Resource: A compendium of HLA types and expression in pediatric cancer models
Bibliographic record
Abstract
Cancer immunotherapy has revolutionized treatment by leveraging the immune system to recognize and destroy tumor cells, offering a promising, less toxic option for pediatric patients. A key component of this response is antigen presentation, which depends on accurate human leukocyte antigen (HLA) typing and expression. However, immune-focused resources for pediatric cancers remain limited. In this study, we present a comprehensive immunogenomic resource covering 231 cancer cell lines and 56 tumor-associated fibroblast cell lines from the Childhood Cancer Model Atlas (CCMA). We inferred high-resolution HLA types, predicted neoantigens arising from somatic single nucleotide variants, gene fusions, and splicing isoforms across multiple tumor types, and quantified HLA expression levels. We also explored immune escape mechanisms, including loss of heterozygosity and allele-specific expression loss of HLA genes. This publicly accessible dataset provides critical insight into the immune landscape of pediatric cancers and serves as a foundational tool for immunotherapy development.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.016 |
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".