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Record W4409631950 · doi:10.1158/1538-7445.am2025-6501

Abstract 6501: The SRG RAT supports in vivo human cell xenotransplantation through enhanced tumor microenvironment interactions

2025· article· en· W4409631950 on OpenAlexaboutno aff
Caitlin M. O'Connnor, Diane Begemann, Kaitlin P. Zawacki, Fallon K. Noto, Grace Walton, Gabrielle Hodges Onishi, Taylor Branyan, Mike Schlosser, Goutham Narla

Bibliographic record

VenueCancer Research · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Research and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsXenotransplantationIn vivoTumor microenvironmentCancer researchMedicineTransplantationImmunologyBiologyTumor cellsInternal medicineGenetics

Abstract

fetched live from OpenAlex

Abstract The use of immunodeficient mice for human tumor engraftment has proven to be an essential model of human cancer, with uses ranging from basic science to translational research. However, low engraftment rates, slow growth, and smaller tumor volumes can be limitations. Previously, we reported a highly immunodeficient rat strain developed by Hera BioLabs, with the functional deletion of both the Rag2 and Il2rg genes on the Sprague-Dawley background (SRG RAT®), which lacks B, T, and NK cells. We have shown that the SRG rat supports the growth of multiple PDX and CDX models, however, the mechanisms underlying the supportive growth are not fully understood. Additionally, direct comparison of tumor growth and the rates of engraftment between the SRG rat and NSG mice has remained limited. Here, we subcutaneously engrafted two CDX and five PDX models into SRG or NSG animals and tracked tumor growth. These models included prostate, lung, ovarian, and uterine cancer models. In all cases, the engraftment and tumor growth rates were better supported in the SRG rat compared to the NSG mouse. Interestingly, the SRG rat is not more immunocompromised than the NSG mouse, suggesting alternative mechanisms leading to the supportive growth in the SRG rat. To understand this, we explored potential differences in the tumor microenvironment (TME) between models grown in the two host animals. We analyzed tumors from the H660 CDX model of castrate-resistant prostate cancer grown in either SRG rats or NSG mice using markers of the TME via immunohistochemistry. This analysis showed differences in the vasculature and macrophages between the two host species. We complemented this analysis using Xenium in situ by 10X genomics to allow for single-cell spatial imaging of engrafted tumors. This analysis showed upregulation of the human CXCL2, LAMP3, and other genes in H660 tumors grown in the SRG rat vs the NSG mouse. Interestingly, high expression of these two genes has been linked to poor prognosis in cancer. Combined, our data demonstrate that the SRG rat supports the growth of multiple human cancer types and displays enhanced tumor microenvironment interactions compared to NSG mice. Citation Format: Caitlin M. O'Connnor, Diane Begemann, Kaitlin P. Zawacki, Kelsey Barrie, Fallon K. Noto, Grace Walton, Gabrielle H. Onishi, Jessica Durant, Taylor Branyan, Mike Schlosser, Goutham Narla. The SRG RAT supports in vivo human cell xenotransplantation through enhanced tumor microenvironment interactions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6501.

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 score0.004
Threshold uncertainty score0.013

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)0.0040.001

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 head0.027
GPT teacher head0.398
Teacher spread0.371 · 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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