MétaCan
Menu
Retour à la cohorte
Enregistrement W4415898627 · doi:10.1136/jitc-2025-sitc2025.1230

1230 Enhancing tissue microarray design for immune profiling: tissue microarray vs whole-slide quantification of CD8 in non-small cell lung carcinoma

2025· article· W4415898627 sur OpenAlexaff
Daphne Wang, Sonali Uttam, Benjamin Green, Eman R. Radwan, A. Uriarte, David M. Berman, Janis M. Taube, Tricia R. Cottrell

Notice bibliographique

RevueRegular and Young Investigator Award Abstracts · 2025
Typearticle
Langue
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueAdvanced Biosensing Techniques and Applications
Établissements canadiensPublic Health OntarioQueen's University
Organismes subventionnairesnon disponible
Mots-clésImmune systemMicroarrayTissue microarrayMicroarray analysis techniquesCD8Lung cancerCytotoxic T cell

Résumé

récupéré en direct d'OpenAlex

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,005
Score d'incertitude au seuil0,017

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0050,002

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.

Tête enseignante Opus0,014
Tête enseignante GPT0,267
Écart entre enseignants0,253 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueRegular and Young Investigator Award AbstractsMême sujetAdvanced Biosensing Techniques and ApplicationsTravaux en français237 207