Abstract A010 The role of <i>TP53</i> on transposable elements in pediatric cancer
Bibliographic record
Abstract
Abstract Background: Transposable elements (TEs) are dynamic repetitive regions which generate mutations and structural variants. TP53 plays a crucial role in suppressing TE movement to maintain genomic stability. The relationship between TP53 and TEs has been extensively studied in tumours, but not the germline. Individuals with germline TP53 pathogenic variants have Li-Fraumeni Syndrome (LFS), a cancer predisposition syndrome with a high lifetime risk of cancer in various tissues. This study aims to characterize the TE landscape in individuals with Li-Fraumeni Syndrome and determine how this contributes to their increased cancer risk. Methods: MELT and xTea were used to identify TEs in children with (n=48) or without (n=198) a germline TP53 variant. TE calls were merged with SURVIVOR and variants were annotated with AnnotSV. To assess the influence of TP53 on TE location, we quantified TEs across genomic windows and identified the top 100 significantly different regions with the Mann-Whitney U test, adjusting for multiple comparisons. We used these regions to develop a gradient-boosted tree model with 5-fold cross-validation to predict TP53 status. Results: Individuals with germline TP53 variants harboured significantly fewer ALU and LINE1 elements in their germline genome compared to the control dataset (p<0.001). The phenomenon was consistent across chromosomes and significant in chromosomes 5 and 11 (FDR<0.05). A gradient-boosted tree model was able to differentiate patients with and without a germline TP53 variant with an AUPRC of 0.77 on an unseen test set. Conclusion: Germline TP53 variants may effect the frequency and location of germline TE insertions, which may influence nearby genomic variations and lead to cancer development. Analyzing TEs in individuals with LFS will enhance our understanding of accelerated cancer development in these patients, informing future research for diagnostic and therapeutic approaches. Citation Format: Brianne Laverty, Shilpa Yadahalli, Vallijah Subasri, David Malkin. The role of TP53 on transposable elements in pediatric cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr A010.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".