On June 17, 2025 Agenus Inc. (Nasdaq: AGEN), a leader in immuno-oncology innovation, and Noetik, a leader in AI-driven spatial and multimodal biology, reported a research collaboration to develop predictive biomarkers of response to Agenus’ lead clinical stage immuno-oncology (IO) combination, botensilimab (BOT, multifunctional Fc-enhanced anti-CTLA-4) and balstilimab (BAL, anti-PD-1) (Press release, Agenus, JUN 17, 2025, View Source [SID1234653963]). The collaboration harnesses Noetik’s proprietary virtual cell foundation models and large-scale, multimodal tumor data to uncover novel insights into the biology of tumor immunology. Together, the teams will deploy Noetik’s first-in-class foundation models directly on clinical results with the aim to enrich clinical efficacy.
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Central to this collaboration is Noetik’s OCTO virtual cell model, a 1.5 billion parameter foundation model trained on one of the largest proprietary multimodal spatial datasets. This dataset brings together spatial proteomics, spatial transcriptomics, H&E pathology, DNA genotyping, and clinical metadata from nearly 200 million tumor and immune cells collected from thousands of patients with cancers such as colorectal cancer, non-small cell lung cancer, ovarian cancers, and sarcomas. By integrating these diverse data types, Noetik’s foundation models provide a systems-level view of the tumor microenvironment in real patients, unlocking novel insights into cancer biology that can drive more precise therapeutic discovery and development.
Botensilimab, alone or in combination with BAL, has been evaluated in more than 1,200 patients across nine tumor types, including colorectal cancer, NSCLC, and sarcomas. By targeting complementary immune pathways, the BOT/BAL combination has shown deep and durable clinical responses—even in tumors considered immunotherapy "cold" or resistant to prior IO treatment. The regimen has generated growing recognition within the medical community, supported by compelling data presented in both late-line and neoadjuvant settings, multiple peer-reviewed publications, and presentations at more than a dozen major medical congresses over the past three years.
The collaboration aims to uncover clear, actionable biomarkers that can help predict which patients are most likely to respond to BOT/BAL treatment. Using OCTO virtual cell models to simulate how tumors behave in the body, Noetik will analyze complex biological data from multiple cancer types. The goal is to identify patterns that can predict treatment outcomes and help classify patient groups who may benefit most. Agenus will have exclusive rights to apply these insights in its drug development and commercialization efforts.
"Enhancing clinical efficacy is the most important problem in developing new medicines, and exactly what we’ve trained our foundation models to do. We are excited to deploy Noetik’s virtual cell foundation models on Agenus’ rich clinical data to uncover biomarkers that can enrich patient therapeutic response, improve trial outcomes, and ultimately deliver more precise therapies," said Ron Alfa, M.D., Ph.D., CEO & Co-Founder, Noetik.
"At Agenus, we are committed to transforming cancer care through scientific innovation and next-generation immunotherapies. This collaboration with Noetik enables us to harness cutting-edge AI to better understand patient biology and tailor treatments more precisely," said Dr. Garo Armen, Chairman and CEO of Agenus. "By integrating Noetik’s virtual cell models with our expansive BOT/BAL clinical dataset, we have the potential to accelerate the identification of predictive biomarkers, enhance the success of our pivotal trials, and ultimately improve outcomes for patients who currently have limited or no treatment options."
This collaboration reflects a growing momentum in oncology toward model-driven trial design and AI-enabled precision medicine—an area increasingly prioritized by the FDA under the guidance of the current U.S. administration as central to advancing more equitable, effective cancer care. By applying these technologies to real patient data, the goal is to accelerate the delivery of more personalized treatments, improve outcomes, and expand access to therapies for patients who need them most.