Dual-omic analysis to reveal gene regulatory network of immune landscape in childhood solid tumors and implications for immunotherapy.
Bibliographic record
Abstract
e14570 Background: Immune checkpoint blockades (ICBs) showed little efficacy in pediatric solid tumors. The reconfiguration of gene regulation in cancers is a major factor in tumor immune microenvironment (TiME) remodeling and ICB resistance. Hypothesis: A subset of pediatric tumors may exhibit an epigenetically altered TiME, potentially responsible for ICB resistance. Objective: classify the TiME of pediatric extracranial solid tumors and describe the gene regulatory networks (GRNs) of immune landscapes. Methods: We studied bulk tumor gene expression and DNA-methylation from 184 pediatric extracranial solid tumors. Similarity network fusion (SNFtool) individualized TiME phenotypes by dual-omic clustering of immune genes. The clusters were compared by differential analysis for gene expression and methylation. We studied the relationship of enhancer methylation to gene expression (ELMER package) to infer methylation reprogramming. An enrichment study of regulatory binding regions (ReMapEnrich) identified the master regulators that define the GRNs unique to each phenotype. Results: SNF clustering identified 3 phenotypes with 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). Different tumor types were present across all phenotypes, but Ewing sarcoma was more frequent in altered, osteosarcoma and neuroblastoma in hot, and Wilms tumor in cold phenotype ( p <0.05). 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-presenting machinery and immune recognition. Methylation regulation was responsible for derepressing MHC-II genes and their regulators in hot tumors. In contrast, gene silencing in altered phenotype relied on hypermethylation/downregulation of the CIITA cofactor domain, the master control of MHC-II genes, and other immune-specific master regulators crucial for anti-tumor immunity and ICB sensitivity. The regulatory landscape of immune gene repression in the altered phenotype involved binding regions specific to the MYC/MAX network and the polycomb-group protein PCR2 ( i.e., EZH2 and SUZ12). Conclusions: We demonstrated that GRN reconfiguration participates in TiME reshaping in pediatric extracranial solid tumors. A subset of tumors with epigenetically altered immune phenotype has an immune recognition capacity repressed by MYC/MAX and PCR2 complexes. Future studies should investigate specific inhibitors to reprogram the GRN to foster immune recognition and ICB sensitivity.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".