TY - JOUR
T1 - Refined histopathological predictors of BRCA1 and BRCA2 mutation status
T2 - a large-scale analysis of breast cancer characteristics from the BCAC, CIMBA, and ENIGMA consortia
AU - ABCTB Investigators
AU - Spurdle, Amanda B
AU - Couch, Fergus J
AU - Parsons, Michael T
AU - McGuffog, Lesley
AU - Barrowdale, Daniel
AU - Bolla, Manjeet K
AU - Wang, Qin
AU - Healey, Sue
AU - Schmutzler, Rita
AU - Wappenschmidt, Barbara
AU - Rhiem, Kerstin
AU - Hahnen, Eric
AU - Engel, Christoph
AU - Meindl, Alfons
AU - Ditsch, Nina
AU - Arnold, Norbert
AU - Plendl, Hansjoerg
AU - Niederacher, Dieter
AU - Sutter, Christian
AU - Wang-Gohrke, Shan
AU - Steinemann, Doris
AU - Preisler-Adams, Sabine
AU - Kast, Karin
AU - Varon-Mateeva, Raymonda
AU - Ellis, Steve
AU - Frost, Debra
AU - Platte, Radka
AU - Perkins, Jo
AU - Evans, D Gareth
AU - Izatt, Louise
AU - Eeles, Ros
AU - Adlard, Julian
AU - Davidson, Rosemarie
AU - Cole, Trevor
AU - Scuvera, Giulietta
AU - Manoukian, Siranoush
AU - Bonanni, Bernardo
AU - Mariette, Frederique
AU - Fortuzzi, Stefano
AU - Viel, Alessandra
AU - Pasini, Barbara
AU - Papi, Laura
AU - Varesco, Liliana
AU - Balleine, Rosemary
AU - Nathanson, Katherine L
AU - Domchek, Susan M
AU - Offitt, Kenneth
AU - Jakubowska, Anna
AU - Lindor, Noralane
AU - Hansen, Thomas V O
PY - 2014/12/23
Y1 - 2014/12/23
N2 - INTRODUCTION: The distribution of histopathological features of invasive breast tumors in BRCA1 or BRCA2 germline mutation carriers differs from that of individuals with no known mutation. Histopathological features thus have utility for mutation prediction, including statistical modeling to assess pathogenicity of BRCA1 or BRCA2 variants of uncertain clinical significance. We analyzed large pathology datasets accrued by the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA) and the Breast Cancer Association Consortium (BCAC) to reassess histopathological predictors of BRCA1 and BRCA2 mutation status, and provide robust likelihood ratio (LR) estimates for statistical modeling.METHODS: Selection criteria for study/center inclusion were estrogen receptor (ER) status or grade data available for invasive breast cancer diagnosed younger than 70 years. The dataset included 4,477 BRCA1 mutation carriers, 2,565 BRCA2 mutation carriers, and 47,565 BCAC breast cancer cases. Country-stratified estimates of the likelihood of mutation status by histopathological markers were derived using a Mantel-Haenszel approach.RESULTS: ER-positive phenotype negatively predicted BRCA1 mutation status, irrespective of grade (LRs from 0.08 to 0.90). ER-negative grade 3 histopathology was more predictive of positive BRCA1 mutation status in women 50 years or older (LR = 4.13 (3.70 to 4.62)) versus younger than 50 years (LR = 3.16 (2.96 to 3.37)). For BRCA2, ER-positive grade 3 phenotype modestly predicted positive mutation status irrespective of age (LR = 1.7-fold), whereas ER-negative grade 3 features modestly predicted positive mutation status at 50 years or older (LR = 1.54 (1.27 to 1.88)). Triple-negative tumor status was highly predictive of BRCA1 mutation status for women younger than 50 years (LR = 3.73 (3.43 to 4.05)) and 50 years or older (LR = 4.41 (3.86 to 5.04)), and modestly predictive of positive BRCA2 mutation status in women 50 years or older (LR = 1.79 (1.42 to 2.24)).CONCLUSIONS: These results refine likelihood-ratio estimates for predicting BRCA1 and BRCA2 mutation status by using commonly measured histopathological features. Age at diagnosis is an important variable for most analyses, and grade is more informative than ER status for BRCA2 mutation carrier prediction. The estimates will improve BRCA1 and BRCA2 variant classification and inform patient mutation testing and clinical management.
AB - INTRODUCTION: The distribution of histopathological features of invasive breast tumors in BRCA1 or BRCA2 germline mutation carriers differs from that of individuals with no known mutation. Histopathological features thus have utility for mutation prediction, including statistical modeling to assess pathogenicity of BRCA1 or BRCA2 variants of uncertain clinical significance. We analyzed large pathology datasets accrued by the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA) and the Breast Cancer Association Consortium (BCAC) to reassess histopathological predictors of BRCA1 and BRCA2 mutation status, and provide robust likelihood ratio (LR) estimates for statistical modeling.METHODS: Selection criteria for study/center inclusion were estrogen receptor (ER) status or grade data available for invasive breast cancer diagnosed younger than 70 years. The dataset included 4,477 BRCA1 mutation carriers, 2,565 BRCA2 mutation carriers, and 47,565 BCAC breast cancer cases. Country-stratified estimates of the likelihood of mutation status by histopathological markers were derived using a Mantel-Haenszel approach.RESULTS: ER-positive phenotype negatively predicted BRCA1 mutation status, irrespective of grade (LRs from 0.08 to 0.90). ER-negative grade 3 histopathology was more predictive of positive BRCA1 mutation status in women 50 years or older (LR = 4.13 (3.70 to 4.62)) versus younger than 50 years (LR = 3.16 (2.96 to 3.37)). For BRCA2, ER-positive grade 3 phenotype modestly predicted positive mutation status irrespective of age (LR = 1.7-fold), whereas ER-negative grade 3 features modestly predicted positive mutation status at 50 years or older (LR = 1.54 (1.27 to 1.88)). Triple-negative tumor status was highly predictive of BRCA1 mutation status for women younger than 50 years (LR = 3.73 (3.43 to 4.05)) and 50 years or older (LR = 4.41 (3.86 to 5.04)), and modestly predictive of positive BRCA2 mutation status in women 50 years or older (LR = 1.79 (1.42 to 2.24)).CONCLUSIONS: These results refine likelihood-ratio estimates for predicting BRCA1 and BRCA2 mutation status by using commonly measured histopathological features. Age at diagnosis is an important variable for most analyses, and grade is more informative than ER status for BRCA2 mutation carrier prediction. The estimates will improve BRCA1 and BRCA2 variant classification and inform patient mutation testing and clinical management.
KW - Adult
KW - Age Factors
KW - Aged
KW - Breast Neoplasms/genetics
KW - Carcinoma/genetics
KW - Female
KW - Genes, BRCA1
KW - Genes, BRCA2
KW - Humans
KW - Likelihood Functions
KW - Middle Aged
KW - Mutation
KW - Neoplasm Grading
KW - Neoplasm Staging
KW - Receptor, ErbB-2/metabolism
KW - Receptors, Estrogen/metabolism
KW - Receptors, Progesterone/metabolism
KW - Triple Negative Breast Neoplasms/genetics
U2 - 10.1186/s13058-014-0474-y
DO - 10.1186/s13058-014-0474-y
M3 - Article
C2 - 25857409
SN - 1465-542X
VL - 16
SP - 3419
JO - Breast Cancer Research
JF - Breast Cancer Research
IS - 6
ER -