Co‐occurrence of breast cancer and neuroendocrine tumours: New genetic insights beyond Multiple Endocrine Neoplasia syndromes
Bibliographic record
Abstract
Abstract Objective Age‐standardized incidence of female breast cancer is 145.1 per 100000/year and 5.86 per 100000/year for neuroendocrine tumours (NET) in Canada. Evidence is scarce about gene variants that may predispose patients to develop both neoplasms. The objective of this study was to identify germline gene variants associated with this combination of tumours. Design and patients A retrospective chart review (2007‐2018) in a tertiary NET referral centre was completed. A series of 9 female patients with concurrent breast cancer and NET is presented. All patients underwent a 37 gene hereditary cancer next‐generation sequencing panel. Results Mean age was 61.4 years (35‐85) at breast cancer diagnosis and 63.4 years (51‐89) at NET diagnosis. Four patients had a pancreatic, three had a small bowel and two had a lung NET. Two patients were known cases of MEN1, and one patient was found to harbour a pathogenic variant in MEN1 and a variant of unknown significance (VUS) in ATM . A second patient was found to harbour a pathogenic variant in APC . A third patient was found to carry a pathogenic variant in PALB2 as well as a VUS in FANCM, MLH1 and STK11 . Another patient was found to harbour a VUS in MSH2 . One patient was found to carry a pathogenic variant in NTHL1 . Conclusion The first cases of a PALB2 , an APC and a NTHL1 pathogenic variants in patients with both breast cancer and NET were presented. NGS testing should be considered in specific patients with this combination of neoplasms, as certain germline variants beyond MEN1 , have important implications for cancer surveillance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".