Abstract 3047: Investigating the interaction between the microbiome and mutant p53 in Li-Fraumeni Syndrome
Bibliographic record
Abstract
Abstract Li-Fraumeni Syndrome (LFS) is a hereditary cancer predisposition syndrome in which affected individuals are prone to developing a wide spectrum of malignancies. LFS is caused by germline mutations in the TP53 tumour suppressor gene. LFS patients develop cancer significantly earlier than the general population and may experience multiple malignancies in their lifetime. Studies have indicated that environmental influences like radiation exposure and tobacco smoke may further increase cancer risk in LFS patients. Currently, there is no cure for LFS, and screening for early tumour detection is the standard of care for LFS patients. Recently, microbes have been found to be involved in cancer development, progression, and treatment responsiveness. Moreover, microbes have anti-tumourigenic properties that can be harnessed for therapeutic intervention. The interaction between microbes and cancer can be through contact-dependent, contact-independent, and/or immunological interactions. Profiling the microbiome in the context of cancer may provide prognostic and/or diagnostic options for patients. However, interactions between the microbiome and mutant p53 in LFS has not been studied to date. We induced dysbiosis using a combination of antibiotics in LFS mice harbouring the heterozygous Trp53 R172H mutation. Following 7 days of treatment, we challenged mice with subcutaneous injections of the MC38 colon adenocarcinoma cell line. We observed an increase in tumour volume in LFS mice treated with antibiotics; this change was not observed in p53 wildtype mice. These findings highlight potentially unique interactions between mutant p53 and the microbiome which influence tumour growth in the context of LFS. We also found that LFS mice treated with metformin have smaller tumours compared to untreated mice. A particular genus of bacteria, Faecalibaculum sp., was found to be upregulated in a cohort of metformin treated mice, and the abundance of this bacterium is inversely proportional to tumour volume. Zagato et al. have shown that Faecalibaculum rodentium has anti-tumourigenic properties through the production of short-chain fatty acid (SCFA). Altogether, these observations suggest that microbes may influence tumour growth in LFS mice. The study of interactions between the microbiome and mutant p53 and the influence of these interactions on cancer development in LFS may, with further study, provide avenues of prognostic and/or diagnostic interest. Differences in the microbiome in cancer-affected and cancer-free LFS patients may also introduce the possibility to exploit the microbiome for treatment and/or preventative purposes in these patients. Citation Format: Noel Wei Ong, Camilla Maria Giovino, Nicholas William Fischer, Pamela Psarianos, David Malkin. Investigating the interaction between the microbiome and mutant p53 in Li-Fraumeni Syndrome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3047.
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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 | 0.001 | 0.001 |
| 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".