Reply to Li and Colleagues
Bibliographic record
Abstract
We thank Dr Li and colleagues (1) for their correspondence in which the following point related to our publication entitled “Heterozygous BRCA1 and BRCA2 and mismatch repair gene pathogenic variants in children and adolescents with cancer” is raised: The frequency of BRCA2 pathogenic variant (PV) carriers in the population (including controls) decreases with age, due to the primarily cancer related death of carriers. This factor leads to an overestimation of the childhood cancer risk of BRCA2 PV carriers in the presented analysis. This is a valid argument. However, the use of an extreme phenotype case–control design (eg, by using healthy elderly controls to search for cancer associated genes) has been successfully employed to maximize power (2). Nevertheless, our literature review and combined meta-analysis and case–control study has several methodological limitations acknowledged in the discussion. Therefore, the analysis represents an early step, and reported findings require independent validation using a more appropriate design (including matched controls as well as identical pipelines to call PVs in cases and controls). In addition to our findings that were at least in part confirmed in a validation cohort and a supplementary analysis, there is growing evidence from childhood cancer sequencing studies suggesting that PVs in genes like BRCA1 and BRCA2, as well as other adult-onset cancer predisposition genes, represent (low penetrance) cancer risk alleles in children and adolescents (3-10). Recent results from integrative somatic-germline mutational signature (SBS3/BRCAness) analyses (3) as well as functional analyses (7) support this notion. Despite several limitations, our findings that require confirmation employing a more sophisticated design agree with other studies providing growing evidence that suggests that variants in BRCA2 and other adult-onset cancer predisposition genes represent low-penetrance cancer risk alleles in children and adolescents. No new data were generated or analyzed for this response. Christian Kratz, MD (Conceptualization; Writing – original draft); Dmitrii Smirnov, MS (Writing – review & editing); Robert Autry, PhD (Writing – review & editing); Natalie Jäger, PhD (Writing – review & editing); Sebastian M. Waszak, PhD (Writing – review & editing); Anika Großhennig, PhD (Writing – review & editing); Riccardo Berutti, PhD (Writing – review & editing); Mareike Wendorff, PhD (Writing – review & editing); Pierre Hainaut, PhD (Writing – review & editing); Stefan M. Pfister, MD (Writing – review & editing); Holger Prokisch, PhD (Writing – review & editing); Tim Ripperger, MD, PhD (Writing – review & editing); David Malkin, MD (Writing – review & editing) CPK and SMP have been supported by the Deutsche Kinderkrebsstiftung (DKS2019.13) and Bundesministerium für Bildung und Forschung (BMBF) ADDRess (01GM1909A and 01GM1909E). RA is supported by the Everest Centre for Low-Grade Paediatric Brain Tumours (the Brain Tumour Charity, UK; GN-000382). SMW is supported by the Research Council of Norway (187615), the South-Eastern Norway Regional Health Authority, and the University of Oslo. TR has been supported by BMBF MyPred (01GM1911B). DM is supported by grants from the Canadian Institutes for Health Research (FDN-143234) and the Terry Fox Research Institute (TFRI #1081). DS and HP have been supported by BMBF (01GM1906B). None exist. The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
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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 | 0.006 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.034 | 0.054 |
| Insufficient payload (model declined to judge) | 0.010 | 0.011 |
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".