Case Studies

Since 2006, a prominent government research institute has been utilizing Labmatrix as its biospecimen information management platform. Labmatrix follows through the entire life-cycle of biospecimen management, from clinical and research information management of the originating patient (study subject), to barcode label generation, and to tracking of internal/external custody transfers for samples. Basic patient demographics data are first synchronized from the institutional electronic medical records (EMR) system; subsequently, additional information about the subjects are captured from a multitude of research activities and study-centric clinical observations (such as form-based questionnaires, disease scoring, and clinical events). Prior to patient procedures, investigators attach pre-generated Labmatrix barcode labels to biospecimen containers and hand them to phlebotomists and/or surgical staff. With the acquisition of the biospecimens, this clinical event is associated along with other downstream processing information such as pathology results, custody transfer, storage inventory, sample aliquots, downstream derivatives, slide images/annotations, and experimental or molecular assay results from the samples. Individual patient reports are generated from real-time clinical, molecular, and study protocol data in Labmatrix, and distributed to physicians in weekly grand rounds as reference information for the physicians to determine the next course of disease treatment.

Problem : Institution consisting of multiple divisions conducting both basic and clinical research in campus-wide laboratories utilizing myriad, non-secure systems to collect, store and analyze patient clinical and molecular data.  Inherited immune disorders division involved in multiple collaborative studies using various assortment of electronic and paper files to manage both laboratory operations, patient data, clinical research specimens, genetic, pedigree, and experimental data while involved in direct patient care and personalized treatment plans.  A lab information management system, pedigree drawing tool, sample data management system, Excel spreadsheets, paper copies of patient encounter notes, Case Report Forms, and home-grown genetic mutation databases were all in use to manage data and operations. Situations arose, which involved delving into old patient-related files in order to determine which mutation to screen for and which workflows/treatment plan to follow, depending on proband status.  Since there was no way to conglomerate information from multiple, disparate data sources to access pedigree and genetic data in real-time, requests from referring physicians, for example, were unable to be answered without significant research time.  In addition, lab operations were fractured. Samples and sample data were lost due to the inability to track samples, sample lineage, chain-of-custody, applied workflows, results, and storage locations.  Time and money were wasted on repeated experiments when records could not be found.  Simple questions, such as determining a patient’s gene mutational status or whether a sample was sent for cytogenetic analysis, could not be answered without significant time and effort.

Solution: Implement Labmatrix™ to manage and query patient clinical and sample data together with genetic, pedigree, and experimental data. Adding pre-formulated queries merged with formatted report templates allowed for rapid reports to referring physicians and sample shipment to other facilities for further analytical workup. Logging of patient and physician inquiries and clinical workflows simplified follow-up.

Results: Time and money savings due to:

•  reduced complexity of data entry and retrieval

•  elimination of multiple, specialized applications and Excel spreadsheets

•  over 2/3 effort saved on cross-cutting queries

•  batch data load

•  interactive pedigree generation tools

•  cross-referenced, linked data records

•  sample lineage and chain of custody tracking

•  internet accessibility from any PC or Mac

Conclusions: Implementation of Labmatrix™ saved time and money by reducing administrative burden, redundant efforts, and complexity in both data management and query. This resulted in enhanced data integrity, collaboration, and operational and scientific insights.  Labmatrix™ was further implemented as a secure, enterprise-wide solution. The technology- and disease-neutral design enabled wide adoption by a variety of investigators spanning the clinic-lab divide and encompassing both human and animal studies.

A multi-center research hospital based study focused on genetic alterations of patients with esophageal cancer uses Labmatrix for the collection of associated phenotypic and molecular data, biobank management, clinical information, and development of family pedigree charts. All data for the study including patient medical history, pathology reports, and biomaterial tracking  are stored and immediately accessible upon entry with PHI protection and proprietary information controlled under user access privileges.  As a result the study sites are able to work as a cohesive unit with more insight and faster access to the latest research findings.

At the NIH, a subset of the patients treated at the Clinical Center consent into various Institutes' research protocols. NCI investigators utilize Labmatrix to securely store, annotate, curate, and track information such as patient data, clinical phenotypes, biospecimen & derivatives data, and experimental research data. Labmatrix enables collaborative exchange and sharing of information amongst research groups and provides an intuitive environment for investigators to query and review their collected data with minimal need for direct IT support.

A major pharmaceutical company had clinical and molecular biomarker data residing in different file directories, spreadsheets, and databases with little or no integration - hence creating a "data bottleneck" for extraction of higher level scientific insights and downstream decision making.

During the lengthy time frames required for data managers and IT-specialists to integrate, retrieve and analyze these scattered data sets, researchers had limited or no means to gain access to any patient clinical information for cross-referencing and further validation of interesting biomolecular observations.

Qiagram was brought in to provide a centralized, tightly linked view into molecular biomarker data and patient clinical data for the research team, enabling researchers to easily explore and derive actionable insights from all project data on their own.

A large pharmaceutical company, with intent to identify a patient cohort sorted by Her2/Neu status for a breast cancer biomarker study, made arrangements to acquire such data through partnership with a comprehensive cancer research hospital. Upon use of the proposed collaborative exploratory environment the pharmaceutical company was able to view the entire landscape of internal data silos available within their own organization and found that the acquisition of additional data was not needed. As a result, both time and money were saved by using this application to quickly gain perspective of the relevant information that was available across non-integrated less than perfect data sources.

Problem: Translational Research Scientists at a major European molecular medicine center are studying prostate cancer in an effort to identify detectable biomarkers in serum that could be markers for invasive cancer.  Currently, approximately 15-20% of patients diagnosed with prostate cancer (Gleason Index 6) develop bone cancer sometime in the ten years following treatment of the primary cancer. The research consortium consists of a clinic that sees prostate cancer patients, records medical histories and clinical parameters which are stored in a relational database.  Biospecimens (serum, urine, tissue biopsies) from these patients are also stored in a tissue biorepository along with biospecimens from hundreds of other patients with a variety of diseases. The clinic is affiliated with universities with state-of-the-art proteomics, metabolomics, and gene expression core facilities, where analyses of patient samples are routinely carried out.  Data from these different platforms is stored locally on dedicated servers associated with the instrumentation and dedicated software, limiting access and utility of the processed data.

Solution: Labmatrix™ interfaces with the clinical, biorepository, and core facilities data sources and provides a common exploratory research environment with which to access integrated information, cross-query the various data sets to fuel discovery, and generate reports.  The Labmatrix™ flexible scientific query builder enabled identification of protein biomarkers:

• differentiating high grade vs. low grade prostate cancer

• redictive of bilateral disease and distant site metastasis

• distinguishing invasiveness to lymph nodes vs. proximal urethra

Results: Utilization of Labmatrix™ enabled combination of various different data sets on both the clinical and molecular analytical sides. Exploration of the data within the query environment in Labmatrix™ enabled the discovery of proteomic biomarkers of prostate cancer and led to patent filings by the organization.

Conclusion: Labmatrix™ enabled discovery of unique prostate cancer biomarkers that can lead to development of blood-based molecular diagnostics and targets for therapeutic intervention.

Problem: Proteomics biotechnology company performing cancer biomarker discovery project via comparative proteomic profiling of five cancers spanning low to high grades. Large replicated data sets for each specimen analyzed at tissue, peptide and protein levels, including statistical and relative confidence values. Data query, sorting, and filtering while maintaining links to specimen data and run information was crucial.

Solution: Labmatrix™ was implemented as a solution to manage sample, workflow, and resulting data sets on tissue, peptide, and protein level. Labmatrix™ query strategy optimized for proteomic biomarker discovery enabled identification of:

•   proteins uniquely expressed in certain cancers

• proteins selectively expressed in metastatic samples

• proteins shared by all cancers but not in normal tissue

• analysis of mass spec run-to-run variability

• other proteomic mass spec-related biomarker  discovery questions

Results: Utilization of Labmatrix™ enabled discovery of a number of cancer-specific biomarkers, which were not previously described, resulting in patent filing by the company.

Conclusions: Labmatrix™ enabled discovery of unique cancer biomarkers and raised the value of the company by generating intellectual property. The utility of Labmatrix™ to address proteomics data and relate it to other clinical parameters was such that the company offers “Labmatrix™ view” of contract research projects for selected studies as part of their deliverables.