Tumor characteristics as an analytic tool for classifying genetic variants of uncertain clinical significance
Bibliographic record
Abstract
It is important to identify a germline mutation in a patient with an inherited cancer syndrome to allow mutation carriers to be included in cancer surveillance programs, which have been proven to save lives. Many of the mutations identified result in premature termination of translation, and thus in loss-of-function of the encoded mutated protein. However, the significance of a large proportion of the sequence changes reported is unknown. Some of these variants will be associated with a high risk of cancer and have direct clinical consequence. Many criteria can be used to classify variants with unknown significance; most criteria are based on the characteristics of the amino acid change, on segregation data and appearance of the variant, on the presence of the variant in controls, or on functional assays. In inherited cancers, tumor characteristics can also be used to classify variants. It is worthwhile to examine the clinical, morphological and molecular features of a patient, and his or her family, when assessing whether the role of a variant is likely to be neutral or pathogenic. Here we describe the advantages and disadvantages of using the tumor characteristics of patients carrying germline variants of uncertain significance (VUS) in BRCA1, BRCA2, or in one of the mismatch repair (MMR) genes, MLH1, MSH2, or MSH6, to infer pathogenicity.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".