Abstract A37: Unique gene fusions inform targeted therapeutic strategies across extremely rare, non-CNS pediatric solid tumors
Bibliographic record
Abstract
Abstract Introduction: In children, approximately 7% of the non-CNS solid tumors include extremely rare histologies such as angiosarcomas and histiocytic neoplasms. While most recurrent histologies can be identified with a commonly occurring alteration or associated with a known biologic mechanism, rare solid tumors remain poorly characterized given their low frequency of occurrence and intrapatient tumor heterogeneity. Our group and others have demonstrated gene fusions (GFs) as unique oncogenic alterations in pediatric gliomas to guide prognosis, diagnosis, and therapeutic interventions. Here, we investigated the role of unique GFs as individualized therapeutic targets in 3 patients with rare non-CNS malignancies and explored GF-directed precision medicine approaches. Methods: We selected a patient with the rare diagnosis of pediatric angiosarcoma (pAS) and two with pediatric histiocytic neoplasms spanning the juvenille xanthogranuloma (JXG) family and malignant histiocytoses groups. Neoplasms were analyzed using the CHOP Comprehensive Next-Generation Sequencing Solid Tumor Panel as well as a targeted RNA-seq panel for 106 fusion partner genes. For novel gene fusions identified, we performed molecular and therapeutic characterization, including subcloning to create heterologous cell models. Cellular assays were used to assess oncogenic transformation, test mechanistic activation of related downstream signaling pathways via cellular assays, and examine targeting of GFs with specific small-molecule inhibitors. Results and Discussion: We identified a novel NOTCH1-ROS1 gene fusion in pAs, and rare BRAF-fusions, MTAP-BRAF and MS4A6A-BRAF, in malignant and JXG family histiocytic neoplasms, respectively. We also identified a germline TP53 p.T123Wfs*12 pathogenic variant, and the cancer predisposition team confirmed Li Fraumeni syndrome in the angiosarcoma patient. Our studies demonstrate that the NOTCH1-ROS1 fusion and both BRAF-fusions are potent oncogenes capable of inducing neoplastic transformation in cells and tumor formation in a murine allograft model. The kinase domains of ROS1 and BRAF are retained and activate the MAPK/PI3K/mTOR and JAK-STAT pathways in respective GF- expressing models, driven by potent dimerization of the GFs. Upon testing ROS1-targeted tyrosine kinase inhibitors (TKI) against NOTCH1-ROS1, we observe dose-dependent suppression of Notch1-Ros1 driven cellular activity and concurrent inhibition of tumor growth as well as prolonged survival after oral monotherapy. Interestingly, BRAF-fusions do not respond to second-generation BRAF inhibitors but can be suppressed by RAF dimer-inhibitor LY3009120 and MEK inhibitors. Overall, these data suggest that ongoing genomic profiling of rare pediatric tumors may reveal actionable drivers, and molecular testing of putative oncogenic fusions is imperative for improvement of patient outcomes through precision therapy. Citation Format: Payal Jain, Lea F. Surrey, Joshua Straka, Tiffany Smith, Sudarshan Iyer, Elizabeth Fox, Monika Davare, Jennifer Picarsic, Marilyn Li, Angela J. Waanders, Adam C. Resnick. Unique gene fusions inform targeted therapeutic strategies across extremely rare, non-CNS pediatric solid tumors [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 A37.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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, 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".