Prevalence of Constitutional Pathogenic Variant in a Cohort of 348 Patients With Multiple Primary Cancer Addressed in Oncogenetic Consultation
Bibliographic record
Abstract
INTRODUCTION: Multiple primary malignancies (MPMs) refer to two or more primary malignant tumors in the same patient. MPMs are frequent: 18.4% of incident cancers represent a second or a higher primary cancer. In order to assess the value of genetic testing for patients with multiple cancers, studies are needed to accurately determine the prevalence of pathogenic variants for these patients. METHODS: All families were seen in our oncogenetics consultation from 2010 to 2022. We compared clinical features and detection rates of pathogenic or likely pathogenic variants in a panel of up to 47 cancer predisposition genes in patients with ≥ 2 primary cancers (n = 348) versus a single primary cancer (n = 1422). RESULTS: A pathogenic or likely pathogenic variant was diagnosed in 27.3% of patients with 348 index patients with MPM, concerning 21 genes: BRCA1 (n = 27), BRCA2 (n = 19), MSH2 (n = 9), ATM (n = 8), MLH1 (n = 5), MSH6 (n = 6), TP53 (n = 4), CHEK2 (n = 4), PALB2 (n = 3), APC (n = 2), MEN1 (n = 1), RAD51C (n = 1), NBN (n = 1), EPCAM (n = 1), PMS2 (n = 1), RB1 (n = 1), PTEN (n = 1), CYLD1 (n = 1), NF1 (n = 1), RAD51D (n = 1), and CDKN2A (n = 1). MPM index cases were more likely to carry a deleterious mutation than cases with a single cancer (27.3% vs. 11.39%, p < 0.001). Pathogenic variants were found more frequently in patients with a suggestive family history (34.2% vs. 20.1%, p < 0.05), with a younger age of cancer diagnosis related to the suspected syndrome (32.7% vs. 22%, p = 0.049). For the 208 index patients with ≥ 2 cancers pertaining to the same predisposition syndrome (HBOC, HNPCC…), the detection rate increased significantly to 36% (vs. 14.3% for MPM patients with unrelated cancers (n = 140), p < 0.001). Conversely, the detection rate for patients with unrelated cancers was not statistically different from the single-cancer population (14.3%-11.39%, p = 0.318). CONCLUSION: Patients referred for oncogenetic testing with MPM are more likely to carry pathogenic variants in cancer predisposition genes than patients with a single primary cancer (p < 0.05), especially if the cancers are related to the same predisposition syndrome. If the cancers are unrelated, no statistical difference in comparison to the single-cancer population was observed. For these latter patients, we recommend using the specific criteria of each tumor to propose appropriate genetic 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".