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43 Harnessing the power of glycoproteomics: a cutting-edge approach for predicting treatment efficacy in metastatic melanoma with immune checkpoint inhibitors
  1. Yana G Najjar1,
  2. Chad Pickering2,
  3. Paul Aiyetan2,
  4. Gege Xu3,
  5. Rachel Rice2,
  6. Alan Mitchell2,
  7. Ranjan Bhadra2,
  8. Chirag Dhar2,
  9. Lisa M Ebert3,
  10. Michael P Brown4,
  11. Gonzalo Tapia-Rico5,
  12. Dennie Frederick6,
  13. Xin Cong2,
  14. Daniel Serie2,
  15. Klaus Lindpaintner2,
  16. Flavio Schwarz2,
  17. Genevieve M Boland7 and
  18. Joseph Markowitz8
  1. 1UPMC Hillman Cancer Center, Pittsburgh, PA, USA
  2. 2InterVenn Biosciences, South San Francisco, CA, USA
  3. 3SA Pathology and University of South Australia, Adelaide, SA, Australia
  4. 4Royal Adelaide Hospital, North Ice, Adelaide, SA, SA, Australia
  5. 5Icon Cancer Centre, Adelaide, SA, Australia
  6. 6Harvard University, Boston, MA, USA
  7. 7Massachusetts General Hospital, Boston, MA, USA
  8. 8Moffitt Cancer Center, Tampa, FL, USA


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.


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Ethics Approval Plasma samples were collected under MGH IRB protocols 12–488 & 11–181and Central Adelaide Local Health Network Human Research Ethics Committee protocol HREC/16/RAH/95. Written informed consent was obtained from all patients prior to inclusion in the study.

Abstract 43 Table 1

Clinical characteristics of the discovery cohort. Patients were recruited from the Massachusetts General Hospital, Boston, USA.

Abstract 43 Table 2

Clinical characteristics of the independent cohort. Patients were recruited from the Royal Adelaide Hospital, Australia.

Abstract 43 Figure 1

OS Kaplan-Meier curves stratified by early failure (EF, death within six months of treatment start, n=40) and sustained controls (SC, death-free beyond three years of treatment; n=56) in the discovery cohort. ‘Other’ defines intermediate phenotypes (n=106) represented in the upper panel. Heatmap of 143 hierarchically-clustered concentration-normalized features that achieve FDR<0.05 in age- and sex-adjusted differential expression comparing early failures (EF) and sustained controls (SC) represented in the lower panel

Abstract 43 Table 3

Performance metrics of novel glycoproteomic classifier in differentiating between ICI responders and non-responders.

Abstract 43 Figure 2

Kaplan-Meir curves depicting overall survival in the discovery cohort based on the GP classifier predicted likelihood to benefit from ICIs (full discovery cohort, training, test, and validation set)

Abstract 43 Figure 3

Kaplan-Meir curves depicting overall survival in the independent cohort based on the GP classifier predicted likelihood to benefit from ICIs

Abstract 43 Figure 4

Kaplan-Meir curves depicting overall survival in the discovery cohort based on the fucose-feature predicted likelihood to benefit from to ICIs

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