Abstract P4-08-02: Identifying unique transcriptomes associated with HER2+ breast cancer recurrence at the single-cell level
Notice bibliographique
Résumé
Abstract Precision medicine aims to provide more effective treatments, detect disease earlier and improve patient outcomes. The development of more targeted therapies relies on advanced technologies like transcriptomics and machine learning to identify new and unique markers of a person’s cancer. Breast cancer remains one of the leading causes of cancer-deaths amongst women world-wide, with 670,000 deaths world-wide in 2022, thus breast cancer is ranked as the second most common cancer globally, and the number one cancer amongst women. HER2 positive (HER2+) breast cancer accounts for 20-30% of all breast cancer cases and is highly aggressive. The mortality rates for HER2+ are higher than other breast cancer subtypes. When treated with Herceptin, survival rates are good, however approximately 20-30% of cases experience recurrence and metastasis. Our question is whether HER2+ breast cancers express transcriptomic signatures that are distinguishable between non-recurrence and recurrence. Objective: The objective of our research is to interrogate breast cancers at the single cell level to identify new predictive biomarkers associated with HER2+ breast cancer recurrence using a single-cell microfluidics platform coupled with DNA barcoding genome-wide single-cell RNA-sequencing (scRNA-seq) and machine learning. Specifically, scRNA-seq allows one to study the heterogeneity of breast cancer cells to better understand molecular mechanisms that support tumorigenesis and recurrence. Herein, we detail the analysis of scRNAseq analysis of resected primary HER2+ breast cancers from patients that received Herceptin adjuvant therapy. Methods: Single-cell transcriptomes (10X Genomics) were characterized from 8 patients identified with HER2+ breast tumors that were treated with Herceptin adjuvant therapy. From these patients, four did not experience recurrence, while four did experience recurrence within 5 years of Herceptin treatment. Tumors were prepared for scRNAseq analysis, followed by computational analysis of data. Results: From the 80,000 single nuclei analyzed, we obtained 5.9 billion reads and detected a total of 27,303 genes following quality processing and read mapping. We performed dimension reduction and clustering analysis at pseudo-bulk level merging normal (8 samples), recurred tumor (4 samples) and non-recurred (4 samples) data. We annotated individual cells in each of the three merged data with 14 different cell types. We found 5,147 DEGs (Avg. 514) are common between the recurred and non-recurred samples, while 62.8% (8,691; avg. 869) DEGs and 66.8% (10,344; avg. 1,034) DEGs were specific to recurred and non-recurred data, respectively. Next, we applied GSEA for transcriptomic characterization using multiple reference databases such as transcription factor (TF) collection, biological pathways, hallmark gene sets and gene ontology (GO) terms. We identified 683 (203 unique) and 1,025 (379 unique) TFs were enriched in recurred and non-recurred tumors, respectively. The number of enriched biological pathways revealed that 50 of 62 pathways were activated in non-recurred tumor cells compared with recurred tumor cells where 38 of 62 pathways were activated. Summary: The outcome of our study reveals significant molecular distinction between recurrent and non-recurrent HER2+ breast cancers, suggesting potential biomarkers for predicting recurrence. Specifically, the identified DEGs and enriched pathways could serve as targets for developing new therapies aimed at preventing recurrence. Future studies will include the integration of scRNAseq data with other omics technologies for a comprehensive understanding of recurrence mechanisms. PM is supported by Breast Cancer Canada. Citation Format: Paola A. Marignani, Jinhong Kim. Identifying unique transcriptomes associated with HER2+ breast cancer recurrence at the single-cell level [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P4-08-02.
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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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,002 |
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 ».