427 Mapping the expression of therapeutically targetable molecules in the tumor microenvironment of malignant pleural mesothelioma
Notice bibliographique
Résumé
Background Malignant pleural mesothelioma (MPM) is an aggressive malignancy with a poor prognosis and limited treatment options. 1 A subset of patients with MPM benefit from immune checkpoint blockade (ICB) targeting PD-(L)1, but response rates are low, and new therapies are needed.2 Histologic subtypes of MPM (e.g., epithelioid, biphasic, and sarcomatoid) are associated with distinct tumor microenvironment (TME) features and patient outcomes, including response to anti-PD-(L)1. Numerous novel therapies that modulate the anti-tumor immune response are currently in clinical trials, with targets including TIM-3, VISTA, TIGIT, CD155, TGFB, and COX-2. Mapping the expression of these molecules in the MPM TME will support biomarker development to guide rational immunotherapy combinations.Methods We quantitatively optimized and validated an 8-marker multiplex immunofluorescence (mIF) panel targeting B7H3, CD155, TIGIT, TIM3, COX2, TGFB, VISTA and pan-membrane using pixel analysis on n=5 solid tumor specimens. We then stained pre-treatment formalin-fixed, paraffin-embedded (FFPE) biopsy specimens from n=18 patients with MPM. Representative high-powered fields (HPFs) were scanned for digital image analysis using InForm software. For each marker, the proportion of positive cells (regardless of cell lineage) and mean per-cell expression intensity were quantified. Comparative analysis across histologic subtypes was performed using non-parametric Mann-Whitney U tests, and spatial co-localization at the HPF level was assessed using Spearman correlations.Results For each marker in the mIF panel, staining performance was validated as comparable to the corresponding single immunohistochemistry stain (<5% difference in percent positive pixels, figure 1). Approximately 2 million cells were analyzed across 627 HPFs from mIF-stained pre-treatment biopsies from n=18 patients with MPM. Specimen-level analyses demonstrated that sarcomatoid tumors exhibited significantly higher proportions of cells expressing COX2 (median 92.6% vs. 80.9%, p<0.001), TGFB (86.3% vs. 65.0%, p<0.001), TIGIT (91.7% vs. 67.4%, p<0.001), TIM3 (59.7% vs. 20.6%, p<0.001), and CD155 (92.7% vs. 80.2%, p<0.001) compared to epithelioid tumors. VISTA expression was observed across all subtypes, but localized predominantly to tumor cells in epithelioid MPM versus multiple cell types in sarcomatoid tumors. Across all specimens, geographic co-localization was demonstrated by positive correlations for expression of TGFB and TIGIT (ρ=0.59, P<0.001), CD155 and TIGIT (ρ=0.43, P<0.001), and B7H3 and TGFB (ρ=0.48, P<0.001) (figure 2).Conclusions This preliminary data suggests distinct TME features by histologic subtype in MPM and coordinated expression among targetable molecules TGFB, B7H3, TIGIT, and CD155. Comprehensive mapping of the MPM TME is ongoing to further delineate infiltrating immune cell populations, PD-L1 expression, and lineage-specific expression of these next-generation immunotherapy targets.References Ceresoli GL, Pasello G. Immune checkpoint inhibitors in mesothelioma: a turning point. Lancet. 2021;397(10272):348–9.Ahmadzada T, Cooper WA, Holmes M, Mahar A, Westman H, Gill AJ, et al. Retrospective evaluation of the use of pembrolizumab in malignant mesothelioma in a real-world Australian population. JTO Clin Res Rep. 2020;1(4):100075.Ethics Approval This study was approved by Queen’s University Human Subjects Research Ethics Board (HSREB# ONGY-600-21).Abstract 427 Figure 1Validation of mIF Panel Against Single IHC Stains. A. Representative images, pixel masks, and histogram compare B7H3 signal in IHC and mIF. B. Validation of all markers across 5 cases confirms comparable expression between IHC and mIF (<5% difference in positive pixels)Abstract 427 Figure 2Spearman correlation between targetable molecules in MPM across 627 HPFs. The Strongest correlations were observed between TGF-β and TIGIT (ρ = 0.59), TIGIT and CD155 (ρ=0.43), and between B7-H3 and TGF-β (ρ = 0.48), suggesting coordinated expression within the TME.
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 | 0,000 | 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) | 0,002 | 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 ».