This podcast explores the groundbreaking scientific result achieved by Google DeepMind and Yale University, marking a major milestone in the use of Artificial Intelligence for biomedical research. Google DeepMind’s latest biological AI system, the 27-billion-parameter foundation model called Cell2Sentence-Scale 27B (C2S-Scale 27B), has generated and experimentally confirmed a new hypothesis for cancer treatment. This model, which is part of Google’s open-source Gemma family, was designed to understand the “language” of individual cells and analyze complex single-cell data.

The discovery specifically addresses a major challenge in cancer immunotherapy: dealing with “cold” tumors, which evade detection by the immune system. Cold tumors have few immune cells (tumor-infiltrating lymphocytes) and weak antigen presentation, making them less responsive to immunotherapy. The AI’s goal was to identify how to make these tumors “hot,” meaning rich in immune cell infiltration and exhibiting stronger immune activity.

The C2S-Scale 27B model analyzed patient tumor data and simulated the effects of over 4,000 drug candidates. It successfully identified silmitasertib (CX-4945), a kinase CK2 inhibitor, as a conditional amplifier drug that could boost immune visibility. The AI predicted this drug would significantly increase antigen presentation—a key immune trigger—but only in immune-active conditions. This prediction was novel, as inhibiting CK2 via silmitasertib had not been previously reported in the literature to explicitly enhance MHC-I expression or antigen presentation.

Laboratory experiments confirmed this prediction. When human neuroendocrine cells were treated with silmitasertib and low-dose interferon, antigen presentation rose by approximately 50 percent, making the tumor cells significantly more visible to the immune system. Researchers described this finding as proof that scaling up biological AI models can lead to entirely new scientific hypotheses and offers a promising new pathway for developing therapies to fight cancer. This work provides a blueprint for a new kind of biological discovery that uses large-scale AI to run virtual drug screens and propose biologically grounded hypotheses for laboratory testing.