The clinical success of immune-checkpoint inhibitors (ICI) in both resected and metastatic melanoma has confirmed the validity of therapeutic strategies that boost the immune system to counteract cancer. However, half of patients with metastatic disease treated with even the most aggressive regimen do not derive durable clinical benefit. Thus, there is a critical need for predictive biomarkers that can identify individuals who are unlikely to benefit with high accuracy so that these patients may be spared the toxicity of treatment without the likely benefit of response. Ideally, such an assay would have a fast turnaround time and minimal invasiveness. Here, we utilize a novel platform that combines mass spectrometry with an artificial intelligence-based data processing engine to interrogate the blood glycoproteome in melanoma patients before receiving ICI therapy. We identify 143 biomarkers that demonstrate a difference in expression between the patients who died within six months of starting ICI treatment and those who remained progression-free for three years. We then develop a glycoproteomic classifier that predicts benefit of immunotherapy (HR=2.7; p=0.026) and achieves a significant separation of patients in an independent cohort (HR=5.6; p=0.027). To understand how circulating glycoproteins may affect efficacy of treatment, we analyze the differences in glycosylation structure and discover a fucosylation signature in patients with shorter overall survival (OS). We then develop a fucosylation-based model that effectively stratifies patients (HR=3.5; p=0.0066). Together, our data demonstrate the utility of plasma glycoproteomics for biomarker discovery and prediction of ICI benefit in patients with metastatic melanoma and suggest that protein fucosylation may be a determinant of anti-tumor immunity.

Minimally invasive technologies for early diagnosis of epithelial ovarian cancer (EOC) remain an unmet clinical need. CA-125, a tumor marker secreted into the circulation, is utilized to monitor treatment response and disease relapse in EOC, but has limited utility in accurately triaging patients with pelvic masses of unknown histology. To address this unmet need, we applied a novel blood-based glycoproteomic platform that relies on mass spectrometry coupled to machine learning tools, and identified glycopeptide biomarkers that differentiate between patients with benign pelvic masses and malignant EOC. We then used a subset of these markers to generate a classifier that discriminated between benign pelvic tumors and EOC with sensitivity and specificity of 83.5% and 90.1% in the training set and 86.7 and 86.7% in the testing set, respectively. On subgroup analyses, we noticed that patients with malignant EOC had higher levels of fucosylated markers, primarily of hepatic origin. Furthermore, patients with late-stage EOC (FIGO stage III and IV) had markedly higher levels of tri- and tetra-antennary glycopeptide markers containing fucose. We used these markers to build an independent algorithm that can differentiate between early- and late-stage EOC. Lastly, we detected a similar upregulation of fucosylated glycans and gene expression signatures suggestive of multi-antennary glycans in late-stage EOC tissues. We posit that common mechanisms - possibly driven by cytokines - affect both the tumor glycocalyx and liver-derived glycoproteins. In summary, we generated blood glycoproteomic profiles resemblant of distinct tumor states and identified biomarkers that differentiate between benign and malignant pelvic masses, and/or between early- and late-stage EOC. We also provide mechanistic insights suggesting a direct link between the tumor site and the circulating glycoproteome. These data may inform the development of robust clinical tests to diagnose and stage patients with EOC.

e17604 Background: Ovarian cancer (OC) is the fifth- leading cause of cancer-related deaths among women, and the most lethal gynecological cancer. Currently available biomarkers, including CA-125 and HE4, show suboptimal diagnostic performance for differentiating among benign and malignant pelvic tumors, and the early recognition of OC. Differentiation of benign and malignant pelvic tumors is required for proper patient triaging and management, yet non-invasive methods remain a largely unmet medical need. Methods: We applied a novel platform for characterizing peripheral blood glycoproteomic biomarkers, combining liquid-chromatography/mass spectrometry (LC-MS) with artificial intelligence/neural networks (AI-NN) for the targeted quantification of serum protein glycosylation at intact glycopeptide level to analyze serum samples from 296 treatment-naïve women with histopathology-confirmed diagnosis of either benign (n = 151) or malignant (n = 145) tumors, and from 55 healthy control subjects, procured from a commercial biobank. Using data-dependent acquisition, a panel of 683 serum glycosylated and non-glycosylated peptides, representing 71 serum proteins, was interrogated. Samples were randomly divided into a training and a testing set for multivariable analysis. Data processing was performed using PB-Net, an in-house-developed high-throughput peak integration software. Raw data were normalized, processed by statistical analysis, and applied to machine learning models. Results: Comparison of glycopeptide abundances among patients with OC and benign pelvic tumors yielded 428 statistically significantly differentially expressed glycopeptides/peptides (at FDR < 0.05). A subpanel of these markers used to generate a score for predicting OC yielded areas under the receiver-operating-characteristic of 0.955 and 0.894 in the training and testing sets, respectively. The predicted probability of malignancy increased with cancer stage, and probability distributions were similar across training and test sets. Applying the model to healthy subjects, not utilized in training, resulted in few misclassifications and a spread nearly equivalent to that of the benign tumor cases. This indicates the signature robustly predicts malignancy and severity of disease. Conclusions: Our novel approach exhibited impressive levels of accuracy for the noninvasive differentiation of benign and malignant pelvic masses, compared with existing biomarkers and algorithms, thereby demonstrating the utility of glycoprotein profiles as a powerful, noninvasive new diagnostic modality.