Abstract P4-08-02: Identifying unique transcriptomes associated with HER2+ breast cancer recurrence at the single-cell level
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".