Realizing the Potential of Social Determinants Data: A Scoping Review of Approaches for Screening, Linkage, Extraction, Analysis and Interventions

Background

Social determinants of health (SDoH) like socioeconomics and neighborhoods strongly influence outcomes, yet standardized SDoH data is lacking in electronic health records (EHR), limiting research and care quality.

Methods

We searched PubMed using keywords "SDOH" and "EHR", underwent title/abstract and full-text screening. Included records were analyzed under five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions.

Results

We identified 685 articles, of which 324 underwent full review. Key findings include tailored screening instruments implemented across settings, census and claims data linkage providing contextual SDoH profiles, rule-based and neural network systems extracting SDoH from notes using NLP, connections found between SDoH data and healthcare utilization/chronic disease control, and integrated care management programs executed. However, considerable variability persists across data sources, tools, and outcomes.

Discussion

Despite progress identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical to fulfill the potential of SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.

Limitations of Noninvasive Tests-Based Population-Level Risk Stratification Strategy for Nonalcoholic Fatty Liver Disease

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) are highly prevalent but underdiagnosed.

AIMS

We used an electronic health record data network to test a population-level risk stratification strategy using noninvasive tests (NITs) of liver fibrosis.

METHODS

Data were obtained from PCORnet® sites in the East, Midwest, Southwest, and Southeast United States from patients aged [Formula: see text] 18 with or without ICD-10-CM diagnosis codes for NAFLD, NASH, and NASH-cirrhosis between 9/1/2017 and 8/31/2020. Average and standard deviations (SD) for Fibrosis-4 index (FIB-4), NAFLD fibrosis score (NFS), and Hepatic Steatosis Index (HSI) were estimated by site for each patient cohort. Sample-wide estimates were calculated as weighted averages across study sites.

RESULTS

Of 11,875,959 patients, 0.8% and 0.1% were coded with NAFLD and NASH, respectively. NAFLD diagnosis rates in White, Black, and Hispanic patients were 0.93%, 0.50%, and 1.25%, respectively, and for NASH 0.19%, 0.04%, and 0.16%, respectively. Among undiagnosed patients, insufficient EHR data for estimating NITs ranged from 68% (FIB-4) to 76% (NFS). Predicted prevalence of NAFLD by HSI was 60%, with estimated prevalence of advanced fibrosis of 13% by NFS and 7% by FIB-4. Approximately, 15% and 23% of patients were classified in the intermediate range by FIB-4 and NFS, respectively. Among NAFLD-cirrhosis patients, a third had FIB-4 scores in the low or intermediate range.

CONCLUSIONS

We identified several potential barriers to a population-level NIT-based screening strategy. HSI-based NAFLD screening appears unrealistic. Further research is needed to define merits of NFS- versus FIB-4-based strategies, which may identify different high-risk groups.

Avenues for Strengthening PCORnet's Capacity to Advance Patient-Centered Economic Outcomes in Patient-Centered Outcomes Research (PCOR)

PCORnet, the National Patient-Centered Clinical Research Network, provides the ability to conduct prospective and observational pragmatic research by leveraging standardized, curated electronic health records data together with patient and stakeholder engagement. PCORnet is funded by the Patient-Centered Outcomes Research Institute (PCORI) and is composed of 8 Clinical Research Networks that incorporate at total of 79 health system "sites." As the network developed, linkage to commercial health plans, federal insurance claims, disease registries, and other data resources demonstrated the value in extending the networks infrastructure to provide a more complete representation of patient's health and lived experiences. Initially, PCORnet studies avoided direct economic comparative effectiveness as a topic. However, PCORI's authorizing law was amended in 2019 to allow studies to incorporate patient-centered economic outcomes in primary research aims. With PCORI's expanded scope and PCORnet's phase 3 beginning in January 2022, there are opportunities to strengthen the network's ability to support economic patient-centered outcomes research. This commentary will discuss approaches that have been incorporated to date by the network and point to opportunities for the network to incorporate economic variables for analysis, informed by patient and stakeholder perspectives. Topics addressed include: (1) data linkage infrastructure; (2) commercial health plan partnerships; (3) Medicare and Medicaid linkage; (4) health system billing-based benchmarking; (5) area-level measures; (6) individual-level measures; (7) pharmacy benefits and retail pharmacy data; and (8) the importance of transparency and engagement while addressing the biases inherent in linking real-world data sources.

REDCap and the National Mesothelioma Virtual Bank-a scalable and sustainable model for rare disease biorepositories

OBJECTIVE

Rare disease research requires data sharing networks to power translational studies. We describe novel use of Research Electronic Data Capture (REDCap), a web application for managing clinical data, by the National Mesothelioma Virtual Bank, a federated biospecimen, and data sharing network.

MATERIALS AND METHODS

National Mesothelioma Virtual Bank (NMVB) uses REDCap to integrate honest broker activities, enabling biospecimen and associated clinical data provisioning to investigators. A Web Portal Query tool was developed to source and visualize REDCap data in interactive, faceted search, enabling cohort discovery by public users. An AWS Lambda function behind an API calculates the counts visually presented, while protecting record level data. The user-friendly interface, quick responsiveness, automatic generation from REDCap, and flexibility to new data, was engineered to sustain the NMVB research community.

RESULTS

NMVB implementations enabled a network of 8 research institutions with over 2000 mesothelioma cases, including clinical annotations and biospecimens, and public users' cohort discovery and summary statistics. NMVB usage and impact is demonstrated by high website visits (>150 unique queries per month), resource use requests (>50 letter of interests), and citations (>900) to papers published using NMVB resources.

DISCUSSION

NMVB's REDCap implementation and query tool is a framework for implementing federated and integrated rare disease biobanks and registries. Advantages of this framework include being low-cost, modular, scalable, and efficient. Future advances to NVMB's implementations will include incorporation of -omics data and development of downstream analysis tools to advance mesothelioma and rare disease research.

CONCLUSION

NVMB presents a framework for integrating biobanks and patient registries to enable translational research for rare diseases.

ReDWINE: A clinical datamart with text analytical capabilities to facilitate rehabilitation research

Rehabilitation research focuses on determining the components of a treatment intervention, the mechanism of how these components lead to recovery and rehabilitation, and ultimately the optimal intervention strategies to maximize patients' physical, psychologic, and social functioning. Traditional randomized clinical trials that study and establish new interventions face challenges, such as high cost and time commitment. Observational studies that use existing clinical data to observe the effect of an intervention have shown several advantages over RCTs. Electronic Health Records (EHRs) have become an increasingly important resource for conducting observational studies. To support these studies, we developed a clinical research datamart, called ReDWINE (Rehabilitation Datamart With Informatics iNfrastructure for rEsearch), that transforms the rehabilitation-related EHR data collected from the UPMC health care system to the Observational Health Data Sciences and Informatics (OHDSI) Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to facilitate rehabilitation research. The standardized EHR data stored in ReDWINE will further reduce the time and effort required by investigators to pool, harmonize, clean, and analyze data from multiple sources, leading to more robust and comprehensive research findings. ReDWINE also includes deployment of data visualization and data analytics tools to facilitate cohort definition and clinical data analysis. These include among others the Open Health Natural Language Processing (OHNLP) toolkit, a high-throughput NLP pipeline, to provide text analytical capabilities at scale in ReDWINE. Using this comprehensive representation of patient data in ReDWINE for rehabilitation research will facilitate real-world evidence for health interventions and outcomes.

Industry, occupation, and exposure history of mesothelioma patients in the U.S. National Mesothelioma Virtual Bank, 2006-2022

BACKGROUND

Malignant mesothelioma is associated with environmental and occupational exposure to certain mineral fibers, especially asbestos. This study aims to examine work histories of mesothelioma patients and their survival time.

METHOD

Using the NIOSH Industry and Occupation Computerized Coding System, we mapped occupations and industries recorded for 748 of 1444 patients in the U.S. National Mesothelioma Virtual Bank (NMVB) during the period 2006-2022. Descriptive and survival analyses were conducted.

RESULTS

Among the 1023 industries recorded for those having mesothelioma, the most frequent cases were found for those in manufacturing (n = 225, 22.0%), construction (138, 13.5%), and education services (66, 6.5%); among the 924 occupation records, the most frequent cases were found for those in construction and extraction (174, 18.8%), production (145, 15.7%), and management (84, 9.1%). Males (583) or persons aged >40 years (658) at the time of diagnosis tended to have worked in industries traditionally associated with mesothelioma (e.g., construction), while females (163) or persons aged 20-40 years (27) tended to have worked in industries not traditionally associated with mesothelioma (e.g., health care). Asbestos, unknown substances, and chemical solvents were the most frequently reported exposure, with females most often reporting an unknown substance. A multi-variable Cox Hazard Regression analysis showed that significant prognostic factors associated with decreased survival in mesothelioma cases are sex (male) and work experience in utility-related industry, while factor associated with increased survival are epithelial or epithelioid histological type, prior history of surgery and immunotherapy, and industry experience in accommodation and food services.

CONCLUSION

The NMVB has the potential of serving as a sentinel surveillance mechanism for identifying industries and occupations not traditionally associated with mesothelioma. Results indicate the importance of considering all potential sources of asbestos exposures including occupational, environmental, and extra-occupational exposures when evaluating mesothelioma patients and advising family members.

Non-asbestiform elongate mineral particles and mesothelioma risk: Human and experimental evidence

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.

Clinical validation of a multi-protein, serum-based assay for disease activity assessments in multiple sclerosis

An 18-protein multiple sclerosis (MS) disease activity (DA) test was validated based on associations between algorithm scores and clinical/radiographic assessments (N = 614 serum samples; Train [n = 426; algorithm development] and Test [n = 188; evaluation] subsets). The multi-protein model was trained based on presence/absence of gadolinium-positive (Gd+) lesions and was also strongly associated with new/enlarging T2 lesions, and active versus stable disease (composite of radiographic and clinical evidence of DA) with improved performance (p < 0.05) compared to the neurofilament light single protein model. The odds of having ≥1 Gd + lesions with a moderate/high DA score were 4.49 times that of a low DA score, and the odds of having ≥2 Gd + lesions with a high DA score were 20.99 times that of a low/moderate DA score. The MSDA Test was clinically validated with improved performance compared to the top-performing single-protein model and can serve as a quantitative tool to enhance the care of MS patients.

Artificial intelligence in clinical and translational science: Successes, challenges and opportunities

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.

Open-source Software Sustainability Models: Initial White Paper From the Informatics Technology for Cancer Research Sustainability and Industry Partnership Working Group

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.

Transcriptomics of bronchoalveolar lavage cells identifies new molecular endotypes of sarcoidosis

BACKGROUND

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.

METHODS

RNA sequencing by Ion Torrent Proton was performed on BAL cells obtained from 215 well-characterised patients with pulmonary sarcoidosis enrolled in the multicentre Genomic Research in Alpha-1 Antitrypsin Deficiency and Sarcoidosis (GRADS) study. Weighted gene co-expression network analysis and nonparametric statistics were used to analyse genome-wide BAL transcriptome. Validation of results was performed using a microarray expression dataset of an independent sarcoidosis cohort (Freiburg, Germany; n=50).

RESULTS

Our supervised analysis found associations between distinct transcriptional programmes and major pulmonary phenotypic manifestations of sarcoidosis including T-helper type 1 (Th1) and Th17 pathways associated with hilar lymphadenopathy, transforming growth factor-β1 (TGFB1) and mechanistic target of rapamycin (MTOR) signalling with parenchymal involvement, and interleukin (IL)-7 and IL-2 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 involvement, 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.

CONCLUSION

Taken together, our results identify BAL gene expression programmes that characterise major pulmonary sarcoidosis phenotypes and suggest the presence of distinct disease molecular endotypes.

An atomic approach to the design and implementation of a research data warehouse

Objective

As 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.

Materials and Methods

We designed and implemented an RDW named Neptune to serve the specific needs of our CTSA. Neptune uses an atomic design where data are 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 electronic health records (EHRs), health plans, and research studies; and includes knowledge for mapping to standard terminologies.

Results

Neptune contains data for more than 5 million patients longitudinally organized as Health Insurance Portability and Accountability Act (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 institutional review board-approved research projects by local investigators and for national projects.

Discussion

The 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 includes several unique aspects, including the physical warehouse straddling the University and UPMC networks and management under an HIPAA Business Associates Agreement.

Conclusion

We describe the design and implementation of an RDW at a large academic healthcare system that uses a distinctive atomic design where data are stored at a high level of granularity.

Malignant Pleural Mesothelioma Interactome with 364 Novel Protein-Protein Interactions

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.

Gene coexpression networks reveal novel molecular endotypes in alpha-1 antitrypsin deficiency

BACKGROUND

Alpha-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.

METHODS

We 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.

RESULT

We 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.

CONCLUSIONS

We 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.

The Ethics of Artificial Intelligence in Pathology and Laboratory Medicine: Principles and Practice

Growing numbers of artificial intelligence applications are being developed and applied to pathology and laboratory medicine. These technologies introduce risks and benefits that must be assessed and managed through the lens of ethics. This article describes how long-standing principles of medical and scientific ethics can be applied to artificial intelligence using examples from pathology and laboratory medicine.

Workshop summary: Potential usefulness and feasibility of a US National Mesothelioma Registry

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.

Integration of Cancer Registry Data into the Text Information Extraction System: Leveraging the Structured Data Import Tool

Introduction/Background

Cancer registries in the US collect timely and systematic data on new cancer cases, extent of disease, staging, biomarker status, treatment, survival, and mortality of cancer cases. Existing methodologies for accessing local cancer registry data for research are time-consuming and often rely on the manual merging of data by staff registrars. In addition, existing registries do not provide direct access to these data nor do they routinely provide linkage to discrete electronic health record (EHR) data, reports, or imaging data. Automation of such linkage can provide an impressive data resource and make valuable data available for translational cancer research.

Methods

The UPMC Network Cancer Registry collects highly structured, longitudinal data on all reportable cancer patients, from the point of the diagnosis throughout treatment and follow-up/outcomes. Using commercial registry software, we collect data in compliance with standards governed by the North American Association of Central Cancer Registries. This standardization ensures that the data are highly structured with standard coding and collection methods, which support data exchange among central cancer registries and the Centers for Disease Control and Prevention.

Results

At the UPMC Hillman Cancer Center and University of Pittsburgh, we explored the feasibility of linking this well-curated, structured cancer registry data with unstructured text (i.e., pathology and radiology reports), using the Text Information Extraction System (TIES). We used the TIES platform to integrate breast cancer cases from the UPMC Network Cancer Registry system and then combine these data with other EHR data as a pilot use case that can be replicated for other cancers.

Conclusions

As a result of this integration, we now have a single searchable repository of information for breast cancer patients from the UPMC registry, combined with their pathology and radiology reports. The system that we developed is easily scalable to other health systems and cancer centers.

Accrual to Clinical Trials (ACT): A Clinical and Translational Science Award Consortium Network

The Accrual to Clinical Trials (ACT) network is a federated network of sites from the National Clinical and Translational Science Award (CTSA) Consortium that has been created to significantly increase participant accrual to multi-site clinical trials. The ACT network represents an unprecedented collaboration among diverse CTSA sites. The network has created governance and regulatory frameworks and a common data model to harmonize electronic health record (EHR) data, and deployed a set of Informatics for Integrating Biology and the Bedside (i2b2) data repositories that are linked by the Shared Health Research Information Network (SHRINE) platform. It provides investigators the ability to query the network in real time and to obtain aggregate counts of patients who meet clinical trial inclusion and exclusion criteria from sites across the United States. The ACT network infrastructure provides a basis for cohort discovery and for developing new informatics tools to identify and recruit participants for multi-site clinical trials.

Factors influencing malignant mesothelioma survival: a retrospective review of the National Mesothelioma Virtual Bank cohort

Background: Malignant mesothelioma (MM) is a rare but deadly malignancy with about 3,000 new cases being diagnosed each year in the US.  Very few studies have been performed to analyze factors associated with mesothelioma survival, especially for peritoneal presentation. The overarching aim of this study is to examine survival of the cohort of patients with malignant mesothelioma enrolled in the National Mesothelioma Virtual Bank (NMVB).   Methods:  888 cases of pleural and peritoneal mesothelioma cases were selected from the NMVB database, which houses data and associated biospecimens for over 1400 cases that were diagnosed from 1990 to 2017. Kaplan Meier's method was performed for survival analysis. The association between prognostic factors and survival was estimated using Cox Hazard Regression method and using R software for analysis. Results: The median overall survival (OS) rate of all MM patients, including pleural and peritoneal mesothelioma cases is 15 months (14 months for pleural and 31 months for peritoneal).  Significant prognostic factors associated with improved survival of malignant mesothelioma cases in this NMVB cohort were younger than 45, female gender, epithelioid histological subtype, stage I, peritoneal occurrence, and having combination treatment of surgical therapy with chemotherapy. Combined surgical and chemotherapy treatment was associated with improved survival of 23 months in comparison to single line therapies. Conclusions: There has not been improvement in the overall survival for patients with malignant mesothelioma over many years with current available treatment options. Our findings show that combined surgical and chemotherapy treatment in peritoneal mesothelioma is associated with improved survival compared to local therapy alone.

Factors influencing malignant mesothelioma survival: a retrospective review of the National Mesothelioma Virtual Bank cohort

Background: Malignant mesothelioma (MM) is a rare but deadly malignancy with about 3,000 new cases being diagnosed each year in the US.  Very few studies have been performed to analyze factors associated with mesothelioma survival, especially for peritoneal presentation. The overarching aim of this study is to examine survival of the cohort of patients with malignant mesothelioma enrolled in the National Mesothelioma Virtual Bank (NMVB).  

Methods:  888 cases of pleural and peritoneal mesothelioma cases were selected from the NMVB database, which houses data and associated biospecimens for over 1400 cases that were diagnosed from 1990 to 2017. Kaplan Meier’s method was performed for survival analysis. The association between prognostic factors and survival was estimated using Cox Hazard Regression method and using R software for analysis.

Results: The median overall survival (OS) rate of all MM patients, including pleural and peritoneal mesothelioma cases is 15 months (14 months for pleural and 31 months for peritoneal).  Significant prognostic factors associated with improved survival of malignant mesothelioma cases in this NMVB cohort were younger than 45, female gender, epithelioid histological subtype, stage I, peritoneal occurrence, and having combination treatment of surgical therapy with chemotherapy. Combined surgical and chemotherapy treatment was associated with improved survival of 23 months in comparison to single line therapies.

Conclusions: There has not been improvement in the overall survival for patients with malignant mesothelioma over many years with current available treatment options. Our findings show that combined surgical and chemotherapy treatment in peritoneal mesothelioma is associated with improved survival compared to local therapy alone.

Factors influencing malignant mesothelioma survival: a retrospective review of the National Mesothelioma Virtual Bank cohort

Background: Malignant mesothelioma (MM) is a rare but deadly malignancy with about 3,000 new cases being diagnosed each year in the US.  Very few studies have been performed to analyze factors associated with mesothelioma survival, especially for peritoneal presentation. The overarching aim of this study is to examine survival of the cohort of patients with malignant mesothelioma enrolled in the National Mesothelioma Virtual Bank (NMVB).   Methods:  888 cases of pleural and peritoneal mesothelioma cases were selected from the NMVB database, which houses data and associated biospecimens for over 1400 cases that were diagnosed from 1990 to 2017. Kaplan Meier's method was performed for survival analysis. The association between prognostic factors and survival was estimated using Cox Hazard Regression method and using R software for analysis. Results: The median overall survival (OS) rate of all MM patients, including pleural and peritoneal mesothelioma cases is 15 months (14 months for pleural and 31 months for peritoneal).  Significant prognostic factors associated with improved survival of malignant mesothelioma cases in this NMVB cohort were younger than 45, female gender, epithelioid histological subtype, stage I, peritoneal occurrence, and having combination treatment of surgical therapy with chemotherapy. Combined surgical and chemotherapy treatment was associated with improved survival of 23 months in comparison to single line therapies. Conclusions: There has not been improvement in the overall survival for patients with malignant mesothelioma over many years with current available treatment options. Our findings show that combined surgical and chemotherapy treatment in peritoneal mesothelioma is associated with improved survival compared to local therapy alone.

A Novel Cross-Disciplinary Multi-Institute Approach to Translational Cancer Research: Lessons Learned from Pennsylvania Cancer Alliance Bioinformatics Consortium (PCABC)

Background

The Pennsylvania Cancer Alliance Bioinformatics Consortium (PCABC, http://www.pcabc.upmc.edu ) is one of the first major project-based initiatives stemming from the Pennsylvania Cancer Alliance that was funded for four years by the Department of Health of the Commonwealth of Pennsylvania. The objective of this was to initiate a prototype biorepository and bioinformatics infrastructure with a robust data warehouse by developing a statewide data model (1) for bioinformatics and a repository of serum and tissue samples; (2) a data model for biomarker data storage; and (3) a public access website for disseminating research results and bioinformatics tools. The members of the Consortium cooperate closely, exploring the opportunity for sharing clinical, genomic and other bioinformatics data on patient samples in oncology, for the purpose of developing collaborative research programs across cancer research institutions in Pennsylvania. The Consortium's intention was to establish a virtual repository of many clinical specimens residing in various centers across the state, in order to make them available for research. One of our primary goals was to facilitate the identification of cancer-specific biomarkers and encourage collaborative research efforts among the participating centers.

Methods

The PCABC has developed unique partnerships so that every region of the state can effectively contribute and participate. It includes over 80 individuals from 14 organizations, and plans to expand to partners outside the State. This has created a network of researchers, clinicians, bioinformaticians, cancer registrars, program directors, and executives from academic and community health systems, as well as external corporate partners - all working together to accomplish a common mission. The various sub-committees have developed a common IRB protocol template, common data elements for standardizing data collections for three organ sites, intellectual property/tech transfer agreements, and material transfer agreements that have been approved by each of the member institutions. This was the foundational work that has led to the development of a centralized data warehouse that has met each of the institutions’ IRB/HIPAA standards.

Results

Currently, this “virtual biorepository” has over 58,000 annotated samples from 11,467 cancer patients available for research purposes. The clinical annotation of tissue samples is either done manually over the internet or semi-automated batch modes through mapping of local data elements with PCABC common data elements. The database currently holds information on 7188 cases (associated with 9278 specimens and 46,666 annotated blocks and blood samples) of prostate cancer, 2736 cases (associated with 3796 specimens and 9336 annotated blocks and blood samples) of breast cancer and 1543 cases (including 1334 specimens and 2671 annotated blocks and blood samples) of melanoma. These numbers continue to grow, and plans to integrate new tumor sites are in progress. Furthermore, the group has also developed a central web-based tool that allows investigators to share their translational (genomics/proteomics) experiment data on research evaluating potential biomarkers via a central location on the Consortium's web site.

Conclusions

The technological achievements and the statewide informatics infrastructure that have been established by the Consortium will enable robust and efficient studies of biomarkers and their relevance to the clinical course of cancer.

Computer Science, Biology and Biomedical Informatics academy: Outcomes from 5 years of Immersing High-school Students into Informatics Research

The University of Pittsburgh's Department of Biomedical Informatics and Division of Pathology Informatics created a Science, Technology, Engineering, and Mathematics (STEM) pipeline in 2011 dedicated to providing cutting-edge informatics research and career preparatory experiences to a diverse group of highly motivated high-school students. In this third editorial installment describing the program, we provide a brief overview of the pipeline, report on achievements of the past scholars, and present results from self-reported assessments by the 2015 cohort of scholars. The pipeline continues to expand with the 2015 addition of the innovation internship, and the introduction of a program in 2016 aimed at offering first-time research experiences to undergraduates who are underrepresented in pathology and biomedical informatics. Achievements of program scholars include authorship of journal articles, symposium and summit presentations, and attendance at top 25 universities. All of our alumni matriculated into higher education and 90% remain in STEM majors. The 2015 high-school program had ten participating scholars who self-reported gains in confidence in their research abilities and understanding of what it means to be a scientist.

The Building Blocks of Interoperability. A Multisite Analysis of Patient Demographic Attributes Available for Matching

BACKGROUND

Patient matching is a key barrier to achieving interoperability. Patient demographic elements must be consistently collected over time and region to be valuable elements for patient matching.

OBJECTIVES

We sought to determine what patient demographic attributes are collected at multiple institutions in the United States and see how their availability changes over time and across clinical sites.

METHODS

We compiled a list of 36 demographic elements that stakeholders previously identified as essential patient demographic attributes that should be collected for the purpose of linking patient records. We studied a convenience sample of 9 health care systems from geographically distinct sites around the country. We identified changes in the availability of individual patient demographic attributes over time and across clinical sites.

RESULTS

Several attributes were consistently available over the study period (2005-2014) including last name (99.96%), first name (99.95%), date of birth (98.82%), gender/sex (99.73%), postal code (94.71%), and full street address (94.65%). Other attributes changed significantly from 2005-2014: Social security number (SSN) availability declined from 83.3% to 50.44% (p<0.0001). Email address availability increased from 8.94% up to 54% availability (p<0.0001). Work phone number increased from 20.61% to 52.33% (p<0.0001).

CONCLUSIONS

Overall, first name, last name, date of birth, gender/sex and address were widely collected across institutional sites and over time. Availability of emerging attributes such as email and phone numbers are increasing while SSN use is declining. Understanding the relative availability of patient attributes can inform strategies for optimal matching in healthcare.

Validation of a Gene Expression Test for Mesothelioma Prognosis in Formalin-Fixed Paraffin-Embedded Tissues

A molecular test performed using fresh-frozen tissue was proposed for use in the prognosis of patients with pleural mesothelioma. The accuracy of the test and its properties was assessed under Clinical Laboratory Improvement Amendments-approved guidelines using FFPE tissue from an independent multicenter patient cohort. Concordance studies were performed using matched frozen and FFPE mesothelioma samples. The prognostic value of the test was evaluated in an independent validation cohort of 73 mesothelioma patients who underwent surgical resection. FFPE-based classification demonstrated overall high concordance (83%) with the matched frozen specimens, on removal of cases with low confidence scores, showing sensitivity and specificity in predicting type B classification (poor outcome) of 43% and 98%, respectively. Concordance between research and clinical methods increased to 87% on removal of low confidence cases. Median survival times in the validation cohort were 18 and 7 months in type A and type B cases, respectively (P = 0.002). Multivariate classification adding pathologic staging information to the gene expression score resulted in significant stratification of risk groups. The median survival times were 52 and 14 months in the low-risk (class 1) and intermediate-risk (class 2) groups, respectively. The prognostic molecular test for mesothelioma can be performed on FFPE tissues to predict survival, and can provide an orthogonal tool, in combination with established pathologic parameters, for risk evaluation.

How can we improve Science, Technology, Engineering, and Math education to encourage careers in Biomedical and Pathology Informatics?

The Computer Science, Biology, and Biomedical Informatics (CoSBBI) program was initiated in 2011 to expose the critical role of informatics in biomedicine to talented high school students.[1] By involving them in Science, Technology, Engineering, and Math (STEM) training at the high school level and providing mentorship and research opportunities throughout the formative years of their education, CoSBBI creates a research infrastructure designed to develop young informaticians. Our central premise is that the trajectory necessary to be an expert in the emerging fields of biomedical informatics and pathology informatics requires accelerated learning at an early age.In our 4^th year of CoSBBI as a part of the University of Pittsburgh Cancer Institute (UPCI) Academy (http://www.upci.upmc.edu/summeracademy/), and our 2nd year of CoSBBI as an independent informatics-based academy, we enhanced our classroom curriculum, added hands-on computer science instruction, and expanded research projects to include clinical informatics. We also conducted a qualitative evaluation of the program to identify areas that need improvement in order to achieve our goal of creating a pipeline of exceptionally well-trained applicants for both the disciplines of pathology informatics and biomedical informatics in the era of big data and personalized medicine.

A Federated Network for Translational Cancer Research Using Clinical Data and Biospecimens

Advances in cancer research and personalized medicine will require significant new bridging infrastructures, including more robust biorepositories that link human tissue to clinical phenotypes and outcomes. In order to meet that challenge, four cancer centers formed the Text Information Extraction System (TIES) Cancer Research Network, a federated network that facilitates data and biospecimen sharing among member institutions. Member sites can access pathology data that are de-identified and processed with the TIES natural language processing system, which creates a repository of rich phenotype data linked to clinical biospecimens. TIES incorporates multiple security and privacy best practices that, combined with legal agreements, network policies, and procedures, enable regulatory compliance. The TIES Cancer Research Network now provides integrated access to investigators at all member institutions, where multiple investigator-driven pilot projects are underway. Examples of federated search across the network illustrate the potential impact on translational research, particularly for studies involving rare cancers, rare phenotypes, and specific biologic behaviors. The network satisfies several key desiderata including local control of data and credentialing, inclusion of rich phenotype information, and applicability to diverse research objectives. The TIES Cancer Research Network presents a model for a national data and biospecimen network.

The center for causal discovery of biomedical knowledge from big data

The Big Data to Knowledge (BD2K) Center for Causal Discovery is developing and disseminating an integrated set of open source tools that support causal modeling and discovery of biomedical knowledge from large and complex biomedical datasets. The Center integrates teams of biomedical and data scientists focused on the refinement of existing and the development of new constraint-based and Bayesian algorithms based on causal Bayesian networks, the optimization of software for efficient operation in a supercomputing environment, and the testing of algorithms and software developed using real data from 3 representative driving biomedical projects: cancer driver mutations, lung disease, and the functional connectome of the human brain. Associated training activities provide both biomedical and data scientists with the knowledge and skills needed to apply and extend these tools. Collaborative activities with the BD2K Consortium further advance causal discovery tools and integrate tools and resources developed by other centers.

Computational Pathology: A Path Ahead

CONTEXT

We define the scope and needs within the new discipline of computational pathology, a discipline critical to the future of both the practice of pathology and, more broadly, medical practice in general.

OBJECTIVE

To define the scope and needs of computational pathology.

DATA SOURCES

A meeting was convened in Boston, Massachusetts, in July 2014 prior to the annual Association of Pathology Chairs meeting, and it was attended by a variety of pathologists, including individuals highly invested in pathology informatics as well as chairs of pathology departments.

CONCLUSIONS

The meeting made recommendations to promote computational pathology, including clearly defining the field and articulating its value propositions; asserting that the value propositions for health care systems must include means to incorporate robust computational approaches to implement data-driven methods that aid in guiding individual and population health care; leveraging computational pathology as a center for data interpretation in modern health care systems; stating that realizing the value proposition will require working with institutional administrations, other departments, and pathology colleagues; declaring that a robust pipeline should be fostered that trains and develops future computational pathologists, for those with both pathology and nonpathology backgrounds; and deciding that computational pathology should serve as a hub for data-related research in health care systems. The dissemination of these recommendations to pathology and bioinformatics departments should help facilitate the development of computational pathology.

Finding collaborators: toward interactive discovery tools for research network systems

Background

Research networking systems hold great promise for helping biomedical scientists identify collaborators with the expertise needed to build interdisciplinary teams. Although efforts to date have focused primarily on collecting and aggregating information, less attention has been paid to the design of end-user tools for using these collections to identify collaborators. To be effective, collaborator search tools must provide researchers with easy access to information relevant to their collaboration needs.

Objective

The aim was to study user requirements and preferences for research networking system collaborator search tools and to design and evaluate a functional prototype.

Methods

Paper prototypes exploring possible interface designs were presented to 18 participants in semistructured interviews aimed at eliciting collaborator search needs. Interview data were coded and analyzed to identify recurrent themes and related software requirements. Analysis results and elements from paper prototypes were used to design a Web-based prototype using the D3 JavaScript library and VIVO data. Preliminary usability studies asked 20 participants to use the tool and to provide feedback through semistructured interviews and completion of the System Usability Scale (SUS).

Results

Initial interviews identified consensus regarding several novel requirements for collaborator search tools, including chronological display of publication and research funding information, the need for conjunctive keyword searches, and tools for tracking candidate collaborators. Participant responses were positive (SUS score: mean 76.4%, SD 13.9). Opportunities for improving the interface design were identified.

Conclusions

Interactive, timeline-based displays that support comparison of researcher productivity in funding and publication have the potential to effectively support searching for collaborators. Further refinement and longitudinal studies may be needed to better understand the implications of collaborator search tools for researcher workflows.

PaTH: towards a learning health system in the Mid-Atlantic region

The PaTH (University of Pittsburgh/UPMC, Penn State College of Medicine, Temple University Hospital, and Johns Hopkins University) clinical data research network initiative is a collaborative effort among four academic health centers in the Mid-Atlantic region. PaTH will provide robust infrastructure to conduct research, explore clinical outcomes, link with biospecimens, and improve methods for sharing and analyzing data across our diverse populations. Our disease foci are idiopathic pulmonary fibrosis, atrial fibrillation, and obesity. The four network sites have extensive experience in using data from electronic health records and have devised robust methods for patient outreach and recruitment. The network will adopt best practices by using the open-source data-sharing tool, Informatics for Integrating Biology and the Bedside (i2b2), at each site to enhance data sharing using centrally defined common data elements, and will use the Shared Health Research Information Network (SHRINE) for distributed queries across the network.

Creating a pipeline of talent for informatics: STEM initiative for high school students in computer science, biology, and biomedical informatics

This editorial provides insights into how informatics can attract highly trained students by involving them in science, technology, engineering, and math (STEM) training at the high school level and continuing to provide mentorship and research opportunities through the formative years of their education. Our central premise is that the trajectory necessary to be expert in the emergent fields in front of them requires acceleration at an early time point. Both pathology (and biomedical) informatics are new disciplines which would benefit from involvement by students at an early stage of their education. In 2009, Michael T Lotze MD, Kirsten Livesey (then a medical student, now a medical resident at University of Pittsburgh Medical Center (UPMC)), Richard Hersheberger, PhD (Currently, Dean at Roswell Park), and Megan Seippel, MS (the administrator) launched the University of Pittsburgh Cancer Institute (UPCI) Summer Academy to bring high school students for an 8 week summer academy focused on Cancer Biology. Initially, pathology and biomedical informatics were involved only in the classroom component of the UPCI Summer Academy. In 2011, due to popular interest, an informatics track called Computer Science, Biology and Biomedical Informatics (CoSBBI) was launched. CoSBBI currently acts as a feeder program for the undergraduate degree program in bioinformatics at the University of Pittsburgh, which is a joint degree offered by the Departments of Biology and Computer Science. We believe training in bioinformatics is the best foundation for students interested in future careers in pathology informatics or biomedical informatics. We describe our approach to the recruitment, training and research mentoring of high school students to create a pipeline of exceptionally well-trained applicants for both the disciplines of pathology informatics and biomedical informatics. We emphasize here how mentoring of high school students in pathology informatics and biomedical informatics will be critical to assuring their success as leaders in the era of big data and personalized medicine.

National Mesothelioma Virtual Bank: A Platform for Collaborative Research and Mesothelioma Biobanking Resource to Support Translational Research

The National Mesothelioma Virtual Bank (NMVB), developed six years ago, gathers clinically annotated human mesothelioma specimens for basic and clinical science research. During this period, this resource has greatly increased its collection of specimens by expanding the number of contributing academic health centers including New York University, University of Pennsylvania, University of Pittsburgh Medical Center, and Mount Sinai School of Medicine. Marketing efforts at both national and international annual conferences increase awareness and availability of the mesothelioma specimens at no cost to approved investigators, who query the web-based NMVB database for cumulative and appropriate patient clinicopathological information on the specimens. The data disclosure and specimen distribution protocols are tightly regulated to maintain compliance with participating institutions' IRB and regulatory committee reviews. The NMVB currently has over 1120 annotated cases available for researchers, including paraffin embedded tissues, fresh frozen tissue, tissue microarrays (TMA), blood samples, and genomic DNA. In addition, the resource offers expertise and assistance for collaborative research. Furthermore, in the last six years, the resource has provided hundreds of specimens to the research community. The investigators can request specimens and/or data by submitting a Letter of Intent (LOI) that is evaluated by NMVB research evaluation panel (REP).

The history of pathology informatics: A global perspective

Pathology informatics has evolved to varying levels around the world. The history of pathology informatics in different countries is a tale with many dimensions. At first glance, it is the familiar story of individuals solving problems that arise in their clinical practice to enhance efficiency, better manage (e.g., digitize) laboratory information, as well as exploit emerging information technologies. Under the surface, however, lie powerful resource, regulatory, and societal forces that helped shape our discipline into what it is today. In this monograph, for the first time in the history of our discipline, we collectively perform a global review of the field of pathology informatics. In doing so, we illustrate how general far-reaching trends such as the advent of computers, the Internet and digital imaging have affected pathology informatics in the world at large. Major drivers in the field included the need for pathologists to comply with national standards for health information technology and telepathology applications to meet the scarcity of pathology services and trained people in certain countries. Following trials by a multitude of investigators, not all of them successful, it is apparent that innovation alone did not assure the success of many informatics tools and solutions. Common, ongoing barriers to the widespread adoption of informatics devices include poor information technology infrastructure in undeveloped areas, the cost of technology, and regulatory issues. This review offers a deeper understanding of how pathology informatics historically developed and provides insights into what the promising future might hold.

Clinical integration of next-generation sequencing technology

Recent advances in next-generation sequencing (NGS) methods and technology have substantially reduced costs and operational complexity leading to production of benchtop sequencers and commercial software solutions for implementation in small research and clinical laboratories. This article addresses requirements and limitations to successful implementation of these systems, including (1) calibration and validation of the instrumentation, experimental paradigm, and primary readout, (2) secure data transfer, storage, and secondary processing, (3) implementation of software tools for targeted analysis, and (4) training of research and clinical personnel to evaluate data fidelity and interpret the molecular significance of the genomic output.

Pathology informatics fellowship retreats: The use of interactive scenarios and case studies as pathology informatics teaching tools

Background:

Last year, our pathology informatics fellowship added informatics-based interactive case studies to its existing educational platform of operational and research rotations, clinical conferences, a common core curriculum with an accompanying didactic course, and national meetings.

Methods:

The structure of the informatics case studies was based on the traditional business school case study format. Three different formats were used, varying in length from short, 15-minute scenarios to more formal multiple hour-long case studies. Case studies were presented over the course of three retreats (Fall 2011, Winter 2012, and Spring 2012) and involved both local and visiting faculty and fellows.

Results:

Both faculty and fellows found the case studies and the retreats educational, valuable, and enjoyable. From this positive feedback, we plan to incorporate the retreats in future academic years as an educational component of our fellowship program.

Conclusions:

Interactive case studies appear to be valuable in teaching several aspects of pathology informatics that are difficult to teach in more traditional venues (rotations and didactic class sessions). Case studies have become an important component of our fellowship's educational platform.

Next generation sequencing in clinical medicine: Challenges and lessons for pathology and biomedical informatics

The Human Genome Project (HGP) provided the initial draft of mankind's DNA sequence in 2001. The HGP was produced by 23 collaborating laboratories using Sanger sequencing of mapped regions as well as shotgun sequencing techniques in a process that occupied 13 years at a cost of ~$3 billion. Today, Next Generation Sequencing (NGS) techniques represent the next phase in the evolution of DNA sequencing technology at dramatically reduced cost compared to traditional Sanger sequencing. A single laboratory today can sequence the entire human genome in a few days for a few thousand dollars in reagents and staff time. Routine whole exome or even whole genome sequencing of clinical patients is well within the realm of affordability for many academic institutions across the country. This paper reviews current sequencing technology methods and upcoming advancements in sequencing technology as well as challenges associated with data generation, data manipulation and data storage. Implementation of routine NGS data in cancer genomics is discussed along with potential pitfalls in the interpretation of the NGS data. The overarching importance of bioinformatics in the clinical implementation of NGS is emphasized.[7] We also review the issue of physician education which also is an important consideration for the successful implementation of NGS in the clinical workplace. NGS technologies represent a golden opportunity for the next generation of pathologists to be at the leading edge of the personalized medicine approaches coming our way. Often under-emphasized issues of data access and control as well as potential ethical implications of whole genome NGS sequencing are also discussed. Despite some challenges, it's hard not to be optimistic about the future of personalized genome sequencing and its potential impact on patient care and the advancement of knowledge of human biology and disease in the near future.

Evaluating de novo locus-disease discoveries in GWAS using the signal-to-noise ratio

A genome-wide association study (GWAS) involves examining representative SNPs obtained using high throughput technologies. A GWAS data set can entail a million SNPs and may soon entail many millions. In a GWAS researchers often investigate the correlation of each SNP with a disease. With so many hypotheses, it is not straightforward how to interpret the results. Strategies include using the Bonferroni correction to determine the significance of a model and Bayesian methods. However, when we are discovering new locus-disease associations, i.e., so called de novo discoveries, we should not just endeavor to determine the significance of particular models, but also concern ourselves with determining whether it is likely that we have any true discoveries, and if so how many of the highest ranking models we should investigate further. We develop a method based on a signal-to-noise ratio that targets this issue. We apply the method to a GWAS Alzheimer's data set.

A bayesian method for evaluating and discovering disease loci associations

BACKGROUND

A genome-wide association study (GWAS) typically involves examining representative SNPs in individuals from some population. A GWAS data set can concern a million SNPs and may soon concern billions. Researchers investigate the association of each SNP individually with a disease, and it is becoming increasingly commonplace to also analyze multi-SNP associations. Techniques for handling so many hypotheses include the Bonferroni correction and recently developed bayesian methods. These methods can encounter problems. Most importantly, they are not applicable to a complex multi-locus hypothesis which has several competing hypotheses rather than only a null hypothesis. A method that computes the posterior probability of complex hypotheses is a pressing need.

METHODOLOGY/FINDINGS

We introduce the bayesian network posterior probability (BNPP) method which addresses the difficulties. The method represents the relationship between a disease and SNPs using a directed acyclic graph (DAG) model, and computes the likelihood of such models using a bayesian network scoring criterion. The posterior probability of a hypothesis is computed based on the likelihoods of all competing hypotheses. The BNPP can not only be used to evaluate a hypothesis that has previously been discovered or suspected, but also to discover new disease loci associations. The results of experiments using simulated and real data sets are presented. Our results concerning simulated data sets indicate that the BNPP exhibits both better evaluation and discovery performance than does a p-value based method. For the real data sets, previous findings in the literature are confirmed and additional findings are found.

CONCLUSIONS/SIGNIFICANCE

We conclude that the BNPP resolves a pressing problem by providing a way to compute the posterior probability of complex multi-locus hypotheses. A researcher can use the BNPP to determine the expected utility of investigating a hypothesis further. Furthermore, we conclude that the BNPP is a promising method for discovering disease loci associations.

Immunohistochemical analysis of ezrin-radixin-moesin-binding phosphoprotein 50 in prostatic adenocarcinoma

Background

Ezrin-radixin-moesin-binding phosphoprotein 50 (EBP50) is an adapter protein which has been shown to play an active role in a wide variety of cellular processes, including interactions with proteins related to both tumor suppression and oncogenesis. Here we use immunohistochemistry to evaluate EBP50's expression in normal donor prostate (NDP), benign prostatic hyperplasia (BPH), high grade prostatic intraepithelial neoplasia (HGPIN), normal tissue adjacent to prostatic adenocarcinoma (NAC), primary prostatic adenocarcinoma (PCa), and metastatic prostatic adenocarcinoma (Mets).

Methods

Tissue microarrays were immunohistochemically stained for EBP50, with the staining intensities quantified using automated image analysis software. The data were statistically analyzed using one-way ANOVA with subsequent Tukey tests for multiple comparisons. Eleven cases of NDP, 37 cases of NAC, 15 cases of BPH, 35 cases of HGPIN, 103 cases of PCa, and 36 cases of Mets were analyzed in the microarrays.

Results

Specimens of PCa and Mets had the lowest absolute staining for EBP50. Mets staining was significantly lower than NDP (p = 0.027), BPH (p = 0.012), NAC (p < 0.001), HGPIN (p < 0.001), and PCa (p = 0.006). Additionally, HGPIN staining was significantly higher than NAC (p < 0.009) and PCa (p < 0.001).

Conclusions

To our knowledge, this represents the first study comparing the immunohistochemical profiles of EBP50 in PCa and Mets to specimens of HGPIN, BPH, NDP, and NAC and suggests that EBP50 expression is decreased in Mets. Given that PCa also had significantly higher expression than Mets, future studies are warranted to assess EBP50's potential as a prognostic biomarker for prostate cancer.

Proteomic analysis of stage I endometrial cancer tissue: identification of proteins associated with oxidative processes and inflammation

OBJECTIVE

The present study aimed to identify differentially expressed proteins employing a high resolution mass spectrometry (MS)-based proteomic analysis of endometrial cancer cells harvested using laser microdissection.

METHODS

A differential MS-based proteomic analysis was conducted from discrete epithelial cell populations gathered by laser microdissection from 91 pathologically reviewed stage I endometrial cancer tissue samples (79 endometrioid and 12 serous) and 10 samples of normal endometrium from postmenopausal women. Hierarchical cluster analysis of protein abundance levels derived from a spectral count analysis revealed a number of proteins whose expression levels were common as well as unique to both histologic types. An independent set of endometrial cancer specimens from 394 patients were used to externally validate the differential expression of select proteins.

RESULTS

209 differentially expressed proteins were identified in a comparison of stage I endometrial cancers and normal post-menopausal endometrium controls (Q<0.005). A number of differentially abundant proteins in stage I endometrial cancer were identified and independently validated by western blot and tissue microarray analyses. Multiple proteins identified with elevated abundance in stage I endometrial cancer are functionally associated with inflammation (annexins) and oxidative processes (peroxiredoxins). PRDX1 and ANXA2 were both confirmed as being overexpressed in stage I cancer compared to normal endometrium by independent TMA (Q=0.008 and Q=0.00002 respectively).

CONCLUSIONS

These data provide the basis for further investigation of previously unrecognized novel pathways involved in early stage endometrial carcinogenesis and provide possible targets for prevention strategies that are inclusive of both endometrioid and serous histologic subtypes.

Immunohistochemical profiles of claudin-3 in primary and metastatic prostatic adenocarcinoma

Background

Claudins are integral membrane proteins that are involved in forming cellular tight junctions. One member of the claudin family, claudin-3, has been shown to be overexpressed in breast, ovarian, and pancreatic cancer. Here we use immunohistochemistry to evaluate its expression in benign prostatic hyperplasia (BPH), prostatic intraepithelial neoplasia (PIN), normal tissue adjacent to prostatic adenocarcinoma (NAC), primary prostatic adenocarcinoma (PCa), and metastatic prostatic adenocarcinoma (Mets).

Methods

Tissue microarrays were immunohistochemically stained for claudin-3, with the staining intensities subsequently quantified and statistically analyzed using a one-way ANOVA with subsequent Tukey tests for multiple comparisons or a nonparametric equivalent. Fifty-three cases of NAC, 17 cases of BPH, 35 cases of PIN, 107 cases of PCa, and 55 cases of Mets were analyzed in the microarrays.

Results

PCa and Mets had the highest absolute staining for claudin-3. Both had significantly higher staining than BPH (p < 0.05 in both cases) and NAC (p < 0.05 in both cases). PIN had a lower, but non-significant, staining score than PCa and Mets, but a statistically higher score than both BPH and NAC (p < 0.05 for both cases). No significant differences were observed between PCa, Mets, and PIN.

Conclusions

To our knowledge, this represents one of the first studies comparing the immunohistochemical profiles of claudin-3 in PCa and NAC to specimens of PIN, BPH, and Mets. These findings provide further evidence that claudin-3 may serve as an important biomarker for prostate cancer, both primary and metastatic, but does not provide evidence that claudin-3 can be used to predict risk of metastasis.

Immunohistochemical staining of radixin and moesin in prostatic adenocarcinoma

BACKGROUND

Some members of the Protein 4.1 superfamily are believed to be involved in cell proliferation and growth, or in the regulation of these processes. While the expression levels of two members of this family, radixin and moesin, have been studied in many tumor types, to our knowledge they have not been investigated in prostate cancer.

METHODS

Tissue microarrays were immunohistochemically stained for either radixin or moesin, with the staining intensities subsequently quantified and statistically analyzed using One-Way ANOVA or nonparametric equivalent with subsequent Student-Newman-Keuls tests for multiple comparisons. There were 11 cases of normal donor prostates (NDP), 14 cases of benign prostatic hyperplasia (BPH), 23 cases of high-grade prostatic intraepithelial neoplasia (HGPIN), 88 cases of prostatic adenocarcinoma (PCa), and 25 cases of normal tissue adjacent to adenocarcinoma (NAC) analyzed in the microarrays.

RESULTS

NDP, BPH, and HGPIN had higher absolute staining scores for radixin than PCa and NAC, but with a significant difference observed between only HGPIN and PCa (p = < 0.001) and HGPIN and NAC (p = 0.001). In the moesin-stained specimens, PCa, NAC, HGPIN, and BPH all received absolute higher staining scores than NDP, but the differences were not significant. Stage 4 moesin-stained PCa had a significantly reduced staining intensity compared to Stage 2 (p = 0.003).

CONCLUSIONS

To our knowledge, these studies represent the first reports on the expression profiles of radixin and moesin in prostatic adenocarcinoma. The current study has shown that there were statistically significant differences observed between HGPIN and PCa and HGPIN and NAC in terms of radixin expression. The differences in the moesin profiles by tissue type were not statistically significant. Additional larger studies with these markers may further elucidate their potential roles in prostatic neoplasia progression.

Automated ancillary cancer history classification for mesothelioma patients from free-text clinical reports

BACKGROUND

Clinical records are often unstructured, free-text documents that create information extraction challenges and costs. Healthcare delivery and research organizations, such as the National Mesothelioma Virtual Bank, require the aggregation of both structured and unstructured data types. Natural language processing offers techniques for automatically extracting information from unstructured, free-text documents.

METHODS

Five hundred and eight history and physical reports from mesothelioma patients were split into development (208) and test sets (300). A reference standard was developed and each report was annotated by experts with regard to the patient's personal history of ancillary cancer and family history of any cancer. The Hx application was developed to process reports, extract relevant features, perform reference resolution and classify them with regard to cancer history. Two methods, Dynamic-Window and ConText, for extracting information were evaluated. Hx's classification responses using each of the two methods were measured against the reference standard. The average Cohen's weighted kappa served as the human benchmark in evaluating the system.

RESULTS

Hx had a high overall accuracy, with each method, scoring 96.2%. F-measures using the Dynamic-Window and ConText methods were 91.8% and 91.6%, which were comparable to the human benchmark of 92.8%. For the personal history classification, Dynamic-Window scored highest with 89.2% and for the family history classification, ConText scored highest with 97.6%, in which both methods were comparable to the human benchmark of 88.3% and 97.2%, respectively.

CONCLUSION

We evaluated an automated application's performance in classifying a mesothelioma patient's personal and family history of cancer from clinical reports. To do so, the Hx application must process reports, identify cancer concepts, distinguish the known mesothelioma from ancillary cancers, recognize negation, perform reference resolution and determine the experiencer. Results indicated that both information extraction methods tested were dependant on the domain-specific lexicon and negation extraction. We showed that the more general method, ConText, performed as well as our task-specific method. Although Dynamic- Window could be modified to retrieve other concepts, ConText is more robust and performs better on inconclusive concepts. Hx could greatly improve and expedite the process of extracting data from free-text, clinical records for a variety of research or healthcare delivery organizations.

A decade of experience in the development and implementation of tissue banking informatics tools for intra and inter-institutional translational research

Context:

Tissue banking informatics deals with standardized annotation, collection and storage of biospecimens that can further be shared by researchers. Over the last decade, the Department of Biomedical Informatics (DBMI) at the University of Pittsburgh has developed various tissue banking informatics tools to expedite translational medicine research. In this review, we describe the technical approach and capabilities of these models.

Design:

Clinical annotation of biospecimens requires data retrieval from various clinical information systems and the de-identification of the data by an honest broker. Based upon these requirements, DBMI, with its collaborators, has developed both Oracle-based organ-specific data marts and a more generic, model-driven architecture for biorepositories. The organ-specific models are developed utilizing Oracle 9.2.0.1 server tools and software applications and the model-driven architecture is implemented in a J2EE framework.

Result:

The organ-specific biorepositories implemented by DBMI include the Cooperative Prostate Cancer Tissue Resource (http://www.cpctr.info/), Pennsylvania Cancer Alliance Bioinformatics Consortium (http://pcabc.upmc.edu/main.cfm), EDRN Colorectal and Pancreatic Neoplasm Database (http://edrn.nci.nih.gov/) and Specialized Programs of Research Excellence (SPORE) Head and Neck Neoplasm Database (http://spores.nci.nih.gov/current/hn/index.htm). The model-based architecture is represented by the National Mesothelioma Virtual Bank (http://mesotissue.org/). These biorepositories provide thousands of well annotated biospecimens for the researchers that are searchable through query interfaces available via the Internet.

Conclusion:

These systems, developed and supported by our institute, serve to form a common platform for cancer research to accelerate progress in clinical and translational research. In addition, they provide a tangible infrastructure and resource for exposing research resources and biospecimen services in collaboration with the clinical anatomic pathology laboratory information system (APLIS) and the cancer registry information systems.

The tissue microarray OWL schema: An open-source tool for sharing tissue microarray data

Background:

Tissue microarrays (TMAs) are enormously useful tools for translational research, but incompatibilities in database systems between various researchers and institutions prevent the efficient sharing of data that could help realize their full potential. Resource Description Framework (RDF) provides a flexible method to represent knowledge in triples, which take the form Subject-Predicate-Object. All data resources are described using Uniform Resource Identifiers (URIs), which are global in scope. We present an OWL (Web Ontology Language) schema that expands upon the TMA data exchange specification to address this issue and assist in data sharing and integration.

Methods:

A minimal OWL schema was designed containing only concepts specific to TMA experiments. More general data elements were incorporated from predefined ontologies such as the NCI thesaurus. URIs were assigned using the Linked Data format.

Results:

We present examples of files utilizing the schema and conversion of XML data (similar to the TMA DES) to OWL.

Conclusion:

By utilizing predefined ontologies and global unique identifiers, this OWL schema provides a solution to the limitations of XML, which represents concepts defined in a localized setting. This will help increase the utilization of tissue resources, facilitating collaborative translational research efforts.