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

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.

A global federated real-world data and analytics platform for research

Objective

This article describes a scalable, performant, sustainable global network of electronic health record data for biomedical and clinical research.

Materials and Methods

TriNetX has created a technology platform characterized by a conservative security and governance model that facilitates collaboration and cooperation between industry participants, such as pharmaceutical companies and contract research organizations, and academic and community-based healthcare organizations (HCOs). HCOs participate on the network in return for access to a suite of analytics capabilities, large networks of de-identified data, and more sponsored trial opportunities. Industry participants provide the financial resources to support, expand, and improve the technology platform in return for access to network data, which provides increased efficiencies in clinical trial design and deployment.

Results

TriNetX is a growing global network, expanding from 55 HCOs and 7 countries in 2017 to over 220 HCOs and 30 countries in 2022. Over 19 000 sponsored clinical trial opportunities have been initiated through the TriNetX network. There have been over 350 peer-reviewed scientific publications based on the network's data.

Conclusions

The continued growth of the TriNetX network and its yield of clinical trial collaborations and published studies indicates that this academic-industry structure is a safe, proven, sustainable path for building and maintaining research-centric data networks.

Development of a distributed international patient data registry for hairy cell leukemia

Hairy cell leukemia (HCL) is a rare lymphoproliferative disorder, comprising only 2% of all leukemias. The Hairy Cell Leukemia Foundation (HCLF) has developed a patient data registry to enable investigators to better study the clinical features, treatment outcomes, and complications of patients with HCL. This system utilizes a centralized registry architecture. Patients are enrolled at HCL Centers of Excellence (COE) or via a web-based portal. All data are de-identified, which reduces regulatory burden and increases opportunities for data access and re-use. To date, 579 patients have been enrolled in the registry. Efforts are underway to engage additional COE's to expand access to patients across the globe. This international PDR will enable researchers to study outcomes in HCL in ways not previously possible due to the rarity of the disease and will serve as a platform for future prospective research.

Assessment of Electronic Health Record for Cancer Research and Patient Care Through a Scoping Review of Cancer Natural Language Processing

PURPOSE

The advancement of natural language processing (NLP) has promoted the use of detailed textual data in electronic health records (EHRs) to support cancer research and to facilitate patient care. In this review, we aim to assess EHR for cancer research and patient care by using the Minimal Common Oncology Data Elements (mCODE), which is a community-driven effort to define a minimal set of data elements for cancer research and practice. Specifically, we aim to assess the alignment of NLP-extracted data elements with mCODE and review existing NLP methodologies for extracting said data elements.

METHODS

Published literature studies were searched to retrieve cancer-related NLP articles that were written in English and published between January 2010 and September 2020 from main literature databases. After the retrieval, articles with EHRs as the data source were manually identified. A charting form was developed for relevant study analysis and used to categorize data including four main topics: metadata, EHR data and targeted cancer types, NLP methodology, and oncology data elements and standards.

RESULTS

A total of 123 publications were selected finally and included in our analysis. We found that cancer research and patient care require some data elements beyond mCODE as expected. Transparency and reproductivity are not sufficient in NLP methods, and inconsistency in NLP evaluation exists.

CONCLUSION

We conducted a comprehensive review of cancer NLP for research and patient care using EHRs data. Issues and barriers for wide adoption of cancer NLP were identified and discussed.

The Value of Federated Data Networks in Oncology: What Research Questions Do They Answer? Outcomes From a Systematic Literature Review

OBJECTIVES

Real-world evidence (RWE) plays an important role in addressing key research questions of interest to healthcare decision makers. Federated data networks (FDNs) apply novel technology to enable the conduct of RWE studies with multiple partners, without the need to share the individual partner's data set. A systematic review of the published literature was performed to determine which types of research questions can best be addressed through FDNs, specifically in the field of oncology.

METHODS

Systematic searches of MEDLINE and Embase were undertaken to identify the types of research questions that had been addressed in studies using FDNs. Additional information was retrieved about study characteristics, statistical methods, and the FDN itself.

RESULTS

In total, 40 publications were included where research questions on the following had been addressed (multiple categories possible): disease natural history (58%), safety surveillance (18%), treatment pathways (15%), comparative effectiveness (10%), and cost/resource use studies (3%)-13% of studies had to be left uncategorized. A total of 50% of the studies were run with data partners in networks of ≤5. The size of the networks ranged from 227 patients to >5 million patients. Statistical methods used included distributed learning and distributed regression methods.

CONCLUSIONS

Further work is needed to raise awareness of the important role that FDNs can play in leveraging readily available RWE to address key research questions of interest in cancer and the benefits to the research community in engaging in federated data initiatives with a long-term perspective.

Informatics Management of Tumor Specimens in the Era of Big Data: Challenges and Solutions

Biomedical data bear the potential to facilitate personalized diagnosis and precision treatment. In the era of Big Data, high-quality annotation of human specimens has become the primary mission of biobankers, especially for tumor biobanks with large amounts of "omics" and clinical data. However, the lack of agreed-upon standardization and the gap among heterogeneous databases make information application and communication a major challenge. International efforts are underway to develop national projects on informatics management. The aim of this review is to provide references in specimen annotation to regulate and take full advantage of biological and biomedical information. First, critical data categories that are vital for specimen applications, including sample attributes, clinical data, preanalytical variations, and analytical records, are systematically listed for subsequent data mining. Second, current standards and guidelines related to biospecimen information are reviewed, and proper standards for tumor biobanks are recommended. In particular, commonly-used approaches and functionalities of data management are summarized and discussed. This review highlights the importance of informatics management of tumor specimens, defines critical data types, recommends data standards, and presents the methodologies of data harmonization for biobankers to reach high quality annotation of biospecimens.

Global Trend in Research and Development of CDK4/6 Inhibitors for Clinical Cancer Therapy: A Bibliometric Analysis

Background: Cyclin-dependent kinase (CDK) 4/6 inhibitors are frequently used anti-cancer agents in hormone receptor-positive breast cancers. This study assessed the course of research and development (R&D) for CDK4/6 inhibitors in terms of publications over the past two decades.

Methods: The Web of Science (WOS) and PubMed databases were searched to identify publications related to research on CDK4/6 inhibitors since 2001. The VOS Viewer software was used to analyze co-occurring keywords to stratify the publication data and collaborations in research.

Results: There were 1395 publications related to research on CDK4/6 inhibitors since 2001. Eight of the top 10 institutions originated from the USA and the other two were a Swiss Pharmaceutical Company and French Research Institute. Bardia A, the first author for some of the articles published in the USA, was the most prolific with 25 publications. The journal with the most publications was Cancer Res with 162 publications. Basic research comprised six of the 10 most frequently cited publications and the rest consisted of three reviews and a clinical trial. The most common keywords for publications since 2011 were “palbociclib”, “abemaciclib”, “ribociclib” and “double blind”, indicating the successful development of CDK4/6 inhibitors as anticancer drugs.

Conclusions: This study provides a comprehensive review of the CDK4/6 inhibitors R&D history. The data imply that drug development in this field is a decade-long process and clinical trials have been performed before clinical applications. Thereafter, research was conducted on the adverse effects and drug resistance associated with the inhibitors.

Construction of the National Brain Tumor Registry of China for better management and more efficient use of data: a protocol

INTRODUCTION

Brain tumours encompass a complex group of intracranial tumours that mostly affect young adults and children, with a high incidence rate and poor prognosis. It remains impossible to systematically collect data on patients with brain tumours in China and difficult to perform in-depth analysis on the status of brain tumours, medical outcomes or other important medical issues through a multicentre clinical study. This study describes the first nation-wide data platform including the entire spectrum of brain tumour entities, which will allow better management and more efficient application of patient data in China.

METHODS AND ANALYSIS

The National Brain Tumor Registry of China (NBTRC) is a registry of real-word clinical data on brain tumours. It is established and managed by the China National Clinical Research Center for Neurological Diseases and administered by its scientific and executive committees. The 54 participating hospitals of the NBTRC are located in 27 provinces/municipalities, performing more than 40 000 brain tumour surgeries per year. The data consist of in-hospital medical records, images and follow-up information after discharge. Data can be uploaded in three ways: the web portal, remote physical servers and offline software. The data quality control scheme is seven-dimensional. Each participating hospital could focus on a single pathology subtype and public subtypes of brain tumour for which they expect to conduct related multicentre clinical research. The standardised workflow to conduct clinical research is based on the benefit-sharing mechanism. Data collection will be conducted continuously from 1 February 2019 to 31 January 2024.

ETHICS AND DISSEMINATION

Informed consent will be obtained from all participants. Consent for the adolescents' participation will be also obtained from their guardians via written consent. The results will be published in professional journals, in both Chinese and English.

TRIAL REGISTRATION NUMBER

Chinese Clinical Trial Registry (ChiCTR1900021096).

MYC Targets Scores Are Associated with Cancer Aggressiveness and Poor Survival in ER-Positive Primary and Metastatic Breast Cancer

MYC is one of the most studied oncogenes that is known to promote cell proliferation. We utilized MYC targets v1 and MYC targets v2 scores of gene set variation analysis and hypothesized that these scores correlate with tumor aggressiveness and survival outcomes. We examined a total of 3109 breast cancer patients from TCGA, METABRIC, and GSE124647 cohorts. In each cohort, the patients were divided into high- and low-score groups using the upper third value as the cut off. As expected, higher scores were related to increased cell proliferation and worse clinical and pathologic features. High MYC targets scores were associated with worse survival, specifically in primary ER-positive breast cancer, consistently in both TCGA and METABRIC cohorts. In ER-positive breast cancer, high MYC targets v1, but not v2 score, was associated with high mutation load, and high MYC targets v1 and v2 scores were both associated with increased infiltration of pro- and anti-cancerous immune cells. We found that high MYC scores were associated with worse survival in metastatic breast cancer. Our findings show that the MYC targets v1 and v2 scores are associated with tumor aggressiveness and poor prognosis in ER-positive primary tumors, as well as in metastatic breast cancer.

CD8 T Cell Score as a Prognostic Biomarker for Triple Negative Breast Cancer

CD8 T cell is an essential component of tumor-infiltrating lymphocytes (TIL) and tumor immune microenvironment (TIME). Using the xCell CD8 T cell score of whole tumor gene expression data, we estimated these cells in total of 3837 breast cancer patients from TCGA, METABRIC and various GEO cohorts. The CD8 score correlated strongly with expression of CD8 genes. The score was highest for triple-negative breast cancer (TNBC), and a high score was associated with high tumor immune cytolytic activity and better survival in TNBC but not other breast cancer subtypes. In TNBC, tumors with a high CD8 score had enriched expression of interferon (IFN)-α and IFN-γ response and allograft rejection gene sets, and greater infiltration of anti-cancerous immune cells. The score strongly correlated with CD4 memory T cells in TNBC, and tumors with both a high CD8 score and high CD4 memory T cell abundance had significantly better survival. Finally, a high CD8 score was significantly associated with high expression of multiple immune checkpoint molecules. In conclusion, a high CD8 T cell score is associated with better survival in TNBC, particularly when tumor CD4 memory T cells were elevated. Our findings also suggest a possible use of the score as a predictive biomarker for response to immune checkpoint therapy.

The E2F Pathway Score as a Predictive Biomarker of Response to Neoadjuvant Therapy in ER+/HER2- Breast Cancer

E2F transcription factors play critical roles in the cell cycle. Therefore, their activity is expected to reflect tumor aggressiveness and responsiveness to therapy. We scored 3905 tumors of nine breast cancer cohorts for this activity based on their gene expression for the Hallmark E2F targets gene set. As expected, tumors with a high score had an increased expression of cell proliferation-related genes. A high score was significantly associated with shorter patient survival, greater MKI67 expression, histological grade, stage, and genomic aberrations. Furthermore, metastatic tumors had higher E2F scores than the primary tumors from which they arose. Although tumors with a high score had greater infiltration by both pro- and anti-cancerous immune cells, they had an increased expression of immune checkpoint genes. Estrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative cancer with a high E2F score achieved a significantly higher pathological complete response (pCR) rate to neoadjuvant chemotherapy. The E2F score was significantly associated with the expression of cyclin-dependent kinase (CDK)-related genes and strongly correlated with sensitivity to CDK inhibition in cell lines. In conclusion, the E2F score is a marker of breast cancer aggressiveness and predicts the responsiveness of ER-positive/HER2-negative patients to neoadjuvant chemotherapy and possibly to CDK and immune checkpoint inhibitors.

Patient treatment and outcome after breast cancer orbital and periorbital metastases: a comprehensive case series including analysis of lobular versus ductal tumor histology

BACKGROUND

Breast cancer is the most common malignancy to spread to the orbit and periorbit, and the invasive lobular carcinoma (ILC) histologic subtype of breast cancer has been reported to form these ophthalmic metastases (OM) more frequently than invasive ductal carcinomas (IDC). We herein report our single academic institution experience with breast cancer OM with respect to anatomical presentation, histology (lobular vs. ductal), treatment, and survival.

METHODS

We employed the natural language processing platform, TIES (Text Information Extraction System), to search 2.3 million de-identified patient pathology and radiology records at our institution in order to identify patients with OM secondary to breast cancer. We then compared the resultant cohort, the "OM cohort," to two other representative metastatic breast cancer patient (MBC) databases from our institution. Histological analysis of selected patients was performed.

RESULTS

Our TIES search and manual refinement ultimately identified 28 patients who were diagnosed with breast cancer between 1995 and 2016 that subsequently developed OM. Median age at diagnosis was 54 (range 28-77) years of age. ER, PR, and HER2 status from the 28 patients with OM did not differ from other patients with MBC from our institution. The relative proportion of patients with ILC was significantly higher in the OM cohort (32.1%) than in other MBC patients in our institution (11.3%, p = 0.007). Median time to first OM in the OM cohort was 46.7 months, and OM were the second most frequent first metastases after bony metastases. After diagnosis of the first distant metastasis of any kind, median survival of patients with ILC (21.4 months) was significantly shorter than that of patients with IDC (55.3 months, p = 0.03). Nine patients developed bilateral OM. We observed a significant co-occurrence of OM and central nervous system metastases (p = 0.0053). The histological analysis revealed an interesting case in which the primary tumor was of a mixed ILC/IDC subtype, while only ILC was present in the OM.

CONCLUSIONS

OM from breast cancer are illustrative of the difference in metastatic behavior of ILC versus IDC and should be considered when treating patients with ILC, especially in those with complaints of visual acuity changes.

Molecular Biological Features of Nottingham Histological Grade 3 Breast Cancers

INTRODUCTION

Cancer biology dominates the behavior and prognosis of a tumor. Although Nottingham histological grade is a subjective pathological determination, it has been accepted as a surrogate model for cancer biology. As such, histologic grade was incorporated into the latest 8th edition of the American Joint Committee on Cancer breast cancer staging system. In this study, we hypothesized that grade 3 breast cancers demonstrate aggressive molecular biological profiles, reflecting worse biology and possible underlying immunogenicity.

METHODS

Transcriptomic and clinical data were obtained from the Molecular Taxonomy of Breast Cancer International Consortium, and the findings were validated by The Cancer Genome Atlas breast cancer cohort and GSE25066.

RESULTS

Overall, 2876 patients were analyzed in this study. Grade 3 tumors were more common in estrogen receptor (ER)-negative, advanced-stage patients, and were associated with human epidermal growth factor receptor 2 and basal subtypes by the PAM50 classifier, as well as with increased MKI67 expression (all p <0.001). Disease-free survival was significantly worse in grade 3 tumors (all cohorts). Gene set enrichment analysis demonstrated that grade 3 tumors were significantly enriched with not only cell proliferation and cell cycle-related gene sets but also immune activity-related gene sets. CIBERSORT confirmed that grade 3 tumors were infiltrated with macrophage M1, follicular helper T cells, and activated natural killer cells (all p <0.001). Furthermore, grade 3 tumors were associated with more diverse T cell receptors (p =0.001) and increased cytolytic activity (p <0.001). Lastly, major T-cell exhaustion markers were significantly elevated in grade 3 breast cancers (p <0.001).

CONCLUSION

Grade 3 breast cancers demonstrated aggressive transcriptomic features with enhanced immunogenicity and elevated T-cell exhaustion markers.

G2M Cell Cycle Pathway Score as a Prognostic Biomarker of Metastasis in Estrogen Receptor (ER)-Positive Breast Cancer

The vast majority of breast cancer death is a result of metastasis. Thus, accurate identification of patients who are likely to have metastasis is expected to improve survival. The G2M checkpoint plays a critical role in cell cycle. We hypothesized that breast cancer tumors with high activity of G2M pathway genes are more aggressive and likely to metastasize. To test this, we used the single-sample gene set variation analysis method to calculate the score for the Hallmark G2M checkpoint pathway using gene expression data of a total of 4626 samples from 12 human breast cancer cohorts. As expected, a high G2M pathway score correlated with enriched tumor expression of other cell proliferation-related gene sets. The score was significantly associated with clinical aggressive features of tumors and patient survival in estrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative breast cancer. Interestingly, a high G2M score of metastasis tumors was also significantly associated with worse survival. In primary as well as metastasis tumors with high scores, the infiltration of both pro- and anti-cancerous immune cells increased. Tumor G2M score was also associated with treatment response to systemic chemotherapy in ER-positive/HER2-negative cancer, and was predictive of response to cyclin-dependent kinase inhibition therapy.

Lymphovascular invasion in breast cancer is associated with gene expression signatures of cell proliferation but not lymphangiogenesis or immune response

BACKGROUND

While the prognostic relevance of lymphovascular invasion (LVI) in breast cancer is well known, its molecular biology is poorly understood. We hypothesized that pathologically determined LVI reflects molecular features of tumors and can be discerned from their genomic and transcriptomic profiles.

METHODS

LVI status and Nottingham histological scores of primary breast tumors of The Cancer Genome Atlas (TCGA) project were assessed from pathology reports; other clinical and molecular data were obtained from TCGA data portals and publications. Two independent datasets (GSE5460 and GSE7849) were combined and used for validation.

RESULTS

LVI status was determinable for 639 and 196 cases of the TCGA and validation cohorts, among whom LVI incidence was 37.8% and 37.2%, respectively. LVI was associated with high tumor Ki67 expression, advanced pathologic stage, and high Nottingham scores. LVI-positive cases had worse overall and progression-free survival regardless of cancer subtype. Surprisingly, in both cohorts, LVI was not associated with lymphangiogenesis or lymphatic vessel density as estimated from tumor expression of lymphatic endothelium-associated genes. LVI-positive tumors had higher genome copy number aberrations, aneuploidy, and homologous recombination defects, but not single-nucleotide variations or intra-tumor genome heterogeneity. Tumor immune cell composition and cytolytic activity was not associated with LVI status. On the other hand, expression of cell proliferation-related genes was significantly increased in LVI-positive tumors.

CONCLUSION

Our study suggests that breast cancer with LVI is a highly proliferative cancer, and it does not correlate with gene expression markers for lymphangiogenesis or immune response.

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.

Optimizing Retrieval of Biospecimens Using the Curated Cancer Clinical Outcomes Database (C3OD)

To fully support their role in translational and personalized medicine, biorepositories and biobanks must continue to advance the annotation of their biospecimens with robust clinical and laboratory data. Translational research and personalized medicine require well-documented and up-to-date information, but the infrastructure used to support biorepositories and biobanks can easily be out of sync with the host institution. To assist researchers and provide them with accurate pathological, epidemiological, and bio-molecular data, the Biospecimen Repository Core Facility (BRCF) at the University of Kansas Medical Center (KUMC) merges data from medical records, the tumor registry, and pathology reports using the Curated Cancer Clinical Outcomes Database (C3OD). In this report, we describe the utilization of C3OD to optimally retrieve and dispense biospecimen samples using these 3 data sources and demonstrate how C3OD greatly increases the efficiency of obtaining biospecimen samples for the researchers.

APOBEC3-Mediated RNA Editing in Breast Cancer is Associated with Heightened Immune Activity and Improved Survival

APOBEC3 enzymes contribute significantly to DNA mutagenesis in cancer. These enzymes are also capable of converting C bases at specific positions of RNAs to U. However, the prevalence and significance of this C-to-U RNA editing in any cancer is currently unknown. We developed a bioinformatics workflow to determine RNA editing levels at known APOBEC3-mediated RNA editing sites using exome and mRNA sequencing data of 1040 breast cancer tumors. Although reliable editing determinations were limited due to sequencing depth, editing was observed in both tumor and adjacent normal tissues. For 440 sites (411 genes), editing was determinable for ≥5 tumors, with editing occurring in 0.6%-100% of tumors (mean 20%, SD 14%) at an average level of 0.6%-20% (mean 7%, SD 4%). Compared to tumors with low RNA editing, editing-high tumors had enriched expression of immune-related gene sets, and higher T cell and M1 macrophage infiltration, B and T cell receptor diversity, and immune cytolytic activity. Concordant with this, patients with increased RNA editing in tumors had better disease- and progression-free survivals (hazard ratio = 1.67-1.75, p < 0.05). Our study identifies that APOBEC3-mediated RNA editing occurs in breast cancer tumors and is positively associated with elevated immune activity and improved survival.

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.

Application of Text Information Extraction System for Real-Time Cancer Case Identification in an Integrated Healthcare Organization

Background:

Surgical pathology reports (SPR) contain rich clinical diagnosis information. The text information extraction system (TIES) is an end-to-end application leveraging natural language processing technologies and focused on the processing of pathology and/or radiology reports.

Methods:

We deployed the TIES system and integrated SPRs into the TIES system on a daily basis at Kaiser Permanente Southern California. The breast cancer cases diagnosed in December 2013 from the Cancer Registry (CANREG) were used to validate the performance of the TIES system. The National Cancer Institute Metathesaurus (NCIM) concept terms and codes to describe breast cancer were identified through the Unified Medical Language System Terminology Service (UTS) application. The identified NCIM codes were used to search for the coded SPRs in the back-end datastore directly. The identified cases were then compared with the breast cancer patients pulled from CANREG.

Results:

A total of 437 breast cancer concept terms and 14 combinations of “breast“and “cancer“ terms were identified from the UTS application. A total of 249 breast cancer cases diagnosed in December 2013 was pulled from CANREG. Out of these 249 cases, 241 were successfully identified by the TIES system from a total of 457 reports. The TIES system also identified an additional 277 cases that were not part of the validation sample. Out of the 277 cases, 11% were determined as highly likely to be cases after manual examinations, and 86% were in CANREG but were diagnosed in months other than December of 2013.

Conclusions:

The study demonstrated that the TIES system can effectively identify potential breast cancer cases in our care setting. Identified potential cases can be easily confirmed by reviewing the corresponding annotated reports through the front-end visualization interface. The TIES system is a great tool for identifying potential various cancer cases in a timely manner and on a regular basis in support of clinical research studies.

Methods to Improve Sustainability of a Large Academic Biorepository

This article discusses the evolution of the University of Pittsburgh (UPitt) Health Sciences Tissue Bank (HSTB) operation and how it has successfully positioned itself, manages to sustain its value, and remains viable in today's research landscape. We describe the various components of our biobanking operation, which are valued by our researchers, thus leading to sustainability for our biorepository. Operating within the infrastructure of a large academic university, we have access to the most cutting-edge database resources for collecting, tracking, and annotating thousands of specimens. We are constantly improving upon our ability to provide real-time longitudinal follow-up data for our collections, thus providing researchers with valuable, highly annotated research specimens. We believe the combination of all that is described within this article helps to create a biobank that will remain sustainable well into the future.

An information model for computable cancer phenotypes

BACKGROUND

Standards, methods, and tools supporting the integration of clinical data and genomic information are an area of significant need and rapid growth in biomedical informatics. Integration of cancer clinical data and cancer genomic information poses unique challenges, because of the high volume and complexity of clinical data, as well as the heterogeneity and instability of cancer genome data when compared with germline data. Current information models of clinical and genomic data are not sufficiently expressive to represent individual observations and to aggregate those observations into longitudinal summaries over the course of cancer care. These models are acutely needed to support the development of systems and tools for generating the so called clinical "deep phenotype" of individual cancer patients, a process which remains almost entirely manual in cancer research and precision medicine.

METHODS

Reviews of existing ontologies and interviews with cancer researchers were used to inform iterative development of a cancer phenotype information model. We translated a subset of the Fast Healthcare Interoperability Resources (FHIR) models into the OWL 2 Description Logic (DL) representation, and added extensions as needed for modeling cancer phenotypes with terms derived from the NCI Thesaurus. Models were validated with domain experts and evaluated against competency questions.

RESULTS

The DeepPhe Information model represents cancer phenotype data at increasing levels of abstraction from mention level in clinical documents to summaries of key events and findings. We describe the model using breast cancer as an example, depicting methods to represent phenotypic features of cancers, tumors, treatment regimens, and specific biologic behaviors that span the entire course of a patient's disease.

CONCLUSIONS

We present a multi-scale information model for representing individual document mentions, document level classifications, episodes along a disease course, and phenotype summarization, linking individual observations to high-level summaries in support of subsequent integration and analysis.

How Will Big Data Improve Clinical and Basic Research in Radiation Therapy?

Historically, basic scientists and clinical researchers have transduced reality into data so that they might explain or predict the world. Because data are fundamental to their craft, these investigators have been on the front lines of the Big Data deluge in recent years. Radiotherapy data are complex and longitudinal data sets are frequently collected to track both tumor and normal tissue response to therapy. As basic, translational and clinical investigators explore with increasingly greater depth the complexity of underlying disease processes and treatment outcomes, larger sample populations are required for research studies and greater quantities of data are being generated. In addition, well-curated research and trial data are being pooled in public data repositories to support large-scale analyses. Thus, the tremendous quantity of information produced in both basic and clinical research in radiation therapy can now be considered as having entered the realm of Big Data.