Abstract P1-10-08: Assessing immune biomarkers of response to anthracyclines in breast cancer
Notice bibliographique
Résumé
Abstract Background: Pathologists have long recognized that the interaction between immune and tumour cells is critical in the development and progression of breast cancer. Studies have demonstrated the presence of tumour-infiltrating lymphocytes (TILs) correlates with improved clinical outcome in breast cancer especially in the triple negative and HER-2 positive subtypes. TILs predict for improved response to certain therapies including chemotherapy and trastuzumab. The predictive value of TILs in ER positive tumours is less clear. It has been demonstrated the higher presence of the immune microenvironment is associated with a better prognosis and as a result a higher likelihood of benefit from chemotherapy and possibly from immunotherapy, whereas cold immune microenvironment carries greater risk of relapse and lower benefit from chemotherapy and possibly immuno-therapies. In this study, we evaluated whether TILs could be used to predict chemotherapy response and characterize the pre-existing tumour microenvironment (TME) using NanoString’s GeoMx Digital Spatial Profiling (DSP) platform. Methods: We assessed haematoxylin and eosin stained slides from the phase III BR9601 adjuvant breast cancer trial using software used in the international ring study 2 for standardized evaluation of TILs integrated in VMscope slide explorer. Evaluation of stromal TILs was based on international guidelines. NanoString’s DSP platform was used to analysis 256 patient samples from the BR9601 clinical trial. For analysis, region of interest were selected and compared for the TME (CD45+ve) and tumour rich (pan cytokeratin) in tissue microarrays. A panel of 56-antibodies were analysed in each ROI. Results: The mean TIL score in this cohort of patients was 15.58% (ranging from 0 to 66.67%). The presence of higher levels of TILs was significantly associated with ER negativity (p<0.001), high grade (p=0.01) and increased lymph nodal involvement (p=0.002). In multivariate analysis, patients whose tumours had medium/high levels of TILS expression had better DRFS (HR: 0.49, 95%CI 0.24-1.02, p=0.057) when treated with E-CMF than those treated with CMF alone. Highest levels of TILs were found in Basal and HER2-like tumours. A T-cell score was generated using the average expression of CD3, CD4 and CD8. The T-cell score was examined in both the tumour and TME. Using the T cell score it was apparent that the cohort had a range of immune “hot” and immune “cold” tumours. It was demonstrated that immune “hot” TME doesn’t not always correlate with immune “hot” tumour expression. Proteins that were most associated with T-cell exclusion (p<0.01) in the TME were Fibronectin, B7-H3, PTEN, ER-α, TGFB1, FAPα and CD34. This would indicate that these proteins are causal inhibitors of T-cell invasion. Conclusion: In conclusion, this study highlights the significance of assessing the entire tumour since TILs, tumour and stromal cells collectively engage in a complex interplay that contributes to disease development and progression. NanoString’s GeoMx DSP is a promising technology for multiplexed analysis. TILs, whether measured using automated software, or estimation by protein profiling, are predictive of chemotherapy benefit. Citation Format: Melanie Spears, Carsten Denkert, Sonia L Villagas, Nicola Lyttle, Linda Liao, Mary Anne Quintayo, Christopher J Twelves. Assessing immune biomarkers of response to anthracyclines in breast cancer [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P1-10-08.
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 | 0,001 |
| 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 | 0,000 | 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) | 0,003 | 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 ».