Abstract 1269: Development of humanized PDX mouse model for high grade serous ovarian cancer
Bibliographic record
Abstract
Abstract Introduction: High-grade serous ovarian cancer (HGSOC) is one the most lethal gynecological cancer, accounting for 70% deaths and with a 5-year survival rate below 50%. Traditionally, HGSOC are considered 'immune-cold' tumors, characterized by low immune infiltration and poor response rates to single agent immune checkpoint blockers (ICB). To improve the therapeutic potential of immunotherapies in HGSOC, combination treatments are being applied such as the Phase 2 MEDIOLA trial, which aims to identify mechanisms of response and resistance. A major challenge in this field remains the lack of suitable preclinical models for ICB-based treatments. Although patient-derived tumor xenografts (PDX) are valuable for developing new therapies, they are limited by the absence of an immunocompetent host. Here we present humanized mouse models incorporating human immune cells. Methods: Female NRG-W41-3GS mice (ENW) aged 7-8 weeks were used to generate the humanized PDX model. HGSOC tumors originally sourced from human patients and maintained in our laboratory’s previous PDX models using NRG mice, were injected subcutaneously into ENW mice. After 3 weeks, 5x104 human hematopoietic CD34+ cells collected from umbilical cord blood of healthy donors were intravenously injected via the tail vein. To monitor human cell engraftment, bone marrow (BM) aspiration was performed at week 6 post-CD34+ cell injection, and peripheral blood (PB) samples were collected from saphenous vein starting from week 7 until the experimental endpoint. BM and PB samples were analyzed by flow cytometry using antibodies against human CD45 and mouse CD45 to determine humanization level. Tumors were harvested at the endpoint, embedded in paraffin, and remaining tumor tissues were cryopreserved for future. To confirm human immune cell infiltration into the tumors, fresh frozen paraffin embedded tumor sections were immunohistochemically stained with rabbit anti-human CD45 antibodies. Results: We successfully generated sixteen huENW PDX mice. Robust engraftment of human CD34+ cells was confirmed in each mouse, with human CD45+ cells ranging from 35% to 90% (mean ± SD: 76.2 ± 18.5%) in BM samples and from 7% to 35% in PB samples. The huENW PDX model have revealed strong evidence of human immune infiltration with CD45+ cells on immunohistochemistry of the PDX tumors and spleen. Conclusion: Humanized mouse models offer a valuable resource for advancing HGSOC research. Our humanized PDX HGSOC mouse model demonstrated significant engraftment of human immune infiltration into HGSOC tumors. Future works will focus on characterizing tumor cell heterogeneity, analyzing spatial gene expression patterns and human immune cells distribution using Xenium in situ coupled with scRNA seq. We believe our model has the potential to enhance our understanding of HGSOC clonal diversity, microenvironment interactions, and contributing to the development of new therapies. Citation Format: Tsz Yin (Jacky) Lam, Farhia Kabeer, Shary Chen, Makoto Kishida, Yuchen Ding, Lorena Zoltan, Yvette Drew, David Huntsman. Development of humanized PDX mouse model for high grade serous ovarian cancer [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 1269.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".