Multi-omic data integration allows baseline immune signatures to predict hepatitis B vaccine response in a small cohort
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PROVIDER: PRJNA675120 | ENA |
REPOSITORIES: ENA
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Project description:Multi-omic data integration allows baseline immune signatures to predict hepatitis B vaccine response in a small cohort
| PRJNA648957 | ENA
Project description:Vaccination remains one of the most effective means of reducing the burden of infectious diseases globally. Climate change is projected to lead to an increase in the burden of vector- and water-borne, as well as zoonotic infectious diseases including COVID-19. Improving our understanding of the molecular basis for effective vaccine response is of paramount importance if we are to ensure the success of future vaccine development efforts. We applied cutting edge multi-omics approaches to characterize temporal molecular responses following vaccination with hepatitis B virus (HBV) vaccine. Data were integrated across cellular, epigenomic, transcriptomic, proteomic, and fecal microbiome profiles, and correlated to final hepatitis B antibody titres. Using both a molecular-interaction network integration method (NetworkAnalyst) and a data-driven integration approach (DIABLO), we overcame the p>>>n problem and uncovered baseline molecular patterns and pathways associated with more effective vaccine responses to HBV. Correlations were identified with signalling pathways such as JAK-STAT and interleukin signalling, Toll-Like Receptor Cascades, interferon signalling and Th17 cell differentiation. This study provides further evidence that baseline cellular and molecular characteristics of an individual’s immune system influence vaccine responses, and highlights the utility of integrating information across many parallel molecular datasets.
2020-12-21 | GSE155198 | GEO
Project description:Vaccination remains one of the most effective means of reducing the burden of infectious diseases globally. Climate change is projected to lead to an increase in the burden of vector- and water-borne, as well as zoonotic infectious diseases including COVID-19. Improving our understanding of the molecular basis for effective vaccine response is of paramount importance if we are to ensure the success of future vaccine development efforts. We applied cutting edge multi-omics approaches to characterize temporal molecular responses following vaccination with hepatitis B virus (HBV) vaccine. Data were integrated across cellular, epigenomic, transcriptomic, proteomic, and fecal microbiome profiles, and correlated to final hepatitis B antibody titres. Using both a molecular-interaction network integration method (NetworkAnalyst) and a data-driven integration approach (DIABLO), we overcame the p>>>n problem (predictors much greater than observations) and uncovered baseline molecular patterns and pathways associated with more effective vaccine responses to HBV. Correlations were identified with signalling pathways such as JAK-STAT and interleukin signalling, Toll-Like Receptor Cascades, interferon signalling and Th17 cell differentiation. This study provides further evidence that baseline cellular and molecular characteristics of an individual’s immune system influence vaccine responses, and highlights the utility of integrating information across many parallel molecular datasets.
2020-12-21 | GSE161020 | GEO
Project description:BackgroundVaccination remains one of the most effective means of reducing the burden of infectious diseases globally. Improving our understanding of the molecular basis for effective vaccine response is of paramount importance if we are to ensure the success of future vaccine development efforts.MethodsWe applied cutting edge multi-omics approaches to extensively characterize temporal molecular responses following vaccination with hepatitis B virus (HBV) vaccine. Data were integrated across cellular, epigenomic, transcriptomic, proteomic, and fecal microbiome profiles, and correlated to final HBV antibody titres.ResultsUsing both an unsupervised molecular-interaction network integration method (NetworkAnalyst) and a data-driven integration approach (DIABLO), we uncovered baseline molecular patterns and pathways associated with more effective vaccine responses to HBV. Biological associations were unravelled, with signalling pathways such as JAK-STAT and interleukin signalling, Toll-like receptor cascades, interferon signalling, and Th17 cell differentiation emerging as important pre-vaccination modulators of response.ConclusionThis study provides further evidence that baseline cellular and molecular characteristics of an individual's immune system influence vaccine responses, and highlights the utility of integrating information across many parallel molecular datasets.
| S-EPMC7734088 | biostudies-literature
Project description:Contemporary analyses focused on a limited number of clinical and molecular features have been unable to accurately predict clinical outcomes in pancreatic ductal adenocarcinoma (PDAC). Here we describe a novel, conceptual approach and use it to analyze clinical, computational pathology, and molecular (DNA, RNA, protein, and lipid) analyte data from 74 patients with resectable PDAC. Multiple, independent, machine learning models were developed and tested on curated singleand multi-omic feature/analyte panels to determine their ability to predict clinical outcomes in patients. The multi-omic models predicted recurrence with an accuracy and positive predictive value (PPV) of 0.90, 0.91, and survival of 0.85, 0.87, respectively, outperforming every singleomic model. In predicting survival, we defined a parsimonious model with only 589 multi-omic analytes that had an accuracy and PPV of 0.85. Our approach enables discovery of parsimonious biomarker panels with similar predictive performance to that of larger and resource consuming panels and thereby has a significant potential to democratize precision cancer medicine worldwide.
2024-01-23 | PXD037038 | Pride
Project description:Contemporary analyses focused on a limited number of clinical and molecular features have been unable to accurately predict clinical outcomes in pancreatic ductal adenocarcinoma (PDAC). Here we describe a novel, conceptual approach and use it to analyze clinical, computational pathology, and molecular (DNA, RNA, protein, and lipid) analyte data from 74 patients with resectable PDAC. Multiple, independent, machine learning models were developed and tested on curated single and multi-omic feature/analyte panels to determine their ability to predict clinical outcomes in patients. The multi-omic models predicted recurrence with an accuracy and positive predictive value (PPV) of 0.90, 0.91, and survival of 0.85, 0.87, respectively, outperforming every single-omic model. In predicting survival, we defined a parsimonious model with only 589 multi-omic analytes that had an accuracy and PPV of 0.85. Our approach enables discovery of parsimonious biomarker panels with similar predictive performance to that of larger and resource consuming panels and thereby has a significant potential to democratize precision cancer medicine worldwide.
2023-10-15 | MSV000093118 | MassIVE
Project description:Muscle biopsy samples were obtained from two groups of male subjects prior to endurance training. The samples were used to predict training responses. Baseline gene expression involving 30 probe sets was able to classify subjects into high and low responders.
2010-02-04 | GSE18583 | GEO
Project description:Hepatitis C virus (HCV) infection induces interferon stimulated genes (ISGs) and downstream innate immune responses. This study investigated whether baseline and on-treatment differences in these responses predict response versus virological breakthrough during therapy with direct acting antivirals (DAA). Microarray and nanostring analyses were performed on paired liver biopsies and analyzed using linear mixed models. As biomarkers for peripheral IFN responses, peripheral blood natural killer cells were assessed for pSTAT1 and TRAIL expression and for cytotoxicity.
2018-12-14 | GSE109278 | GEO
Project description:Muscle biopsy samples were obtained from two groups of male subjects prior to endurance training. The samples were used to predict training responses. Baseline gene expression involving 30 probe sets was able to classify subjects into high and low responders. Resting skeletal muscle sample after an overnight fast.
2010-02-03 | E-GEOD-18583 | biostudies-arrayexpress
Project description:Despite the tremendous advances in the use of omic-based technologies to serve the new paradigm of precision medicine, efforts are still required to expedite their routine use in drug discovery. In this work, we present a case study describing the use of multi-omics to push the development of 3-chloropiperidines (3-CePs), a new class of potential anticancer DNA alkylating agents. The combined analyses of transcriptome and chromatin accessibility efficiently identified proteostasis and DNA repair unbalances as the mechanisms underlying the tropism of 3-CePs against a pancreatic adenocarcinoma cell line. Further on, we implemented a new versatile strategy for the integration of RNA-seq and ATAC-seq information, which could be easily exported to accelerate and extend the separate analyses of omic layers. In addition, this platform offered a new anchor for the construction of a perturbation-informed basal signature able to efficiently predict cell lines sensitivity to 3-CePs and to further direct their development against specific tumor types. Overall, this approach offered a clinically-applicable pipeline to support both the early phases of drug discovery and the correct positioning of therapeutics in the medical practice.
2022-08-17 | GSE179059 | GEO