Abstract 1221: Comprehensive cell-type classification of tumor and normal cells from single cell RNA sequencing in pan cancer settings
Notice bibliographique
Résumé
Abstract Single-cell RNA sequencing (scRNA-seq) allows for the study of the transcriptome at a cellular level, where populations of cells are annotated based on the expression of marker genes, providing a tool to gain cell-specific, subtle insights on cancer biology. However, precise annotation of cell type remains a challenge, hindering the efficiency of data interpretation. Several existing tools for cell-type annotation have been developed to improve resolution and reproducibility, yet their performance is reduced when the reference dataset contains many cell types, subclasses of similar cell types, or malignant cells. Interpatient malignant cell heterogeneity often leads to reduced accuracy when classifying cancer cells as most methods rely on correlations to a reference from a different source. Given the challenges in the annotation of scRNA-seq data of cancer and its high impact for elucidating mechanisms associated with tumor heterogeneity, pathogenesis, and treatment, we developed a comprehensive, hierarchically organized, multi-layered classifier spanning diverse malignant and normal cells of the tumor microenvironment. We found that performance improves when each layer focuses on a smaller number of classes and each cell sequentially moves down a series of classifiers with increased cell type resolution. When applied to an external validation dataset of over 300 primary solid tumor biopsies spanning diverse cancer types, the classifier accurately annotated the tissue of origin of malignant cells, and relevant subtypes of stromal and blood cells, with average F1 scores of 0.91, 0.95 and 0.99 respectively. Using confidence thresholds at each layer, the classifier abstains from classifying ambiguous cells. We applied 4 existing annotators provided with the same reference to the external test dataset and found that cancer cells are misclassified or unclassified, while the blood and stromal cells are accurately classified, highlighting our tool’s unique ability to classify cancer cells. Moreover, we applied our classifier’s to scRNA-seq data derived from breast cancer metastasis to the liver and were able to uncover the tissue of origin, demonstrating the potential use for determining the source of a metastatic tumour. Finally, given that our classifier is modular, we leveraged two recently published single cell breast cancer atlases to add a breast cancer subtype classification layer, that consistently identified the correct clinical subtype of single breast cancer cells in external data. This study provides a flexible model for the annotation of cells comprising the tumor microenvironment in pan cancer settings, while existing methods require tissue-specific references for every cancer type. Our classifier provides a powerful method for investigating intercellular communication pathways between tumor cells and non-malignant cells of the tumor microenvironment. Citation Format: Ido Nofech-Mozes, Philip Awadalla, Sagi Abelson. Comprehensive cell-type classification of tumor and normal cells from single cell RNA sequencing in pan cancer settings [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1221.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».