Abstract 1270: Glycoproteomics-based liquid biopsy informs optimal checkpoint-inhibitor drug choice

Abstract

Abstract Introduction: While immune checkpoint inhibitor (ICI) therapy has added a powerful new arsenal of highly effective drugs for the subset of cancer patients who respond to these agents, their use remains burdened by the fact that we lack reliable biomarkers to identify likely responders, to avoid the adverse event incidence and cost of treating likely non-responders. Likewise, we currently have no tools which would help identify the optimal choice of agents; current prescribing practice is not guided by any objective criteria. We have recently demonstrated that interrogating the serum glycoproteome, using a proprietary platform that couples artificial intelligence to targeted liquid chromatography-mass spectrometry yields highly informative biomarkers for a range of use cases, including prediction of response to ICI treatment. We recently demonstrated this for metastatic malignant melanoma (MM). In the current study, we examined if we could also predict preferential response to individual ICIs. Experimental Procedures: We carried out glycoproteomic analysis of pretreatment blood samples in advanced MM patients treated with pembrolizumab (P; n=24) or nivolumab-ipilimumab (N; n=11). Individual glycopeptide (GP)signatures derived from 67 serum proteins were analyzed and correlated with treatment, and progression-free survival (PFS). Summary of New Data: Two response groups were defined based on PFS: early failures (EF; PFS event within 6 months) and sustained control (SC; no events for ≥ 12 months). Differential relative abundances for 498 serum GPs were calculated between SC and EF patients to determine GPs more abundant in SC vs. EF by treatment group. A score was developed for each treatment group based on the 20 GPs within each treatment group identified as most statistically significant (one-sided Wilcoxon test). For any patient, the score is the proportion of GPs with relative abundance exceeding their median abundance. A low score is associated with high risk for EF. When examined in all patients in the cohort (regardless of treatment), both scores isolated EF from SC. Only 2 glycopeptides overlapped between the treatment group scores, suggesting that the information is indeed drug specific. Algorithmic assignment was performed by choosing the treatment with the highest treatment-specific score (e.g., if N- score > P-score, then assign to N). PFS was superior for cases where the assigned treatment matched the treatment received. Log-rank p-values comparing PFS by assigned treatment within P- and N -treated cases were 0.009 and 0.0004, respectively. Conclusions: Our results show that a proprietary serum glycoproteomic analytical approach can guide individualized treatment assignment to the most likely successful agent among different ICIs. This could importantly improve the clinical use of immuno-therapy. Citation Format: Klaus Lindpaintner, Gege Xu, Rachel Rice, Alan Mitchell, Dennie Frederick, Genevieve Boland, Daniel Seie. Glycoproteomics-based liquid biopsy informs optimal checkpoint-inhibitor drug choice [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1270.