Background Immune checkpoint inhibitors (ICIs) have revolutionized melanoma treatment, necessitating predictive biomarkers to identify patients likely to benefit. To that end, this study leverages a novel platform that combines liquid chromatography/mass spectrometry with a proprietary artificial-intelligence-based data processing engine, allowing for highly scalable and reproducible interrogation of glycoproteins with site-and glycan-specificity, capable of identifying blood-based predictive biomarkers using pre-treatment plasma samples from metastatic melanoma (MM) patients.
Methods We interrogated 521 glycopeptide (GP) and 75 peptide biomarkers in a discovery cohort of pre-treatment plasma samples obtained from 202 patients with metastatic melanoma (MM) treated with anti-PD-1 monotherapy (pembrolizumab or nivolumab (57%), or anti-CTLA-4 (ipilimumab) with/without nivolumab (43%) (table 1). In addition to using age- and sex-adjusted regression to identify differentially abundant biomarkers where overall survival (OS) from ICI therapy start was the primary endpoint, patients were divided into those having early treatment failures (death within 6-months), intermediate controls (progression of death l between 6-months and 3-years), and sustained controls (progression-free for at least 3 years). Next, the discovery cohort was divided into a training, test, and validation set to develop and assess a repeated cross-validated LASSO-regularized Cox-based glycoproteomic classifier. To externally validate the classifier, an independent cohort of 27 MM patients were tested (table 2). Lastly, given the link between fucosylation and MM, engineered fucosylation-features were used in a second classifier.
Results We identified 143 markers that significantly distinguished patients with early treatment failure from those with sustained controls (figure 1). A 14-marker classifier achieved a high degree of separation (table 3-detailed performance metrics) between those likely to benefit (i.e. those predicted to achieve long-term clinical benefit) and unlikely to benefit (Cox proportional hazard ratio/H.R. = 2.7, p-value = 0.026) (figure 2) while also yielding comparable performance in an independent cohort (H.R = 5.6, p-value = 0.027) (table 3). The secondary fucosylated-based classifier was also able to distinguish patients with and without long-term benefit (H.R = 3.5, p-value = 0.0066) (figure 4).
Conclusions Using glycoproteomic profiling, our classifier predicted which MM patients treated with ICIs had nearly a 3-fold greater likelihood of durable benefit, with the finding validated in an independent cohort. Our results also suggest circulating glycoprotein fucosylation may be an important determinant of anti-tumor immunity. These data demonstrate the utility of plasma glycoproteomics for biomarker discovery and prediction of ICI benefit in patients with MM. Future directions include prospective confirmatory testing.
Acknowledgements The authors thank James Richard Hartness, Jr. and Kim Vigal for their alliance management efforts and critical inputs for this abstract.
Shum B, Larkin J, Turajlic S. Predictive biomarkers for response to immune checkpoint inhibition. Semin Cancer Biol. 2022 Feb;79:4–17. doi: 10.1016/j.semcancer.2021.03.036. Epub 2021 Apr 2. PMID: 33819567.
Dhar C, Ramachandran P, Xu G, Pickering C, Caval T, Rice R, Zhou B, Srinivasan A, Hundal I, Cheng R, Aiyetan P. Diagnosing and staging epithelial ovarian cancer by serum glycoproteomic profiling. medRxiv 2023.03.20.23287422 [Preprint]. March 20, 2023 [cited 2023 Jun 26]. Available from: https://doi.org/10.1101/2023.03.20.23287422
Agrawal P, Fontanals-Cirera B, Sokolova E, Jacob S, Vaiana CA, Argibay D, Davalos V, McDermott M, Nayak S, Darvishian F, Castillo M, Ueberheide B, Osman I, Fenyö D, Mahal LK, Hernando E. A Systems Biology Approach Identifies FUT8 as a Driver of Melanoma Metastasis. Cancer Cell. 2017 Jun 12;31(6):804–819.e7. doi: 10.1016/j.ccell.2017.05.007. PMID: 28609658; PMCID: PMC5649440.
This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See http://creativecommons.org/licenses/by-nc/4.0/.
Statistics from Altmetric.com
If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.