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Abstract B15: Investigating the contribution of mTORC1-dependent stromal signaling to cancer onset in Li-Fraumeni syndrome

2020· article· en· W3092630632 on OpenAlexaff
Camilla Giovino, Nish Patel, Sangeetha Paramathas, David Malkin, Ran Kafri

Bibliographic record

VenueMolecular Cancer Research · 2020
Typearticle
Languageen
FieldMedicine
TopicCancer-related Molecular Pathways
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsCarcinogenesismTORC1Stromal cellCancer researchParacrine signallingCancerBiologyTumor microenvironmentSignal transductionPI3K/AKT/mTOR pathwayCell biologyGeneticsReceptor

Abstract

fetched live from OpenAlex

Abstract In this study, we are investigating the role of stromal mTORC1 signaling in the development of malignancy in individuals with Li-Fraumeni syndrome (LFS). LFS is a 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 tumor onset. Inhibitors of the mTORC1 pathway, namely rapamycin and metformin, have been shown to promote lifespan extension and delay or even prevent tumor onset in LFS mouse models. These findings are unsurprising—since wild-type p53 inhibits mTORC1—yet noteworthy, since they suggest that mTORC1 signaling plays a central role in favoring tumorigenesis in LFS. mTORC1 signaling has been implicated in promoting the protumorigenic 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. Interestingly, an increased incidence of senescence has been reported in LFS patients, and mutant p53 has been shown to promote the secretion of a quantitatively and qualitatively more protumorigenic SASP. As such, we believe that mTORC1 signaling in LFS stromal cells favors the production and secretion of protumorigenic factors, which may induce changes in the surrounding tissue to promote development of a precancerous niche, thus favoring tumorigenesis. Our objectives are 1) to investigate the contribution of stromal cells and stromal secreted factors in favoring tumorigenesis in LFS, and 2) to investigate the role of mTORC1 signaling in favoring these altered stromal phenotypes. To address these objectives, we co-injected patient-derived LFS fibroblasts with tumor cells of a common LFS subtype (rhabdomyosarcoma [RMS]) into immunocompromised mice and observed that tumors formed from the co-injection of RMS cells with LFS fibroblasts grew to be larger than tumors formed from co-injection with non-LFS fibroblasts. To determine whether LFS fibroblasts secrete more protumorigenic cytokines than non-LFS fibroblasts, we performed protein arrays with conditioned media (CM) from fibroblast cultures and saw that LFS fibroblasts secrete markedly higher levels of protumorigenic, SASP-related cytokines. To investigate the protumorigenic effects of fibroblast secreted factors, we treated spheroids formed from tumor cells of a common LFS subtype (osteosarcoma) with CM from LFS and non-LFS fibroblast cultures. LFS fibroblast CM had a robust proproliferative effect on spheroids, while non-LFS fibroblast CM inhibited spheroid growth. Thus far, we have observed that LFS stromal constituents favor tumor cell proliferation in vitro and in vivo, and based on the current literature, we believe that mTORC1 signaling plays a role in favoring these processes. Citation Format: Camilla M. Giovino, Nish Patel, Sangeetha Paramathas, David Malkin, Ran Kafri. Investigating the contribution of mTORC1-dependent stromal signaling to cancer onset in Li-Fraumeni syndrome [abstract]. In: Proceedings of the AACR Special Conference on Targeting PI3K/mTOR Signaling; 2018 Nov 30-Dec 8; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Res 2020;18(10_Suppl):Abstract nr B15.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.942

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.074
GPT teacher head0.382
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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