Abstract A039: Pediatric pan-cancer characterization of transposable elements and their modulation by germline <i>TP53</i> variants
Bibliographic record
Abstract
Abstract Transposable elements (TEs) are dynamic repetitive regions constituting 50% of the genome. In adult cancers, TEs contribute to carcinogenesis through structural rearrangements that disrupt tumor suppressors and by onco-exaptation, which drives aberrant oncogene expression. TEs are characterized in adult malignancies, with epithelial cancers harbouring significantly more new insertions. However, the role of TEs in pediatric cancer is unknown. Many pediatric cancers arise from predisposition, such as Li-Fraumeni Syndrome, which is caused by germline TP53 (gTP53) variants. TP53 suppresses TEs by binding L1 elements to inhibit transcription and by inducing apoptosis in cells with high retrotransposition. Moreover, adult cancers with somatic TP53 variants harbour significantly more TEs than wildtype tumors. To investigate the pediatric context, we conducted a pan-cancer analysis of germline and tumor TE landscapes in pediatric patients with (n = 66 germline; n = 40 tumor) and without (n = 465 germline; n = 276 tumor) gTP53 variants. Germline TEs were identified using MELT, xTEA, and INSurVeyor, while tumor TEs were called with xTEA and TotalReCall. TEs were consolidated across individuals using JASMINE and annotated with AnnotSV. We excluded 21% of germline and 2% of tumor TEs classified as common, defined as present in over 3% of gnomAD or pediatric non-cancer controls (n=189). Overall, 11% of pediatric tumors harbored at least one L1 insertion, with a median of 0 insertions per tumor, substantially lower than the 21% reported in adult cancers using the same TE callers (Solovyov et al). Frequency in germline or tumor TEs did not vary significantly by tumor type, age of onset, sex, prior treatment, or disease state. Germline and tumor TEs were found in 35 and 147 cancer genes respectively. Tumor TEs significantly affected many cancer pathways, including those involved in mitosis. While no structural variants (SVs) involved TEs at both breakpoints, 171 SVs in six samples were associated with a TE at one breakpoint. Importantly, both germline and tumor TE burden did not differ between gTP53 carriers and wildtype individuals, nor did the number of evolutionarily young or full-length elements. Pathway enrichment analyses revealed no significant differences in TE-affected pathways between gTP53 and wildtype groups. However, a gradient-boosted tree model trained on TE distribution across genomic windows predicted gTP53 status with an AUPRC of 0.77, suggesting that gTP53 variants shape the germline TE landscape at a positional level rather than through global frequency changes. In summary, pediatric cancers exhibit a quiescent TE landscape, with low insertion rates across tumor types. gTP53 variants do not alter the germline or tumor TE frequency,but influence the location of germline insertions. TE analysis in pediatric cancer and gTP53 carriers will enhance our understanding of tumorigenesis, informing future treatment approaches. Citation Format: Brianne Laverty, Shilpa Yadahalli, Safa Majeed, Ashby Kissoondoyal, Alexander Solovyov, Scott Davidson, Yisu Li, Mehdi Layeghifard, Adam Shlien, Vallijah Subasri, David Malkin. Pediatric pan-cancer characterization of transposable elements and their modulation by germline TP53 variants [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr A039.
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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 | 0.001 |
| 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".