Abstract 4215: Single-cell proteomic characterization of the tumor microenvironment in humanized NOG-EXL patient-derived xenograft (PDX) models
Bibliographic record
Abstract
Abstract Introduction: CD34+ hematopoietic stem cell (HSC) derived humanized mice engineered to express human cytokine transgenes are promising models for the evaluation of novel immunotherapeutic treatments. While flow cytometry-based analyses have confirmed the presence of many human immune cell populations within humanized mice bearing patient-derived tumor xenografts (PDX), comprehensive high-dimensional immunophenotyping of the tumor microenvironment (TME) of these models remains limited. Beyond this, the relative influence of PDX vs. donor CD34+ HSC populations on TME composition is poorly understood. Here, we characterize the quantity and diversity of immune cell subpopulations in the TME, bone marrow, and spleen of humanized NOG-EXL (huNOG-EXL) mice using Cytometry by Time-Of-Flight (CyTOF) data with > 60 features. Methods: We performed CyTOF analysis on 250 dissociated samples from primary tumors (n = 15; n = 6 head and neck cancer, n = 6 ovarian cancer, n = 3 renal cell carcinoma), as well as xenograft (n = 59), spleen (n = 88), and bone marrow (n = 88) samples from huNOG-EXL mice (Taconic). Each sample was profiled with two partially overlapping antibody panels; an innate immunity-focused panel (40 features), and an adaptive immunity-focused panel (40 features). Results: CyTOF data were collected from over 10 million live immune cells for each of the adaptive and innate antibody panels. Unbiased clustering of single-cell marker expression revealed clusters corresponding to all major lymphoid and myeloid cell types. Therapeutically relevant cell states were identified including exhausted CD8+ T cells marked by PD-1 and Tim-3 expression, and PD-L1+ macrophages. Hierarchical clustering of bone marrow and spleen samples suggested that systemically, immune composition was not driven by tumor-intrinsic factors alone with many samples clustering on the basis of CD34+ stem cell donor ID. Conversely, clustering PDX samples revealed strong consistency in xenografts originating from the same primary tumor sample regardless of the chosen stem cell donor. The immune composition of primary tumor samples showed greater similarity with matched PDX samples versus unmatched PDX samples, further supporting that tumor-intrinsic factors drive immune composition in xenografts. Conclusions: We provide a high-resolution characterization of the immune phenotypes present in huNOG-EXL PDX models across three cancer types. These data captured substantial heterogeneity in the immune composition of humanized xenograft samples, and we demonstrate that immune composition in humanized xenografts is strongly driven by tumor-intrinsic features. Overall, this resource data supports the use of these models for immuno-oncology studies with future work focused on functionally validating specific immune cell populations of interest. Citation Format: Daniel Stueckmann, Jalna Meens, Sally Zhang, Shirley Hui, Sujana Sivapatham, Sephane Chevrier, Jennifer Pfeil, Lisa Martin, Maria Komisarenko, Philip Dube, Susan Prendeville, Hartland Jackson, Antonio Finelli, Gary Bader, Bernd Bodenmiller, Keith A. Lawson, Laurie Ailles. Single-cell proteomic characterization of the tumor microenvironment in humanized NOG-EXL patient-derived xenograft (PDX) models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4215.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".