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Abstract PR13: Surgical excision of the primary tumor in osteosarcoma model results in enhanced metastatic growth by modulating the lung immune microenvironment

2020· article· en· W3043655441 on OpenAlexaboutno aff
Michelle P. Kallis, Caroline Maloney, Morris Edelman, Samuel Z. Soffer, Marc Symons, Bettie M. Steinberg

Bibliographic record

VenueCancer Research · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Research and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMetastasisGefitinibOsteosarcomaPrimary tumorImmune systemPerioperativeLungContext (archaeology)Cancer researchTumor microenvironmentPathologyInternal medicineCancerImmunologySurgeryEpidermal growth factor receptorBiology

Abstract

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Abstract Surgery has been shown to enhance metastasis in a number of cancers. To examine this in the context of osteosarcoma, we implanted syngeneic murine K7M2 cells into the tibia of BALB/c mice, which we had shown previously to result in immediate seeding of K7M2 cells into the lung. One week post-implantation, mice were assigned to either control or surgical groups and all mice were euthanized at week 4 post-implantation. Mice that underwent amputation of the primary tumor displayed an increase in the number of pulmonary surface nodules as compared to tumor-bearing control animals (17 vs. 9.5; p<0.05), showing that excision of the primary tumor indeed enhances metastasis in this model. As we previously showed that K7M2 metastasis without surgical intervention is strongly inhibited by re-educating tumor-associated macrophages (TAMs) with gefitinib, we also examined the effect of gefitinib on surgery-accelerated metastasis. We found that perioperative gefitinib abrogated the effects of primary tumor removal on the development of pulmonary metastases (17 vs. 6; p< 0.01), strongly suggesting that TAMs also mediate surgery-accelerated metastasis. To examine changes in the pulmonary immune milieu, we utilized the same model of surgery-accelerated metastasis. Lungs were homogenized and immune cell populations were analyzed using flow cytometric analysis at 48 hours and 3 weeks after surgery. We found that surgery caused a significant reduction in antitumor macrophages, identified by MHCII positivity (21.1% vs. 13.2%; p<0.01), and a significant increase in protumor macrophages, identified by CD206 positivity (33.0% vs. 42.4%; p<0.01), at 48 hours after surgical resection. There was no change in the proportion of CD4+ or CD8+ T cells at this time point. At 3 weeks post-surgery the proportion of macrophage populations became similar for both groups, but there was now a small but significant reduction in the CD4+ and CD8+ T cells in the surgical group (68.4% vs. 60.3%; p<0.05 and 25.0% vs. 19.3%; p<0.01). In conclusion, surgical removal of the primary tumor accelerated the growth of pulmonary metastases in a murine model of OS. Gefitinib was able to abrogate this effect, suggesting macrophages as key cellular mediators. Furthermore, surgery altered the macrophage phenotype within the lungs in the acute postoperative period toward a protumor state, subsequently reducing the CD4+ or CD8+ T-cell populations. Taken together these data suggest that surgery contributes to enhanced metastatic outgrowth by altering the immune microenvironment to an immunosuppressive and protumor state. This abstract is also being presented as Poster B38. Citation Format: Michelle P. Kallis, Caroline Maloney, Morris Edelman, Samuel Z. Soffer, Marc Symons, Bettie M. Steinberg. Surgical excision of the primary tumor in osteosarcoma model results in enhanced metastatic growth by modulating the lung immune microenvironment [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 PR13.

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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)0.000
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 score0.022
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.034
GPT teacher head0.342
Teacher spread0.308 · 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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Citations2
Published2020
Admission routes1
Has abstractyes

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