The 10,000 Immunomes Project: Building a Resource for Human Immunology

Zalocusky, Kelly A.; Kan, Matthew J.; Hu, Zicheng; Dunn, Patrick; Thomson, Elizabeth; Wiser, Jeffrey; Bhattacharya, Sanchita; Butte, Atul J.

doi:10.1016/j.celrep.2018.09.021

Cited by 44 publications

(26 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…As is commonly encountered in flow cytometry studies, comparisons of our data with prior studies are difficult due to varying methodologies and patient populations [24,25]. A recent publication by Zalocusky et.…”

Section: Discussionmentioning

confidence: 97%

See 1 more Smart Citation

Establishment of normative ranges of the healthy human immune system with comprehensive polychromatic flow cytometry profiling

Rosa-Bray

Staats

et al. 2019

PLoS ONE

View full text Add to dashboard Cite

Existing normative flow cytometry data have several limitations including small sample sizes, incompletely described study populations, variable flow cytometry methodology, and limited depth for defining lymphocyte subpopulations. To overcome these issues, we defined high-dimensional flow cytometry reference ranges for the healthy human immune system using Human Immunology Project Consortium methodologies after carefully screening 127 subjects deemed healthy through clinical and laboratory testing. We enrolled subjects in the following age cohorts: 18-29 years, 30-39, 40-49, and 50-66 and enrolled cohorts to ensure an even gender distribution and at least 30% non-Caucasians. From peripheral blood mononuclear cells, flow cytometry reference ranges were defined for >50 immune subsets including T-cell (activation, maturation, T follicular helper and regulatory T cell), B-cell, and innate cells. We also developed a web tool for visualization of the dataset and download of raw data. This dataset provides the immunology community with a resource to compare and extract data from rigorously characterized healthy subjects across age groups, gender and race.

show abstract

Section: Discussionmentioning

confidence: 97%

“…al. undertook a large bioinformatics project to isolate data of healthy normal human subjects from the ImmPort database, and integrate the data for visualization [25]. They were able to capture 10,344 subjects from 83 studies, and normalized and integrated flow cytometry, protein, and gene expression datasets.…”

Section: Discussionmentioning

confidence: 99%

Establishment of normative ranges of the healthy human immune system with comprehensive polychromatic flow cytometry profiling

Rosa-Bray

Staats

et al. 2019

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Independent prognostic value of six immune-gene signature and clinical parameters Revealing the relationship between prognostic signature and tumor-infiltrating immune cells in TME initiation and progression [25]. Further investigation indicated that the presence of infiltrating immune cells can be used as biomarker for immunotherapy response [26].…”

Section: Validation Of the Predictive Performance Of Immune Prognosismentioning

confidence: 99%

Identification the prognostic value of immune gene signature and infiltrating immune cells of esophageal cancer patients

Wang

Qian

Zhang

et al. 2020

Preprint

View full text Add to dashboard Cite

Background Esophageal cancer (ESCA) is one of the deadliest solid malignancies with worse survival in the world. The poor prognosis of ESCA is not only related to malignant cells, but also affected by the microenvironment. We aimed to establish prognostic signature consisting of immune genes to predict the survival outcome of patients and estimate the prognosis value of infiltrating immune cells in tumor microenvironment (TME). Methods Based on integrated analysis of gene expression profiling and immune gene database, differentially immune-related genes were filtered out. Then, stepwise Cox regression analysis was applied to identify survival related immune genes and construct prognosis signature. Functional enrichment analysis was performed to explore biology function. Kaplan-Meier (K-M) curves and receiver operating characteristic (ROC) curves were performed to validate the predictive effect of predictive signature. We also verified the clinical value of prognostic signature under the influence of different clinical parameters. For deeper analysis, we evaluated the correlation between prognosis signature and infiltrating immune cells by Tumor Immune Estimation Resource (TIMER) and CIBERSORT. Results Finally, we identified 303 differentially immune genes as candidate and constructed immune prognosis signature composed of six immune genes. Furthermore, we observed that the prognosis signature was enriched in cytokine-mediated signaling pathway, lymphocyte activation, immune effector process, cancer pathway, NF-kappa B signaling pathway. K-M survival curves showed that the prognosis signature indeed have good predictive ability in entire ESCA set ( P =0.003), validation set 1 ( P =0.008) and validation set 2 ( P =0.036). The area under the curve (AUC) of ROC curves validated the predictive accuracy of immune signature in three cohorts (AUC=0.757, 0.800 and 0.701), respectively. In addition, we identified the prognosis value of infiltrating-immune cells including activated memory CD4 T cells, T cells follicular helper cells and monocytes and provided a landscape of TME. Conclusions The results indicated that immune prognosis signature can be a novel biomarker to predict survival outcome, which can provide new targets for immunotherapy and individualized therapies in ESCA and open up a new prospect for improving the prognosis of ESCA patients in the era of immunotherapy.

show abstract

“…Plusieurs études récentes portant sur le phénotypage génétique et immunologique intégrés dans des populations saines ont commencé à définir les facteurs à l'origine des variations que l'on observe entre réactions immunitaires des individus [1]. Il s'agit notamment de cohortes établies pour répondre à des questions similaires à celles posées dans le cadre de Milieu Intérieur : en particulier, le Human Functional Genomics Project [7], le Human Immunology Project Consortium (HIPC) [8], et le 10K Immunomes [9]. Collectivement, ces études et les premiers résultats obtenus dans le cadre de Milieu Intérieur 4 apportent une meilleure définition de la variabilité de la réponse immunitaire et des facteurs clés qui la déterminent.…”

Section: Resultsunclassified

Milieu Intérieur : définir les limites d’une réponse immunitaire saine pour mieux comprendre la maladie

Duffy

2019

Med Sci (Paris)

View full text Add to dashboard Cite

Un point commun de tous les individus réside dans le fait qu’ils sont justement tous différents. Certaines des plus grandes différences entre deux individus peuvent être liées à leurs réponses immunitaires, entraînant des conséquences variables face à une infection, à la gravité d’une maladie et à la réponse aux traitements médicaux. Les différences dans les réponses immunitaires reposent en partie sur des forces évolutives agissant au niveau de la génétique de l’hôte. Elles peuvent également être dues aux différences que nous rencontrons dans notre environnement et notre mode de vie. Malgré ces différences interindividuelles bien connues, cette variabilité inhérente est rarement incluse dans les approches cliniques ou le développement de médicaments.

show abstract

The 10,000 Immunomes Project: Building a Resource for Human Immunology

Cited by 44 publications

References 59 publications

Establishment of normative ranges of the healthy human immune system with comprehensive polychromatic flow cytometry profiling

Establishment of normative ranges of the healthy human immune system with comprehensive polychromatic flow cytometry profiling

Identification the prognostic value of immune gene signature and infiltrating immune cells of esophageal cancer patients

Milieu Intérieur : définir les limites d’une réponse immunitaire saine pour mieux comprendre la maladie

Contact Info

Product

Resources

About