Estimated number of adult survivors of childhood cancer in United States with cancer‐predisposing germline variants
Bibliographic record
Abstract
PURPOSE: To estimate the absolute number of adult survivors of childhood cancer in the U.S. population who carry a pathogenic or likely pathogenic variant in a cancer predisposition gene. METHODS: Using the Surveillance, Epidemiology, and End Results (SEER) Program, we estimated the number of childhood cancer survivors on December 31, 2016 for each childhood cancer diagnosis, multiplied this by the proportion of carriers of pathogenic/likely pathogenic variants in the St. Jude Lifetime Cohort (SJLIFE) study, and projected the resulting number onto the U.S. RESULTS: Based on genome sequence data, 11.8% of 2450 SJLIFE participants carry a pathogenic/likely pathogenic variant in one of 156 cancer predisposition genes. Given this information, we estimate that 21 800 adult survivors of childhood cancer in the United States carry a pathogenic/likely pathogenic variant in one of these genes. The highest estimated absolute number of variant carriers are among survivors of central nervous system tumors (n = 4300), particularly astrocytoma (n = 1800) and other gliomas (n = 1700), acute lymphoblastic leukemia (n = 4300), and retinoblastoma (n = 3500). The most frequently mutated genes are RB1 (n = 3000), NF1 (n = 2300), and BRCA2 (n = 800). CONCLUSION: Given the increasing number of childhood cancer survivors in the United States, clinicians should counsel survivors regarding their potential genetic risk, consider referral for genetic counseling and testing, and, as appropriate, implement syndrome-specific cancer surveillance or risk-reducing measures.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
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".