Dataset Information

A standardized immune phenotyping and automated data analysis platform for multicenter biomarker studies.

ABSTRACT: The analysis and validation of flow cytometry-based biomarkers in clinical studies are limited by the lack of standardized protocols that are reproducible across multiple centers and suitable for use with either unfractionated blood or cryopreserved PBMCs. Here we report the development of a platform that standardizes a set of flow cytometry panels across multiple centers, with high reproducibility in blood or PBMCs from either healthy subjects or patients 100 days after hematopoietic stem cell transplantation. Inter-center comparisons of replicate samples showed low variation, with interindividual variation exceeding inter-center variation for most populations (coefficients of variability <20% and interclass correlation coefficients >0.75). Exceptions included low-abundance populations defined by markers with indistinct expression boundaries (e.g., plasmablasts, monocyte subsets) or populations defined by markers sensitive to cryopreservation, such as CD62L and CD45RA. Automated gating pipelines were developed and validated on an independent data set, revealing high Spearman's correlations (rs >0.9) with manual analyses. This workflow, which includes pre-formatted antibody cocktails, standardized protocols for acquisition, and validated automated analysis pipelines, can be readily implemented in multicenter clinical trials. This approach facilitates the collection of robust immune phenotyping data and comparison of data from independent studies.

SUBMITTER: Ivison S

PROVIDER: S-EPMC6328091 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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A standardized immune phenotyping and automated data analysis platform for multicenter biomarker studies.

Ivison Sabine S Malek Mehrnoush M Garcia Rosa V RV Broady Raewyn R Halpin Anne A Richaud Manon M Brant Rollin F RF Wang Szu-I SI Goupil Mathieu M Guan Qingdong Q Ashton Peter P Warren Jason J Rajab Amr A Urschel Simon S Kumar Deepali D Streitz Mathias M Sawitzki Birgit B Schlickeiser Stephan S Bijl Janetta J JJ Wall Donna A DA Delisle Jean-Sebastien JS West Lori J LJ Brinkman Ryan R RR Levings Megan K MK

JCI insight 20181206 23

The analysis and validation of flow cytometry-based biomarkers in clinical studies are limited by the lack of standardized protocols that are reproducible across multiple centers and suitable for use with either unfractionated blood or cryopreserved PBMCs. Here we report the development of a platform that standardizes a set of flow cytometry panels across multiple centers, with high reproducibility in blood or PBMCs from either healthy subjects or patients 100 days after hematopoietic stem cell ...[more]

PMID: 30518691

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Project description:BackgroundLanguage and the ability to communicate effectively are key factors in mental health and well-being. Despite this critical importance, research on language is limited by the lack of a scalable phenotyping toolkit.MethodsHere, we describe and showcase Lingo - a flexible online battery of language and nonverbal reasoning skills based on seven widely used tasks (COWAT, picture narration, vocal rhythm entrainment, rapid automatized naming, following directions, sentence repetition, and nonverbal reasoning). The current version of Lingo takes approximately 30 minutes to complete, is entirely open source, and allows for a wide variety of performance metrics to be extracted. We asked > 1,300 individuals from multiple samples to complete Lingo, then investigated the validity and utility of the resulting data.ResultsWe conducted an exploratory factor analysis across 14 features derived from the seven assessments, identifying five factors. Four of the five factors showed acceptable test-retest reliability (Pearson's R > 0.7). Factor 2 showed the highest reliability (Pearson's R = 0.95) and loaded primarily on sentence repetition task performance. We validated Lingo with objective measures of language ability by comparing performance to gold-standard assessments: CELF-5 and the VABS-3. Factor 2 was significantly associated with the CELF-5 "core language ability" scale (Pearson's R = 0.77, p-value < 0.05) and the VABS-3 "communication" scale (Pearson's R = 0.74, p-value < 0.05). Factor 2 was positively associated with phenotypic and genetic measures of socieconomic status. Interestingly, we found the parents of children with language impairments had lower Factor 2 scores (p-value < 0.01). Finally, we found Lingo factor scores were significantly predictive of numerous psychiatric and neurodevelopmental conditions.ConclusionsTogether, these analyses support Lingo as a powerful platform for scalable deep phenotyping of language and other cognitive abilities. Additionally, exploratory analyses provide supporting evidence for the heritability of language ability and the complex relationship between mental health and language.

Project description:Background Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpose To standardize a set of 174 radiomic features. Materials and Methods Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features. Results Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI). Conclusion A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kuhl and Truhn in this issue.

Dataset Information

A standardized immune phenotyping and automated data analysis platform for multicenter biomarker studies.

Publications

A standardized immune phenotyping and automated data analysis platform for multicenter biomarker studies.

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