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The presentations in this session of the Monticello II conference were aimed at summarizing what is known about asbestiform and non-asbestiform elongate mineral particles (EMPs) and mesothelioma risks based on evidence from experimental and epidemiology studies. Dr. Case discussed case reports of mesothelioma over the last several decades. Dr. Taioli indicated that the epidemiology evidence concerning non-asbestiform EMPs is weak or lacking, and that progress would be limited unless mesothelioma registries are established. One exception discussed is that of taconite miners, who are exposed to grunerite. Drs. Mandel and Odo noted that studies of taconite miners in Minnesota have revealed an excess rate of mesothelioma, but the role of non-asbestiform EMPs in this excess incidence of mesothelioma is unclear. Dr. Becich discussed the National Mesothelioma Virtual Bank (NMVB), a virtual mesothelioma patient registry that includes mesothelioma patients' lifetime work histories, exposure histories, biospecimens, proteogenomic information, and imaging data that can be used in epidemiology research on mesothelioma. Dr. Bernstein indicated that there is a strong consensus that long, highly durable respirable asbestiform EMPs have the potential to cause mesothelioma, but there is continued debate concerning the biodurability required, and the dimensions (both length and diameter), the shape, and the dose associated with mesothelioma risk. Finally, Dr. Nel discussed how experimental studies of High Aspect Ratio Engineered Nanomaterials have clarified dimensional and durability features that impact disease risk, the impact of inflammation and oxidative stress on the epigenetic regulation of tumor suppressor genes, and the generation of immune suppressive effects in the mesothelioma tumor microenvironment. The session ended with a discussion of future research needs.

Background and objectivesAn unmet need exists for validated quantitative tools to measure multiple sclerosis (MS) disease activity and progression. We developed a custom immunoassay-based MS disease activity (MSDA) Test incorporating 18 protein concentrations into an algorithm to calculate four Disease Pathway scores (Immunomodulation, Neuroinflammation, Myelin Biology, and Neuroaxonal Integrity) and an overall Disease Activity score. The objective was to clinically validate the MSDA Test based on associations between scores and clinical/radiographic assessments.MethodsSerum samples (N=614) from patients with MS at multiple sites were split into Train (n=426; algorithm development) and Test (n=188; evaluation) subsets. Subsets were stratified by demographics, sample counts per site, and gadolinium-positive (Gd+) lesion counts; age and sex were used to demographically adjust protein concentrations. MSDA Test results were evaluated for potential association with Gd+ lesion presence/absence, new and enlarging (N/E) T2 lesion presence, and active versus stable disease status (composite endpoint combining radiographic and clinical evidence of disease activity).ResultsA multi-protein model was developed (trained and cross-validated) using the Train subset. When applied to the Test subset, the model classified the Gd+ lesion presence/absence, N/E T2 lesion presence, and active versus stable disease status assessments with an area under the receiver operating characteristic (AUROC) of 0.781, 0.750, and 0.768, respectively. In each case, the multi-protein model had significantly (bootstrapped, one-sidedpDiscussionThe MSDA Test was clinically validated; the multi-protein model had greater performance compared with the top-performing single-protein model. The MSDA Test may serve as a quantitative and objective tool to enhance care for MS.

Malignant pleural mesothelioma (MPM), an aggressive cancer of the mesothelial cells lining the pleural cavity, lacks effective treatments. Multiple somatic mutations and copy number losses in tumor suppressor genes (TSGs) BAP1, CDKN2A/B, and NF2 are frequently associated with MPM. The impact of single versus multiple genomic alterations of TSG on MPM biology, the immune tumor microenvironment, clinical outcomes, and treatment responses are unknown. Tumors with genomic alterations in BAP1 alone were associated with a longer overall patient survival rate compared to tumors with CDKN2A/B and/or NF2 alterations with or without BAP1 and formed a distinct immunogenic subtype with altered transcription factor and pathway activity patterns. CDKN2A/B genomic alterations consistently contributed to an adverse clinical outcome. Since the genomic alterations of only BAP1 was associated with the PD-1 therapy response signature and higher LAG3 and VISTA gene expression, it might be a candidate marker for immune checkpoint blockade therapy. Our results on the impact of TSG genotypes on MPM and the correlations between TSG alterations and molecular pathways provide a foundation for developing individualized MPM therapies.

Malignant pleural mesothelioma (MPM), an aggressive cancer of the mesothelial cells lining the pleural cavity, lacks effective treatments. Multiple somatic mutations and copy number losses in tumor suppressor genes (TSGs)

BackgroundAlpha-1 antitrypsin deficiency (AATD) is a genetic condition that causes early onset pulmonary emphysema and airways obstruction. The complete mechanisms via which AATD causes lung disease are not fully understood. To improve our understanding of the pathogenesis of AATD, we investigated gene expression profiles of bronchoalveolar lavage (BAL) and peripheral blood mononuclear cells (PBMCs) in AATD individuals.MethodsWe performed RNA-Seq on RNA extracted from matched BAL and PBMC samples isolated from 89 subjects enrolled in the Genomic Research in Alpha-1 Antitrypsin Deficiency and Sarcoidosis (GRADS) study. Subjects were stratified by genotype and augmentation therapy. Supervised and unsupervised differential gene expression analyses were performed using Weighted Gene Co-expression Network Analysis (WGCNA) to identify gene profiles associated with subjects’ clinical variables. The genes in the most significant WGCNA module were used to cluster AATD individuals. Gene validation was performed by NanoString nCounter Gene Expression Assay.ResultWe observed modest effects of AATD genotype and augmentation therapy on gene expression. When WGCNA was applied to BAL transcriptome, one gene module, ME31 (2312 genes), correlated with the highest number of clinical variables and was functionally enriched with numerous immune T-lymphocyte related pathways. This gene module identified two distinct clusters of AATD individuals with different disease severity and distinct PBMC gene expression patterns.ConclusionsWe successfully identified novel clusters of AATD individuals where severity correlated with increased immune response independent of individuals’ genotype and augmentation therapy. These findings may suggest the presence of previously unrecognised disease endotypes in AATD that associate with T-lymphocyte immunity and disease severity.

Pathologists are adopting whole slide images (WSIs) for diagnosis, thanks to recent FDA approval of WSI systems as class II medical devices. In response to new market forces and recent technology advances outside of pathology, a new field of computational pathology has emerged that applies artificial intelligence (AI) and machine learning algorithms to WSIs. Computational pathology has great potential for augmenting pathologists' accuracy and efficiency, but there are important concerns regarding trust of AI due to the opaque, black-box nature of most AI algorithms. In addition, there is a lack of consensus on how pathologists should incorporate computational pathology systems into their workflow. To address these concerns, building computational pathology systems with explainable AI (xAI) mechanisms is a powerful and transparent alternative to black-box AI models. xAI can reveal underlying causes for its decisions; this is intended to promote safety and reliability of AI for critical tasks such as pathology diagnosis. This article outlines xAI enabled applications in anatomic pathology workflow that improves efficiency and accuracy of the practice. In addition, we describe HistoMapr-Breast, an initial xAI enabled software application for breast core biopsies. HistoMapr-Breast automatically previews breast core WSIs and recognizes the regions of interest to rapidly present the key diagnostic areas in an interactive and explainable manner. We anticipate xAI will ultimately serve pathologists as an interactive computational guide for computer-assisted primary diagnosis.

Sarcoidosis is a multisystem granulomatous disease of unknown origin with a variable and often unpredictable course and pattern of organ involvement. In this study we sought to identify specific bronchoalveolar lavage (BAL) cell gene expression patterns indicative of distinct disease phenotypic traits.RNA sequencing by Ion Torrent Proton was performed on BAL cells obtained from 215 well characterized patients with pulmonary sarcoidosis enrolled in the multicenter Genomic Research in Alpha-1 Antitrypsin Deficiency and Sarcoidosis (GRADS) study. Weighted Gene Co-expression Network Analysis (WGCNA) and non-parametric statistics were used to analyze genome wide BAL transcriptome. Validation of results was performed using a microarray expression data set of an independent sarcoidosis cohort (Freiburg, Germany (n=50)).Our supervised analysis found associations between distinct transcriptional programs and major pulmonary phenotypic manifestations of sarcoidosis including; TH1 and TH17 pathways associated with hilar lymphadenopathy; TGFB1 and MTOR signaling with parenchymal involvement, and IL7 and IL2 with airway involvement. Our unsupervised analysis revealed gene modules that uncovered four potential sarcoidosis endotypes including hilar lymphadenopathy with increased acute T cell immune response; extraocular organ involvement with PI3K activation pathways; chronic and multiorgan disease with increased immune response pathways; and multiorgan with increased IL-1 and IL-18 immune and inflammatory responses. We validated the occurrence of these endotypes using gene expression, pulmonary function tests and cell differentials from Freiburg. Taken together our results identify BAL gene expression programs that characterize major pulmonary sarcoidosis phenotypes and suggest the presence of distinct disease molecular endotypes.Take home messageGenome wide BAL transcriptomics identified novel gene expression profiles associated with distinct phenotypic traits in sarcoidosis and is suggestive of the presence of novel molecular and clinical sarcoidosis endotypes that could help with further understanding of this heterogenous disease.

Artificial intelligence (AI) is transforming many domains, including finance, agriculture, defense, and biomedicine. In this paper, we focus on the role of AI in clinical and translational research (CTR), including preclinical research (T1), clinical research (T2), clinical implementation (T3), and public (or population) health (T4). Given the rapid evolution of AI in CTR, we present three complementary perspectives: (1) scoping literature review, (2) survey, and (3) analysis of federally funded projects. For each CTR phase, we addressed challenges, successes, failures, and opportunities for AI. We surveyed Clinical and Translational Science Award (CTSA) hubs regarding AI projects at their institutions. Nineteen of 63 CTSA hubs (30%) responded to the survey. The most common funding source (48.5%) was the federal government. The most common translational phase was T2 (clinical research, 40.2%). Clinicians were the intended users in 44.6% of projects and researchers in 32.3% of projects. The most common computational approaches were supervised machine learning (38.6%) and deep learning (34.2%). The number of projects steadily increased from 2012 to 2020. Finally, we analyzed 2604 AI projects at CTSA hubs using the National Institutes of Health Research Portfolio Online Reporting Tools (RePORTER) database for 2011–2019. We mapped available abstracts to medical subject headings and found that nervous system (16.3%) and mental disorders (16.2) were the most common topics addressed. From a computational perspective, big data (32.3%) and deep learning (30.0%) were most common. This work represents a snapshot in time of the role of AI in the CTSA program.

The burden and prognosis of malignant mesothelioma in the United States have remained largely unchanged for decades, with approximately 3200 new cases and 2400 deaths reported annually. To address care and research gaps contributing to poor outcomes, in March of 2019 the Mesothelioma Applied Research Foundation convened a workshop on the potential usefulness and feasibility of a national mesothelioma registry. The workshop included formal presentations by subject matter experts and a moderated group discussion. Workshop participants identified top priorities for a registry to be (a) connecting patients with high-quality care and clinical trials soon after diagnosis, and (b) making useful data and biospecimens available to researchers in a timely manner. Existing databases that capture mesothelioma cases are limited by factors such as delays in reporting, deidentification, and lack of exposure information critical to understanding as yet unrecognized causes of disease. National disease registries for amyotrophic lateral sclerosis (ALS) in the United States and for mesothelioma in other countries, provide examples of how a registry could be structured to meet the needs of patients and the scientific community. Small-scale pilot initiatives should be undertaken to validate methods for rapid case identification, develop procedures to facilitate patient access to guidelines-based standard care and investigational therapies, and explore approaches to data sharing with researchers. Ultimately, federal coordination and funding will be critical to the success of a National Mesothelioma Registry in improving mesothelioma outcomes and preventing future cases of this devastating disease.

DNA was obtained from matching micro-dissected, primary tumor cells, paired metastases, and peripheral blood mononuclear cells (germline) from patients with appendiceal mucinous neoplasms. We compared specimens from patient cohorts comprising low-grade adenomucinous neoplasm versus high-grade mucinous adenocarcinoma using a targeted, amplicon sequencing panel of 409 cancer related genes (Ion Torrent Comprehensive Cancer Panel, Thermo-Fisher, Waltham, MA). Copy number variants, single nucleotide variants and small insertions/deletions were identified using a multiplex algorithm pipeline (GATK, VarScan2, MuTect2, SIFT, SIFT-INDEL, PolyPhen-2, Provean). There were significantly more damaging variants in high-grade versus low-grade tumor cohorts. Both cohorts contained damaging, heterozygous germline variants (catenin β1; notch receptor 1 and 4) in pathways associated with cell-lineage specification (WNT, NOTCH). Damaging, somatic KRAS proto-oncogene, GTPase mutations were present in both cohorts, while somatic GNAS complex locus mutations were confined to low-grade neoplasms. Variants predominantly affected transcription factors, kinases, and stem cell signaling molecules in canonical pathways including epithelial to mesenchymal transition, stem cell pluripotency, p53, PTEN, and NF-қB signaling pathways. High-grade tumors demonstrated MYC proto-oncogene, bHLH transcription factor (MYC) and death domain associated protein (DAXX) amplification and damaging somatic variants in tumor protein p53 (TP53), likely to amplify an aggressive phenotype. Damaging APC, WNT signaling pathway regulator (APC) deletions were identified in metastatic tissue of both cohorts suggesting a role in invasive disease. Our data suggest that germline dysregulation of WNT and/or NOTCH pathways predisposes patients toward a secretory cell phenotype (i.e., goblet-like cells) upon acquisition of somatic KRAS mutations. Additional somatically acquired variants activating oncogenes MYC and DAXX and inhibiting the critical tumor suppressor, tumor protein TP53, were consistent with manifestation of a high-grade phenotype. These additional changes within the epithelial to mesenchymal transition signaling network (WNT, NOTCH, RAS/ERK/PI3K, PTEN, NF-қB), produce aggressive high-grade tumor characteristics by actively driving cells towards dedifferentiation, proliferation, and migration.

ObjectiveAs a long-standing Clinical and Translational Science Awards (CTSA) Program hub, the University of Pittsburgh and the University of Pittsburgh Medical Center (UPMC) developed and implemented a modern research data warehouse (RDW) to efficiently provision electronic patient data for clinical and translational research.MethodsBecause UPMC is one of the largest health care systems in the US with multiple vendors’ electronic health record (EHR) systems, we designed and implemented an RDW named Neptune to serve the specific needs of our CTSA. Neptune uses an atomic design where data is stored at a high level of granularity as represented in source systems. Neptune contains robust patient identity management tailored for research; integrates patient data from multiple sources, including EHRs, health plans, and research studies; and includes knowledge for mapping to standard terminologies. Neptune enables efficient provisioning of data to large analytics-oriented data models and to individual investigators.ResultsNeptune contains data for more than 5 million patients longitudinally organized as HIPAA Limited Data with dates and includes structured EHR data, clinical documents, health insurance claims, and research data. Neptune is used as a source for patient data for hundreds of IRB-approved research projects by local investigators and for national projects such as the Accrual to Clinical Trials (ACT) network, the All of Us Research Program, and the National Patient-Centered Clinical Research Network.DiscussionThe design of Neptune was heavily influenced by the large size of UPMC, the varied data sources, and the rich partnership between the University and the healthcare system. It features several desiderata of an RDW, including robust protected health information management, an extensible information storage model, and binding to standard terminologies at the time of data delivery. It also includes several unique aspects, including the physical warehouse straddling the University of Pittsburgh and UPMC networks and management under a HIPAA Business Associates Agreement.ConclusionWe describe the design and implementation of an RDW at a large academic health care system that uses a distinctive atomic design where data is stored at a high level of granularity.

Simple Summary Internal organs like the heart and lungs, and body cavities like the thoracic and abdominal cavities, are covered by a thin, slippery layer called the mesothelium. Malignant pleural mesothelioma (MPM) is an aggressive cancer of the lining of the lung, where genetics and asbestos exposure play a role. It is not diagnosable until it becomes invasive, offering only a short survival time to the patient. To help understand the role of the genes that relate to this disease most of which are poorly understood, we constructed the ‘MPM interactome’, including in it the protein-protein interactions that we predicted computationally and those that are previously known in the literature. Five novel protein-protein interactions (PPIs) were tested and validated experimentally. 85.65% of the interactome is supported by genetic variant, transcriptomic, and proteomic evidence. Comparative transcriptome analysis revealed 5 repurposable drugs targeting the interactome proteins. We make the interactome available on a freely accessible web application, Wiki-MPM. Abstract Malignant pleural mesothelioma (MPM) is an aggressive cancer affecting the outer lining of the lung, with a median survival of less than one year. We constructed an ‘MPM interactome’ with over 300 computationally predicted protein-protein interactions (PPIs) and over 2400 known PPIs of 62 literature-curated genes whose activity affects MPM. Known PPIs of the 62 MPM associated genes were derived from Biological General Repository for Interaction Datasets (BioGRID) and Human Protein Reference Database (HPRD). Novel PPIs were predicted by applying the HiPPIP algorithm, which computes features of protein pairs such as cellular localization, molecular function, biological process membership, genomic location of the gene, and gene expression in microarray experiments, and classifies the pairwise features as interacting or non-interacting based on a random forest model. We validated five novel predicted PPIs experimentally. The interactome is significantly enriched with genes differentially ex-pressed in MPM tumors compared with normal pleura and with other thoracic tumors, genes whose high expression has been correlated with unfavorable prognosis in lung cancer, genes differentially expressed on crocidolite exposure, and exosome-derived proteins identified from malignant mesothelioma cell lines. 28 of the interactors of MPM proteins are targets of 147 U.S. Food and Drug Administration (FDA)-approved drugs. By comparing disease-associated versus drug-induced differential expression profiles, we identified five potentially repurposable drugs, namely cabazitaxel, primaquine, pyrimethamine, trimethoprim and gliclazide. Preclinical studies may be con-ducted in vitro to validate these computational results. Interactome analysis of disease-associated genes is a powerful approach with high translational impact. It shows how MPM-associated genes identified by various high throughput studies are functionally linked, leading to clinically translatable results such as repurposed drugs. The PPIs are made available on a webserver with interactive user interface, visualization and advanced search capabilities.

BACKGROUND The National Cancer Institute Informatics Technology for Cancer Research (ITCR) program provides a series of funding mechanisms to create an ecosystem of open-source software (OSS) that serves the needs of cancer research. As the ITCR ecosystem substantially grows, it faces the challenge of the long-term sustainability of the software being developed by ITCR grantees. To address this challenge, the ITCR sustainability and industry partnership working group (SIP-WG) was convened in 2019. OBJECTIVE The charter of the SIP-WG is to investigate options to enhance the long-term sustainability of the OSS being developed by ITCR, in part by developing a collection of business model archetypes that can serve as sustainability plans for ITCR OSS development initiatives. The working group assembled models from the ITCR program, from other studies, and from the engagement of its extensive network of relationships with other organizations (eg, Chan Zuckerberg Initiative, Open Source Initiative, and Software Sustainability Institute) in support of this objective. METHODS This paper reviews the existing sustainability models and describes 10 OSS use cases disseminated by the SIP-WG and others, including 3D Slicer, Bioconductor, Cytoscape, Globus, i2b2 (Informatics for Integrating Biology and the Bedside) and tranSMART, Insight Toolkit, Linux, Observational Health Data Sciences and Informatics tools, R, and REDCap (Research Electronic Data Capture), in 10 sustainability aspects: governance, documentation, code quality, support, ecosystem collaboration, security, legal, finance, marketing, and dependency hygiene. RESULTS Information available to the public reveals that all 10 OSS have effective governance, comprehensive documentation, high code quality, reliable dependency hygiene, strong user and developer support, and active marketing. These OSS include a variety of licensing models (eg, general public license version 2, general public license version 3, Berkeley Software Distribution, and Apache 3) and financial models (eg, federal research funding, industry and membership support, and commercial support). However, detailed information on ecosystem collaboration and security is not publicly provided by most OSS. CONCLUSIONS We recommend 6 essential attributes for research software: alignment with unmet scientific needs, a dedicated development team, a vibrant user community, a feasible licensing model, a sustainable financial model, and effective product management. We also stress important actions to be considered in future ITCR activities that involve the discussion of the sustainability and licensing models for ITCR OSS, the establishment of a central library, the allocation of consulting resources to code quality control, ecosystem collaboration, security, and dependency hygiene.

Academic industry partnership (AIP) represents an important alliance between academic researchers and industry that helps translate technology and complete the innovation cycle within academic health systems. Despite diverging missions and skillsets the culture for academia and industry is changing in response to the current digital era which is spawning greater collaboration between physicians and businesses in this marketplace. In the field of pathology, this is further driven by the fact that traditional funding sources cannot keep pace with the innovation needed in digital pathology and artificial intelligence. This concept article from the Digital Pathology Association (DPA) describes the rules of engagement for pathology innovators in academia and for their corporate partners to help establish best practices in this critical area. Stakeholders include pathologists, basic and translational researchers, university technology transfer and sponsored research offices, as well as industry relations officers. The article discusses the benefits and pitfalls of an AIP, reviews different partnership models, examines the role of pathologists in the innovation cycle, explains various agreements that may need to be signed, covers conflict of interest and intellectual property issues, and offers recommendations for ensuring successful partnerships.