It is widely perceived at present that pharmacogenetics and pharmacogenomics are about to revolutionize the face of medicine. In a more realistic assessment, the implementation of molecular genetics ...and biology will provide us with better ways to treat illnesses, and has already begun to do so in an incremental and evolutionary fashion. However, it is unlikely to change fundamentally the direction of medical progress. Advances are most likely to be made in the area of pharmacodynamics, as we learn to differentiate broader conventional clinical diagnoses into separate molecular subtypes.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
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.
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.
Heterogeneity in the underlying mechanisms of disease processes and inter-patient variability in drug responses are major challenges in drug development. To address these challenges, biomarker ...strategies based on a range of platforms, such as microarray gene-expression technologies, are increasingly being applied to elucidate these sources of variability and thereby potentially increase drug development success rates. With the aim of enhancing understanding of the regulatory significance of such biomarker data by regulators and sponsors, the US Food and Drug Administration initiated a programme in 2004 to allow sponsors to submit exploratory genomic data voluntarily, without immediate regulatory impact. In this article, a selection of case studies from the first 5 years of this programme - which is now known as the voluntary exploratory data submission programme, and also involves collaboration with the European Medicines Agency - are discussed, and general lessons are highlighted.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Previous genome-wide association studies for type 2 diabetes susceptibility genes have confirmed that a common variant, rs9939609, in the fat mass and obesity associated (FTO) gene region is ...associated with body mass index (BMI) in European children and adults. A significant association of the same risk allele has been described in Asian adult populations, but the results are conflicting. In addition, no replication studies have been conducted in children and adolescents of Asian ancestry.
A population-based survey was carried out among 3503 children and adolescents (6-18 years of age) in Beijing, China, including 1229 obese and 2274 non-obese subjects. We investigated the association of rs9939609 with BMI and the risk of obesity. In addition, we tested the association of rs9939609 with weight, height, waist circumference, waist-to-height ratio, fat mass percentage, birth weight, blood pressure and related metabolic traits.
We found significant associations of rs9939609 variant with weight, BMI, BMI standard deviation score (BMI-SDS), waist circumference, waist-to-height ratio, and fat mass percentage in children and adolescents (p for trend = 3.29 x 10-5, 1.39 x 10-6, 3.76 x 10-6, 2.26 x 10-5, 1.94 x 10-5, and 9.75 x 10-5, respectively). No significant associations were detected with height, birth weight, systolic and diastolic blood pressure and related metabolic traits such as total cholesterol, triglycerides, HDL-cholesterol, LDL-cholesterol and fasting plasma glucose (all p > 0.05). Each additional copy of the rs9939609 A allele was associated with a BMI increase of 0.79 95% Confidence interval (CI) 0.47 to 1.10 kg/m2, equivalent to 0.25 (95%CI 0.14 to 0.35) BMI-SDS units. This rs9939609 variant is significantly associated with the risk of obesity under an additive model Odds ratio (OR) = 1.29, 95% CI 1.11 to 1.50 after adjusting for age and gender. Moreover, an interaction between the FTO rs9939609 genotype and physical activity (p < 0.001) was detected on BMI levels, the effect of rs9939609-A allele on BMI being (0.95 +/- 0.10), (0.77 +/- 0.08) and (0.67 +/- 0.05) kg/m2, for subjects who performed low, moderate and severe intensity physical activity.
The FTO rs9939609 variant is strongly associated with BMI and the risk of obesity in a population of children and adolescents in Beijing, China.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
BackgroundImmune checkpoint blockade is an integral component of first-line therapy for most patients with ad-vanced non-small cell lung cancer (NSCLC), however individual patient outcomes are highly ...variable and improved biomarkers are needed. Protein glycosylation is an emerging mechanism of immune evasion in cancer. We examined blood-based glycopeptide signatures in a cohort of advanced NSCLC patients treated with first-line immune checkpoint blockade.MethodsPretreatment blood samples were obtained from 46 advanced NSCLC patients treated with first line pembrolizumab or pembrolizumab + carboplatin + pemetrexed. All patients provided written in-formed consent to the institutional review board–approved protocols (#02–180 and 13–367) at the Da-na-Farber/Harvard Cancer Center (Boston, MA), and the study was conducted in accordance with the Declaration of Helsinki. Samples were analyzed using an advanced glycoproteomics platform (Inter-Venn Biosciences) that combines ultra-high-performance liquid chromatography coupled to triple quadrupole mass spectrometry with a proprietary neural-network-based data processing engine. 409 individual glycopeptide (GP) signatures derived from 67 abundant serum proteins were analyzed and correlated with overall survival (OS) and other clinical outcomes.ResultsWe identified 30 GPs with abundance differences using a False Discovery Rate (FDR) threshold of 0.05. Using the 5 most predictive GP markers, we created a multivariable model for OS by generating leave-one-out cross-validation (LOOCV) scores and determining an optimized cutoff value of -0.83 (range: -2.2 - 3.4) for these scores using Harrell’s concordance index. The median overall survival was 2.8 years for patients (n=14) whose GP classifier value was above the cutoff and 0.8 years for patients (n=32) whose GP classifier value was below the cutoff (HR 7.4, 95% CI 1.7–32.1, p=0.007) The model’s perfor-mance was not affected by sex, age, or treatment regimen.ConclusionsBlood-based glycopeptide signatures may represent novel, non-invasive biomarkers of clinical out-come to first-line immune checkpoint blockade in advanced NSCLC. Additional research is needed to validate these findings in larger cohorts and to explore potential applications relevant to clinical decision-making.Ethics ApprovalThe study obtained ethics approval from the institutional review board (approved protocol #02–180 and 13–367) at the Dana-Farber/Harvard Cancer Center (Boston, MA), and the study was conducted in accordance with the Declaration of Helsinki.ConsentAll patients provided written informed consent to the institutional review board–approved protocols (#02–180 and 13–367) at the Dana-Farber/Harvard Cancer Center (Boston, MA), and the study was conducted in accordance with the Declaration of Helsinki.
BackgroundImmune 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.MethodsWe 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.ResultsWe 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).ConclusionsUsing 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.AcknowledgementsThe authors thank James Richard Hartness, Jr. and Kim Vigal for their alliance management efforts and critical inputs for this abstract.ReferencesShum 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/10.1101/2023.03.20.23287422Agrawal 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.Ethics ApprovalPlasma 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 1Clinical characteristics of the discovery cohort. Patients were recruited from the Massachusetts General Hospital, Boston, USA.Abstract 43 Table 2Clinical characteristics of the independent cohort. Patients were recruited from the Royal Adelaide Hospital, Australia.Abstract 43 Figure 1OS 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 panelAbstract 43 Table 3Performance metrics of novel glycoproteomic classifier in differentiating between ICI responders and non-responders.Abstract 43 Figure 2Kaplan-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 3Kaplan-Meir curves depicting overall survival in the independent cohort based on the GP classifier predicted likelihood to benefit from ICIsAbstract 43 Figure 4Kaplan-Meir curves depicting overall survival in the discovery cohort based on the fucose-feature predicted likelihood to benefit from to ICIs
Antibodies are quintessential affinity reagents for the investigation and determination of a protein's expression patterns, localization, quantitation, modifications, purification, and functional ...understanding. Antibodies are typically used in techniques such as Western blot, immunohistochemistry (IHC), and enzyme-linked immunosorbent assays (ELISA), among others. The methods employed to generate antibodies can have a profound impact on their success in any of these applications. We raised antibodies against 10 serum proteins using 3 immunization methods: peptide antigens (3 per protein), DNA prime/protein fragment-boost ("DNA immunization"; 3 per protein), and full length protein. Antibodies thus generated were systematically evaluated using several different assay technologies (ELISA, IHC, and Western blot). Antibodies raised against peptides worked predominantly in applications where the target protein was denatured (57% success in Western blot, 66% success in immunohistochemistry), although 37% of the antibodies thus generated did not work in any of these applications. In contrast, antibodies produced by DNA immunization performed well against both denatured and native targets with a high level of success: 93% success in Western blots, 100% success in immunohistochemistry, and 79% success in ELISA. Importantly, success in one assay method was not predictive of success in another. Immunization with full length protein consistently yielded the best results; however, this method is not typically available for new targets, due to the difficulty of generating full length protein. We conclude that DNA immunization strategies which are not encumbered by the limitations of efficacy (peptides) or requirements for full length proteins can be quite successful, particularly when multiple constructs for each protein are used.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
PDF
