744 Mapping immune cell responses in whole blood and solid tumor tissue biopsies of renal cancer with CyTOF and imaging mass cytometry
Notice bibliographique
Résumé
Background Immunotherapy has limited efficacy in treating solid tumor. The main hurdles include T cell exhaustion, an immunosuppressive tumor microenvironment (TME), a lack of tumor-specific antigens and a complex immune landscape. To overcome the limitation of current immunotherapies and develop novel, safe and effective treatment strategies, a comprehensive understanding of both spatially localized and systemic immune responses is essential. Multimodal analysis provides a comprehensive, holistic view of the tumor and immune environment, which is essential for accurately predicting and improving patient responses to treatment.Methods We applied an integrative multimodal approach to map localized and systemic immune responses by employing CyTOF™ and Imaging Mass Cytometry™ (IMC™) technologies in matched peripheral blood mononuclear cells (PBMC), tumor-derived cells (TDCs) and formalin-fixed, paraffin-embedded tumor tissues from clear cell renal carcinoma (ccRCC) . A 40-plus-marker CyTOF panel was used to stain PBMC and TDC samples and acquired using CyTOF XT. Tumor tissues were stained using a 40-plus-marker immuno-oncology IMC panel and acquired using Hyperion™ XTi attached to CyTOF XT. Subsequent pixel-clustering and single-cell segmentation analyses quantified expression patterns of structural and immune markers in IMC data ( figure 1).Results CyTOF profiling of TDC and PBMC revealed distinct functional states. PBMC showed higher frequencies of functional monocytes and naïve/cytotoxic T cells, while TDCs were enriched for memory T cells with increased regulatory and exhausted phenotypes. These findings suggest a shift toward immune suppression within the TME compared to systemic immunity. Using IMC, multiple tertiary lymphoid- structures (TLSs) with mature morphologies were found within the tumor and at tumor margins. Immunosuppressive M2 macrophages surrounded TLSs with a small degree of penetration. Pixel-clustering analysis provided a detailed view of various clusters present in the sample, including a necrotic cluster and a macrophage cluster within the tumor stroma ( figure 2). Interestingly, we identified a region of tumor cells with high TIM-3 expression and low immune infiltration. Finally, functional cell types identified with CyTOF were spatially mapped to ccRCC tissue acquired on IMC, revealing potential involvement of M2 macrophages in immune cell exhaustion in the TME.Conclusions Our integrated, multimodal approach demonstrates the power of CyTOF and IMC technologies for comprehensive functional profiling of immune cells and their spatial dynamics, respectively. Application of this approach can identify predictive biomarkers and promote the development of novel therapeutic strategies against cancer.For Research Use Only. Not for use in diagnostic procedures.Abstract 744 Figure 1Workflow overview. Tumor tissue and whole blood from ccRCC patient were obtained. Tumor tissue was processed in two ways: (1) FFPE tumor sections were stained with IMC antibody panel, run on Hyperion XTi, (2) PBMC and TDC were stained with CyTOF antibody panel run on CyTOFAbstract 744 Figure 2High-resolution visualization of regions of interest (A) and single-cell segmentation analysis of selected phenotypes (B). ROI from Stage 3 ccRCC containing TLSs (Panel A and B, white box). The single-cell segmentation algorithm was trained to recognize functional phenotypes of T cells and M2 macrophages (Panel B)
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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 | 0,000 |
| 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) | 0,002 | 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 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 ».