Abstract
Current methods for biomarker discovery and target identification in immuno-oncology rely on static snapshots of tumor immunity. To thoroughly characterize the temporal nature of antitumor immune responses, we developed a 34-parameter spectral flow cytometry panel and performed high-throughput analyses in critical contexts. We leveraged two distinct preclinical models that recapitulate cancer immunoediting (NPK-C1) and immune checkpoint blockade (ICB) response (MC38), respectively, and profiled multiple relevant tissues at and around key inflection points of immune surveillance and escape and/or ICB response. Machine learning-driven data analysis revealed a pattern of KLRG1 expression that uniquely identified intratumoral effector CD4 T cell populations that constitutively associate with tumor burden across tumor models, and are lost in tumors undergoing regression in response to ICB. Similarly, a Helios - KLRG1 + subset of tumor-infiltrating regulatory T cells (Tregs) was associated with tumor progression from immune equilibrium to escape, and were also lost in tumors responding to ICB. Validation studies confirmed KLRG1 signatures in human tumorinfiltrating CD4 T cells associate with disease progression in renal cancer. These findings nominate KLRG1 + CD4 T cell populations as subsets for further investigation in cancer immunity and demonstrate the utility of longitudinal spectral flow profiling as an engine of dynamic biomarker and/or target discovery.
Full Text Availability
The license terms selected by the author(s) for this preprint version do not permit archiving in PMC. The full text is available from the preprint server.