Diagnosing and staging epithelial ovarian cancer by serum glycoproteomic profiling.

BACKGROUND: There is a need for diagnostic tests for screening, triaging and staging of epithelial ovarian cancer (EOC). Glycoproteomics of blood samples has shown promise for biomarker discovery. METHODS: We applied glycoproteomics to serum of people with EOC or benign pelvic masses and healthy controls. A total of 653 analytes were quantified and assessed in multivariable models, which were tested in an independent cohort. Additionally, we analyzed glycosylation patterns in serum markers and in tissues. RESULTS: We identified a biomarker panel that distinguished benign lesions from EOC with sensitivity and specificity of 83.5% and 90.1% in the training set, and of 86.7 and 86.7% in the test set, respectively. ROC analysis demonstrated strong performance across a range of cutoffs. Fucosylated multi-antennary glycopeptide markers were higher in late-stage than in early-stage EOC. A comparable pattern was found in late-stage EOC tissues. CONCLUSIONS: Blood glycopeptide biomarkers have the potential to distinguish benign from malignant pelvic masses, and early- from late-stage EOC. Glycosylation of circulating and tumor tissue proteins may be related. This study supports the hypothesis that blood glycoproteomic profiling can be used for EOC diagnosis and staging and it warrants further clinical evaluation.

Plasma glycoproteomic biomarkers identify metastatic melanoma patients with reduced clinical benefit from immune checkpoint inhibitor therapy.

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.

Differential Peripheral Blood Glycoprotein Profiles in Symptomatic and Asymptomatic COVID-19.

Glycosylation is the most common form of post-translational modification of proteins, critically affecting their structure and function. Using liquid chromatography and mass spectrometry for high-resolution site-specific quantification of glycopeptides coupled with high-throughput artificial intelligence-powered data processing, we analyzed differential protein glycoisoform distributions of 597 abundant serum glycopeptides and nonglycosylated peptides in 50 individuals who had been seriously ill with COVID-19 and in 22 individuals who had recovered after an asymptomatic course of COVID-19. As additional comparison reference phenotypes, we included 12 individuals with a history of infection with a common cold coronavirus, 16 patients with bacterial sepsis, and 15 healthy subjects without history of coronavirus exposure. We found statistically significant differences, at FDR < 0.05, for normalized abundances of 374 of the 597 peptides and glycopeptides interrogated between symptomatic and asymptomatic COVID-19 patients. Similar statistically significant differences were seen when comparing symptomatic COVID-19 patients to healthy controls (350 differentially abundant peptides and glycopeptides) and common cold coronavirus seropositive subjects (353 differentially abundant peptides and glycopeptides). Among healthy controls and sepsis patients, 326 peptides and glycopeptides were found to be differentially abundant, of which 277 overlapped with biomarkers that showed differential expression between symptomatic COVID-19 cases and healthy controls. Among symptomatic COVID-19 cases and sepsis patients, 101 glycopeptide and peptide biomarkers were found to be statistically significantly abundant. Using both supervised and unsupervised machine learning techniques, we found specific glycoprotein profiles to be strongly predictive of symptomatic COVID-19 infection. LASSO-regularized multivariable logistic regression and K-means clustering yielded accuracies of 100% in an independent test set and of 96% overall, respectively. Our findings are consistent with the interpretation that a majority of glycoprotein modifications observed which are shared among symptomatic COVID-19 and sepsis patients likely represent a generic consequence of a severe systemic immune and inflammatory state. However, there are glycoisoform changes that are specific and particular to severe COVID-19 infection. These may be representative of either COVID-19-specific consequences or susceptibility to or predisposition for a severe course of the disease. Our findings support the potential value of glycoproteomic biomarkers in the biomedical understanding and, potentially, the clinical management of serious acute infectious conditions.

Support for Transgender Military Service from Active Duty United States Military Personnel.

INTRODUCTION: Most transgender individuals are banned from serving in and joining the U.S. military. Historically, exclusions and limits have been placed on women, people of color, and sexual minority people seeking to serve and advance within the U.S. military. However, both history and prior research demonstrate that diversity contributes to social and institutional advancement within both U.S. and international militaries. METHODS: We used an adapted respondent-driven sampling (RDS) approach to recruit transgender and cisgender heterosexual and LGB active duty military members in a first-of-its-kind study funded by the Department of Defense. We recruited 540 active duty service members serving one of the four major branches of the U.S. military between August 2017 and March 2018. We examined data from 486 heterosexual cisgender and LGB cisgender service members to understand their support for transgender people serving in the U.S. military. RESULTS: Findings indicate broad support for transgender military service across all four branches of the military and military ranks, with some statistically significant differences in support emerging by gender, sexual orientation, and race/ethnicity. DISCUSSION: Results suggest that the ban, in part, based on a belief that transgender service members degrade unit readiness, contradicts our findings of broad support for transgender service among active duty service members. POLICY IMPLICATIONS: Policies limiting transgender service in the U.S. military should be lifted given these data.

Mental Health and Health Risk Behaviors of Active Duty Sexual Minority and Transgender Service Members in the United States Military.

Purpose: The aim of this study was to examine health risk behaviors and mental health outcomes among sexual minority and transgender active duty military service members and their heterosexual and cisgender counterparts. Methods: Participants (N = 544) were recruited by using respondent-driven sampling between August 2017 and March 2018 and completed an online survey by using validated measures of cigarette smoking, alcohol use, anxiety, depression, post-traumatic stress disorder (PTSD), and suicidality. Bayesian random intercept multiple logistic regressions were used to understand differences between sexual minority participants and heterosexual participants as well as between transgender participants and both their cisgender sexual minority and cisgender heterosexual peers. Results: Cisgender sexual minority women service members were more likely to meet criteria for problematic alcohol use (adjusted odds ratio [aOR] = 10.11) and cigarette smoking (aOR = 7.12) than cisgender heterosexual women. Cisgender sexual minority men had greater odds of suicidality (aOR = 4.73) than their cisgender heterosexual counterparts. Transgender service members had greater odds of anxiety, PTSD, depression, and suicidality than their cisgender peers. Conclusion: Military researchers and policymakers who seek to improve the overall health and well-being of sexual minority and transgender service members should consider programs and policies that are tailored to specific health outcomes and unique sexual minority and transgender subgroups.