Akoya Biosciences (AKYA) Status Update summary

AI strategy and spatial profiling in cancer research

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

• A novel immune scoring system based on five genes was developed, achieving a predictive accuracy of 0.82 for liver cancer recurrence, outperforming traditional clinical prognostic factors.

• The study validated findings across RNA, protein, ex vivo, and in vivo models, ensuring robustness and biological relevance of the AI-driven discoveries.

• The approach enables efficient patient stratification for clinical trials, potentially improving recruitment and outcomes by leveraging H&E 2.0 virtual staining and AI prediction.

• Expansion into spatial mass spectrometry and dark proteomics revealed additional protein markers and mechanisms, with ongoing work to automate and generalize the scoring system for broader clinical use.

Clinical and translational implications

• The immune scoring system allows identification of high-risk liver cancer patients, enabling targeted adjuvant therapies to delay or prevent relapse.

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

• The methodology supports rapid, AI-powered analysis for clinical decision-making, with potential for two-week turnaround times.

• Efforts are underway to deploy these tools in global health settings, including Africa and Southeast Asia, to address disparities in cancer care.

• Future work includes developing cloud-based, automated quantification platforms and expanding the approach to other multiplexed assays.

Key takeaways and future directions

• Strategic AI application and structured data reduction are essential for meaningful insights from large-scale spatial datasets.

• Validation across multiple technologies and biological models ensures findings are not due to chance and are mechanistically sound.

• Ongoing research focuses on improving technical platforms, expanding clinical utility, and supporting early-career scientists in the field.