Complex rearrangements fuel ER+ and HER2+ breast tumours
Bibliographic record
Abstract
Breast cancer is a highly heterogeneous disease whose prognosis and treatment as defined by the expression of three receptors—oestrogen receptor (ER), progesterone receptor and human epidermal growth factor receptor 2 (HER2; encoded by ERBB2)—is insufficient to capture the full spectrum of clinical outcomes and therapeutic vulnerabilities. Previously, we demonstrated that transcriptional and genomic profiles define eleven integrative subtypes with distinct clinical outcomes, including four ER+ subtypes with increased risk of relapse decades after diagnosis1,2. Here, to determine whether these subtypes reflect distinct evolutionary histories, interactions with the immune system and pathway dependencies, we established a meta-cohort of 1,828 breast tumours spanning pre-invasive, primary invasive and metastatic disease with whole-genome and transcriptome sequencing. We demonstrate that breast tumours fall along a continuum constrained by three genomic archetypes. The ER+ high-risk integrative subgroup is characterized by complex focal amplifications, similar to HER2+ tumours, including cyclic extrachromosomal DNA amplifications induced by ER through R-loop formation and APOBEC3B-editing, which arise in pre-invasive lesions. By contrast, triple-negative tumours exhibit genome-wide instability and tandem duplications and are enriched for homologous repair deficiency-like signatures, whereas ER+ typical-risk tumours are largely genomically stable. These genomic archetypes, which replicate in an independent cohort of 2,659 primary tumours, are established early during tumorigenesis, sculpt the tumour microenvironment and are conserved in metastatic disease. These complex structural alterations contribute to replication stress and immune evasion, and persist throughout tumour evolution, unveiling potential vulnerabilities. A study identifies three dominant genomic archetypes of breast cancer induced by discrete mutational processes, describing a continuum of genomic profiles and detailing the mechanisms underlying the progression of breast cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".