Abstract 2032: Investigating the contribution of mTORC1-dependent signaling in the microenvironment to cancer onset in Li-Fraumeni syndrome
Bibliographic record
Abstract
Abstract We are investigating the role of stromal mTORC1 signaling in the development of malignancy in individuals with Li-Fraumeni Syndrome (LFS). LFS is a hereditary cancer predisposition syndrome that is associated with germline mutations in TP53. Individuals with LFS are faced with a significant lifetime risk of developing a wide spectrum of early-onset malignancies, thus necessitating the identification of pharmacologic agents to prevent tumour onset. Chemical inhibitors of the mTORC1 pathway have been shown to promote lifespan extension and delay or even prevent tumour onset in LFS mouse models. These findings are noteworthy, since they suggest that mTORC1 signaling plays a central role in favoring tumorigenesis in LFS. mTORC1 signaling has been implicated in promoting the pro-tumorigenic senescence-associated secretory phenotype (SASP). The SASP constitutes the release of soluble factors, such as cytokines, growth factors, and proteases from senescent cells into the microenvironment, thus promoting oncogenesis in a paracrine manner. Mutant p53 has also been shown to promote the secretion of a variety of pro-tumorigenic factors from various cell types. We believe that mTORC1 signaling in LFS stromal cells favors the production and secretion of pro-tumorigenic factors, which may induce changes in the surrounding tissue to promote development of a precancerous niche, thus favoring tumorigenesis. Our objectives are to investigate the contribution of stromal cells and stromal secreted factors in favoring tumorigenesis in LFS, and to investigate the role of mTORC1 signaling in favoring these altered stromal phenotypes. To determine whether patient-derived LFS fibroblasts with TP53 mutations secrete more pro-tumorigenic cytokines than non-LFS fibroblasts, we performed protein arrays with conditioned media (CM) from fibroblast cultures. We saw that LFS fibroblasts secrete markedly higher levels of pro-tumorigenic, SASP-related cytokines. Next, we treated spheroids formed from cancer cells of a common LFS subtype (osteosarcoma) with CM from LFS and non-LFS fibroblast cultures. LFS fibroblast CM had a robust pro-proliferative effect on spheroids, while non-LFS fibroblast CM inhibited spheroid growth. To further address these aims in vivo, we co-injected LFS fibroblasts with cancer cells of a common LFS subtype [rhabdomyosarcoma (RMS)] into Nude mice. We found that tumours formed from the co-injection of RMS cells with LFS fibroblasts grew to be larger than tumours formed from co-injection with non-LFS fibroblasts. Thus far, we have observed that LFS stromal constituents favor cancer cell proliferation in vitro and in vivo. We are currently investigating the role that mTORC1 signaling plays in these processes by repeating these studies following chemical and genetic perturbation of mTORC1 signaling in the LFS fibroblasts. Citation Format: Camilla Giovino, Nish Patel, Sangeetha Paramathas, Gaetano Zafarana, Ran Kafri, David Malkin. Investigating the contribution of mTORC1-dependent signaling in the microenvironment to cancer onset in Li-Fraumeni syndrome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2032.
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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".