Abstract A013 Exploring the role of microbiota in cancer development in Li-Fraumeni Syndrome
Bibliographic record
Abstract
Abstract Li-Fraumeni Syndrome (LFS) is an inherited cancer predisposition syndrome caused by pathogenic TP53 germline variants and associated with an elevated risk to develop a wide spectrum of malignancies. LFS patients are prone to develop multiple cancers throughout their lifetime, with significantly earlier onset compared to the general population. Currently, there is no cure for LFS; the primary approach to managing LFS is early tumor surveillance to detect tumors in their early state while they are relatively easier to treat. Microbes have been increasingly recognized for their roles in cancer development and progression. Microbes can contribute to cancer risk via the production of DNA-damaging toxins and carcinogenic metabolites which can induce cancer-promoting inflammation. However, the interactions between the microbiome and mutant p53 in Li-Fraumeni Syndrome have not been fully explored. To investigate the contribution of microbes to cancer development in LFS, we depleted the microbiota of Trp53R172H/WT mice (LFS mice; TP53-R175H human homolog) using a wide spectrum of antibiotics. Following 4 days of antibiotic treatment, LFS mice were subcutaneously injected with the MC38 colon adenocarcinoma cell line. Antibiotic treatment was continued until the endpoint of the study. We observed that the antibiotic-treated LFS mice exhibited smaller tumor mass compared to untreated LFS mice, while no antibiotic-associated changes were observed in wildtype littermates. This finding might suggest that the gut microbiome contributes to cancer development in LFS mice. To interrogate whether the gut microbiome of LFS mice promotes tumor development, we conducted faecal filtrate transplant (FFT) from LFS mice—which transfers the microbial metabolites from the faeces in the absence of the microbes themselves—into the wildtype littermates via oral gavage. We observed that the FFT transferred from LFS mice to wildtype littermates was associated with increased tumor growth, which may further support the notion that the gut microbiome in LFS mice promotes tumorigenesis. Microbes can influence tumor development via many different mechanisms, one of which is by inducing cancer-promoting inflammation. The NF-κB pathway plays a central role in inflammation, and it is a crucial component of the immune response to microbial infection. NF-κB signalling is also linked to cancer initiation and progression through the promotion of chronic inflammation. Studies have demonstrated that mutant p53 upregulates the NF-κB pathway in vitro, and is activated in response to microbial infection. Here, we show that NF-κB activity was upregulated in the intestines of LFS mice. Upon FFT-treatment in wildtype p53 mice, the NF-κB activity in the intestine was upregulated. This finding suggests that the gut microbiome in LFS mice promotes inflammation in the intestine, which may further contribute to cancer-causing systemic inflammation. In summary, this work provides insight into the influence of the microbiome on cancer susceptibility in LFS. Citation Format: Noel WY Ong, Camilla M. Giovino, Nicholas W. Fischer, Pamela Psarianos, David Malkin. Exploring the role of microbiota in cancer development 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 A013.
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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 | 0.001 |
| 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".