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Abstract PR12: IL6-mediated self-seeding functions to prevent osteosarcoma metastasis

2020· article· en· W3047655686 on OpenAlexaboutno aff
Amanda J. Saraf, Amy C. Gross, Helene LePommellet, Sophia Vatelle, Ryan D. Roberts

Bibliographic record

VenueCancer Research · 2020
Typearticle
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsnot available
Fundersnot available
KeywordsMetastasisPrimary tumorOsteosarcomaCirculating tumor cellCancer researchTumor microenvironmentChemotaxisMedicinePathologyTumor cellsCancerImmunologyBiologyInternal medicineReceptor

Abstract

fetched live from OpenAlex

Abstract Osteosarcoma (OS) is the most common primary bone tumor, striking primarily adolescents and young adults. OS mortality occurs primarily as a consequence of metastatic spread to the lungs, and when OS metastasizes it resides in the lung 98% of the time. Oncologists have long observed that metastases tend to appear after removal of a primary tumor. Previously, others have explained this phenomenon by invoking the production of “metastasis suppressors” by the primary tumor. Recently, we and others have described how circulating tumor cells most commonly return to the primary tumor, where the microenvironment is particularly favorable to tumor growth. This occurs by way of a process called “self-seeding.” However, if OS cells exhibit true self-seeding, this raises a distinct possibility that resection of the primary tumor could affect the behavior of circulating tumor cells. We therefore sought to answer the question: does resection of a primary OS tumor drive redirection of circulating tumor cells toward sites of metastasis (“next best” sites)? To answer this, we first sought to identify candidate mediators of tumor cell chemotaxis that drive self-seeding. We examined production of a panel of candidate mediators by ELISA of conditioned OS supernatants. Transwell migration assays using the same candidates as chemoattractants identified responses to cytokines produced by OS cells. The results show that specific cytokines produced by most OS cells reliably mediate chemoattraction. We suspected that these cytokines play a role in OS self-seeding and that removal of a primary tumor would remove this signal, terminating self-seeding and redirecting circulating tumor cells toward colonization of the lung. To test this hypothesis we moved our studies into in vivo models. First, we examined tumor cell burden in the lung after amputation of an orthotopic tumor compared to that in mice that retained their primary tumor. Results showed that amputation of the primary tumor results in increased OS tumor cell burden in the lungs. We next asked how the presence of a primary tumor impacts the migration of tail vein-injected OS cells to the lung. Comparison of lungs from mice bearing a primary tumor to those with no primary tumor showed a decreased number of OS cells recovered from the lung. These labeled tumor cells were easily recovered in the primary tumors, suggesting a shift in recruitment from the lungs to the primary tumor. Our data suggest that a loss of self-seeding in OS on excision of the primary tumor increases metastatic burden. We are now exploring ways to leverage this biology in the development of novel therapies that prevent metastatic disease. This abstract is also being presented as Poster B31. Citation Format: Amanda Saraf, Amy C. Gross, Helene LePommellet, Sophia M. Vatelle, Ryan D. Roberts. IL6-mediated self-seeding functions to prevent osteosarcoma metastasis [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr PR12.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
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 head0.146
GPT teacher head0.415
Teacher spread0.269 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2020
Admission routes1
Has abstractyes

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