1230 Enhancing tissue microarray design for immune profiling: tissue microarray vs whole-slide quantification of CD8 in non-small cell lung carcinoma
Notice bibliographique
Résumé
Background Tissue microarrays (TMAs) are efficient, high-throughput tools for tissue-based analyses, yet optimal design strategies for studying spatially heterogeneous markers such as immune infiltration remain undefined. 1 2 CD8+ T-cell density is associated with improved prognosis in non-small cell lung carcinoma (NSCLC), particularly at the tumor-stroma interface, termed the invasive margin (IM).3–5 There is currently no standardized operational definition of the IM in NSCLC6–8 and TMAs have traditionally been tailored to tumor-intrinsic features, rather than immune contexture.9–11 Methods CD8 immunohistochemistry was performed on 35 NSCLC whole-slide resection specimens and a corresponding TMA comprising 3 cores from the central tumor (CT) and from the IM (50% tumor: 50% stroma) per specimen. CD8 + cell densities were digitally mapped in 50-μm increments across the tumor-stroma boundary to empirically define the IM on the whole-slide resections. Actual and digitally-simulated TMA cores of varying number and size were assessed for concordance with whole-slide CD8+ cell densities and nearest-neighbor distances at the CT and IM to optimize core size and number.Results A characteristic CD8 + cell density peak was consistently observed within 200 µm beyond the tumor border, defining the extent of the tumor-immune interface. Accordingly, cores were required to contain ≥80% tumor by area (CT) or ≥10% stroma and 80%≥ tumor >0% (IM) (figure 1). TMA core inadequacy was significantly higher at the IM (56%) than at the CT (6.2%), primarily due to geographic displacement away from the 200-µm IM peak (χ2, p<0.001). Modeling showed that core radius (≥0.5 mm) more strongly affected nearest-neighbor measurements than core number, while returns diminished beyond 4 IM or 3 CT cores in CD8+ cell density sampling (figure 2). CD8+ clusters were smaller and more dispersed at the IM than in the CT, emphasizing the need for larger sampling areas to capture immune heterogeneity (figure 1B). We recommend designing 8 IM and 3 CT cores ≥0.5 mm in radius per specimen to anticipate core loss and ensure analytical fidelity in quantifying CD8+ cells.Conclusions This study defines the IM as the 200-μm stromal region beyond the tumor edge and provides a data-driven framework for optimizing TMA design to study heterogeneously expressed immunological markers in NSCLC, including strategy for TMA core quality assessment. By refining TMA strategies to account for spatial immune heterogeneity, we enhance the translational potential of TMAs, supporting their broader use in scaling immune-related biomarker monitoring across clinical trial cohorts and in evaluating immunotherapy responses in NSCLC and beyond.References Jones S, Prasad ML. Comparative evaluation of high-throughput small-core (0.6-mm) and large-core (2-mm) thyroid tissue microarray: is larger better? Archives of Pathology & Laboratory Medicine. 2021;136:199–203.Eckel-Passow JE, et al. Tissue microarrays: one size does not fit all. Diagnostic Pathology. 2010;5:48.Trojan A, et al. Immune activation status of CD8+ T cells infiltrating non-small cell lung cancer. Lung Cancer. 2004;44:143–147.Ghiringhelli F, et al. Immunoscore immune checkpoint using spatial quantitative analysis of CD8 and PD-L1 markers is predictive of the efficacy of anti- PD1/PD-L1 immunotherapy in non-small cell lung cancer. eBioMedicine. 2023;92.Donnem T, et al. Stromal CD8+ T-cell density—a promising supplement to TNM staging in non-small cell lung cancer. Clinical Cancer Research. 2015;21:2635–2643.Galon J, et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science. 2006;313:1960–1964.Gong C, et al. Quantitative characterization of CD8+ T cell clustering and spatial heterogeneity in solid tumors. Front Oncol. 2019;8:649.Marliot F, Lafontaine L, Galon J. Immunoscore assay for the immune classification of solid tumors: technical aspects, improvements and clinical perspectives. Methods Enzymol. 2020;636:109–128.Eskaros AR, et al. Larger core size has superior technical and analytical accuracy in bladder tissue microarray. Laboratory Investigation. 2017;97:335–342.Wampfler JA, et al. Determining the optimal numbers of cores based on tissue microarray antibody assessment in non-small cell lung cancer. Journal of Cancer Science and Therapy. 2011;3:120–124.Alkushi A. Validation of tissue microarray biomarker expression of breast carcinomas in Saudi women. Hematology/Oncology and Stem Cell Therapy. 2009;2:394–398.Ethics Approval This study was approved by Queen’s University Human Subjects Research Ethics Board (HSREB# ONGY-600-21).Abstract 1230 Figure 1Distinct CD8 patterns distinguish the IM from the CT on whole-slide specimens. A) A histogram of whole-slide CD8 density (normalized to the intratumoral mean) shows the 200-µm IM peak beyond the tumor border. B) A histogram of core placement relative to the tumor border shows cores that miss the IM peak due to geographic variationAbstract 1230 Figure 2Increasing core number improves whole-slide CD8+ cell density concordance and larger cores better approximate nearest-neighbor measurements. A) A heatmap displays CD8 density correlations (Spearman) between simulated cores and whole-slides. Mean density standard deviation (B) and nearest-neighbor distance error (C) are shown for simulated cores. D) An iteration of simulation is illustrated
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 | 0,001 |
| 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) | 0,005 | 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 ».