Abstract IA021: Novel genomic features of POLE-mutant tumors: the utility for tumor classification and identification of new driver alleles
Bibliographic record
Abstract
Abstract Alterations in the exonuclease domain of POLE drive the development of ultramutated endometrial tumors with better prognosis. A hallmark of these tumors is the accumulation of G>T mutations in AGA sequences; however, understanding of their genomic features beyond the trinucleotide motifs is limited. We applied a novel computational framework to the whole-exome sequencing data of 524 endometrial tumors reported by The Cancer Genome Atlas network to analyze the extended DNA sequence context of ultramutation. We found that the presence of POLE driver alleles is associated with an increased frequency of G>T mutations in polypurine tracts. Sequences containing three consecutive purines, including the classic AGA context, are only moderately mutable, but the mutability increased abruptly as the tract length reached six or more purines. Moreover, mutations showed a strong preference for certain positions within the tracts, creating a characteristic quantifiable pattern. Using this signature, we developed a machine learning classifier to identify tumors with hitherto unknown POLE drivers and validated two new drivers, POLE-E978G and POLE-S461L, by functional assays in yeast. Unlike previously known pathogenic variants, the E978G substitution affects the DNA polymerase rather than exonuclease domain of POLE. We further show that the extended genomic signature of POLE ultramutation is shared by tumors with POLD1 drivers. These findings uncover new mechanisms of ultramutation and highlight the limitations of the current focus on POLE exonuclease domain variants when stratifying patients. Citation Format: Daria Ostroverkhova, Kathryn Tyryshkin, Annette K. Beach, Elizabeth A. Moore, Yosef Masoudi-Sobhanzadeh, Stephanie R. Barbari, Igor B Rogozin, Konstantin V Shaitan, Anna R Panchenko, Polina V. Shcherbakova. Novel genomic features of POLE-mutant tumors: the utility for tumor classification and identification of new driver alleles [abstract]. In: Proceedings of the AACR Special Conference on Endometrial Cancer: Transforming Care through Science; 2023 Nov 16-18; Boston, Massachusetts. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(5_Suppl):Abstract nr IA021.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".