Abstract PO4-13-11: Epigenetically defined sub-clonal heterogeneity drives therapy resistance in triple-negative breast cancer
Bibliographic record
Abstract
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.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".