Gene Panel Testing in Hereditary Breast Cancer.
Bibliographic record
Abstract
BACKGROUND: Breast cancer (BC) is a highly complex, heterogeneous and multifactorial disease and is the most commonly diagnosed cancer and the leading cause of cancer-related mortality in women worldwide. Family history and genetic mutations are important risk factors for BC. While studies in twins have estimated that about 10%-30% of BC are due to hereditary factors, only 4%-5% of them are due to mutations in BRCA1 or BRCA2 genes. Our aim was to investigate the role of other BC genes in familial BC among the Iranian population. METHODS: We selected 61 BC patients who were wild-type for BRCA1 and BRCA2 mutations but who met the criteria for hereditary BC based on the American College of Medical Genetics and Genomics (ACMG) and the National Comprehensive Cancer Network (NCCN) guidelines. We performed targeted sequencing covering the exons of 130 known cancer susceptibility genes based on the Cancer Gene Census list. RESULTS: We found seven mutations in seven known BC susceptibility genes (RAD50, PTEN, TP53, POLH, DKC1, WRN and CHEK2) in seven patients including two pathogenic frameshift variants in RAD50 and WRN genes, four pathogenic missense variants in TP53, PTEN, POLH, and DKC1 genes and a pathogenic splice donor variant in the CHEK2 gene. The presence of all these variants was confirmed by Sanger sequencing and Gap reverse transcription-polymerase chain reaction (RT-PCR) for the splice variant. In silico analysis of all of these variants predicted them to be pathogenic. CONCLUSION: Panel testing of BC patients who met the established criteria for hereditary BC but who were negative for BRCA1/2 mutations provided additional relevant clinical information for approximately 11.5% of the families. Our findings indicate that next generation sequencing (NGS) is a powerful tool to investigative putative mutagenic variants among patients who meet the criteria for hereditary BC, but with negative results on BRCA1/2 testing.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.005 | 0.001 |
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".