Abstract B004: Mapping loss of heterozygosity in Li-Fraumeni syndrome to uncover early molecular drivers of tumorigenesis
Bibliographic record
Abstract
Abstract Li-Fraumeni syndrome (LFS) is an autosomal dominant cancer predisposition syndrome caused by pathogenic germline mutations in TP53. Mutant p53 impairs DNA damage repair and causes a dysregulation in cell growth and division. LFS patients have a 40% chance of developing cancer during childhood and early adolescence, and an almost 100% lifetime risk of developing a wide range of cancers. Recent studies found that while 86% of tumors in LFS patients exhibit loss of the wild-type allele (loss of heterozygosity (LOH)), this event was absent in matched healthy tissue, indicating it is specific to pre-malignant and malignant cells. Moreover, this LOH appeared to occur several years before tumor diagnosis, likely in utero or early infancy, suggesting it plays a key, early role in LFS precancer niche development and eventual tumorigenesis (Light et al Nature Comm 2023). In cancers with somatic TP53 mutations, LOH is commonly a critical early event in tumor evolution, leading to a cascade of detrimental genomic events and accelerated tumor development. While the aftermath of TP53 LOH has been explored in sporadic cancers, the exact extent and contribution of LOH to cancer evolution and development in LFS remains poorly understood. We leveraged our extensive bank of LFS patient-derived skin fibroblast cell line collected either pre- or post-cancer dianosis to map the timeline of LOH in vitro across continuous passages. Droplet digital PCR (ddPCR) was used to determine the allelic ratio between the unaffected (WT) and mutated copy of TP53. Intrestingly, a signifcant increase in the abundance of the mutated allele was seen in post-cancer fibroblasts, suggesting LOH of the WT allele, which was not observed in pre-cancer fibroblasts. Bulk RNA sequencing (RNAseq) will be used to identify transcriptomic changes and subsequent GO analysis will reveal which biological pathways are affected before, during and after the LOH event, generating specific mapped LOH signatures in vitro. Pathways identified will also be mapped back to an in vivo LFS mouse model (Trp53 R172H/WT), probing for these in vitro transcriptomic signatures in single-cell RNA-sequencing (scRNAseq) data collected across stages of embryonic mouse development. LOH of WT TP53 precedes tumorigenesis, many years before tumor diagnosis in LFS patients. Knowing this, it is important to better understand mechanisms influencing, contributing and responding to LOH in LFS. These events are critical to cell evolution and precancer development in LFS and may offer insight into opportunities for tumor prevention or interception. This project is generating the first map of LOH in LFS patient-derived fibroblasts, integrating allelic analyses and transcriptomic data, providing critical insights into the earliest stages of precancer and tumor evolution. By uncovering pathways that are differently regulated across distinct LOH states, mechanisms of precancer niche development and potential therapeutic targets will be revealed. Citation Format: Hailey M. Stack, Ashby Kissoondoyal, David Malkin. Mapping loss of heterozygosity in Li-Fraumeni syndrome to uncover early molecular drivers of tumorigenesis [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Cancer Evolution: The Dynamics of Progression and Persistence; 2025 Dec 4-6; Albuquerque, NM. Philadelphia (PA): AACR; Cancer Res 2025;85(23_Suppl):Abstract nr B004.
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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) | 0.003 | 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".