Abstract B09: DNA polymerase mutations trigger rapid onset of ultra-hypermutant malignant brain tumors in children with biallelic mismatch repair deficiency
Bibliographic record
Abstract
Abstract Background: Biallelic Mismatch Repair Deficiency (bMMRD) is a childhood cancer predisposition syndrome caused by germline mutations in MSH2, MSH6, MLH1, and PMS2. The leading cause of death is malignant brain tumors. The genomic landscape and secondary somatic mutations of bMMRD brain tumors are unknown. Methods: We analyzed 27 cancers and corresponding normal tissues from bMMRD patients using genome, exome sequencing and SNP-arrays. Additionally, we performed sequential sequencing from five primary and recurrent tumor pairs. Results: BMMRD malignant brain tumors harbored massive numbers of substitution mutations (>250/Mb), 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. All ultra-hypermutated bMMRD brain cancers acquired early and conserved somatic mutations in DNA polymerases ε or δ. We examined a panel of eight genes involved in brain tumor pathogenesis (TP53, EGFR,NF1, RB1, ATRX, PDGFRA, BRAF, ACVR1) and found that 80% of ultra-hypermutant tumors carried a mutation in five or more of these genes, with 70% of the mutations fitting the bMMRD/POL signature. Sequential tumor analysis revealed that brain tumors acquired over 20,000 mutations in less than 6 months during malignant transformation. However, recurrent glioblastomas did not display a higher mutation load than ultra-hypermutant primary tumors with a polymerase mutation. Conclusions/Significance: Early-onset brain tumors from bMMRD patients have a unique mechanism of malignant progression through secondary mutations in DNA polymerases. During transformation, brain tumors quickly reach a threshold of mutations developed in a rapid burst once a mutation in a DNA polymerase is acquired. The high mutation load and threshold of bMMRD cancers may be its Achilles' heel, exploitable for diagnosis and therapeutic intervention. Note: This abstract was not presented at the conference. Citation Format: Adam Shlien, Brittany B. Campbell, Richard de Borja, Ludmil B. Alexandrov, Daniele Merico, David Wedge, Peter Van Loo, Patrick S. Tarpey, Paul Coupland, Aaron Pollett, Tatiana Lipman, Abolfazl Heidari, Shriya Deshmukh, Moritz Gerstung, Diana Merino, Manasa Ramakrishna, Marc Remke, Roland Arnold, Gagan B. Panigrahi, Samina Afzal, Valerie Larouche, Harriet Druker, Jordan Lerner-Ellis, Matthew Mistry, Rina Dvir, Ronald Grant, Ronit Elhasid, Roula Farah, Glenn P. Taylor, Paul C. Nathan, Sarah Alexander, Shay Ben-Shachar, Nada Jabado, Steven Gallinger, Shlohmi Constantini, Peter Dirks, Annie Huang, Steven W. Scherer, Richard G. Grundy, Carol Durno, Melyssa Aronson, M Stephen Meyn, Michael D. Taylor, Zachary F. Pursell, Christopher E. Pearson, David Malkin, P Andrew Futreal, Cynthia Hawkins, Eric Bouffet, Michael D. Taylor, Peter J. Campbell, Uri Tabori. DNA polymerase mutations trigger rapid onset of ultra-hypermutant malignant brain tumors in children with biallelic mismatch repair deficiency. [abstract]. In: Proceedings of the AACR Special Conference: Advances in Brain Cancer Research; May 27-30, 2015; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2015;75(23 Suppl):Abstract nr B09.
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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) | 0.002 | 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".