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Abstract B016: Cytokines derived from tumor-initiating osteosarcoma cells mediate a novel self-seeding mechanism relevant to growth of primary and metastatic tumors

2022· article· en· W4296230870 on OpenAlexaboutno aff
Ryan D. Roberts, Amy C. Gross, James B. Reinecke, Amanda J. Saraf

Bibliographic record

VenueClinical Cancer Research · 2022
Typearticle
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsOsteosarcomaPrimary tumorMetastasisMedicineCancer researchCytokinePathologyCancerImmunologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Osteosarcoma is the most common pediatric bone tumor. Death from osteosarcoma is almost always due to metastatic spread to the lungs, which largely occurs after the primary tumor has been resected and treated with chemotherapy. Others have suggested that the primary tumor elaborates factors that suppress metastasis, though mechanisms explaining this phenomenon have not been well-validated suggested that primary osteosarcoma tumors suppress metastasis by recruiting circulating tumor cells back to the tumor via self-seeding, though the mechanism(s) of recruitment remained poorly defined. We found that osteosarcoma cells induce chemotaxis of other osteosarcoma cells in vitro. We previously demonstrated that osteosarcoma cells produce IL6 and CXCL8. Here, we found that both cytokines induced chemotaxis of osteosarcoma cells. Conversely, inhibition of both cytokines prevented osteosarcoma-induced osteosarcoma chemotaxis. These results suggested a model wherein tumor cells attract other tumor cells via inflammatory cytokine signaling. Within a living organism, such mechanisms might have implications for therapy. We wondered whether resection of a primary osteosarcoma tumor might redirect circulating tumor cells from the primary tumor to the lung (the “next best” site). To test this hypothesis, we generated orthotopic tibial tumors in mice. One cohort of mice underwent amputation of the primary tumor while another retained their primary tumors. At euthanasia, mice that underwent amputation had significantly increased tumor cell burden in the lungs. In separate experiments, lungs from mice with established primary tumors showed significantly less osteosarcoma lung burden than was seen in mice that did not have a primary tumor. Many infiltrating (labeled) tumor cells were subsequently identified within the primary tumor, strongly supporting the self-seeding model. Finally, we asked whether this same self-seeding mechanism might drive events important to lung metastasis. To test this hypothesis, mice were first inoculated with green-labeled osteosarcoma cells to induce lung metastasis. Fourteen days later, the same mice received inoculation with red labeled osteosarcoma cells. Strikingly, we found that lesions contained many more red- than green-labeled osteosarcoma cells. In addition, we have demonstrated that inhibition of IL6/CXCL8 blocks circulating tumor cell recruitment to established lung niches. This suggests circulating tumor cells preferentially target an established niche within the lung in an IL6/CXCL8 dependent manner in the absence of a primary tumor. Collectively, our data suggest that a loss of self-seeding in osteosarcoma on the excision of the primary tumor increases the metastatic burden. We are now exploring ways to leverage this biology in the development of novel therapies that prevent metastatic disease. Citation Format: Ryan D. Roberts, Amy C. Gross, James B Reinecke, Amanda Saraf. Cytokines derived from tumor-initiating osteosarcoma cells mediate a novel self-seeding mechanism relevant to growth of primary and metastatic tumors [abstract]. In: Proceedings of the AACR Special Conference: Sarcomas; 2022 May 9-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(18_Suppl):Abstract nr B016.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.162
GPT teacher head0.422
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), 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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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