Akoya Biosciences (AKYA) investor relations material

AI-driven spatial profiling and clinical application

• AI-powered analysis of trillions of immune cell data points identified key predictors of cancer recurrence risk, focusing on spatial distribution and single-cell resolution.

• Developed an AI strategy to filter massive datasets into a few clinically relevant biomarkers, enabling explainable and visualizable predictions at the single-cell level.

• Introduced the concept of H&E 2.0, leveraging AI and cloud-based tools to enhance traditional pathology and streamline clinical trial recruitment.

• AI-enabled virtual staining and immune scoring systems outperformed traditional prognostic factors in predicting liver cancer recurrence.

• Human expertise remains integral in AI decision-making, ensuring clinical relevance and safety.

Key research findings and validation

• Identified a five-gene immune signature, primarily involving NK cells, that accurately stratifies liver cancer patients by recurrence risk.

• Immune scoring system based on these markers achieved an accuracy (AUC) of 0.82, surpassing existing clinical predictors.

• Ex vivo and in vivo experiments confirmed the mechanistic role of NK cells and their interaction with CD8 T cells in tumor control.

• High immune scores correlated with better response to immunotherapy, converting high-risk patients to favorable outcomes.

• Spatial proteomics and mass spectrometry approaches revealed additional protein markers, including unannotated "dark proteins."

Future directions and broader impact

• Ongoing work aims to automate image quantification and reporting for multiplexed assays, making clinical translation more efficient.

• Efforts are underway to generalize the AI-driven scoring system for broader clinical and global health applications, including resource-limited settings.

• The team is advancing H&E 2.0 and exploring further improvements, such as cloud-based platforms and new biomarker panels.

• Emphasis on rigorous validation across technologies and settings to ensure findings are robust and biologically meaningful.

• Encouragement for early-career scientists to persist, collaborate, and maintain integrity in research.