Epigenome erosion drives neural crest-like phenotypic mimicry in triple-negative breast cancer and other SOX10+ malignancies
Bibliographic record
Abstract
A bstract Background Intratumoural heterogeneity is a poor prognostic feature in triple-negative breast cancer (TNBC) and other high-grade malignancies. It is caused by genomic instability and phenotypic plasticity, but how these features co-evolve during tumour development remains unclear. SOX10 is a transcription factor, neural crest stem cell (NCSC) specifier and candidate mediator of cancer-associated phenotypic plasticity. Methods Using immunophenotyping, we investigated the expression of SOX10 in normal human breast tissue and breast cancer (n=21 cosmetic breast reduction and 1,860 tumour samples with clinical annotation). We then defined the context and evolution of its expression in TNBC compared to 21 other malignancies using systems-level transcriptomics. Results SOX10 was detected in nuclei of normal mammary luminal progenitor cells, the histogenic origin of most TNBCs. In breast cancer, nuclear SOX10 predicted poor outcome amongst cross-sectional (log-rank p=0.0015, hazard ratio 2.02, n=224) and metaplastic (log-rank p=0.04, n=66) TNBCs. Systems-level transcriptional network analysis identified a core module in SOX10’s normal mammary epithelial transcription program that is rewired to NCSC genes in TNBC. Reprogramming was proportional to DNA damage and genome-wide promoter hypomethylation, particularly at CpG island shores. Using a novel network analysis pipeline, we found that NCSC-like transcriptional reprogramming is also strongly associated with promoter hypomethylation in other SOX10+ malignancies: glioma and melanoma. Conclusions We propose that cancer-associated genome hypomethylation simulates the open chromatin landscape of more primitive cell states, and that on this relatively unrestricted background, SOX10 recreates its ancestral gene regulatory circuits by default. These findings provide new insights about the basis of intratumoural heterogeneity and resurrection of developmental phenotypes in cancer; and highlight the potential for therapeutics that limit chromatin remodelling.
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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".