Abstract A100: A dynamic view of adaptive resistance enables the development of precision immune-therapies for people living with cancer

Abstract T-cell immune checkpoint inhibitor (ICI) therapies, such as anti-PD1 or anti-CTLA4, represent breakthroughs in cancer care, delivering cures for some patients. However, 75-85% of patients prescribed these ICIs show only transient responses or outright resistance. Major investments in academia and biopharma industry over the past decade had failed to develop new medicines or combinations that significantly enhance the overall response rates of ICIs. A key hurdle has been the lack of understanding as to (a) what druggable mechanism is driving resistance in human patients and (b) how to find patients who would benefit from drugs targeting such mechanism. Without this precision strategy, the likelihood of successfully developing new ICI monotherapy or combination will remains low. The current approach to precision relies heavily on static pre-treatment tumor analyses or preclinical models, neither can capture the dynamic, adaptive nature of the human immune system. The immune system is a complex adaptive system wired to maintain homeostasis [1]​. Like other complex systems, its behavior cannot be fully predicted from static measurements as it is non-linear and emergent in response to a perturbation (e.g. a treatment). Moreover, causal drivers of such response or resistance only reveal themselves through the adaptive changes post-perturbation (e.g. on-treatment). Thus, precision strategies that rely on identifying molecular features in static pre-treatment tumor samples or in preclinical models (e.g. mouse or organoids in culture, which poorly recapitulate the complexity of the human immune system) are unlikely to predict or identify resistance mechanisms operative in human patients being treated with ICI. Indeed, early study has shown that the molecular signals for clinical response or non-response to ICI are evident only after initiation of treatment [2]. Employing a dynamic approach to precision (Dynamic Precision™), we have analyzed serial longitudinal clinical and molecular data from patients on ICI standard-of-care treatment. This approach identified dynamic signatures predictive of non-response (therapy failure) and uncovered dynamically regulated nodes that exert outsized influence on the behavior of the immune system, hence potential therapeutic targets for precision drug discovery. In summary, by applying complex systems thinking, we have developed the Dynamic Precision™ approach to move beyond static view of the cancer immune system, deciphering adaptive changes that define the responder or non-responder immune states to pinpoint emergent druggable resistance mechanisms, and linking such to blood-based biomarkers to inform precision drug discovery and development. Proof-of- concept study will be presented. 1. Uthamacumaran A. Complex Systems in Cancer Networks: Identifying Critical Nodes. BioMed Research International. 2020. 2. Chen PL, et al. Analysis of immune signatures in longitudinal tumor samples yields insight into biomarkers of response and mechanisms of resistance to immune checkpoint blockade. Cancer Discovery 2016;6(8):827-37. Citation Format: Ronan O'Hagan, Lynda Chin. A dynamic view of adaptive resistance enables the development of precision immune-therapies for people living with cancer [abstract]. In: Proceedings of the AACR IO Conference: Discovery and Innovation in Cancer Immunology: Revolutionizing Treatment through Immunotherapy; 2025 Feb 23-26; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Immunol Res 2025;13(2 Suppl):Abstract nr A100.