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Tumour morphology predicts PALB2 germline mutation status.

ABSTRACT: Population-based studies of breast cancer have estimated that at least some PALB2 mutations are associated with high breast cancer risk. For women carrying PALB2 mutations, knowing their carrier status could be useful in directing them towards effective cancer risk management and therapeutic strategies. We sought to determine whether morphological features of breast tumours can predict PALB2 germline mutation status.Systematic pathology review was conducted on breast tumours from 28 female carriers of PALB2 mutations (non-carriers of other known high-risk mutations, recruited through various resources with varying ascertainment) and on breast tumours from a population-based sample of 828 Australian women diagnosed before the age of 60 years (which included 40 BRCA1 and 18 BRCA2 mutation carriers). Tumour morphological features of the 28 PALB2 mutation carriers were compared with those of 770 women without high-risk mutations.Tumours arising in PALB2 mutation carriers were associated with minimal sclerosis (odds ratio (OR)=19.7; 95% confidence interval (CI)=6.0-64.6; P=5 × 10(-7)). Minimal sclerosis was also a feature that distinguished PALB2 mutation carriers from BRCA1 (P=0.05) and BRCA2 (P=0.04) mutation carriers.This study identified minimal sclerosis to be a predictor of germline PALB2 mutation status. Morphological review can therefore facilitate the identification of women most likely to carry mutations in PALB2.

SUBMITTER: Teo ZL

PROVIDER: S-EPMC3708559 | biostudies-other | 2013 Jul

REPOSITORIES: biostudies-other

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Project description:BackgroundIdentifying breast cancer patients with DNA repair pathway-related germline pathogenic variants (GPVs) is important for effectively employing systemic treatment strategies and risk-reducing interventions. However, current criteria and risk prediction models for prioritizing genetic testing among breast cancer patients do not meet the demands of clinical practice due to insufficient accuracy.MethodsThe study population comprised 3041 breast cancer patients enrolled from seven hospitals between October 2017 and 11 August 2019, who underwent germline genetic testing of 50 cancer predisposition genes (CPGs). Associations among GPVs in different CPGs and endophenotypes were evaluated using a case-control analysis. A phenotype-based GPV risk prediction model named DNA-repair Associated Breast Cancer (DrABC) was developed based on hierarchical neural network architecture and validated in an independent multicenter cohort. The predictive performance of DrABC was compared with currently used models including BRCAPRO, BOADICEA, Myriad, PENN II, and the NCCN criteria.ResultsIn total, 332 (11.3%) patients harbored GPVs in CPGs, including 134 (4.6%) in BRCA2, 131 (4.5%) in BRCA1, 33 (1.1%) in PALB2, and 37 (1.3%) in other CPGs. GPVs in CPGs were associated with distinct endophenotypes including the age at diagnosis, cancer history, family cancer history, and pathological characteristics. We developed a DrABC model to predict the risk of GPV carrier status in BRCA1/2 and other important CPGs. In predicting GPVs in BRCA1/2, the performance of DrABC (AUC = 0.79 [95% CI, 0.74-0.85], sensitivity = 82.1%, specificity = 63.1% in the independent validation cohort) was better than that of previous models (AUC range = 0.57-0.70). In predicting GPVs in any CPG, DrABC (AUC = 0.74 [95% CI, 0.69-0.79], sensitivity = 83.8%, specificity = 51.3% in the independent validation cohort) was also superior to previous models in their current versions (AUC range = 0.55-0.65). After training these previous models with the Chinese-specific dataset, DrABC still outperformed all other methods except for BOADICEA, which was the only previous model with the inclusion of pathological features. The DrABC model also showed higher sensitivity and specificity than the NCCN criteria in the multi-center validation cohort (83.8% and 51.3% vs. 78.8% and 31.2%, respectively, in predicting GPVs in any CPG). The DrABC model implementation is available online at http://gifts.bio-data.cn/ .ConclusionsBy considering the distinct endophenotypes associated with different CPGs in breast cancer patients, a phenotype-driven prediction model based on hierarchical neural network architecture was created for identification of hereditary breast cancer. The model achieved superior performance in identifying GPV carriers among Chinese breast cancer patients.

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Tumour morphology predicts PALB2 germline mutation status.

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