Abstract B004: Mapping loss of heterozygosity in Li-Fraumeni syndrome to uncover early molecular drivers of tumorigenesis
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».