1219 High-plex spatial profiling of tumor metabolic reprogramming and cell signaling dynamics in breast cancer using imaging mass cytometry
Notice bibliographique
Résumé
Background Understanding intricate cellular interactions within the tumor microenvironment (TME) is essential for understanding disease progression and advancing cancer treatments, such as immunotherapy. The cancer ecosystem is complex, composed of cells with dysregulated metabolism and signaling pathways, contributing to tumor heterogeneity, growth and differential treatment response. Targeting metabolic and signaling pathways represents a growing strategy to enhance immunotherapy treatments, often in combination with standard of care treatments. Imaging Mass Cytometry™ (IMC™) is a spatial biology imaging technique utilizing CyTOF™ technology, which enables deep characterization of 40-plus markers in TME simultaneously. IMC offers scalable and high-throughput acquisition while generating high-quality data with true dynamic range of signal without amplification or fluorescence-based limitations such as spectral overlap and autofluorescence.Methods IMC was used to explore the TME and interrogate key pathways in metabolic reprogramming and signaling by utilizing antibody panels that integrate markers from the Human Immuno-Oncology IMC Panel (201509) combined with the Human Metabolism IMC Panel (201521) or Human Cell Signaling IMC Panels (201522). This enabled investigation of energy production, cellular homeostasis and mitogenic signaling pathways in human breast cancer samples. To phenotype immune and tumor cells and assess the activation status of immune cells, we used Preview Mode to acquire the whole tissue, followed by higher-resolution imaging of regions of interest using Cell Mode or whole tissue sections using Tissue Mode ( figure 1).Results IMC analysis elucidated the spatial organization and metabolic profile of cells in breast cancer ( figure 2A). Heterogeneity within tumor is highlighted by differential utilization of energy sources across the tumor. Immune cells primarily infiltrated tumor areas utilizing fatty acid oxidation, while tumor cells using anaerobic metabolism or aerobic respiration were classified as immune deserts. Differences in signaling pathways were also observed in these tumor cell populations (figure 2B). Elevated glycolysis and mTOR pathway activation suggested adaptations to hypoxia and anabolic growth. Wnt signaling and PTEN expression were mainly localized in tumor cells, whereas MAP kinase signaling was localized in stroma. Unsupervised pixel clustering and hierarchical clustering using MCD™ SmartViewer highlighted metabolic activity and activation of signaling pathways within tumor regions.Conclusions Comprehensive spatial profiling using IMC technology illuminates the heterogeneity of metabolism and signaling pathways in tumors. IMC allows us to detect many clinically relevant targets simultaneously with intact spatial resolution. This is crucial for developing future prognostic assessments and guiding more effective, personalized cancer therapies.For Research Use Only. Not for use in diagnostic procedures.Abstract 1219 Figure 1Whole tissue evaluation of metabolic activity in human breast cancer. Whole slide Tissue Mode image, human breast cancer stained with IMC panels. A) Multi-color IMC image, metabolic activity in breast cancer. B) Shows differential activation of signaling cascades in breast cancer cellsAbstract 1219 Figure 2High-resolution evaluation of metabolic and cell signaling activity in human breast cancer. Cell Mode IMC images from 3 separate regions of interest from human breast cancer stained with A) Human Metabolism IMC Panel or B) Human Cell Signaling IMC Panels
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,002 |
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 ».