Abstract PO-084: Protein marker heterogeneity in breast cancer subtypes measured using immunofluorescence protein multiplexing and quantitative, single cell image analysis
Notice bibliographique
Résumé
Abstract The extent of intra-tumoral heterogeneity - the variation in the composition of cells in a given tumor and those in the local tumor microenvironment - could have potential impact on diagnosis, treatment planning and subsequent response to treatment. To evaluate the extent of cancer cellular heterogeneity, we conducted quantitative protein marker multiplex imaging to study the variations in protein marker expression patterns on individual cells and spatial localizations. A multiplexed immunofluorescence imaging platform (MxIF, Cell DIVE) was used to measure the cellular expression of Estrogen Receptor (ER), Progesterone Receptor (PR), Epidermal Growth Factor Receptor 2 (HER2), Ki67, p53, p21WAF1 and p16INK4A in cancer epithelium. Analysis was conducted on a tissue microarray (TMA) representing subtypes classified as Luminal A-like, Luminal B-like (HER2-negative), Luminal B-like (HER2-positive), HER2-positive (non-luminal) or Triple-negative based on tumor grade and immune activity according to the St. Gallen surrogate classification. Of the 101 cores from 59 cases studied, high levels of heterogeneity were observed in ER and PR expression among the hormonal receptor-positive tumors. As expected Luminal A-like cancers exhibited higher proportions of individual cells co-expressing ER and PR, while cells in Luminal B-like, HER2-negative cancers showed ER expression only. Luminal B-like, HER2-positive cores were composed of cells with strong HER2 staining, and some cells co-expressing PR and HER2. Single cells with strong ER and HER2 labelling were rarely observed. Spatial visualizations illustrated that cells with similar expression signatures tend to be clustered together. Among cases which showed p53 overexpression with immunohistochemistry, the overall MxIF-measured p53 level was highest in TNBC compared to HER2+ and Luminal B-like cases. TNBC exhibited the highest proliferative fraction and most incidence of abnormal p53 and p16. We did not observe an association of p21 expression to P53 or P16 patterns, yet a slightly higher proportion of Luminal B-like cancers showed increased P21 levels compared to the other subtypes. Our study demonstrated the application of protein marker multiplexing and quantitative image analysis in measuring heterogeneity of protein co-expression signatures within breast cancer subtypes. Our next step is to apply the methods developed here to study a cohort where molecular profiling and radiomics were conducted (Bayani et al.) to reveal the extent of heterogeneity of breast cancer with a multi-omics approach. Citation Format: Alison M. Cheung, Dan Wang, Kela Liu, Fiona Ginty, Sharon Nofech-Mozes, Jane Bayani, John M.S. Bartlett, Anne Martel, Martin J. Yaffe. Protein marker heterogeneity in breast cancer subtypes measured using immunofluorescence protein multiplexing and quantitative, single cell image analysis [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-084.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| 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 | 0,001 |
| 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) | 0,000 | 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 tête enseignante, 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 ».