Abstract B019: Characterization of the extracellular vesicle proteome in Li-Fraumeni Syndrome
Bibliographic record
Abstract
Abstract Li-Fraumeni Syndrome (LFS) is a hereditary cancer predisposition syndrome associated with germline mutations in the TP53 tumor suppressor gene. People with LFS are burdened with an increased lifetime risk to develop a broad spectrum of malignancies, with a 21.2-fold higher relative risk for cancer development in children (0-19 years) versus non-LFS children. Mutations in TP53 (mutp53) abrogate normal tumor-suppressive functions, including DNA repair, cell cycle arrest, metabolism, and apoptosis. This causes cells to accumulate DNA damage, alters cellular behaviors inducing pro-proliferative effects, and alters the cellular secretome, including extracellular vesicles (EVs). In cancer, EVs are a key component of the tumor microenvironment and facilitate core cancer hallmark pathways to promote tumorigenesis and metastasis. LFS mutp53 carriers may be “primed” for cancer initiation, wherein pathogenic germline TP53 variants establish a pre-cancerous niche within the body. However, to our knowledge, no study has investigated the role of EVs in non-malignant mutp53 cells on the pre-cancerous state. We hypothesize that LFS cell-derived EVs contain unique cargo which play local and systemic roles to promote pre-cancer niche priming and tumorigenesis in LFS. EVs were isolated from cell culture conditioned media by differential ultracentrifugation from skin biopsy-derived dermal fibroblasts from LFS patients (R248Q, R273C, R273H mutations) and wildtype (WT) controls, each in triplicate. EVs were characterized using Western blotting, transmission electron microscopy, and nanoparticle tracking analysis. Liquid chromatography-tandem mass spectrometry identified and quantified EV protein cargo for differential expression analysis in R. Fibroblast-derived EV proteomes in both LFS and WT were distinguishable from their parent cell proteomes by principal component analysis. Within EV groups, we identified four significantly differentially expressed proteins (p<0.05; GAS6, P3H1, PSG4, VNN1), and key functions in LFS EVs compared to WT EVS. We further identified differences between EVs derived from specific LFS hotspot mutations, R248Q and R273C, including an additional four differentially expressed proteins (p<0.05; CDH2, KRT10, RPLP0, TPI1). Key functions and processes associated with inflammation, invasion, apoptosis, and immune cell interactions are being further explored through in vitro and in vivo methods. For the first time, we have demonstrated that basal differences exist in the EV protein cargo of LFS cells. Liquid biopsies provide a minimally invasive method to assess a patient’s cancer risk for a personalized medicine approach to LFS surveillance protocols. Further characterization of the LFS EV proteome may identify liquid biopsy biomarkers for early detection of cancer development and risk assessment for LFS individuals. These findings also suggest a novel approach to therapeutics in targeting LFS derived EVs. Investigating differentially expressed LFS EV proteins will identify potential targets unique to LFS individuals. Citation Format: Paula R. Quaglietta, Ashby Kissoondoyal, David Malkin. Characterization of the extracellular vesicle proteome in Li-Fraumeni Syndrome [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 B019.
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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) | 0.001 | 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".