Abstract 2689: Investigating the molecular mechanisms linking disrupted growth homeostasis to tumourigenesis in Li-Fraumeni Syndrome
Bibliographic record
Abstract
Abstract Li-Fraumeni Syndrome (LFS) is a highly penetrant cancer predisposition syndrome caused by germline TP53 mutations. Patients face an increased lifetime risk to develop a broad spectrum of cancers. Implementation of comprehensive cancer screening protocols has led to improvements in survival for LFS patients. However, these interventions pose a significant burden to both patient quality of life and health resource utilization. While these interventions will always be important, recent evidence has suggested an additional approach which may improve patient outcomes: pharmacological prevention of cancer onset. mTORC1 inhibition selectively targets tumor initiation. While rapamycin, an mTORC1 inhibitor, has not shown efficacy in treatment of established cancers, it is reported to prevent tumor onset in mice that are genetically predisposed to cancer, including mouse models of LFS. mTORC1 is associated with protein synthesis and cellular growth. Hyperactivated cellular growth is associated with gain-of-function (GOF) TP53 mutations that underlie many cases of LFS. Wild-type p53 (wtp53) inhibits mTORC1 and, as a consequence, biosynthesis. We have shown that mutant p53 with oncogenic GOF (mutp53) may promote the opposite effect. Under conditions of inappropriately elevated protein synthesis, as is seen in cancer, stress responses, such as the unfolded protein response (UPR), are activated. UPR activation contributes to the secretion of pro-tumorigenic cytokines. We hypothesized that under conditions of elevated protein synthesis, mutp53 engages in a feedforward activation of biosynthesis which hyperactivates UPR-mediated secretion of pro-tumorigenic cytokines. In order to harness the cancer risk reduction potential of mTORC1 inhibition, we undertook investigation of the stress responses which may underlie tumorigenesis in LFS. We sought to establish whether mTORC1 ablation in the tumor microenvironment could influence growth of tumor xenografts formed from the co-injection of LFS patient-derived mutp53 fibroblasts and RH4 rhabdomyosarcoma cells, a representative LFS spectrum cancer cell line. While both wtp53 fibroblasts and mutp53 fibroblasts supported tumor growth, only the tumor growth reinforced by mutp53 fibroblasts was suppressed by rapamycin. Additionally, we utilized protein arrays to profile conditioned media collected from mutp53 and wtp53 fibroblast cultures. Mutp53 fibroblasts secreted higher levels of various pro-tumorigenic cytokines compared to wtp53 fibroblasts, which was partially negated by rapamycin. UPR target gene expression is elevated in fibroblasts expressing mutp53, and this effect is negated by rapamycin. Altogether, our work supports a role for mutp53-driven, mTORC1-mediated hyperactivated protein biosynthesis and subsequent engagement of the UPR, resulting in production of a pro-tumorigenic secretory milieu. Citation Format: Camilla Giovino, Frank Telfer, Nish Patel, Sangeetha Paramathas, Ran Kafri, David Malkin. Investigating the molecular mechanisms linking disrupted growth homeostasis to tumourigenesis in Li-Fraumeni Syndrome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2689.
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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) | 0.005 | 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".