Multi-locus inherited neoplasia alleles syndromes in cancer: implications for clinical practice
Bibliographic record
Abstract
The popularity of multi-gene testing has identified more families with two or more pathogenic variants (PV) in cancer predisposition genes, also known as 'MINAS' (multilocus inherited neoplasia alleles syndromes). They are at risk of suboptimal treatment and management as little on this topic is known. We conducted a systematic review of published MINAS cases within cancer predisposition genes to understand their association with more severe presentations. We analysed 413 MINAS carriers, which included 33 novel cases from the Cancer Genetics Service, National Cancer Centre Singapore. Statistical tests were conducted to assess association between carrier characteristics and the number PV identified. Results suggest that MINAS carriers have more malignancies (31.7% vs 21.5% vs 10.3% %; p < 0.001), a younger median age of first cancer diagnosis (40.0 vs. 44.0 vs. 49.0 years; p < 0.001) and an early onset of cancer (defined as <5% PV-associated cancer risk at age of diagnosis) (24.9% vs 7.7% vs 4.7%; p < 0.001) compared to monoallelic and non-carriers. We also studied the association of clinical characteristics by the dominant or recessive nature of PV harboured, where more dominant-dominant (D-D) carriers reported multiple malignancies (34.0%), compared to dominant-recessive (D-R) (23.9%) and recessive-recessive (R-R) carriers (20%;) (p = 0.051). Our findings suggest that MINAS carriers are prone to more and younger malignancies and the dominant or recessive nature of PV within double carriers can affect clinical presentation. We suggest a framework to guide management based on the dominant or recessive nature of PV within double PV carriers.
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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 | 0.013 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".