Epigenomic Rewiring of Transcriptional Regulators by Chromatin Variants Drives Tumor Evolution in Triple Negative Breast Cancer
Bibliographic record
Abstract
Intra-tumor heterogeneity (ITH) supports cancer progression and therapeutic resistance, raising the need to identify the fundamental mechanisms that sustain ITH to constrain tumor evolution. While somatic genetic alterations contribute to this process, mounting evidence suggest a predominant role of non-genetic alterations in supporting cellular plasticity that fuels ITH. Using preclinical patient-derived xenograft models of triple-negative breast cancer (TNBC), we investigated how epigenomic and transcriptional variation underlie cellular plasticity and promote therapy resistance. Integrating single-cell chromatin accessibility and gene expression profiles from matched chemotherapy-sensitive and acquired-resistant TNBC tumors, we report cellular plasticity associated with epigenomic patient-specific rewiring of regulatory networks along disease progression. Despite interpatient heterogeneity, rewired regulatory networks converge on a conserved set of biological processes driving resistance, including mitotic spindle organization, G2–M checkpoint control in early phases, and TGFβ signaling with metabolic reprogramming in late phases. These processes are orchestrated by a small group of recurrent transcriptional regulators, namely ELF1, AGO2, ZNF217, TCF3, RUNX1, and LARP7. Functional perturbation of TCF3 re-sensitizes resistant tumors to chemotherapy, establishing a proof-of-concept that disrupting convergent transcriptional dependencies can constrain tumor evolution. Collectively, our findings position epigenomic variation as a central feature of ITH and therapy resistance in TNBC, revealing shared transcriptional regulators as actionable vulnerabilities for early or late-stage intervention.
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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) | 0.001 | 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".