Abstract 2813: A multiplexed, multispectral approach to analyzing the immune microenvironment of oral potentially malignant lesions
Notice bibliographique
Résumé
Abstract Objectives: Tissue sections of oral potentially malignant lesions (OPML) can be used not only to provide pathological assessment (diagnosis) but could also be used to analyze the interactions between cellular populations, signaling molecules, and structural proteins that impact the clinical course of disease. However, much of this information remains unpacked due to methodological limitations. Traditional immunohistochemistry (IHC) limits the number of proteins able to be simultaneously analyzed within a tissue section and is also prone to human error in its analysis. Newer multiplexed IHC (mIHC) methods involving repeated cycles of staining allow for quantification of a greater number of markers; however, tissue integrity may be compromised, and complexity is added to the interpretation of the results. There is a need to develop an immunostaining method that overcomes these barriers. Hypothesis: mIHC and an in-house Hyperspectral Cell Sociology (HCS) platform will allow for robust and detailed investigation of the immune microenvironment of OPML compared to traditional IHC staining and scoring techniques. Methods: Automated mIHC staining with a seven immune marker panel was completed on annotated formalin-fixed paraffin-embedded OPML tissue. A cocktail of three primary antibodies was applied, then antigens and chromogens stripped using SDS-glycine before second round staining with four antibodies and a hematoxylin counterstain was completed. Slides were then digitally imaged, regions of interest selected in conjunction with an oral pathologist and staining quantified computationally using the HCS platform. Traditional IHC was completed on a randomized subset of cases using sequentially cut tissue sections and a double-staining technique. Scoring was completed by two blinded clinicians. Results: One cycle of multiplexed staining and de-staining allowed for the detection of seven markers on one section while minimizing loss of tissue integrity. The HCS platform captures a single tissue section at multiple wavelengths, enabling the unmixing of multiple overlapping, colocalized chromogens. The resulting set of images displayed each stain separately, allowing for nuclei segmentation and the generation of a map of the epithelium and underlying connective tissue. True positive cells for each stain were demarcated on this map, allowing for investigation of marker positivity, co-positivity, cell to cell spatial relationships, and layer-based analysis, compared to cell count and density analyses obtained with traditional IHC. Conclusion: The immune microenvironment contains a wealth of information pertaining to the biology, pathogenesis, and outcome of disease. Multiplexed staining and imaging methods to analyze and unpack this information is of great clinical utility. Citation Format: Iris Lin, Kouther Noureddine, Paul Gallagher, Martial Guillaud, Lewei Zhang, Leigha Rock, Miriam Rosin, Denise Laronde. A multiplexed, multispectral approach to analyzing the immune microenvironment of oral potentially malignant lesions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2813.
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».