Interplay Between the Epigenome, the Microenvironment, and the Immune System in Neuroblastoma
Bibliographic record
Abstract
Neuroblastoma (NB) is the most prevalent extracranial childhood tumor and the third leading cause of death from cancer in children. Despite having a high overall survival rate for low- and intermediate-risk patients, survival rates for high-risk cases remain unsatisfactory. The current standard treatment for high-risk NB involves surgery, chemotherapy, radiotherapy, autologous stem cell transplantation, immunotherapy with anti-ganglioside GD2, and differentiation therapy with isotretinoin. Besides not being enough to achieve a high survival rate in high-risk patients, these treatments are associated with significant side effects. With next-generation sequencing technologies, a better understanding of the genetic and epigenetic landscapes of NB has been achieved. This has led to the study of novel treatments to improve the overall survival rate of high-risk NB and reduce the toxicity of conventional treatments. Current research is focusing on the development of targeted drugs for genetic and epigenetic alterations, and protein degraders. Moreover, immunotherapy to enhance anticancer immune responses and by using cell-engineering techniques with chimeric antigen receptor (CAR) T and NK cells are being explored to target NB cells. Here, we review promising novel treatment strategies for NB, which target genetics, epigenetics, the tumor microenvironment, and the immune landscape, highlighting preclinical studies and ongoing clinical trials.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".