GE-09 * COMBINED HEREDITARY AND SOMATIC MUTATIONS OF REPLICATION ERROR REPAIR GENES RESULT IN RAPID ONSET OF ULTRA-HYPERMUTATED MALIGNANT BRAIN TUMORS IN CHILDREN
Bibliographic record
Abstract
BACKGROUND: Biallelic Mismatch Repair Deficiency (bMMRD) Syndrome is a childhood cancer predisposition syndrome with a wide tumor spectrum where malignant brain tumors predominate. Mismatch repair and DNA polymerase proofreading are two components necessary to repair DNA replication − associated mutations. The consequences of disruption to both repair components in humans are not well studied. Furthermore, it is unknown how defects in DNA repair processes cause and perpetuate childhood CNS tumorigenesis. METHODS: We analyzed17 bMMRD cancers using genome, exome sequencing and microarrays. Additionally, we sequenced non-neoplastic tissues from patients and compared the mutational landscape of bMMRD tumors to a reference data set of all childhood and adult cancers. RESULTS: BMMRD malignant brain tumors harbored massive numbers of substitution mutations (>250/Mb), which was greater than all childhood and most adult cancers (>7,000 analyzed). These cancers lacked copy number alterations (p < 0.01) and microsatellite instability as seen in sporadic glioblastoma and adult deficient MMR cancers respectively. These mutation signatures and numbers are unique and diagnostic of childhood germ-line bMMRD (P < 10−13). Strikingly, all ultra-hypermutated bMMRD cancers acquired early and conserved somatic mutations in DNA polymerases ɛ or δ. Polymerase driver mutations resulted in a unique mutation signature reflective of the repair processes and unique to MMRD/polymerase mutant cancers. Sequential tumor biopsy analysis revealed that MMR mutations and combined MMRD/polymerase mutations add a log-scale increment to cancer mutation load. BMMRD/polymerase mutant cancers rapidly amass mutations (∼600 mutations/cell division), reaching a threshold of ∼20,000 exonic mutations in <6 months. CONCLUSIONS/SIGNIFICANCE: Early-onset brain tumors from bMMRD patients offer an unobstructed view of mutation types and secondary pathways that drive carcinogenesis. We suggest a new mechanism of cancer progression where mutations develop in a rapid burst after ablation of replication repair. The high mutation load and threshold may be this cancer's Achilles' heel, exploitable for diagnosis and therapeutic intervention.
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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 | 0.001 | 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) | 0.004 | 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".