DRES-09. IN VIVO FUNCTIONAL GENOMICS IDENTIFIES DRIVERS OF CHEMORESISTANCE IN MEDULLOBLASTOMA
Bibliographic record
Abstract
Brain tumours are the main cause of cancer-related death during childhood and medulloblastoma an aggressive embryonal tumour that arises in the posterior fossa – is the most common malignant tumour in this age group. Chemotherapy is a cornerstone of the postsurgical treatment, particularly in younger children in whom craniospinal irradiation is omitted due to the devastating side effects in the developing brain. Medulloblastoma often progresses or recurs after chemotherapy with a dismal prognosis. We used the Sleeping Beauty (SB) transposon-driven Ptch+/−/Math1-SB11/T2Onc sonic hedgehog (SHH) medulloblastoma murine model as a functional genomic tool to perform a genome-wide screen and identify genes and pathways that promote resistance to chemotherapy. After sub-total resection of the primary tumours, the mice were treated with repeated cycles of chemotherapy (cisplatin 5 mg/kg IP once on day 1 followed by cyclophosphamide 150 mg/kg IP daily from day 2 – 5) every 2 weeks for up to 3 cycles and monitored for tumour recurrence. The primary tumours (pre-treatment) and the tumours and metastasis that regrew after chemotherapy were deep sequenced to determine the transposon insertion sites. We identified recurrence-specific clonally selected insertions that promoted tumour growth despite therapy, including p53 (recurrently mutated in human tumours at relapse) and several other genes involved in DNA repair. Using cerebellar orthotopic models of p53-mutated SHH medulloblastoma, we observed a significant improvement in survival when the ATM inhibitor AZ32 was added to the chemotherapy backbone. This provides a rationale for developing therapeutic approaches targeting DNA repair in combination with conventional chemotherapy to prevent chemoresistance and medulloblastoma recurrence.
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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) | 0.004 | 0.001 |
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".