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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:

Join our webinar on March 22 to learn more about the QuartzBio® enterprise Biomarker Data Management solution:

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The challenge: Maintaining clinical trial timelines dependent on vendor performance

A global biotechnology company with seven clinical-phase programs approached QuartzBio with a challenge shared by many organizations in the industry — overall assessment of clinical trial vendors and sites was time-consuming and unreliable, given global site footprints, complex sample lifecycles, and inconsistent naming across sites and vendors.

Tasked with preparing regular executive summaries, the company’s clinical and biomarker operations teams were spending significant time deriving trial-wide key performance indicators (KPIs), lacking visibility into vendor performance – and challenged to translate current experience into future study planning.

This lack of visibility was exacerbated by the absence of standard nomenclature and inconsistent data entry across vendors. Clinical operations teams were spending time manually mapping and reconciling data and metadata from multiple independent sources.

The QuartzBio approach: Continuously updated visibility through consolidated dashboards

The client implemented the virtual Sample Inventory Management solution, powered by the QuartzBio® Data Platform.

Using the platform’s built-in dynamic dashboards (examples shown in Figure 1), augmented by some bespoke reports custom-built by QuartzBio for the client, the clinical operations team was able to easily monitor and report on multiple vendor performance parameters:

Turnaround times from sample collection to sample processing to test result generation
Completion rate of samples tested within stability window
Sample processing quality KPIs by site and vendor
Rate of missed/incomplete baseline and on treatment collections
Data discrepancies by site and vendor
Protocol violations by site and vendor
Consent violations by site and vendor

Core to QuartzBio’s approach is rigorous sample data integration. The virtual Sample Inventory Management solution automatically harmonizes sample information from multiple sites, vendors, and other sources, regardless of format or nomenclature. QuartzBio applies an adaptive data dictionary that learns and updates common nomenclatures over time, creating a flexible model. Using this model, it harmonizes the non-standard nomenclature across vendors and studies, enabling clear visibility and eliminating manual processes.

The outcome: Fast, accurate reports, timely decisions, course correction on-study

Empowered by 24/7 access to vendor performance, visibility into expected sample schedules, and full reconciliation oversight, the client’s clinical operations teams started using the QuartzBio Data Platform for generating visualizations to guide executives and inform investors.

These visualizations enabled the client to take action at specific sites and vendors that were underperforming. Armed with more reliable trends in sample schedules and turnaround times, the biomarker operations team could more accurately project data delivery timelines and anticipate when key decisions could be made.

translational-KPI-dashboard — Figure 1. Clinical Site Sample Collection Performance Dashboard. Using this and other clinical KPI dashboards on the QuartzBio Data Platform, the client could evaluate vendor and site performance within and across studies.

Watch our webinar, “Biomarker Operations KPIs,” to see these dashboards in action, as well as the latest reporting capabilities available to users of QuartzBio’s vSIM solution.

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flow cytometry data management QuartzBio

Our client’s translational research team needed to be able to monitor clinical outcomes with respect to patients and biomarker-defined subgroups. Within weeks, QuartzBio deployed virtual Sample Inventory Management and Biomarker Data Management solutions for the clinical trial in progress, enabling the team to:

Gain centralized visibility into status of samples as they move from sites to central and specialty labs
Reveal insights on actual samples collected versus 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

Explore Biomarker Data Management

One of the core challenges faced by clients in biomarker-informed clinical trials is the integration of key sample and subject metadata derived from multiple independent sources. One case involved multiple immuno-oncology studies, with data coming from multiple sources, in multiple formats.

The QuartzBio^® platform was deployed to create a Master Sample Inventory view, yielding answers to key on-study questions, such as:

How does the number of available plasma samples compare to the number of available FFPE samples for Study X?
What proportion of samples have data available? What’s the breakdown of data availability by sample type?

Explore virtual Sample Inventory Management

The translational research teams at a clinical-stage biopharma client rely on QuartzBio to report to stakeholders on how their clinical programs are progressing. Together with the clinical data management team, they can monitor vendors, turnaround times, and gaps in contracts – across the entire program.

Using dashboards on the QuartzBio Data Platform, the team can deliver executive summaries and presentations to investors, guiding key decisions and projected timelines.

Explore virtual Sample Inventory Management

The clinical operations team at a mid-sized oncology biotech client depends on QuartzBio’s virtual Sample Inventory Management solution to gain global visibility into their expected sample schedule, across all collection sites.

They can identify gaps in sample data and sample shipments. Full oversight enables the team to identify data discrepancies, consent violations, and subject exclusions.

Explore virtual Sample Inventory Management

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.

Explore Biomarker Data Management

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.