Abstract 42: <i>In vivo</i> subcutaneous and orthotopic cancer xenograft modeling in the SRG immunodeficient rat
Bibliographic record
Abstract
Abstract Human cancer xenografts are a vital tool for understanding tumor biology, growth kinetics, and therapeutic efficacy using animal models. Historically, immunodeficient mice have been the standard rodent species for cancer xenograft modeling. However, an immunodeficient rat that supports a wide variety of human cancer cell types would provide a larger rodent strain for easier surgical manipulation, serial blood sampling, and provide a single model in which efficacy, pharmacokinetics, and toxicology can be performed. We have created a Sprague Dawley Rag2 -/-, Il2rg -/- rat (SRGTM OncoRat®) that provides a highly supportive environment for growing tumors of human origin. The SRG rat lacks B, T, and NK cells and readily supports the growth of multiple human cancer cell lines. The SRG rat is more immunodeficient than the Nude rat, suggesting it may be permissive to a wider variety of human cancer types. Here we demonstrate the utility of the SRG rat for both subcutaneous and orthotopic xenograft modeling. The SRG rat supports the growth of both lung and liver orthotopic cancers. In addition, the SRG rat supports the growth of orthoptic human glioblastoma multiforme in the brain. We use in vivo imaging to visualize tumor establishment and growth in subcutaneous, orthotopic, and metastatic models. Furthermore, our data show the ability of the SRG rat to support the growth of multiple different human cancer cell types subcutaneously in two different matrices, Matrigel® and VitroGel®. These data confirm that the SRG rat is an excellent host for studying human cancer. Our data demonstrate that the SRG rat has a high utility for studies using in vivo imaging, orthotopic tumor implantation, and standard subcutaneous tumor modeling. As the most immunodeficient rat commercially available, the SRG rat supports the growth of multiple human cancer types in a larger rodent strain relative to commercially available mouse models. Citation Format: Diane Begemann, Aida Javidan, Cynthia Dunn, Nicolas Johnston, R. Grace Walton, Valeriya Steffey, Ian Corbin, Niveen Fulcher, Cleusa De Oliveira, Hu Xu, Mila Uzelac, Andrew Deweyert, John A. Ronald, Susanne Schmid, Matthew O. Hebb, Fallon K. Noto. In vivo subcutaneous and orthotopic cancer xenograft modeling in the SRG immunodeficient rat [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 42.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".