Abstract PD7-11: The role of multi-gene hereditary cancer panels in male patients with breast cancer
Bibliographic record
Abstract
Abstract Background/Statement of Purpose: The role of cancer susceptibility genes in the male breast cancer population beyond BRCA1 and BRCA2 (BRCA) is not well defined. While breast cancer has been documented in men with pathogenic variants in a number of other breast cancer susceptibility genes (e.g. CHEK2, PALB2, PTEN), the yield of testing is not well documented nor are predictive clinical features of those likely to harbor causative variants. This study assesses the yield of pathogenic/likely pathogenic variants (collectively, PV) in male breast cancer patients who underwent multi-gene hereditary cancer panel testing. In addition, we aim to examine predictors of identifying a PV in this population. Methods: Clinical histories and test results were reviewed for men with a diagnosis of breast cancer who underwent panel testing that included a minimum of eight well-described breast cancer susceptibility genes (ATM, BRCA1, BRCA2, CDH1, CHEK2, PALB2, PTEN, TP53) and up to 24 additional genes. Using t-test and two-tailed Fisher's exact test, we assessed whether age at diagnosis, family history of breast cancer, or the presence of selected second primary cancers (second breast, prostate, pancreatic, colon, or melanoma cancers) were associated with a greater likelihood of identifying a PV. Results: The clinical histories and test results of 381 men with breast cancer were reviewed, of whom 12.1% had at least one PV (46/381). When we limited our assessment to men who had not had prior negative BRCA testing, 13.3% had at least one PV (42/315). Variants were most commonly detected in BRCA2 (21) and CHEK2 (17), followed by PALB2 (4), BRCA1 (4), and ATM (2). A two sample t-test showed no significant difference (p=0.39) in the average age of diagnosis for those with a PV (62.5y, n=47) compared to those without a PV (60.9y, n=334). Two-tailed Fisher's exact test showed no association between having a PV and a history of a selected second primary (SP) cancer [10.8% (7/65) w/SP vs 12.3% (39/316) w/out SP; p=0.84]. Lastly, two-tailed Fisher's exact test showed those with a family history of breast cancer (fhx br) were more likely to have a PV [15.1% (32/212) fhx br vs. 8.3% (14/169) no fhx br], although this did not reach statistical significance (p=0.06). Conclusions: While BRCA2 remains the most common gene in which PVs are identified in men with breast cancer, a significant proportion of patients will have a PV in another well-described breast cancer susceptibility gene, particularly CHEK2, PALB2, and ATM. Therefore, it is reasonable to utilize a panel that is inclusive of these genes when testing male breast cancer patients. As the likelihood to harbor a PV was not significantly associated with age of onset, family history of breast cancer, or presence of a second primary, all men with breast cancer could consider genetic testing. Further study is warranted as the current sample size may limit the power to detect associations. Citation Format: Vogel Postula KJ, Andolina LM, Theobald K, McGill AK, Sutcliffe E, Arvai KJ, Murphy PD, Klein RT, Hruska KS. The role of multi-gene hereditary cancer panels in male patients with breast cancer [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr PD7-11.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".