The Transcriptional and Epigenetic Landscape of Cancer Cell Lineage Plasticity
Bibliographic record
Abstract
Lineage plasticity, a process whereby cells change their phenotype to take on a different molecular and/or histologic identity, is a key driver of cancer progression and therapy resistance. Although underlying genetic changes within the tumor can enhance lineage plasticity, it is predominantly a dynamic process controlled by transcriptional and epigenetic dysregulation. This review explores the transcriptional and epigenetic regulators of lineage plasticity and their interplay with other features of malignancy, such as dysregulated metabolism, the tumor microenvironment, and immune evasion. We also discuss strategies for the detection and treatment of highly plastic tumors. SIGNIFICANCE: Lineage plasticity is a hallmark of cancer and a critical facilitator of other oncogenic features such as metastasis, therapy resistance, dysregulated metabolism, and immune evasion. It is essential that the molecular mechanisms of lineage plasticity are elucidated to enable the development of strategies to effectively target this phenomenon. In this review, we describe key transcriptional and epigenetic regulators of cancer cell plasticity, in the process highlighting therapeutic approaches that may be harnessed for patient benefit.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".