IL-13 receptors as possible therapeutic targets in diffuse intrinsic pontine glioma
Bibliographic record
Abstract
Diffuse intrinsic pontine glioma (DIPG) is a universally fatal childhood cancer of the brain. Despite the introduction of conventional chemotherapy and radiotherapy, improvements in survival have been marginal and long-term survivorship is uncommon. Thus, new targets for therapeutics are critically needed. Early phase clinical trials exploring molecularly-targeted therapies against the epidermal growth factor receptor (EGFR) and novel immunotherapies targeting interleukin receptor-13α2 (IL-13Rα2) have demonstrated activity in this disease. To identify additional therapeutic markers for cell surface receptors, we performed exome sequencing (16 new samples, 22 previously published samples, total 38 with 26 matched normal DNA samples), RNA deep sequencing (17 new samples, 11 previously published samples, total 28 with 18 matched normal RNA samples), and immunohistochemistry (17 DIPG tissue samples) to examine the expression of the interleukin-4 (IL-4) signaling axis components (IL-4, interleukin 13 (IL-13), and their respective receptors IL-4Rα, IL-13Rα1, and IL-13Rα2). In addition, we correlated cytokine and receptor expression with expression of the oncogenes EGFR and c-MET. In DIPG tissues, transcript-level analysis found significant expression of IL-4, IL-13, and IL-13Rα1/2, with strong differential expression of IL-13Rα1/2 in tumor versus normal brain. At the protein level, immunohistochemical studies revealed high content of IL-4 and IL-13Rα1/2 but notably low expression of IL-13. Additionally, a strong positive correlation was observed between c-Met and IL-4Rα. The genomic and transcriptional landscape across all samples was also summarized. These data create a foundation for the design of potential new immunotherapies targeting IL-13 cell surface receptors in DIPG.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".