Selective advantage of epigenetically disrupted cancer cells via phenotypic inertia
Bibliographic record
Abstract
The evolution of established cancers is driven by selection of cells with enhanced fitness.Subclonal mutations in numerous epigenetic regulator genes are common across cancer types, yet their functional impact has been unclear.Here, we show that disruption of the epigenetic regulatory network increases the tolerance of cancer cells to unfavorable environments experienced within growing tumors by promoting the emergence of stress-resistant subpopulations.Disruption of epigenetic control does not promote selection of genetically defined subclones or favor a phenotypic switch in response to environmental changes.Instead, it prevents cells from mounting an efficient stress response via modulation of global transcriptional activity.This ''transcriptional numbness'' lowers the probability of cell death at early stages, increasing the chance of long-term adaptation at the population level.Our findings provide a mechanistic explanation for the widespread selection of subclonal epigenetic-related mutations in cancer and uncover phenotypic inertia as a cellular trait that drives subclone expansion.Disruption of the epigenetic regulatory network enhances cell fitness under environmental stress In physiology, epigenetic mechanisms mediate the cellular response to environmental cues.We therefore asked whether disruption of epigenetic control in cancer cells may affect their interactions with the tumor microenvironment (TME).We selected two cancer types originating from distinct lineages: NSCLC lung (D) Visualization of subclonal mutations affecting epigenetic regulator genes in the TRACERx patient cohort.Genes grouped by functional class.Colors legend as in (A).(E) Ratio of nonsynonymous over synonymous mutations in individual genes in the indicated groups.N = 190, 65, and 10,025 genes, respectively.Red lines denote median values.p values relative to the rest of the genes from one-tailed Student's t test after random sampling.(F) Phylogenetic trees visualizing emerging subclones in two tumors.Mutated epigenetic regulator genes identified in the trunk (gray line, clonal mutations) or in the branches (black lines, subclonal mutations) are indicated.Genes color-coded as in (A).See also Figure S1 and Table S1. DECLARATION OF INTERESTSH.P. is currently an employee of Seqera Labs.
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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 | 0.001 |
| 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".