Seer and Korea University share early cancer screening proteomics data

Seer, Inc. (Nasdaq: SEER) and Korea University presented preliminary data at the 2026 American Society for Mass Spectrometry (ASMS) Annual Conference on June 1 in San Diego, showing early results from an AI-driven plasma proteomics study spanning ten major cancer types.

The findings, presented by professors Sang-Won Lee and Jaewoo Kang of Korea University, drew on more than 5,500 plasma samples. Researchers reported an average of more than 14,000 protein groups per sample, which they described as deep and reproducible plasma proteome coverage at population scale.

The study employed what the researchers call an ID-Free AI framework, which applies self-supervised AI models to proteomic data that conventional identification-based workflows do not fully utilize. The approach is intended to extract biological signals from mass spectrometry data that would otherwise remain uncharacterized.

"By combining deep proteomic datasets generated using the Proteograph and Orbitrap Astral mass spectrometer combined with our ID-Free AI framework, we can learn directly from a substantially larger portion of the underlying data and uncover biological patterns that may otherwise remain inaccessible," said Dr. Lee.

The work is part of an ongoing collaboration between Seer and Korea University that is expected to ultimately analyze more than 20,000 clinical plasma samples across ten of Korea's highest-incidence cancer types. The data presented at ASMS represent an early milestone in that effort.

Seer's Proteograph Product Suite, which was used to generate the proteomic datasets, integrates engineered nanoparticles, automation instrumentation, consumables, and analytical software. The company noted its products are for research use only and are not intended for diagnostic procedures.

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