MB-100DIVERGENT CLONAL SELECTION DOMINATES MEDULLOBLASTOMA AT RECURRENCE
Bibliographic record
Abstract
The development of targeted anti-cancer therapies through the study of cancer genomes is intended to improve survival and, at the same time, decrease the adverse effects of conventional therapy. We treated a forward genetic model of medulloblastoma with ‘humanized’ in vivo therapy (microneurosurgical tumor resection followed by multi-fractionated, image-guided radiotherapy). Genetic events in radiation treated recurrent murine medulloblastoma exhibit a very poor overlap with those in matched diagnostic samples, but do involve known cancer genes which were not mutated in the dominant clone at presentation. Thirthy-three pairs of human diagnostic / post-therapy medulloblastomas were also studied by whole genome sequencing (WGS) and demonstrated drastic genetic divergence in the dominant clones after therapy (<10% diagnostic events retained at recurrence). Both SNVs and CNAs were vastly different between therapy naïve and recurrent human tumors, despite retention of subgroup affiliation at recurrence. In both mice and humans, the dominant clones at recurrence arose through clonal selection. Targeted therapies are unlikely to be effective in the absence of the target; therefore targeted therapy in medulloblastoma should not be based on targets disovered in therapy-naive samples. Clinical trials where target discovery and validation was performed on therapy naïve samples followed by recruitment of children with highly treated recurrent tumors to test therapies at the time of recurrence have little chance to work. Finally, our results suggest the opportunity of second biopsies of recurrent tumors before enrollment into targeted therapies clinical trials.
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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 | 0.000 |
| 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".