Abstract 6354: Using imaging mass cytometry to visualize the multiple myeloma tumor microenvironment post immune priming
Notice bibliographique
Résumé
Abstract Background: Multiple myeloma (MM) is an incurable cancer characterized by clonal plasma cell proliferation in the bone marrow, accounting for approximately 10% of all hematologic malignancies. Recently, patients with relapsed or refractory disease have been treated with a combination of the oncolytic reovirus Pelareorep, bortezomib, and dexamethasone, which was well-tolerated and led to prolonged progression free survival of over 3 years in a subset of patients. To understand the complex tumor immune microenvironment (TiME) and immune responses in patients before and after this treatment, we used imaging mass cytometry (IMC) to perform single cell, highly multiplexed, analysis of these patients’ bone marrow samples. Methods: We comprehensively characterized the changes in the MM TIME in pre and post bone marrow biopsy specimens taken from patients treated on a Phase 1b study with a combination of Pelareorep, bortezomib, and dexamethasone. For analysis with IMC, a marker panel of 35 antibodies was assembled to interrogate the various immune subsets of the bone marrow biopsies; each of these antibodies were conjugated to a unique metal isotope. After validation, the antibody cocktail was used to stain the biopsies. Pixel-based classification was performed in Ilastik to generate cell probability masks and processed in Cellprofiler. PhenoGraph was run in HistoCAT to identify the unique phenotypes. Rstudio was used for t-stochastic neighborhood embedding (tSNE) plot generation, and nearest neighbor analyses. ImaCytE was used for image visualization and spatial analysis. Results: Initial visualization of the raw, unsegmented data showed increased infiltration of natural killer cells and T cells in the post-treatment samples when compared against the pre-treatment samples. These changes correlated with immunohistochemical findings, clinical response to treatment, and changes in T cell clonality. After segmentation, the marker expression heatmaps for each of the clusters identified by PhenoGraph and the further subphenotyping in Rstudio showed complex ecosystems of cell-cell interactions. Nearest neighbor spatial analysis of the post-treatment samples revealed that NK cells (NKG2D+ and NKG2A+ subsets), monocytes (CD14+), macrophages (CD68+), cytotoxic T cells (CD3+, CD8+), and T helper cells (CD3+, CD4+) were significantly closer to the Pelareorep-primed MM than the non-primed MM. Further analysis in ImaCytE highlighted specific instances of these immune neighborhoods. Conclusions: IMC allows us to analyze the potent immune response and cellular interactions in the tumor microenvironment in multiple myeloma treated with Pelareorep and Bortezomib. Characterization of these complex interactions allows for a deeper understanding of the key mechanisms of action of these treatments and planning of future combination studies. Citation Format: Julian Olea, Kaijin Wu, Anthony Colombo, Claudia Villa Celi, Thomas Heineman, Matt Coffey, Steffan T. Nawrocki, Akil Merchant, Kevin R. Kelly. Using imaging mass cytometry to visualize the multiple myeloma tumor microenvironment post immune priming [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 6354.
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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».