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Record W4414871663 · doi:10.1101/2025.10.05.680584

Differential Impact of Lymphocytes on Radiation Response in Autochthonous versus Transplant Sarcomas in Syngeneic Mice

2025· preprint· en· W4414871663 on OpenAlexaff
Yvonne M. Mowery, Aastha Sobti, Alex M. Bassil, Amy J. Wisdom, Collin Kent, Chang Su, Jonathon E. Himes, Eric S. Xu, Nerissa T. Williams, Lixia Luo, David G. Kirsch

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
FundersNational Institutes of HealthAmerican Society of Clinical Oncology
KeywordsImmune systemImmunotherapyRadiation therapyTransplantationTumor cellsSarcomaImmunity

Abstract

fetched live from OpenAlex

Abstract Preclinical studies in transplant tumor models showing high cure rates with combined radiotherapy (RT) and immunotherapy have rarely translated to clinical success, and studies in autochthonous tumor models are limited. We hypothesized that lymphocytes differentially affect RT response in transplant versus autochthonous tumor models. Here, we compared tumor onset and growth delay after 0 or 20 Gy for autochthonous versus transplant soft tissue sarcomas in Rag2 −/− mice lacking an adaptive immune system versus immune-intact Rag2 +/− mice. While time to tumor onset did not differ between Rag2 −/− and Rag2 +/− mice in the autochthonous model, transplant tumor onset was significantly slower in immunocompetent mice. No transplant tumors in Rag2 −/− mice or autochthonous tumors were cured by RT, whereas 30.4% of Rag2 +/− mice with transplant tumors were cured. These results highlight the importance of including autochthonous tumors as complementary model systems to study the interplay between the immune system and RT response.

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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.269
Teacher spread0.253 · 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
Published2025
Admission routes1
Has abstractyes

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