SOPHiA GENETICS & AstraZeneca Extend AI Collaboration for Breast Cancer Insights

The collaboration will use SOPHiA GENETICS's multimodal AI Factories to assess treatment efficacy, generate real-world evidence, and support the development of predictive models.

SOPHiA GENETICS announced a multi-year expansion of its partnership with AstraZeneca, which aims to improve outcomes in breast cancer care using AI-powered analytics.

The collaboration will use SOPHiA GENETICS's multimodal AI Factories to assess treatment efficacy, generate real-world evidence, and support the development of predictive models.

"We are proud to deepen our partnership with AstraZeneca through this significant new initiative, highlighting the growing demand for secure, compliant, and scalable real-world AI applications," said Ross Muken, President of SOPHiA GENETICS. "Our platform is purpose-built to manage complex healthcare data environments, and this collaboration reinforces our shared commitment to driving better patient outcomes through trusted, federated analytics powered by data and AI."

The AI Factories will analyze diverse datasets, including genomics, imaging, and clinical data, to provide deeper insights into treatment response and patient prognosis. The partnership will also include real-world data generation efforts in Europe and North America to identify drivers of treatment success and address knowledge gaps in clinical oncology.

Leaders Highlight Value of Federated AI in Healthcare

Jorge Reis-Filho, Chief AI and Data Scientist at AstraZeneca, added, "At AstraZeneca, a core component of our AI strategy has been rooted in deploying frontier AI solutions across oncology clinical development. Fine-tuning and augmenting our models with multimodal data—including our own data and the data that will be generated as part of this collaboration—is helping us generate a more holistic understanding of disease biology and biomarkers to tailor the most effective treatment to patients living with cancer."

Additionally, the initiative is centered on generating insights specifically for breast cancer care, aiming to optimize treatment decisions through analysis of real-world patient data. Both companies emphasized the role of federated data models and advanced computing protocols in advancing precision oncology.

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