Abstract A004: Radiation-induced changes to the immune microenvironment in an immunocompetent mouse model of Ewing sarcoma
Bibliographic record
Abstract
Abstract Background: Ewing sarcoma is a rare, aggressive, fusion oncoprotein-driven pediatric cancer. Patients with upfront metastatic or surgically unresectable disease commonly undergo radiation as part of standard of care therapy. Currently, little is known about the effect of radiation specifically on the immune microenvironment of Ewing tumors, as no routine biopsies or other clinical samples are acquired during radiation. Further, historically, the field has lacked an immunocompetent mouse model of Ewing sarcoma in which to study Ewing tumor-immune cell interactions. Given our interest in understanding the anti-tumor immune response specifically during times of DNA damage, we sought to model Ewing sarcoma in a humanized mouse model (where human immune cell interactions with human tumor cells can be examined) in order to examine the Ewing tumor immune microenvironment (TIME) during radiation. Here, we determine the likeness of our established humanized mouse model of Ewing sarcoma to that of human Ewing tumors at baseline and examine changes in the Ewing TIME upon delivery of radiation. Methods: Blood from humanized NSG mice was analyzed by flow cytometry to confirm human immune cell reconstitution. Human Ewing tumor cells were injected into humanized mice and allowed to grow for approximately three weeks. Tumors were treated with radiation (either single dose or fractioned doses x 5 days) using the MultiRad350 Precision irradiator and lead shields were used to isolate the tumor area in the radiation field. Immune cells infiltrating tumors with/without radiation exposure were subsequently analyzed by multiplexed immunohistochemistry, flow cytometry, PCR, and RNAseq analysis. Ewing tumor immune infiltrates (as baseline/without radiation) from humanized mice models were compared to that of patient Ewing tumors. Results: Ewing tumors from both established and primary cell lines were successfully established in humanized mice. Similar to patient primary Ewing tumor samples, local Ewing tumors in humanized mice demonstrate a proportion of T-cell infiltration, although the overall number of infiltrating immune cells is low. Macrophage populations in Ewing sarcoma are not as predominant as in tumors such as osteosarcoma and are represented in this model. Analyses detailing the spatial localization and transcriptional profiles of Ewing tumor immune infiltrates following radiation are ongoing. Conclusions: In the absence of syngeneic and transgenic models of Ewing sarcoma, the use of humanized mouse models is a feasible alternative to address specific questions regarding the Ewing sarcoma TIME. Understanding the Ewing immune microenvironment during radiation therapy, a commonly used treatment modality in Ewing sarcoma, provides clues as to promising agents that may be worthy of preclinical testing to enhance the anti-tumor immune response during radiation in high-risk patients. Citation Format: Jessica D. Daley, Elina M. Mukherjee, Anthony R. Cillo, Adriana C. Tufino, Nathanael G. Bailey, Tullia C. Bruno, Linda M. McAllister-Lucas, Dario A. Vignali, Kelly M. Bailey. Radiation-induced changes to the immune microenvironment in an immunocompetent mouse model of Ewing sarcoma [abstract]. In: Proceedings of the AACR Special Conference: Sarcomas; 2022 May 9-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(18_Suppl):Abstract nr A004.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".