Recommended Guidelines for Validation, Quality Control, and Reporting of <i>TP53</i> Variants in Clinical Practice
Bibliographic record
Abstract
Abstract Accurate assessment of TP53 gene status in sporadic tumors and in the germline of individuals at high risk of cancer due to Li–Fraumeni Syndrome (LFS) has important clinical implications for diagnosis, surveillance, and therapy. Genomic data from more than 20,000 cancer genomes provide a wealth of information on cancer gene alterations and have confirmed TP53 as the most commonly mutated gene in human cancer. Analysis of a database of 70,000 TP53 variants reveals that the two newly discovered exons of the gene, exons 9β and 9γ, generated by alternative splicing, are the targets of inactivating mutation events in breast, liver, and head and neck tumors. Furthermore, germline rearrange-ments in intron 1 of TP53 are associated with LFS and are frequently observed in sporadic osteosarcoma. In this context of constantly growing genomic data, we discuss how screening strategies must be improved when assessing TP53 status in clinical samples. Finally, we discuss how TP53 alterations should be described by using accurate nomenclature to avoid confusion in scientific and clinical reports. Cancer Res; 77(6); 1250–60. ©2017 AACR.
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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 | 0.189 | 0.264 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.014 | 0.006 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.009 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".