Google DeepMind, in collaboration with Yale University, has unveiled C2S-Scale 27B, a new 27 billion parameter foundation model built on the Gemma family. This advanced AI has generated a novel hypothesis about cancer cellular behavior, subsequently confirmed through experimental validation in living cells. The discovery reveals a promising new pathway for developing therapies to fight cancer, marking a significant AI cancer breakthrough.
The C2S-Scale 27B model is designed to understand the "language" of individual cells, representing a new frontier in single-cell analysis. A major challenge in cancer immunotherapy involves "cold" tumors, which evade the immune system. The model was tasked with finding a drug to conditionally amplify immune signals, specifically boosting antigen presentation only in an "immune-context-positive" environment where low levels of interferon were present but insufficient on their own. This complex conditional reasoning capability emerged with the model's scale, a feat smaller models could not achieve.
To accomplish this, the AI employed a dual-context virtual screen, simulating over 4,000 drugs across patient samples with intact tumor-immune interactions and isolated cell line data. This process identified drugs that would selectively enhance antigen presentation in the patient-relevant setting. While some hits were previously known, a significant fraction (10-30%) were surprising, novel candidates with no prior reported link to the screening context.
Validating the AI's Insight
The model's predictions were remarkably clear, identifying a striking "context split" for the kinase CK2 inhibitor silmitasertib (CX-4945). It predicted a strong increase in antigen presentation when silmitasertib was applied in the "immune-context-positive" setting, with minimal effect otherwise. This was a novel idea; although CK2 is known to modulate the immune system, silmitasertib had not been reported to explicitly enhance MHC-I expression or antigen presentation.
Experimental validation in human neuroendocrine cell models, unseen by the model during training, confirmed the AI's hypothesis. Treating cells with silmitasertib alone had no effect, and low-dose interferon alone had a modest impact. However, the combination of silmitasertib and low-dose interferon produced a marked, synergistic amplification, resulting in a roughly 50% increase in antigen presentation. This effectively makes "cold" tumors "hot," potentially rendering them more responsive to immunotherapy.
This result provides a powerful blueprint for biological discovery, demonstrating that scaled AI models can run high-throughput virtual screens, uncover context-conditioned biology, and generate experimentally validated hypotheses. Teams at Yale are now exploring the uncovered mechanism and testing additional AI-generated predictions. This approach promises to accelerate the path to new combination therapies, leveraging AI to unlock previously unknown biological interactions.
