Abstract A033: Identification of gemcitabine-resistant populations using scRNA-sequencing in triple negative breast cancer patient-derived xenograft
Notice bibliographique
Résumé
Abstract Despite major advances in the treatment of breast cancer (BC), it remains the most diagnosed and second most deadly cancer among American women. BC is a heterogeneous disease consisting of distinct subtypes, including hormone receptor-positive, HER2 receptor-positive and cancers that lack these receptors categorized as triple-negative breast cancers (TNBC). The TNBC subtype presents the worst outcome and the highest rates of recurrence and metastasis. The absence of targets prevents the use of established precision therapies in TNBC, and the standard of care remains neoadjuvant chemotherapy. While this is effective in some patients, about 50% develop resistance, leading to the development of metastasis. It is known that selective pressures exerted by chemotherapy treatment can promote the outgrowth of resistant tumor subclones. However, the diverse intra-tumoral population and the mechanisms that lead to chemotherapy resistance in TNBC are still poorly understood. We hypothesized that therapeutic regimens influence tumor plasticity by exerting selective pressures leading to the outgrowth of resistant subpopulations with the greatest survival advantage. To evaluate this hypothesis, we aimed to generate in vivo models of chemotherapy resistance and to investigate the plasticity of tumor cell subpopulations challenged with standard-of-care chemotherapies. To this end, we selected a multi-drug resistant (Doxorubicin, Cyclophosphamide, Cisplatin, and Paclitaxel) BC patient and developed a patient-derived xenograft (PDX) from the primary tumor and the lung metastasis. The metastasis PDX model was initially responsive to Gemcitabine (as observed in the BC patient) but eventually developed resistance. We challenged this metastasis PDX with several cycles of Gemcitabine and obtained residual, rebound, and resistant tumor samples. We performed single-cell RNA sequencing (scRNAseq) of these models using a droplet-based technology from 10X Genomics. This scRNAseq data was used to compare the changes in the proportions of cellular subpopulations in each model. Interestingly, our data shows that the rebound model presents greater similarity to the untreated metastasis, while the resistant model has significant differences in cell population expression profiles. We identified a hypoxic population in the primary tumor and its matched metastasis. This population was validated in these models by Nanostring GeoMx Digital Spatial Profiler. Our recent analyses have identified that this hypoxic population persists in the residual, rebound and resistant models. In addition, we have identified other populations that vary in these models. We are currently investigating their cellular mechanisms and gene expression patterns. Using scRNA-sequencing to understand the clonal expansion of resistant subpopulations following chemotherapy reveals distinctive resistant cell features enabling the identification of the vulnerabilities of these tumors. Citation Format: Sandrine Busque, Constanza Martinez Ramirez, Hellen Kuasne, Paul Savage, Anne-Marie Fortier, Anie Monast, Atilla Omeroglu, Jamil Asselah, Nathaniel Bouganim, Sarkis Meterissian, Claudia Kleinman, Mark Basik, Morag Park. Identification of gemcitabine-resistant populations using scRNA-sequencing in triple negative breast cancer patient-derived xenograft [abstract]. In: Proceedings of the AACR Special Conference: Cancer Metastasis; 2022 Nov 14-17; Portland, OR. Philadelphia (PA): AACR; Cancer Res 2022;83(2 Suppl_2):Abstract nr A033.
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) | 0,002 | 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 ».