Abstract PR17: Immunogenomic landscape of pediatric solid malignancies
Bibliographic record
Abstract
Abstract Malignancy remains the leading cause of disease-related death in children. To identify potential tumor-driving molecular targets and immunogenomic profiles in pediatric cancers, we performed RNA-seq analysis on a cohort of 788 pediatric solid malignant tumors across 14 different diagnoses in conjunction with additional 147 normal tissues for comparison. Sequencing data were analyzed for expressed mutations, fusion events, and expressional patterns, providing therapeutic targets and rich cancer biology for these childhood cancers. Furthermore, we describe immunogenomic features of these solid tumors including immune cell infiltrate, neoantigen expression, expression of immunomodulatory molecules, and T-cell receptor repertoire. Across the cohort, we observed a striking correlation between the expressed neoantigen burden in tumors and enrichment of the effector immune signatures. Histology-specific immunogenomic patterns were also apparent. Several of the pediatric cancers such as alveolar soft part sarcoma and osteosarcoma exhibit rich immune cell infiltration and evidence for activated T-cell activities, whereas others such as Wilms’ tumors and synovial sarcoma generally have a very low T-cell infiltration. We demonstrate that RNA-seq is a powerful tool to identify clinically relevant and histology-specific recurrent mutations, novel oncogenic fusions, and translationally relevant immunogenomic patterns for pediatric cancers. This study also represents one of the largest of its type to date and provides a framework for future translational efforts in pediatric cancer. This abstract is also being presented as Poster A69. Citation Format: Jun S. Wei, Andrew S. Brohl, Sivasish Sindiri, Young K. Song, Sushma Najaraj, Vineela Gangalapudi, Xinyu Wen, Marc Ladanyi, Javed Khan. Immunogenomic landscape of pediatric solid malignancies [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr PR17.
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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 | 0.001 |
| 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) | 0.003 | 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".