Transposable elements as novel therapeutic targets for PARPi-induced synthetic lethality in PcG-mutated blood cancer
Bibliographic record
Abstract
ABSTRACT: Loss-of-function (LoF) mutations frequently found in human cancers are generally intractable by classical small molecule inhibitor approaches. Among them are mutations affecting Polycomb-group (PcG) epigenetic regulators, enhancer of zeste homolog 2 (EZH2) and Additional sex combs like 1 (ASXL1), frequently found in hematological malignancies of myeloid or lymphoid lineage, and their concurrent mutations associates with particularly poor prognosis. Although there is a clear need to develop novel and effective treatments for these patients, the lack of appropriate disease models and mechanistic insights have significantly hindered the progress. Here, we show that genetic inactivation of Asxl1 and Ezh2 in murine hematopoietic stem/progenitor cells results in highly penetrant hematological malignancies as observed in corresponding human diseases. These PcG proteins regulate both coding and noncoding genomes, leading to marked reactivation of transposable elements (TEs) and DNA damage responses in PcG LoF-mutated cells, which create a novel vulnerability for poly(ADP-ribose) polymerase (PARP) inhibitor (PARPi)-induced synthetic lethality. Using both mouse models and primary patient samples, we demonstrate that Asxl1/Ezh2-mutated cells are highly sensitive to PARPis that induce excessive DNA damage and significantly extend disease latency. Intriguingly, the observed PARPi sensitivity can be specifically overridden by reverse transcriptase inhibitors that interrupt target site-primed reverse transcription and life cycle of TEs. This mechanism is contrastingly different from the current concept of BRCAness associated PARPi-induced synthetic lethality, which largely rely on deficient homologous recombination, and is independent on reverse transcriptase inhibitors. Together, this study reveals a novel application and mechanism of PARPi-induced synthetic lethal targeting of blood cancers with reactivated TEs such as those carrying PcG epigenetic mutations.
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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) | 0.001 | 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".