Abstract 1533: Characterizing tumor-immune surveillance in Li-Fraumeni syndrome
Bibliographic record
Abstract
Abstract Li-Fraumeni Syndrome (LFS) is a cancer predisposition syndrome associated with germline TP53 mutations and increased lifetime risk of developing multiple cancers. p53, the protein encoded by TP53, plays a role in regulating various aspects of immune cell biology. Accordingly, inactivation of p53 in tumor cells shapes interactions with the immune system and influences the anti-tumor immune response. Mutation or deletion of p53 in immune cells also favors tumor development and progression. However, to date, a comprehensive study of immune cell populations and the anti-tumor immune response has not been undertaken in the context of LFS. We hypothesized that germline TP53 mutation carriers possess dysfunctional immune cells and/or immune responses, supporting LFS-associated tumor progression. Our preliminary data indicate a profound defect in tumor-immune surveillance in an LFS murine model. We observed that MC38 cancer cells develop significantly faster into tumors when injected into LFS mice (harboring a gain-of-function mutation Trp53R172H/WT) compared to wildtype (WT) control mice. Importantly, we identified significant alterations in immune cell compositions and phenotypes within the tumor microenvironment as well as immune organs using flow cytometry. We are currently using ex vivo proliferation assays and cytotoxicity assays to investigate the impact of mutant p53 on the tumor-killing ability of immune effector cells. Altogether, an improved understanding of anti-tumor immune dysfunction in the context of germline TP53 mutation may reveal the potential for innovative therapies to target different facets of the immune system to intercept cancer progression in LFS patients. Citation Format: Camilla Giovino, Nicholas Fischer, Noel Ong, Ashby Kissoondoyal, Ran Kafri, David Malkin. Characterizing tumor-immune surveillance in Li-Fraumeni syndrome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1533.
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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) | 0.002 | 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".