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Overcoming challenges to integrating biomarker data into clinical trials

Life sciences researchers and oncologists are learning more about cancer pathogenesis and treatment every day, largely thanks to explosive innovation in the world of biomarkers.

 

Biomarkers are the foundation of clinical science and encompass the broadest possible array of physical indicators of a biological state or condition. This includes everything from a person’s blood pressure readings to the unique gene mutations of a tumor and can create enormous opportunities – and enormous complexity – around identifying targets and metrics of interest and organizing this data so it can be used effectively for driving therapeutic care decisions that lead to better outcomes.

 

In cancer care, we tend to focus on the most intricate end of the spectrum: the genes, proteins, and metabolic pathways in cancer cells that can help define the origins and features of the malignancy, as well as predict and track the patient’s response to certain therapies.

 

Scientists are discovering new cancer biomarkers at a breakneck pace as advances in computing power and investment from life science leaders contribute to the ability to dig into the finest details of how cancer cells function and evolve.

 

As we learn more about how cancer works and apply the learnings to clinical care, we need to make sure this knowledge isn’t just academic. We have to actively use the rich, multimodal, real-world data (RWD) that is generated to inform the design of clinical trials, recruit diverse and representative participants, and provide meaningful insight into how individuals and whole populations respond to novel therapies.

 

The challenges of integrating biomarker data into real-world research

Biomarker data comes from a variety of sources, including traditional blood tests, next-generation sequencing (NGS), fluorescence in situ hybridization (FISH) testing, and more. The sheer volume, diversity, complexity, and unstructured nature of this data can be problematic all on its own, especially since it’s growing and changing at an extremely rapid rate.

 

Making sense of the information gets even more complicated due to the fact that there are no universal standards for the reporting of many biomarker data, especially those tested by NGS. This lack of standardization makes it difficult to aggregate biomarker information across different organizations or testing types, preventing researchers from creating a comprehensive portrait of all the factors associated with outcomes of interest in targeted populations.

 

Further complicating the issue is the fact that biomarker testing is unevenly performed across racial, ethnic, and economic groups. Many health plans only offer limited coverage of testing, while research consistently shows disparities in biomarker testing and clinical trial enrollment along racial and ethnic lines.

 

Inaccessible, incomplete, or poorly representative biomarker datasets can make it difficult for researchers to hone in on the specific questions and answers that could identify unmet needs in current populations and inform future standards of care for people with hard-to-treat conditions.

 

How to leverage valuable biomarker data in the clinical trial environment

To make the best use of biomarker data, it has to be aggregated, curated, and accessible to researchers. That can be a tall order for clinical trial sponsors, especially as the number of clinically significant biomarkers and testing vendors continues to increase. Overcoming the obstacles and creating an environment where biomarker data can be used to its full potential will require a collaborative, multifaceted approach.

Make sure everyone who needs a test gets a test

There should be no such thing as a generic cancer diagnosis anymore. All patients with cancer should have the opportunity to be tested for relevant genetic mutations and other biomarkers to personalize their treatments and maximize their odds of a positive outcome.


Healthcare providers, clinical trial sponsors, and health plans need to work closely together to expand the availability and coverage of biomarker testing among traditionally underserved populations so researchers and clinicians can offer informed treatment recommendations to individuals while expanding our overall knowledge of how cancer works.

Create and enforce standards for biomarker reporting

Clean, complete, accurate data is the lynchpin for success with clinical trial design and execution. Biomarker data is voluminous and the reports generally unstructured, making it vital to create and adopt shared standards for reporting this information across disparate entities.

 

While some efforts to do this are already underway, clinical trial sponsors need to continue to advocate for industry-wide reporting guidelines that make it faster and easier to use and understand.

Use the right strategies for curating biomarker data

Abstracting and curating biomarker data can be difficult and time-consuming for organizations that are not fully focused on this task. While artificial intelligence and large language models (LLMs) might someday ease this burden, these techniques aren’t yet ready to manage the massive and complex undertaking of turning raw, poorly standardized information into usable insights.

 

Instead, clinical trial sponsors looking for actionable biomarker data should consider partnering with data abstraction and curation experts that use deep clinical knowledge to capture the nuances of these unwieldy datasets. Sponsors should look for a partner that offers highly curated biomarker data as part of their core datasets to ensure it is accessible and integrated into the sponsor’s broader knowledge base.

Prepare for future use cases and applications

Expanding the testing pool, creating standards, and reliably curating biomarker data will help researchers and clinicians prepare for the never-ending march of progress in cancer research. With more and more biomarkers becoming clinically relevant every day, researchers need to be sure they can go back and reexamine existing datasets for new findings that could change their hypotheses or care decisions in the future.

 

Developing a trustworthy process for collecting, curating, and integrating complete and representative biomarker data into clinical research will be essential for this task. It will also help entities prepare for engaging in emerging research strategies, such as external control arms (ECAs), that can change the way we envision cancer research.

 

By collaborating to make real-world biomarker data a high-value, easily accessible asset for life science researchers and clinicians, we can chart a more personalized future for cancer care and ensure precise and effective treatments for all patients.