427 Mapping the expression of therapeutically targetable molecules in the tumor microenvironment of malignant pleural mesothelioma
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".