Biomarker Data Management Archives

QuartzBio Use Case 202303 Biomarker Data Management

The client: Clinical development and translational research teams executing phase I and II studies for precision oncology

A clinical-stage biotechnology company developing precision oncology therapeutics recently worked with QuartzBio to tackle challenges facing execution of three phase I and two phase II biomarker-informed studies.

Challenges facing clinical development included:

Limited pool of potential patients to recruit for studies
Challenges facing patient screening and stratification – ideal biomarker profile not well-defined

Specific challenge: integrating siloed data

The clinical development team was challenged to solve the problems of patient qualification and stratification by integrating data from multiple streams, across multiple clinical programs:

Electronic medical records
Lab data
Biomarker profiling with indication data for patients
Immune profiling data
Publicly available data
Preclinical data: cell data and animal (cytokine and biomarker) data

The data sets being generated as a result of the clients’ clinical programs were scattered across multiple, disconnected cloud storage solutions. The team needed data to be integrated in a unified, query-friendly database that complied with regulatory requirements.

The solution: QuartzBio® enterprise Biomarker Data Management (eBDM) Software-as-a-Service (SaaS) Application

The client’s clinical development team worked with QuartzBio’s informatics subject matter experts and data engineers to deploy the eBDM platform within 6 weeks.

QuartzBio® eBDM, an enterprise SaaS solution, enabled scalable, user-driven data management of the client’s entire ecosystem of biological data (Figure 1).

QuartzBio eBDM solution deployed across five clinical studies — Figure 1. Overview of the QuartzBio® enterprise Biomarker Data Management solution as deployed across 5 clinical studies. Multiple data types were automatically loaded into the data platform. After indexing, quality control (QC), and mapping in the landing, ingestion, and configuration layers of the platform, client teams could interrogate the data ecosystem using both the web interface and API access.

Because the QuartzBio® data platform is data type-, data source-, and vendor-agnostic, every one of the clients’ data streams could be acquired, ingested, quality-controlled, and harmonized within the platform.

Data type-specific templates were used to automatically harmonize and annotate data from different vendors at the time of or after data import
QuartzBio’s library of configuration-ready workflows spanned all assay technologies used by the client: genomics, high-content imaging, flow cytometry, immunohistochemistry

The outcome: freedom to explore and transform data via web-based UI

Once data was set up within the platform, users had the freedom to explore and transform data via a web-based user interface (UI) with data access controls, version history and audit trails.

The enterprise Biomarker Data Management application enabled collaboration among a diverse user base across the client’s organization. Not only did the clinical development team uncover insights to inform trial execution, but the clients’ bioinformatics, translational biology, and biomarker operations teams also used QuartzBio’s visualization, data analysis, and top-line reporting capabilities as follows:

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QuartzBio Helps Keep Oncology Trial on Track at a Clinical-Stage Biotech

A clinical-stage biopharmaceutical company has been developing novel therapeutics that selectively modulate gene expression to address unmet medical needs in cancer patients.

To keep their phase I study of an epigenetic modulator on track, the program’s translational research team needed to be able to monitor clinical outcomes with respect to patients and with respect to biomarker-defined subgroups.

To achieve this goal, the team needed to manage multiple data streams:

Central lab sample inventory
EDC/Clinical data
Assay results data from multiple specialty labs, including IHC, flow cytometry, PK, and gene expression

Biomarker Data Management (BDM) & virtual Sample Inventory Management (vSIM)

In weeks, QuartzBio deployed its vSIM and BDM solutions to the clinical trial in progress, enabling the translational research team to:

Gain centralized visibility into status of samples as they move from sites to central and specialty labs
Revealed insights on actual samples collected vs. expected samples collected
Generate hypotheses based on biomarker assay results linked to patient clinical data and dosage information
Eliminate time-consuming and manual data cleaning and cross-referencing processes

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QuartzBio’s approach to biomarker data management can be seen in the context of a phase 2/3 dose evaluation and expansion oncology study with 5 assays run at 4 different labs. Using QuartzBio’s technology-driven solution, the sponsor could:

Pull together and visualize PK, cytokine, and flow cytometry data to inform optimal dosing
Integrate mutation, IHC, and cytokine data to develop a multimarker signature to stratify patients

The cross-functional QuartzBio team—including data scientists, biomarker data management programmers, and data managers—delivered a centralized data hub for biomarker data transfer, integration, and collaboration, as well as interactive reports and support for ongoing trial activities.

The client found that our approach transformed their ability to reach key clinical trial objectives.

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Functional Team	How the team used enterprise Biomarker Data Management
Biomarker Operations	On-study reporting of data quality, data conformity, data availability, vendor performance, and turnaround times.
Bioinformatics / Computational Biology	Generated visualizations (e.g., line plots, clustering, dimensionality reduction) and were able to connect data to existing tools (e.g., SpotFire, Prism)
Translational Research	Obtained early insights into the ideal genomic profile of patients that showed response to the therapeutic candidate.