Abstract B057: Dual-omic characterization of pediatric solid tumors identified a subset of tumors with epigenetically altered immune phenotype
Bibliographic record
Abstract
Abstract Background: Immune checkpoint blockades (ICBs) lack clinical efficacy in all-coming pediatric solid tumors. Gene expression-based classification of the tumor immune environment (TiME) can identify a subset of pediatric tumors with a rich immune environment, potentially actionable by ICBs. Methylation reprogramming is a major player in TiME modeling and ICB resistance, its role in pediatric tumors has been little 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 RNA-sequencing and DNA-methylation quantitative data from pediatric extra-cranial solid tumors. We used similarity network fusion (SNF) to individualize immune phenotypes. A preselected customized panel of 1226 immune genes, and their corresponding probes, were used for dual-omics clustering. Differential expression (DE) and methylation (DM) were performed between clusters using volcano3D. Scatter plots visualized the cis-regulation of probe-gene pairs with significant DE-DM. Gene set enrichment analyses (GSEA) were performed with clusterProfiler and gometh packages for DE and DM, respectively. Adjusted p values <0.01 were retained. Results: 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 was characterized by a low expression of immune genes (cold phenotype), cl2 by overexpression in immune genes (hot phenotype), and cl3 by global hypermethylation (epigenetically altered phenotype). Both cl2 and cl3 overexpressed genes of immune checkpoints (CD274, PDCD1) and T-cell activator chemokines (CXCL9, CXCL10), central for ICB sensitivity. However, only cl2 overexpressed major histocompatibility complex (MHC) class I-II genes and their regulators (CIITA, TAP2), essential for antigen (Ag)-presenting machinery and immune recognition. Cl2 and 3 were also enriched for T-, B-cell and pro-inflammatory interferon gamma signaling pathways, known as ICB sensitivity biomarkers. Only cl2 was enriched in Ag processing and presentation pathway, confirming prior observation. DE-DM correlation showed that DNA-methylation programming induced overexpression of most immune genes (78%) in cl2, including CIITA, CXCR3 and MHC genes, when most immune genes (63%) were repressed by methylation in cl3. Methylation-based GSEA confirmed the epigenetic regulation of T- and B-cell signaling in cl2 and cl3, and for Ag presentation in cl2 only. Conclusion We demonstrated that DNA-methylation can reshape the TiME of pediatric solid tumors. We identified a subset of tumors with epigenetically altered immune phenotype characterized by inflamed immune environment but altered for Ag presentation by methylation reprogramming. Future studies should investigate methylation modulators to reverse these mechanisms and enable ICB sensitivity. Citation Format: Stéphanie Bianco, Anas Belaktib, Virgile Raufaste-Cazavieille, Charles Joly-Beauparlant, 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 pediatric solid tumors identified a subset of tumors with epigenetically altered immune phenotype [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 B057.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".