DDDR-33. TARGETING TGFΒ PATHWAY DEPENDENCIES IN GROUP 3 MEDULLOBLASTOMA
Bibliographic record
Abstract
Abstract Medulloblastoma (MB) is one of the most prevalent malignant brain tumors in children, with tremendous cognitive and neuroendocrine disability among survivors. Group 3 (G3) MBs have poor overall survival at < 50%, few recurrent mutations, higher frequency of metastasis, and no targeted therapies. Amplification of MYC (c-myc) and activation of TGFβ signaling are frequent in G3 MB. We hypothesize that the TGFβ pathway and MYC contribute to the intrinsic resistance of G3 MB through deregulation of key genes and pathways. We previously established humanized models for SHH MB by introducing MYCN or PTCH1 deletions into neuroepithelial stem (NES) cells derived from normal human induced pluripotent stem cells (hIPSCs). In this study, we transduced NES cells with TGFb effectors activated in G3 MB (ACVR2A, TGFbR1, TGFb1, TGFb3, and SMAD5) alone and/or in combination with MYC, prioritizing combinations observed in patients. Excitingly, both MYC and TGFβ effectors drove tumor formation in vivo with the combination of TGFβ effectors with MYC leading to more aggressive tumors. We thus describe six new humanized isogenic models for both non-MYC and MYC driven G3 MB. We next found that NES cells expressing MYC with either TGFβR1 or TGFβ1 showed resistance to clinical TGFβR1 inhibitors, compared to cells driven by either TGFβR1 or TGFβ1 alone. To decipher mechanisms of resistance, we integrated CUT & RUN to probe for MYC genomic localization and relevant histone PTMs with RNA-seq analysis of MYC and TGFβ pathway driven NES cells. We discovered a subset of genes upregulated in MYC and TGFb-driven lines that are targets of the histone demethylase KDM2B. We postulate that epigenetic remodeling via MYC and recruitment of other MYC-interacting cofactors culminates in transcriptional changes that lead to aggressive disease. Overall, our studies provide insights on identifying new therapeutic avenues for patients with MYC and TGFβ driven G3 MB.
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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".