Abstract B061: Dissecting pediatric sarcoma microenvironment using single-cell and spatial multi-omics
Bibliographic record
Abstract
Abstract Introduction: Pediatric sarcomas are the second most common solid tumor in children and adolescence. Despite advances in multimodal therapies, one-third of sarcoma patients do not survive the disease. The development of new treatments for sarcomas is hindered by a limited understanding of the tumor microenvironment (TME), especially the role of immune cells such as tumor-associated macrophages (TAMs). The abundance of TAMs significantly impacts the clinical outcomes of pediatric sarcoma patients, yet our knowledge of their molecular heterogeneity and interactions with other cell types in the TME remains limited. Methods: To characterize the transcriptomic and epigenomic heterogeneity of sarcoma TME, we profiled 32 treatment-naïve tumor samples from 5 pediatric sarcoma subtypes using the single-nuclei RNA+ATAC-Seq multiome assay. We also interrogated the spatial organization of the sarcoma TME using the multiplexed immunohistochemistry assay, Co-detection by indexing (CODEX), with a focus on the diversity and inter-cellular interactions of TAMs. We utilized in vitro co-culture models of human monocyte-derived macrophages, CD8 T cells and malignant sarcoma cells to validate immunosuppressive signaling interactions between TAMs and CD8 T cells predicted based on our multiome and CODEX data. Results: We sequenced 175,964 and 178,000 high-quality cells from RNA and ATAC modality respectively. Analysis of cell type frequencies across sarcoma subtypes showed that TAMs were the most abundant immune cells in the TME. We identified seven TAM subpopulations with distinct gene expression and epigenomic signatures. Among these, SPP1+ and C1QC+ TAM subsets exhibited higher immunosuppressive potential. By integrating gene expression and chromatin accessibility data, we identified key transcription factors that drive the transcriptional phenotypes of SPP1+/C1QC+ TAMs, including RUNX1 and ELF2. By ligand-receptor analysis using our snRNA-Seq data, we identified signaling ligand-receptor pairs that potentially mediate immunosuppression by SPP1+/C1QC+ TAMs, including SPP1-CD44 and HLA-E-KLRC1. Additionally, interactions like SEMA3D-NRP1 and LTBP3-ITGB5 between malignant cells and SPP1+/C1QC+ TAMs were observed in multiple sarcoma subtypes and significantly associated with inferior prognosis. Furthermore, our CODEX data revealed spatial co-localization between SPP1+TAMs and T cells, further corroborating the immune regulatory role of SPP1+TAMs in the sarcoma TME. Finally, our in vitro experiments revealed that macrophages co-cultured with sarcoma cell lines (RD and RH30) showed increased expression of C1QC/SPP1+ TAM markers, including CD163 and CD109, and demonstrated enhanced immunosuppressive effect on CD8+ T cells. Conclusion: This study presents a comprehensive single-cell multi-omics and spatial omics analysis of the pediatric sarcoma microenvironment. By identifying specific pro-tumorigenic TAM subsets and signaling interactions associated with inferior patient outcome, this study reveals novel insights into the role of TAMs in pediatric sarcomas. Citation Format: Zhan Zhang, Kyung Jin Ahn, Rumeysa Biyik-Sit, Changya Chen, Anusha Thadi, Chia-hui Chen, William Molina, Brian Lockhart, Theodore Laetsch, Lea Surrey, Malay Haldar, Kathrin Bernt, Vinodh Pillai, Kai Tan. Dissecting pediatric sarcoma microenvironment using single-cell and spatial multi-omics [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr B061.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".