Abstract P4-06-10: Rates of successful engraftment in breast cancer xenograft models based on tissue type: Primary vs relapsed disease
Notice bibliographique
Résumé
Abstract Purpose: As we have published expertise in breast cancer xenograft models and clonal dynamics, our aim was to explore rates of engraftment based on type of tissue for attempted xenografting (primary vs relapsed/metastatic disease) and clinical breast biomarker subtype. Methods: Tissue from patients (pts) enrolled in a locally advanced/metastatic study and a breast tumour tissue repository (ie. resectable primaries) between Sept. 2008 and July 2015 underwent xenografting using NodScid/IL2rgKO (NSG) mice. Xenografts were passaged when tumour volume reached 1 cm3. Mice with no engraftment after 12 months (mos) were sacrificed. Pt charts were reviewed to determine biomarker status (hormone receptor [HR], HER2), date and type of tissue collection for xenografting. Prediction of successful engraftment based on tissue type and biomarker status was performed using nominal logistic regression. Results: A total of 70 tissue samples with known engraftment status were included in the analysis: 51 from primary breast tumour, 10 from relapsed disease (dz) with ≤ 1 line of therapy in the advanced setting and 9 from relapsed dz with > 1 line of therapy in the advanced setting. Tumours from pts treated with > 1 line of therapy were more likely to engraft compared to primary or recurrent dz with ≤ 1 line of therapy (89%, 35%, and 40% respectively; p=.008). HR- primary tumours were more likely to engraft compared to HR+ primary tumours: 71% of HR-/HER2- (triple negative) and 67% of HR-/HER2+ tumours versus 4% of HR+/HER2- and 38% of HR+/HER2+ tumours; p<.0001. Combining all tissue types, HR- tumours were more likely to engraft compared to HR+ tumours: 76% of HR-/HER2- and 67% of HR-/HER2+ tumours versus 37% of HR+/HER2+ and 22% of HR+/HER2- tumours; p=.0007. Table 1 shows the rate of engraftment for each tissue type and biomarker status. Combining these 2 variables predicts engraftment in 80% of cases. Conclusion: This preliminary study highlights potential differences in successful xenoengraftment based on biomarker status at diagnosis and type of tissue, primary vs relapsed tumour, the latter suggesting that the underlying biology of primary or first relapsed recurrent disease is distinct from more refractory disease, and warrants further exploration. This work is ongoing. (Funded by CBCRA, BCCF) Engraftment of primary tumour vs relapsed disease Primary tumour (N=52) N, (%)Recurrent disease and ≤ 1 line of Rx in advanced setting (N=10) N, (%)Recurrent disease and > 1 line of Rx in advanced setting (N=9) N, (%)Engraftment Yes18 (35)4 (40)8 (89)HR-/HER2-10 (55)1 (25)2 (25)HR-/HER2+4 (22)1 (25)1 (13)HR+/HER2+3 (17)00HR+/HER2-1 (6)2 (50)5 (62)Engraftment No33 (65)6 (60)1 (11)HR-/HER2-4 (12)00HR-/HER2+2 (6)1 (17)0HR+/HER2+5 (15)00HR+/HER2-22 (67)5 (83)1 (100) Citation Format: den Brok W-l, Chia S, Kalloger S, Bates C, Aparicio S, Mar C, Gelmon K, Eirew P. Rates of successful engraftment in breast cancer xenograft models based on tissue type: Primary vs relapsed disease [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P4-06-10.
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 | 0,000 |
| 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) | 0,002 | 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 ».