Abstract 7434: Deployment of a visualization tool directly linked to the CellEngine cytometry analysis software to accelerate analysis of complex cytometry data sets in the context of ongoing clinical trials
Notice bibliographique
Résumé
Abstract Introduction High-dimension cytometry panels have become a hallmark of clinical phase 1 and 2 trials. These panels can provide hundreds of readouts and need to be combined with subject metadata (e.g., health data, demographics, timepoint, and dose) to provide insights on the identification of biomarkers. Several bioinformatics tools are available to interpret these complex datasets; however, these usually require significant expertise in data manipulation, which sometimes forces the person doing the data interpretation to outsource the data visualization and statistical analysis to a bioinformatics team. As a consequence, the scientist can be one step-removed from the “raw” data, critically preventing the interpretation of biomarker effects in the context of the raw staining profiles and associated quality controls (e.g. in-run controls). Here, we show how a new tool integrated with the CellEngine cytometry analysis software can accelerate data interpretation without removing the scientist from the raw data. Methods The visualization tool pulls live data from CellEngine, displaying both study- and cohort-level information in summary views as well as the sample-level staining profiles. As a live link, any changes made in CellEngine, such as ones to gating and compensation, or the addition of new samples during an ongoing study, are automatically reflected in the dashboard. This link also gives the ability to consult staining profiles and performance of controls directly within the tool without having to switch between applications. CellEngine is capable of analyzing 10,000s of samples at once, and thus can handle the largest of studies. Results Here, we show how the visualization tool was used to accelerate the data interpretation of two different flow cytometry assays. In the first example, lymphodepletion and immune system reconstitution in patients undergoing CAR-T cell therapy was monitored using the tool. The live update of analysed data within the tool, coupled with the ability to verify the performance of the two controls ran with each sample, accelerated the data interpretation and improved the data quality. In the second example, we show how the software was used to follow cytokines responses across different timepoints in a vaccination program where samples were analysed using a polyfunctional intra-cellular cytokine staining panel. The tool greatly accelerated the interrogation of this complex data set and allowed the identification of trends within the different T cell responses. Conclusion This new tool can help maximize the utility of high-dimensional cytometry in clinical trial analysis while allowing to keep the crucial link between graphical representation of large number of samples and the data associated with each individual flow cytometry staining profile. Citation Format: Damien Montamat-Sicotte, Zach Bjornson, Dean Franckaert, Nicholas Dupuis, Eustache Paramithiotis. Deployment of a visualization tool directly linked to the CellEngine cytometry analysis software to accelerate analysis of complex cytometry data sets in the context of ongoing clinical trials [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7434.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,036 | 0,015 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».