Abstract B049: Deep Learning Enables Identification of Cell Types and Clusters (iCTC) in Immune Tumor Ecosystems for Prognostic Assessment in Cancer
Notice bibliographique
Résumé
Abstract Background: Spatial multiomic profiling has been transforming the understanding of local tumor ecosystems. Yet, the spatial analyses of tumor-immune interactions at systemic levels, such as in liquid biopsies, are challenging. Within the last 10 years, we have longitudinally collected nearly 3,000 patient blood samples for multiplexing imaging of circulating tumor cells (CTCs) and their interactions with white blood cells (WBCs). Multicellular CTC clusters exhibit enhanced metastatic potential. The detection of CTCs and characterization of tumor immune ecosystems are constrained by (1) low frequency of CTCs in blood samples; (2) specific lineages of immune cells are not recognized by limited channels of current imaging methods, (3) reliance on labor-intensive manual analysis slows down the discovery of biomarkers for predicting therapy response and survival in cancer patients. We hypothesize that an AI-powered platform will accelerate the lineage and spatial characterization of tumor immune ecosystems for prognostic evaluations. Methods: Leveraging FDA-approved CellSearch technology, we collected 2,853 blood specimens longitudinally from 1358 patients with advanced cancer (breast, prostate, etc. Integrating machine learning and deep learning tools, we developed a novel platform -identification of Cell Types and Clusters (iCTC) - to automate the detection and identification of CTCs, immune cell types , and their interactions. Using machine-learning image analysis, we extracted over 270 cellular and nuclear features of cytokeratin, CD45, and DAPI expression patterns, enabling precise characterization of CTCs and WBCs including differentiation of sub-types of WBCs, CTC clusters, and their intercellular interactions with one another. Results: The iCTC platform enabled automated identification of CTCs and WBCs (granulocytes, T cells, monocytes, B cells, NK) at specificity and sensitivity >0.97-1.0. It also recognized homotypic CTC clusters, heterogenous CTC-WBC clusters, and immune cell clusters, providing insights into cell morphology and spatial organization within hours for 50+ millions cells from nearly 3,000 blood tests. These features correlated with patient survival, disease progression, and treatment response. Our findings highlight the clinical significance of CTC–immune cell interactions and dynamic alterations of CTCs (singles and clusters) and underscore their potential in stratifying patients into distinct risk categories. Conclusions: This study demonstrates the transformative potential of machine learning in accelerating and enabling large-scale spatial data analyses of tumor immune ecosystem in blood biopsies and can readily extend the method to other imaging data analyses, integrating imaging data with large cohorts of patient data. By automating and enhancing the analysis of CTC-immune cell interactions, we present a robust framework for developing predictive models with direct clinical relevance. This work opens avenues for personalized treatment strategies, underscoring the impact of AI in advancing precision oncology. Citation Format: Joshua R. Squires, Yuanfei Sun, Andrew D. Hoffmann, Youbin Zhang, Allegra C. Minor, Anmol Singh, David Scholten, Hannah Ding, Chengsheng Mao, Leonidas C. Platanias, Yuan Luo, Deyu Fang, William J. Gradishar, Massimo Cristofanilli, Carsen Stringer, Huiping Liu. Deep Learning Enables Identification of Cell Types and Clusters (iCTC) in Immune Tumor Ecosystems for Prognostic Assessment in Cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr B049.
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 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 | 0,001 | 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 ».