EXTH-09. HSP90 INHIBITION AS A NOVEL THERAPY FOR DIFFUSE INTRINSIC PONTINE GLIOMA (DIPG)
Bibliographic record
Abstract
Abstract Diffuse intrinsic pontine glioma (DIPG) is an aggressive pediatric brain tumor. The mean age of onset is 7-9 years with a median survival of 9 months following diagnosis. Histone 3 (H3) mutations (H3K27M) have been identified in approximately 80% of patients, representing an intriguing target, but how to do this is unclear. To address this, we performed a synthetic lethality drug screen of over 2400 compounds on isogenic cells expressing H3K27M and empty vector. 37 drugs were synthetically lethal with H3K27M, including HSP90 inhibitors. In cancer, HSP90 has a higher isoelectric point which can be targeted specifically by PU-H71. Using PU-H71, HSP90 complexed with oncogenic clients can be inhibited to reduce tumor cell viability. We tested PU-H71 on primary patient derived DIPG lines demonstrating caspase-3/7 mediated cell death within 24 hours (n=3) and an IC50 of 100-200 nM (n=5) at 72 hours. HSP90 is a molecular chaperone that supports protein activation and localization; thus, its inhibition may allow simultaneous targeting of multiple oncogenic pathways. To determine which pathways are targeted in DIPG we used PU-H71-conjugated beads to pull-down HSP90 complexed with client proteins. LC-MS/MS characterization of the HSP90 interactome from three DIPG cell lines (SU-DIPG XVII, SU-DIPGXXV, SU-DIPG 50) identified 339 overlapping proteins including MAPK3, SMS, SRM, CTSP1, OAT, GMPS, and PYG. Pathway enrichment analysis highlighted roles in cell cycle regulation and metabolic processes. Treating DIPG with PU-H71 successfully reduced tumor cell viability but PU-H71 has poor brain penetration. To overcome this, a molecular isoform of PU-H71 has been developed, PU-HZ151, which will be used to test the in vivo efficacy of HSP90 inhibition for DIPG.
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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".