NEW YORK – Freenome said Tuesday that it has launched a new study of its multiomics platform to detect multiple cancers, supported by both traditional and real-world data.
The so-called Sanderson Study will collect data from approximately 8,000 high- and elevated-risk populations, with the goal of clinically validating Freenome's early detection platform, which analyzes both tumor and non-tumor signals from a blood sample, using machine learning to detect cancer in its earliest stages.
Investigators also intend to use study data to refine the platform's cancer classification and risk prediction models, with a focus on cancers with significant unmet needs such as pancreatic and lung cancer.
Freenome said it will recruit patients from its clinical study partner network and numerous regional health systems across the United States. Key collaborators include Oracle Cerner and its Learning Health Network (LHN), a group of more than 90 diverse US health systems that was founded in 2020 to advance research and increase equitable access to clinical trials by contributing de-identified data to the LHN.
Elligo Health Research will also collaborate with LHN members, using existing health data to identify and enroll patients significantly faster than traditional recruitment models.
"We're incorporating real-world data with a precision health mindset on clinical actionability," Freenome Chief Medical Officer Lance Baldo said in a statement. "Our goal is to identify the right patient for the right screening tests at the right time, with clear next steps. We believe this approach will ultimately save more lives."
"Achieving earlier detection of cancer is critical to improving health outcomes, and we're honored to bring this groundbreaking research to our community," said Ruth Colby, president and CEO of Silver Cross Hospital, an LHN member in the Chicago area. "This opportunity enables us to expand clinical research opportunities for patients and further our mission to care for members of our community."