Overlapping Genomic and Clinical Data Through Real-World Data Insights

The Basis of the Collaboration

“Flatiron is a health tech company — one side of our business is an electronic health record company. But we're a two-sided business,” began Reisinger. “We take the electronic health records out of one side of the business and curate them for research. Then, we use that clinical data for research.”

She emphasized that multimodal data is a recent trend in data analytics. The EHR side of the company provides perspective — the patient journey and clinical perspective — however, the clinical side alone leaves out a lot of components.

Data Linking

“One of the ways that we fill in missing pieces is by linking. It's de-identified privacy, preserving linkages to other datasets,” noted Reisinger.

According to Sax Institute, an Australian research center, data linkage allows individuals to overlay datasets to identify emerging trends and comprehensively study ongoing problems.

For example, one of the earliest types of data linkage was connecting EHR data with administrative information or insurance claims to provide a more comprehensive clinical or reimbursement picture.

“More recently, data linking, which is the focus of this collaboration, has connected EHR data to genomic data. In this case, it's with a company called Caris and the dataset that they have,” added Reisinger. “They're a diagnostic testing company that captures genomic data on their patients. It's a dataset on the full human genome of more than 22,000 genes.”

The collaboration allows the companies to connect genomic information to clinical information and patient outcomes.

Clinicogenomnics

Optum notes that linking genomic and clinical data, an area of study commonly called clinicogenomnics, can offer well-rounded insights into disease pathways, potential drug targets, variations in disease trends, and drug safety or efficacy.

For example, a 2024 study published in Nature Medicine emphasized the utility of clinicogenomic data by evaluating the impact of whole genome sequencing in the United Kingdom National Healthcare System.

Researchers in the publication revealed that whole genome sequencing data layered with longitudinal life course clinical data allowed them to effectively assess treatment outcomes and divide or filter the assessments based on pangenomic markers.

According to the company’s white paper, a high-quality clinicogenomic dataset also has the potential to improve disease understanding, guide more efficient early-stage drug discovery and development, provide insight into diverse patient populations, help develop fast, cost-effective clinical trials, increase FDA approval rates, enhance opportunities to develop companion diagnostics, promote safe, effective drug discovery with fewer side effects, and reduce trial-and-error prescribing.

A comprehensive, inclusive, and robust clinicogenomic dataset ensures these goals can be accomplished.

With that understanding, Reisinger told LifeSciencesIntelligence, “The strength of this dataset is that it links the full human genome — RNA and DNA — to our data, which has high-quality clinical outcomes.”

Typically, genomic datasets identify new drug targets, pathways, and other unknowns. However, by connecting these genomic datasets to EHR data, researchers can tie these patterns to clinical outcomes.

“If we identify a subset of patients with a genomic pattern, we can see the clinical data on those patients and start to better understand whether those genes in that pattern can drive a particular outcome,” she explained.

Although the data can be sorted by particular trends or themes, Reisinger commented, “In discovery, [researchers] don't know what [they] don't know. We can certainly select cohorts by particular types of cancers and filter that. But in drug discovery, they’re looking for unknowns.”

Filtering specific data may cause companies and researchers to miss out on unknown trends.

Data Diversity

Another component of high-quality data is a robust dataset that includes information from diverse patient populations. A 2023 article from Harvard Medical School noted that diversity in clinical and research data is imperative, as a lack of diversity can compromise patient outcomes by yielding unrepresentative information or inaccurate conclusions.

Ongoing issues in clinical trial and research diversity have pushed biopharmaceutical companies and leaders to look for ways to incorporate participant data that accurately reflects disease patient populations.

One of the added benefits of this collaboration is Flatiron’s ability to leverage extensive EHR data from a broad range of patients with varying backgrounds.

“Our own EHR system is community oncology-based,” she said. “It's outside the academic medical centers, so it's more rural and socioeconomically and racially diverse.”

While the company does have some collaborations with academic medical institutions, the focus on community oncology drives an increasingly diverse data set.

Although Flatiron’s system includes insight from over 3 million patients, the overlap with Caris’s genomic data is slightly smaller, including roughly 45,000 patients.

However, Reisinger countered, “It’s not nearly the full three 3.3 million yet, but we expect 30 30% growth year over year. So, we expect the overlap and diversity to continue improving.”

“We're actively adding additional Caris patients to our community oncology centers,” she continued.

Future Goals

Although it’s unclear how the collaboration between the two companies will pan out, Reisinger revealed that the long-term goal is to make this clinical genomic dataset the standard for discovery and research purposes.

However, she explained, “We intentionally structured this as a lightweight, fast partnership because we want to learn from how our clients are using it and be able to point and pivot the offering toward the highest value use cases.”