Abstract 1277: <i>Ex ovo</i> system for rapid and quantitative modelling of immunotherapy responses in pre-clinical and patient-derived xenografts: PDX<i>ovo</i>
Bibliographic record
Abstract
Abstract Precision oncology for triple-negative breast cancer (TNBC) is urgently needed because not all patients will benefit from PDL1-based immunotherapy. Unfortunately, the expression of PDL1 or abundance of tumor infiltrated lymphocytes (TILs) does not correlate with objective response rates and/or overall survival. We have developed a novel patient-derived xenograft (PDX) model system that offers phenotype-based metrics to predict immunotherapy response prior to receiving treatment. In our PDX model (PDXovo), the patient’s tumour is engrafted onto the chorioallantoic membrane (CAM) of chick embryos. Our PDX model offers significant advantages over mouse PDXs which are expensive, time-consuming, and cannot evaluate immunotherapies. To test the efficacy of this model, we implanted immunocompetent mice with a murine mammary cancer cell line (serving as a surrogate for patients), and implanted tumours into the PDXovo system. We found our model to reliably recapitulate immunotherapy responses observed in mice, with anti-PDL1-treated tumours being significantly smaller in volume as compared to IgG-treated tumours. We also found our model to reliably predict patient response to adoptive cell therapy, wherein TILs were FACS-sorted from murine tumour digests, activated and expanded in vitro, and then engrafted together with cultured murine mammary carcinoma cells. We are now testing this model with patient-derived tumours of a variety of origins, in partnership with the Ontario Institute of Cancer Research. To date we have tested tumour responses to Pembrolizumab in five patient-derived samples from various sites, with an 80% success rate in xenograft formation. We have observed varied responses to immunotherapy, which did not correlate with PDL1 expression on TILs in these samples, highlighting the importance of such a qualitative model for precision oncology. This technology has the potential to make a positive impact in the clinic and improve objective response rates, increase progression-free survival intervals, and increase overall survival. Citation Format: Olivia R. Grafinger, Kabir A. Khan, Esther I. Matus, Sara Mar, Yan Li, David Goertz, Jean Gariépy, Katarzyna J. Jerzak, Robert Kerbel, Hon S. Leong. Ex ovo system for rapid and quantitative modelling of immunotherapy responses in pre-clinical and patient-derived xenografts: PDXovo [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 1277.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".