Abstract A021: Standardized Oncogenic Classification Guidance of Critical Diagnostic and Therapeutic markers in pediatric cancers: NTRK fusions
Bibliographic record
Abstract
Abstract Gene fusions involving neurotrophic receptor tyrosine kinase genes (NTRK1, NTRK2, & NTRK3) are well-established oncogenic drivers and serve as critical diagnostic and therapeutic markers in pediatric hematologic malignancies and solid tumors. The accurate and consistent interpretation of their clinical significance is a high priority given FDA approval of TRK inhibitors; however, this remains challenging due to the rapid pace of fusion discovery, the diversity of fusion partners and tumor types, inconsistent and incomplete reporting of fusion data elements, and the lack of standardized fusion-specific classification guidelines. The Clinical Genome Resource (ClinGen) NTRK Fusions Somatic Cancer Variant Curation Expert Panel (SC-VCEP) is addressing these challenges and creating a publicly available resource of high-quality clinically significant NTRK fusion classifications in the Clinical Interpretation of Variants in Cancer (CIViC; civicdb.org) knowledgebase to support patient care. ClinGen SC-VCEPs follow a rigorous 4-step process to reach approval status. Following the definition of scope and membership recruitment (Step 1), standardized guidance was created to determine the oncogenicity of NTRK fusions (Step 2). This guidance was piloted on 12 NTRK fusions ranging from rare to common (Step 3). After incorporating modifications to the classification guidelines directly influenced by the pilot, we established protocols for prioritizing NTRK fusions for classification (Step 4). The NTRK SC-VCEP created the first-ever standardized guidance to classify the oncogenicity of NTRK fusions through the systematic compilation, review, and discussion of fundamental fusion element annotations. The NTRK fusion-specific oncogenicity guidelines classify NTRK fusions as Oncogenic, Likely Oncogenic, Fusion of Unknown Significance (FUS), or Benign based on Fusion Structure (orientation/breakpoints/reading frame), Cancer Association (number of unique cases), Clinical Validity (targeted inhibitor response), and Functional Status (pathway activation or expression). Over 190 evidence items from 93 publications have been curated into CIViC, with over 20% tagged with Human Phenotype Ontology age of onset terms as part of our pediatric dataset. For the pilot, 12 Oncogenic classifications (6 Oncogenic, 1 Likely Oncogenic, 2 FUS, 3 Benign) were created along with 5 diagnostic and 12 therapeutic response classifications. We established sustained protocols to direct our ongoing coordinated team effort to evaluate the 90-plus NTRK fusions we’ve identified from public databases and private member laboratory lists and to maintain their up-to-date record in CIViC with broader distribution in ClinVar. Completing the ClinGen 4-step approval process assures access, accuracy, and transparency of the variant-level evidence, assessment process, and classifications of the NTRK SC-VCEP. As the first SC-VCEP to navigate this process, the work of the NTRK SC-VCEP provides the blueprint for other SC-VCEPs and, most importantly, aids clinicians in their pursuit of precision medicine. Citation Format: Jason Saliba, Shivani Golem, Arpad Danos, Laura B Corson, Morteza Seifi, Jan Clement Santiago, Emma G Sullivan, Scott Myrand, Elan Hahn, Valentina Nardi, Theodore W Laetsch, Marilyn M Li, Obi L Griffith, Malachi Griffith, Gordana Raca, Larissa V Furtado, Angshumoy Roy, Alanna J Church. Standardized Oncogenic Classification Guidance of Critical Diagnostic and Therapeutic markers in pediatric cancers: NTRK fusions [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Discovery and Innovation in Pediatric Cancer— From Biology to Breakthrough Therapies; 2025 Sep 25-28; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl_2):Abstract nr A021.
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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 | 0.073 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.034 | 0.032 |
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".