areas continue to serve as barriers to global essential immunizations (EI). Microplanning, a population-based, healthcare intervention planning method has begun to leverage geographic information system (GIS) technology and geospatial methods to improve the remote identification and mapping of vulnerable populations to ensure inclusion in outreach and immunization services, when feasible. We compare two methods of accomplishing a remote inventory of building locations to assess their accuracy and similarity to currently employed microplan line-lists in the study area.MethodsThe outputs of a crowd-sourced digitization effort, or mapathon, were compared to those of a machine-learning algorithm for digitization, referred to as automatic feature extraction (AFE). The following accuracy assessments were employed to determine the performance of each feature generation method: (1) an agreement analysis of the two methods assessed the occurrence of matches across the two outputs, where agreements were labeled as “befriended” and disagreements as “lonely”; (2) true and false positive percentages of each method were calculated in comparison to satellite imagery; (3) counts of features generated from both the mapathon and AFE were statistically compared to the number of features listed in the microplan line-list for the study area; and (4) population estimates for both feature generation method were determined for every structure identified assuming a total of three households per compound, with each household averaging two adults and 5 children.ResultsThe mapathon and AFE outputs detected 92,713 and 53,150 features, respectively. A higher proportion (30%) of AFE features were befriended compared with befriended mapathon points (28%). The AFE had a higher true positive rate (90.5%) of identifying structures than the mapathon (84.5%). The difference in the average number of features identified per area between the microplan and mapathon points was larger (t = 3.56) than the microplan and AFE (t = − 2.09) (alpha = 0.05).ConclusionsOur findings indicate AFE outputs had higher agreement (i.e., befriended), slightly higher likelihood of correctly identifying a structure, and were more similar to the local microplan line-lists than the mapathon outputs. These findings suggest AFE may be more accurate for identifying structures in high-resolution satellite imagery than mapathons. However, they both had their advantages and the ideal method would utilize both methods in tandem.

Focus on How Plants Engage With Beneficial Microorganisms While at the Same Time Restricting Pathogens.

Plants live in a world filled with microbes, and spend their lives engaged in the delicate dance of nurturing beneficial interactions while simultaneously reducing disease-causing interactions. How do plants engage with beneficial microorganisms while at the same time restricting pathogens? was recently selected in a crowd-sourced effort as the top, unanswered question in the field of molecular plant-microbe interactions. Elaborating on this question and setting the stage for this focus issue, the Top10 review by Thoms, Liang and Haney examines the way multiple inputs are integrated to initiate programs of immunity or mutualistic symbiosis, and how this shapes the microbiome. This comprehensive review describes the current landscape of the field, focusing on the plant-microbe-soil continuum, but providing ideas for extending these concepts to leaves, where much of the research on immunity has centered. Other papers in this issue examine the simultaneous interaction of plants with beneficial and pathogenic microorganisms, as well as many diverse relationships with beneficial microbes that can improve plant health by increasing access to nutrients or by decreasing disease.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

A Crowdsourcing Effort about Mobilizing Students to Forward Thinking of Their Studies

The paper presents some preliminary research results, obtained within the framework of the project Multiparticipant cooperative decision-making: consensus building vs crowdsourcingbased decisions, concerning the introduction to a crowdsourcing effort meant to mobilize students to forward thinking of their studies A study was conducted by Bucharest University of Economic Studies and by Warsaw School of Information Technology under the auspices of the Polish Academy of Sciences and relevant data were collected using a questionnaire delivered to Romanian and Polish students The results were interpreted in order to extract relevant information about expected nature of work after graduating, based on gender, nationality and university Crowdsourcing solutions are presented in order to reveal their advantages applicable in collaborative environments

Exploring linguistic hybridity and lexical creativity in the UK’s Greek Cypriot diaspora: the Grenglish Project

We present findings from the Grenglish Project, a public engagement initiative that brought together members of the UK’s Greek Cypriot diaspora in a crowdsourcing effort to collect linguistic material that reflected the community’s linguistic history. After a brief sociohistorical overview of Cypriot migration to the UK, we present the types of material contributors submitted to the project website with reference to parallel developments in Greek migrant varieties spoken in other parts of the world. We focus on English loanwords that were integrated into the morphological system of Cypriot Greek by the addition of native derivational and inflectional suffixes like πάσον /páson/ ‘bus’ and πασέρης /paséris/ ‘bus driver’. We finally consider the implications of the project’s findings for the status of British Cypriot Greek as a variety on its own right and address the ideological and attitudinal factors that shape the prospects for its intergenerational transmission.

Integration of the Drug-Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts.

The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) is a web resource that provides information on drug-gene interactions and druggable genes from publications, databases, and other web-based sources. Drug, gene, and interaction data are normalized and merged into conceptual groups. The information contained in this resource is available to users through a straightforward search interface, an application programming interface (API), and TSV data downloads. DGIdb 4.0 is the latest major version release of this database. A primary focus of this update was integration with crowdsourced efforts, leveraging the Drug Target Commons for community-contributed interaction data, Wikidata to facilitate term normalization, and export to NDEx for drug-gene interaction network representations. Seven new sources have been added since the last major version release, bringing the total number of sources included to 41. Of the previously aggregated sources, 15 have been updated. DGIdb 4.0 also includes improvements to the process of drug normalization and grouping of imported sources. Other notable updates include the introduction of a more sophisticated Query Score for interaction search results, an updated Interaction Score, the inclusion of interaction directionality, and several additional improvements to search features, data releases, licensing documentation and the application framework.

CSVS, a crowdsourcing database of the Spanish population genetic variability.

The knowledge of the genetic variability of the local population is of utmost importance in personalized medicine and has been revealed as a critical factor for the discovery of new disease variants. Here, we present the Collaborative Spanish Variability Server (CSVS), which currently contains more than 2000 genomes and exomes of unrelated Spanish individuals. This database has been generated in a collaborative crowdsourcing effort collecting sequencing data produced by local genomic projects and for other purposes. Sequences have been grouped by ICD10 upper categories. A web interface allows querying the database removing one or more ICD10 categories. In this way, aggregated counts of allele frequencies of the pseudo-control Spanish population can be obtained for diseases belonging to the category removed. Interestingly, in addition to pseudo-control studies, some population studies can be made, as, for example, prevalence of pharmacogenomic variants, etc. In addition, this genomic data has been used to define the first Spanish Genome Reference Panel (SGRP1.0) for imputation. This is the first local repository of variability entirely produced by a crowdsourcing effort and constitutes an example for future initiatives to characterize local variability worldwide. CSVS is also part of the GA4GH Beacon network.CSVS can be accessed at: http://csvs.babelomics.org/.

Tracking the deployment of TLS 1.3 on the web

Transport Layer Security (TLS) 1.3 is a redesign of the Web's most important security protocol. It was standardized in August 2018 after a four year-long, unprecedented design process involving many cryptographers and industry stakeholders. We use the rare opportunity to track deployment, uptake, and use of a new mission-critical security protocol from the early design phase until well over a year after standardization. For a profound view, we combine and analyze data from active domain scans, passive monitoring of large networks, and a crowd-sourcing effort on Android devices. In contrast to TLS 1.2, where adoption took more than five years and was prompted by severe attacks on previous versions, TLS 1.3 is deployed surprisingly speedily and without security concerns calling for it. Just 15 months after standardization, it is used in about 20% of connections we observe. Deployment on popular domains is at 30% and at about 10% across the com/net/org top-level domains (TLDs). We show that the development and fast deployment of TLS 1.3 is best understood as a story of experimentation and centralization. Very few giant, global actors drive the development. We show that Cloudflare alone brings deployment to sizable numbers and describe how actors like Facebook and Google use their control over both client and server endpoints to experiment with the protocol and ultimately deploy it at scale. This story cannot be captured by a single dataset alone, highlighting the need for multi-perspective studies on Internet evolution.

The Importance of Being Discovered: The Werner Von Boltenstern Shanghai Photograph and Negative Collection

The Werner von Boltenstern Shanghai Photograph and Negative Collection, housed in Loyola Marymount University’s William H. Hannon Library, is a series of photographs of 1930s–1940s Shanghai taken by Werner von Boltenstern. The images capture a time and place at a crossroads of culture and history. World War II and the Second Sino-Japanese War were raging and the city, a trade center populated by numerous peoples, including Chinese citizens, British, French, and American nationals, Sephardic and Russian Jews, and the occupying Japanese military, was receiving an influx of European Jews fleeing Nazi Europe. The rediscovery of this collection (it sat unused for many years) led to its digitization, a successful crowdsourcing effort to gather more metadata, and the incorporation of the collection into an LMU Literature of the Holocaust class digital project. Through these endeavors, the library has increased its understanding of the collection’s historical value, in particular as it relates to Holocaust studies and Jewish studies more broadly.

Improving Data Quality in Crowdsourced Data for Indonesian Election Monitor: A Case Study in KawalPilpres

ICT has enabled democratic process to be more transparent and enabled citizens’ participation in the election process. However, public trust is a mandatory requirement for a good democratic transition. Participation in monitoring election process could be implemented as a crowdsourcing effort to improve public trust in the election result which in this article based on a case study of KawalPilpres to monitor 2019 Indonesian presidential election. Trust factor is a key success for monitoring effort. Therefore, data quality becomes necessity. Data quality is assessed using Loshin’s maturity assessment and analysed using Loshin’s improvement strategies. Based on our assessments, there are three top categories for improvements namely governance, expectations, and policies of data quality management.

Crowdsourcing a crisis response for COVID-19 in oncology.

Crowdsourcing efforts are currently underway to collect and analyze data from patients with cancer who are affected by the COVID-19 pandemic. These community-led initiatives will fill key knowledge gaps to tackle crucial clinical questions on the complexities of infection with the causative coronavirus SARS-Cov-2 in the large, heterogeneous group of vulnerable patients with cancer.

Cartographers of North Korea: Who are they and what are the technical, political, and social issues involved in mapping North Korea

Volunteered Geographic Information (VGI) has made possible the expansion the mapping to previously unmapped areas, thanks to technological advancements such as Web 2.0 and satellite imagery. However, more than a technical feature, VGI has brought to light the political aspects of cartography, when multiple stakeholders have access to mapping tools. This is the case of the collaborative mapping strategies used to map North Korea, one of the most, if not the most, closed countries in the world in terms of diplomatic relations, tourism and its economy. OpenStreetMap (OSM) enables “armchair mappers” to map opaque territories in which local governments control citizens’ access to the internet. In this paper, we discuss the following questions: Who is mapping North Korea in OSM? Which tools and methods do contributors use to gain access to information about the country and to represent it? What are the motivations behind this mapping endeavor? We analyze technical aspects of OSM data for North Korea and structured correspondence exchanged with 889 contributors. We argue that crowdsourced efforts can make good the dearth of knowledge resulting from the physical, cultural and political barriers associated with uncharted territories and that, although the motivations for such efforts vary greatly, they all have their origin in a sense of empathy and the power technology has to penetrate geopolitical barriers.

Multiple Myeloma DREAM Challenge reveals epigenetic regulator PHF19 as marker of aggressive disease

While the past decade has seen meaningful improvements in clinical outcomes for multiple myeloma patients, a subset of patients does not benefit from current therapeutics for unclear reasons. Many gene expression-based models of risk have been developed, but each model uses a different combination of genes and often involves assaying many genes making them difficult to implement. We organized the Multiple Myeloma DREAM Challenge, a crowdsourced effort to develop models of rapid progression in newly diagnosed myeloma patients and to benchmark these against previously published models. This effort lead to more robust predictors and found that incorporating specific demographic and clinical features improved gene expression-based models of high risk. Furthermore, post-challenge analysis identified a novel expression-based risk marker, PHF19, which has recently been found to have an important biological role in multiple myeloma. Lastly, we show that a simple four feature predictor composed of age, ISS, and expression of PHF19 and MMSET performs similarly to more complex models with many more gene expression features included.

Crowdsourced High-Risk Classifiers for Multiple Myeloma Patients Commonly Identify PHF19 As a Robust Progression Biomarker

Multiple myeloma (MM) is a hematological malignancy of terminally differentiated plasma cells residing within the bone marrow with 25,000-30,000 patients diagnosed in the United States each year. The disease's clinical course depends on a complex interplay chromosomal abnormalities and mutations within plasma cells and patient socio-demographic factors. Novel treatments extended the time to disease progression and overall survival for the majority of patients. However, a subset of 15%-20% of MM patients exhibit an aggressive disease course with rapid disease progression and poor overall survival regardless of treatment. Accurately predicting which patients are at high-risk is critical to designing studies with a better understanding of myeloma progression and enabling the discovery of novel therapeutics that extend the progression free period of these patients. To date, most MM risk models use patient demographic data, clinical laboratory results and cytogenetic assays to predict clinical outcome. High-risk associated cytogenetic alterations include deletion of 17p or gain of 1q as well as t(14;16), t(14;20), and most commonly t(4,14), which leads to juxtaposition of MMSET with the immunoglobulin heavy chain locus promoter, resulting in overexpression of the MMSET oncogene. While cytogenetic assays, in particular fluorescence in situ hybridization (FISH), are widely available, their risk prediction is sub-optimal and recently developed gene expression based classifiers predict more accurately rapid progression. To investigate possible improvements to models of myeloma risk, we organized the Multiple Myeloma DREAM Challenge, focusing on predicting high-risk, defined as disease progression or death prior to 18 months from diagnosis. This effort combined 4 discovery datasets providing participants with clinical, cytogenetic, demographic and gene expression data to facilitate model development while retaining 4 additional datasets, whose clinical outcome was not publicly available, in order to benchmark submitted models. This crowd-sourced effort resulted in the unbiased assessment of 171 predictive algorithms on the validation dataset (N = 823 unique patient samples). Analysis of top performing methods identified high expression of PHF19, a histone methyltransferase, as the gene most strongly associated with disease progression, showing greater predictive power than the expression level of the putative high-risk gene MMSET. We show that a simple 4 feature model composed of age, stage and the gene expression of PHF19 and MMSET is as accurate as much larger published models composed of over 50 genes combined with ISS and age. Results from this work suggest that combination of gene expression and clinical data increases accuracy of high risk models which would improve patient selection in the clinic. Disclosures Towfic: Celgene Corporation: Employment, Equity Ownership. Dalton:MILLENNIUM PHARMACEUTICALS, INC.: Honoraria. Goldschmidt:Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; John-Hopkins University: Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Mundipharma: Research Funding; Amgen: Consultancy, Research Funding; Chugai: Honoraria, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Molecular Partners: Research Funding; MSD: Research Funding; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive Biotechnology: Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Research Funding; Dietmar-Hopp-Stiftung: Research Funding; John-Hopkins University: Research Funding. Avet-Loiseau:takeda: Consultancy, Other: travel fees, lecture fees, Research Funding; celgene: Consultancy, Other: travel fees, lecture fees, Research Funding. Ortiz:Celgene Corporation: Employment, Equity Ownership. Trotter:Celgene Corporation: Employment, Equity Ownership. Dervan:Celgene: Employment. Flynt:Celgene Corporation: Employment, Equity Ownership. Dai:M2Gen: Employment. Bassett:Celgene: Employment, Equity Ownership. Sonneveld:SkylineDx: Research Funding; Takeda: Honoraria, Research Funding; Karyopharm: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; BMS: Honoraria; Amgen: Honoraria, Research Funding. Shain:Amgen: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; AbbVie: Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees; Sanofi Genzyme: Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Consultancy. Munshi:Abbvie: Consultancy; Takeda: Consultancy; Oncopep: Consultancy; Celgene: Consultancy; Adaptive: Consultancy; Amgen: Consultancy; Janssen: Consultancy. Morgan:Bristol-Myers Squibb, Celgene Corporation, Takeda: Consultancy, Honoraria; Celgene Corporation, Janssen: Research Funding; Amgen, Janssen, Takeda, Celgene Corporation: Other: Travel expenses. Walker:Celgene: Research Funding. Thakurta:Celgene: Employment, Equity Ownership.