DIPG-73. SENESCENCE ASSOCIATED SECRETORY PHENOTYPE AS A MECHANISM OF RESISTANCE AND THERAPEUTIC VULNERABILITY IN BMI1 INHIBITOR TREATED DIPG
Bibliographic record
Abstract
Abstract BACKGROUND Diffuse intrinsic pontine gliomas (DIPGs) driven by mutations in the histone 3 (H3) gene (H3K27M) are aggressive pediatric brain tumors for which there is no curative therapy. METHODS To identify novel therapeutic targets we performed a high throughput drug screen combined with an epigenetically targeted RNAi screen using H3K27M and H3.3 WT DIPG cells. RESULTS Chemical and genetic depletion of BMI1 in vitro resulted in inhibition of clonogenicity and cell self-renewal consistent with previous studies. We show for the first time that clinically relevant BMI1 inhibitors attenuates growth of orthotopic DIPG xenografts as measured by MRI and prolong survival in vivo. We found that BMI1 inhibition drives phenotypic cellular senescence and that the senescent cells were able reactivate to form new neurospheres in vitro and tumor growth in vivo. RNA-seq, ChIP-Seq and immuno-proteomic analysis revealed that the senescent cells induced the expression of the Senescence Associated Secretory Phenotype (SASP) cytokines by increasing occupancy of activated histone marks at SASP factor promoters. The SASP results in increased expression of anti-apoptotic BH3 proteins including BCLxl, and BCL2. Treatment of the PTC028 treated senescent DIPG cells with BH3 mimetics induces apoptosis and clears the senescent cells. Combining BH3 mimetics with BMI1 inhibition attenuates tumor growth in vivo synergistically and significantly prolongs survival of DIPG bearing mice compared to BMI1 inhibition alone. CONCLUSION These data inform the current trial of BMI1 inhibition as a monotherapy and predict the need for adding BH3 mimetics to achieve efficacy.
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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) | 0.002 | 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".