The classification of pediatric and young adult renal cell carcinomas registered on the children's oncology group (COG) protocol AREN03B2 after focused genetic testing
Bibliographic record
Abstract
BACKGROUND: Renal cell carcinomas (RCCs) are rare in young patients. Knowledge of their pathologic and molecular spectrum remains limited, and no prospective studies have been performed to date in this population. This study analyzes patients diagnosed with RCC who were prospectively enrolled in the AREN03B2 Children's Oncology Group (COG). The objective was to classify these tumors with the aid of focused genetic testing and to characterize their features. METHODS: All tumors registered as RCC by central review were retrospectively re-reviewed and underwent additional ancillary studies. Tumors were classified according to the 2016 World Health Organization classification system when possible. RESULTS: In total, 212 tumors were identified, and these were classified as microphthalmia transcription factor (MiT) translocation RCC (MiT-RCC) (41.5%), papillary RCC (16.5%), renal medullary carcinoma (12.3%), chromophobe RCC (6.6%), clear cell RCC (3.3%), fumarate hydratase-deficient RCC (1.4%), and succinate dehydrogenase-deficient RCC (0.5%). Other subtypes included tuberous sclerosis-associated RCC (4.2%), anaplastic lymphoma kinase (ALK)-rearranged RCC (3.8%), thyroid-like RCC (1.4%), myoepithelial carcinoma (0.9%), and unclassified (7.5%). MiT-RCCs were classified as either transcription factor E3 (TFE3) (93.2%) or EB (TFEB) (6.8%) translocations, and characterization of fusion partners was possible in most tumors. CONCLUSIONS: The current study delineates the frequency of distinct RCC subtypes in a large prospective series of young patients and contributes knowledge to the diagnostic, clinical, and genetic features of MiT-RCC, the most common subtype among this age group. The identification of rare subtypes expands the spectrum of RCC in young patients, supporting the need for a thorough diagnostic workup. These studies may aid in the introduction of specific therapies for different RCC subtypes in the future. Cancer 2018. © 2018 American Cancer Society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".