Mount Sinai Health System has adopted the SOPHiA DDM platform from Sophia Genetics (NASDAQ: SOPH), a cloud-native AI genomics company, to support next-generation sequencing workflows across blood cancers and solid tumors. The arrangement, announced at the AACR Annual Meeting in San Diego on April 16, 2026, will be deployed across Mount Sinai’s oncology program, which serves more than 4,000 patients annually. Financial terms were not disclosed.
The SOPHiA DDM platform is a cloud-based software system that processes raw sequencing data and applies machine learning models to detect and classify genomic variants. The system is designed to integrate multiple steps in the analysis pipeline and operate across sequencing instruments without requiring instrument-specific configurations. Mount Sinai will run the platform as a laboratory developed test within its CLIA-certified, CAP-accredited laboratory, while Sophia Genetics will provide the analytical software but will not perform diagnostic testing. The institution will initially deploy SOPHiA DDM for Blood Cancers and SOPHiA DDM for Solid Tumors.
The platform’s core function is variant detection and interpretation, distinguishing clinically relevant alterations from sequencing noise using models trained across a global network of participating institutions. Sophia Genetics reports that more than 990 hospitals and laboratories use the platform, enabling a federated learning approach in which standardized, anonymized data contribute to shared model improvement without transferring raw patient data. For Mount Sinai, the primary operational rationale is improved throughput and standardization in genomic analysis, with potential impacts on turnaround time for molecular testing and clinical decision-making in oncology.
For Sophia Genetics, the collaboration adds a major US academic medical center to its network as it competes for adoption among large oncology institutions. The company operates a platform-based commercial model in which laboratories license access to its software rather than relying on instrument-specific ecosystems or per-assay reagents. For Mount Sinai, the adoption reflects a shift toward externally maintained bioinformatics infrastructure, reducing the burden of updating in-house pipelines while supporting both clinical reporting and translational research. No drug development or co-development components were disclosed as part of the agreement.
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