Abstract PO4-13-11: Epigenetically defined sub-clonal heterogeneity drives therapy resistance in triple-negative breast cancer
Notice bibliographique
Résumé
Abstract Background Triple-negative breast cancer (TNBC) is a highly aggressive and heterogeneous disease with an average survival of less than 50%. Although immunotherapy is showing promising results in early TNBC, many patients do not respond, and chemotherapy remains the main treatment. High recurrence rates in TNBC are in part due to its inherent molecular heterogeneity and sub-clonal diversity, whereby cells present in minority sub-populations escape therapeutic pressure. Recurrent genomic alterations are not selected for upon therapy resistance in neoadjuvant chemotherapy (NAC) resistant TNBC, however sub-clonal transcriptionally defined cells are “primed” to drive resistance and are pre-existing, suggesting phenotypic heterogeneity is governed by inheritable activation of epigenetically defined regulatory regions. Here, we sought to characterise the sub-clonal and spatial diversity of chemotherapy resistant TNBC to identify the epigenetic hallmarks of lethal sub-populations of cells that may induce cell state transition, therapy resistance and ultimately metastatic progression. Methods Sixteen primary untreated triple-negative breast tumours (eight chemotherapy sensitive (ChemoS) and eight chemotherapy resistant (ChemoR, who died of their disease within three years)) were subjected to cell-matched high-depth multiome single nuclei RNA- and ATAC-sequencing. Driver DNA mutations and copy number alterations (CNA’s) were identified using a clinically validated targeted capture panel. Four tumours were subject to spatial RNA and ATAC profiling. Fourteen TNBC PDX models generated from patients with residual disease post NAC were subjected to single nuclei sequencing and used for validation. Meta-clustering of epithelial cells from the RNA profiles was used to identify sub-populations of cells harbouring distinct epigenetic and transcriptomic features enriched in ChemoR patients. Findings were validated in the BrighTNess TNBC NAC clinical trial and in chemotherapy treated TNBC patients from the SCAN-B study. Results Unsupervised meta-clustering (MC) of epithelial cells in G1 phase of the cell cycle identified nine meta-clusters across the dataset. A distinct immune-like MC was contributed by epithelial G1 cells, suggesting a cross-talk of epithelial cells with spatially proximal immune cells. Two MCs were dominated by cells from ChemoR patients and were defined by pathways related to hypoxia, EMT, and extra-cellular matrix signalling. Using scATAC profiles, we identified that the sub-populations of cells in the ChemoR dominant MCs were underpinned by both promoter and distal differential chromatin accessibility, that mapped to H3K27ac enhancer sites, suggesting a role of distal enhancer chromatin modification in regulating MC transcription. None of the meta-clusters were dominated by tumours with specific genetic mutations or CNA’s. Spatial ATAC and RNA profiling of a subset of tumours identified spatial epigenetically defined regions that showed distinct chromatin profiles, suggesting that the epigenetic regulation of genes that define the MCs are spatially distinct. ChemoR clusters were independently reproduced in a cohort of fourteen residual disease TNBC PDXs. Genes pertaining to the immune-like MC were associated with a good prognosis in independent cohorts of contemporary treated TNBC (SCAN-B) and associated with response to NAC in the BrighTNess trial. Marker genes from ChemoR dominant MCs were associated with poor prognosis and significant levels of residual disease following NAC. Conclusions Our multi-modal integrative analysis reveals unprecedented insight into the role of epigenetically defined spatially distinct, sub-clonal populations that pre-exist in treatment naïve TNBC and predict therapy response. This suggests pre-existing populations of cells have already acquired the epigenetic footprints and hence transcriptomic features to allow them to evade therapy. Citation Format: Fatemeh Ahmadimoghari, Yu Zhang, Ioanna Mavrommati, Shefali Thakur, Chris Starling, Ioannis Roxanis, Syed Haider, Rachael Natrajan. Epigenetically defined sub-clonal heterogeneity drives therapy resistance in triple-negative breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-13-11.
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 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».