Abstract 5897: Tumor-derived bacteria drive breast cancer metastasis
Notice bibliographique
Résumé
Abstract Metastasis is a major barrier to long-term survival and therapeutic options for aggressive, metastatic forms of breast cancer remain limited. Studies using patient samples have identified tumor-resident bacteria that preferentially associate with specific breast cancer types, however, it is not yet understood how intratumoral bacteria directly contributes to disease progression and metastatic propensity independent of other prognostic factors. It is therefore the goal of the Dedhar and Finlay labs to identify how specific bacteria within metastatic breast cancer control immune and tumor cell functions to regulate metastatic potential and determine the outcome of disease progression. Using the syngenic, immunocompetent 4T1 and 67NR breast cancer models of metastatic and non-metastatic disease, we found microbiome depletion significantly reduces primary tumor growth of highly metastatic 4T1 tumors specifically. We also found bacterial depletion reduces metastatic burden and extends survival time compared to microbiome-replete controls. Microbiome depletion also induces changes in immune cell function that occur specifically in the metastatic 4T1 tumors, revealing differential microbial-based regulation of metastatic versus non-metastatic disease. To identify bacteria that control metastasis in microbiome-replete controls, we plated surgically resected tumor suspensions on bacterial growth media and compared bacteria from the 4T1 and 67NR primary tumors. We identified several species of the Bacillus genus that were unique to 4T1 tumors and were present both within the primary tumor as well as metastatic nodules. We then designed several in vivo model systems to directly test the ability of the isolated bacteria to promote metastasis. Using an orthotopic inoculation model with 4T1 or EMT6 cells, we found that following intratumoral injection, the 4T1-derived Bacillus species was actually able to augment metastasis when introduced directly back into primary tumors. To determine the specificity of this phenomenon, we then compared the effects of the 4T1 and 67NR-isolated bacteria on metastasis by injecting 4T1 cells that had been co-cultured with either bacteria prior to injection. Interestingly, we found that while the 67NR-derived bacteria had little effect on metastasis, the 4T1-derived Bacillus species significantly enhanced metastatic tumor burden compared to all other groups including those cultured with the 67NR-derived bacteria. These data demonstrate the ability of certain bacteria to promote metastatic disease. Based on these findings, we hypothesize specific bacteria play a causative role in augmenting metastatic propensity, and seek to determine functional differences between intratumoral bacteria to identify mechanistic targets for prevention of metastasis. We also seek to expand this work into clinical models to identify potential prognostic factors as well as mechanistic targets for disease treatment. Citation Format: Zachary J. Gerbec, Antonio Serapio-Palacios, Sarah Woodward, Jorge Pena-Diaz, B Brett Finlay, Shoukat Dedhar. Tumor-derived bacteria drive breast cancer metastasis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5897.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».