As oncology care continues to advance, laboratories are increasingly seeking solutions that offer high-impact, decentralized, and accurate genomic testing. The third session of the Precision Oncology Showcase series introduces a transformative approach to precision oncology, built through collaboration between Memorial Sloan Kettering Cancer Center (MSK) and SOPHiA GENETICS. 

Join Pierluigi Iapicca, Senior Subject Matter Expert at SOPHiA GENETICS, and Dr. Kojo Elenitoba-Johnson, Chair of the Department of Pathology and Laboratory Medicine at MSK, as they unveil MSK-IMPACT® powered with SOPHiA DDM™, a decentralized comprehensive genomic profiling (CGP) solution that expands access to MSK’s world-class genomic profiling. 

This solution applies the same core technology principles behind MSK-ACCESS® powered with SOPHiA DDM™ to a format compatible with FFPE tissue samples. Using a matched tumor-normal approach, it enhances somatic mutation detection and filters out germline variants, thereby reducing biological false positives. It can also identify clonal hematopoietic variants (CHIP), revealing mutations of true somatic origin. 

MSK-IMPACT® powered with SOPHiA DDM™ offers high concordance with single-site MSK-IMPACT® results across SNVs, indels, TMB, MSI, and copy number alterations, all while leveraging the robust analytics and global scalability of the SOPHiA DDM™ Platform. By combining MSK’s clinical expertise with SOPHiA GENETICS' technological strength, this end-to-end application enables labs to adopt a comprehensive, decentralized CGP model that improves interpretation, enhances accuracy, and expands equitable access to precision oncology. 

Learning Objectives 

• Discover how matched tumor-normal sequencing improves somatic detection and interpretation in decentralized CGP. 
• Learn how MSK-IMPACT® powered with SOPHiA DDM™ delivers high biomarker concordance and enables labs to implement MSK-level CGP. 
• Understand how this platform supports equitable access to precision oncology through a scalable, cloud-based network.