Abstract 6453: Dual-omic characterization of the immune landscape of pediatric solid tumors revealed putative epigenetic mechanisms of immune evasion
Bibliographic record
Abstract
Abstract Background: There is a general lack of efficacy of immune checkpoint blockades (ICBs) in pediatric solid tumors. Methylation reprogramming is a major player in tumor immune microenvironment (TiME) modeling and ICB resistance. Its role in pediatric-specific TiME has yet to be explored. Hypothesis: Dual-omic classification, combining DNA-methylation and RNA-sequencing, can identify tumors with epigenetically altered TiME, potentially responsible for ICB resistance. Objective: Identify and characterize pediatric solid tumors with epigenetically altered immune phenotypes. Methods: We studied bulk tumor gene expression and DNA-methylation profiles from 184 pediatric extracranial solid tumors. To individualize immune phenotypes, we used similarity network fusion (SNFtool) for dual-omic clustering of immune genes. Clusters were compared for differential expression and differential methylation using volcano3D. We studied the relationship of enhancer methylation to gene expression (ELMER package) to reconstruct regulatory networks (genes and pathways). Transcription factor and cofactor binding motifs were identified to characterize the regulatory element landscape specific to each phenotype. Adjusted p-values <0.01 were deemed significant. Results: A total of 184 samples were included. SNF clustering identified 3 phenotypes regrouping 52 (28%), 83 (45%), 49 (27%) samples in clusters (cl) 1, 2 and 3, respectively. Cl1 exhibited low expression of immune genes (“cold” phenotype), cl2 overexpressed immune genes (“hot” phenotype), and cl3 featured global hypermethylation (epigenetically “altered” phenotype). Both hot and altered phenotypes overexpressed immune checkpoints (CD274, PDCD1), T-cell activator chemokines (CXCL9, CXCL10) and pro-inflammatory pathways, central to ICB sensitivity. However, only hot tumors overexpressed genes and pathways essential for antigen (Ag)-presenting machinery and immune recognition. The methylation regulatory network indicated that the derepression of genes and pathways related to Ag-presenting machinery (mainly MHC-II and regulators) was specific to hot tumors. In contrast, gene silencing in the altered phenotype relied on hypermethylation/downregulation of the CIITA cofactor domain, the master control of MHC-II genes, interferon-regulated factors (IRFs), and immune-specific master regulators (SPI1 and BATF2). These factors are crucial for anti-tumor immunity and ICB sensitivity. Conclusion: We demonstrated that DNA-methylation participates in reshaping the TiME of pediatric solid tumors. A subset of tumors with epigenetically altered immune phenotype is characterized by an inflamed immune environment but altered antigen presentation by methylation reprogramming. Future studies should investigate methylation modulators to reverse these mechanisms and enable ICB sensitivity. Citation Format: Stephanie Bianco, Anas Belaktib, Virgile Raufaste Cazavieille, Charles Joly-Beauparlant, Mona Patoughi, Lara Herrmann, Emeric Texeraud, Sylvie Langlois, Thomas Sontag, Alex Richard-St-Hilaire, Vincent-Philippe Lavallée, Thai Hoa Tran, Sonia Cellot, Daniel Sinnett, Arnaud Droit, Raoul Santiago. Dual-omic characterization of the immune landscape of pediatric solid tumors revealed putative epigenetic mechanisms of immune evasion [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 6453.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".