Abstract B039: Exploring the early genetic determinants of tumor risk in Li-Fraumeni Syndrome
Bibliographic record
Abstract
Abstract Background Li-Fraumeni Syndrome (LFS), caused by germline pathogenic variants in the TP53 tumor suppressor gene, is a highly penetrant cancer predisposition syndrome with lifetime cancer risk approaching 100%. Surveillance protocols have led to improved survival, and it is optimal to identify patients before tumor development. LFS exhibits broad phenotypic heterogeneity in age of onset and tumor subtype, likely related to other germline genetic/epigenetic variants and early acquired somatic variants. We have previously shown that loss of heterozygosity (LOH) and mutant TP53 copy gain arise years before tumor diagnosis (Light N, 2023). Hypothesis TP53 LOH and other somatic cancer driver mutations arise prenatally, with further clonal evolution after birth, and dictate the postnatal tumor risk in LFS. Aims Explore prenatal cancer driver mutations and their postnatal clonal evolution to define tumor risk in LFS. Methods We have recruited 20 children with LFS and collected their clinical history. Their archived Newborn Screening Dried Blood Spots (DBS), and those from 5 healthy controls, will be sequenced (leukocyte genomic DNA and cell-free DNA) using a deep, comprehensive cancer gene panel (905 genes) and Whole Genome Sequencing. Sequencing data will be analyzed using our established bioinformatic pipeline. We will focus on TP53 LOH and established cancer driver mutations seen in tumors associated with LFS, accompanied by global mutational landscape analysis. These findings will be compared to sequencing data from matched postnatal germline and tumor samples, accessible through our previous genomic studies. Results 25 participants have been enrolled in our study: 5 controls and 20 children with LFS, of which 65% (13/20) are female. 55% (11/20) have had a prior malignancy, one each of osteosarcoma, CNS sarcoma, and low grade glioma, two each of acute lymphoblastic leukemia, rhabdomyosarcoma and adrenocortical carcinoma, and four with astrocytoma. 15% (3/20) have a history of two prior malignancies. 35% (7/20) have had paired tumor/germline sequencing through the KiCS (Kids Cancer Sequencing) Program or the SickKids LFS registry. DNA extraction is currently underway for the first 10 DBS samples from patients with LFS, followed by DNA sequencing and analysis. Impact The data will provide critical insights into the prenatal origin of cancer and the unique clonal evolution related to cancer development. Our study has the potential to impact surveillance practices and discover new links between prenatally acquired mutations and pediatric cancers, which may reveal novel approaches for early therapeutic interception and prevention. Citation Format: Laura Raiti, Tanvi Anandampillai, Anita Villani, Adam Shlien, David Malkin, Yiming Wang. Exploring the early genetic determinants of tumor risk in Li-Fraumeni Syndrome [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Discovery and Innovation in Pediatric Cancer— From Biology to Breakthrough Therapies; 2025 Sep 25-28; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl_2):Abstract nr B039.
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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 | 0.001 |
| 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) | 0.003 | 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".