DFFB suppresses interferon to enable cancer persister cell regrowth
Bibliographic record
Abstract
Abstract Oncogene targeted cancer therapies can provide deep responses but frequently suffer from acquired resistance. 1 Therapeutic approaches to treat tumours which have acquired drug resistance are complicated by continual tumour evolution and multiple co-occurring resistance mechanisms. 2,3 Rather than treating resistance after it emerges, it may possible to prevent it by inhibiting the adaptive processes which initiate resistance but these are poorly understood. 4 Here we report that residual cancer persister cells that survive oncogene targeted therapy are growth arrested by drug stress-induced intrinsic Type I interferon (IFN) signaling. To escape growth arrest, persister cells leverage apoptotic machinery to transcriptionally suppress interferon-stimulated genes (ISGs). Mechanistically, persister cells sublethally engage apoptotic caspases to activate DNA endonuclease DNA Fragmentation Factor B (DFFB, also known as Caspase-Activated DNase (CAD)) which induces DNA damage, mutagenesis, and stress response factor Activating Transcription Factor 3 (ATF3). ATF3 limits Activator Protein-1 (AP1)-mediated ISG expression sufficiently to allow persister cell regrowth. Persister cells deficient in DFFB or ATF3 exhibit high ISG expression and are consequently unable to regrow. Therefore, sublethal apoptotic stress paradoxically promotes regrowth of residual cancer cells that survive drug treatment.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".