DIPG-55. TARGETING SENESCENT CELLS WITH ABT-263 ENHANCES CELL DEATH INDUCED BY BMI1 INHIBITION AND IONIZING RADIATION IN DIPG
Bibliographic record
Abstract
Ionizing Radiation (IR) is a key treatment modality for DIPG, but it provides only temporary relief as the tumor cells develop resistance to radiation. Recently, we and others have shown that inhibition of BMI1 either alone or in combination with radiation attenuates DIPG cell proliferation in vitro. While we are demonstrating the in vivo efficacy of pharmacological inhibition of BMI1 and understanding the mechanism of anti-tumor effect of BMI1 inhibition in DIPG, the existence of treatment-resistant cells remains a major obstacle for a prolonged cure. Both IR and genetic or pharmacological inhibition of BMI1 induces cellular senescence as a mechanism to suppress tumor cell proliferation, implying that senescence can be considered as tumor suppressor. Paradoxically, recent studies have shown that accelerated senescence can mediate tumor recurrence due to the development of pro-oncogenic environment. In line with this, we are investigating whether clearance of treatment-induced senescent cells enhances treatment outcomes. DIPG cells exposed to different doses of radiation followed by treatment with ABT-263 (Navitoclax), a drug which selectively clears the senescent cells, resulted in increased radiosensitization. Treatment of pre-radiated DIPG cells with ABT-263 decreased the activity of senescence-associated beta-galactosidase and anti-apoptotic protein expression. Similarly, chemical inhibition of BMI1 in combination with ABT-263 showed synergistic killing of DIPG cells. The synergy was most pronounced in DIPG cells harboring wildtype p53. Our study highlights the importance of eliminating treatment-induced senescent cells while inhibiting proliferation of DIPG tumors, a combination which can immensely improve therapeutic efficacy in DIPG patients.
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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".