Freenome Partners with Siemens Healthineers for Breast Cancer Research

Collaboration leverages imaging, diagnostic and multiomic expertise to identify new, significant biomarkers to improve the detection of breast cancer 

SOUTH SAN FRANCISCO, December 1, 2021 — Freenome, a privately held biotech company, announced a partnership with Siemens Healthineers to collaborate in multiomics and radiomic breast cancer diagnostics to identify suitable markers for early breast cancer detection through blood to augment existing imaging technologies. 

The partnership leverages Freenome’s expertise in machine learning and multiomics to detect early cancer, utilizing epigenetic, proteomic, genomic, immunologic and other data types to maximize clinical accuracy for future screening tests. This collaboration will allow Freenome and Siemens Healthineers to share their technologies by connecting imaging and clinical data with molecular data to identify new suitable markers of breast cancer that are complementary to those identified using current imaging. 

“With their multiomics approach in molecular diagnostics, Freenome is our partner of choice for this study,” said Rangarajan Sampath, Head of the Center for Innovation in Diagnostics (CID), Siemens Healthineers. “Our collaboration in the identification and development of new biomarkers will allow us to work together toward a new patient-centric pathway to diagnose early-stage breast cancer.”

By modeling Freenome’s multiomics data enabled by artificial intelligence and machine learning based methodologies, researchers seek to identify the most effective biomarkers and molecular features to improve the identification of breast cancer.

“Siemens Healthineers is an established leader in the development of imaging and diagnostic technologies, especially in breast cancer screening with more recent improvements leveraging 3D mammograms or digital breast tomosynthesis,” said Mike Nolan (Freenome CEO). “This collaboration will give us even more insights on how we can incorporate unique data types to address the unmet medical needs for one of the most common cancers.”