TY - JOUR
T1 - Multigene testing of moderate-risk genes
T2 - Be mindful of the missense
AU - Young, E. L.
AU - Feng, B. J.
AU - Stark, A. W.
AU - Damiola, F.
AU - Durand, G.
AU - Forey, N.
AU - Francy, T. C.
AU - Gammon, A.
AU - Kohlmann, W. K.
AU - Kaphingst, K. A.
AU - McKay-Chopin, S.
AU - Nguyen-Dumont, T.
AU - Oliver, J.
AU - Paquette, A. M.
AU - Pertesi, M.
AU - Robinot, N.
AU - Rosenthal, J. S.
AU - Vallee, M.
AU - Voegele, C.
AU - Hopper, J. L.
AU - Southey, M. C.
AU - Andrulis, I. L.
AU - John, E. M.
AU - Hashibe, M.
AU - Gertz, J.
AU - Breast Cancer Family Registry
AU - Le Calvez-Kelm, F.
AU - Lesueur, F.
AU - Goldgar, D. E.
AU - Tavtigian, Sean V.
PY - 2016/6/1
Y1 - 2016/6/1
N2 - Background Moderate-risk genes have not been extensively studied, and missense substitutions in them are generally returned to patients as variants of uncertain significance lacking clearly defined risk estimates. The fraction of early-onset breast cancer cases carrying moderate-risk genotypes and quantitative methods for flagging variants for further analysis have not been established. Methods We evaluated rare missense substitutions identified from a mutation screen of ATM, CHEK2, MRE11A, RAD50, NBN, RAD51, RINT1, XRCC2 and BARD1 in 1297 cases of early-onset breast cancer and 1121 controls via scores from Align-Grantham Variation Grantham Deviation (GVGD), combined annotation dependent depletion (CADD), multivariate analysis of protein polymorphism (MAPP) and PolyPhen-2. We also evaluated subjects by polygenotype from 18 breast cancer risk SNPs. From these analyses, we estimated the fraction of cases and controls that reach a breast cancer OR≥2.5 threshold. Results Analysis of mutation screening data from the nine genes revealed that 7.5% of cases and 2.4% of controls were carriers of at least one rare variant with an average OR≥2.5. 2.1% of cases and 1.2% of controls had a polygenotype with an average OR≥2.5. Conclusions Among early-onset breast cancer cases, 9.6% had a genotype associated with an increased risk sufficient to affect clinical management recommendations. Over two-thirds of variants conferring this level of risk were rare missense substitutions in moderate-risk genes. Placement in the estimated OR≥2.5 group by at least two of these missense analysis programs should be used to prioritise variants for further study. Panel testing often creates more heat than light; quantitative approaches to variant prioritisation and classification may facilitate more efficient clinical classification of variants.
AB - Background Moderate-risk genes have not been extensively studied, and missense substitutions in them are generally returned to patients as variants of uncertain significance lacking clearly defined risk estimates. The fraction of early-onset breast cancer cases carrying moderate-risk genotypes and quantitative methods for flagging variants for further analysis have not been established. Methods We evaluated rare missense substitutions identified from a mutation screen of ATM, CHEK2, MRE11A, RAD50, NBN, RAD51, RINT1, XRCC2 and BARD1 in 1297 cases of early-onset breast cancer and 1121 controls via scores from Align-Grantham Variation Grantham Deviation (GVGD), combined annotation dependent depletion (CADD), multivariate analysis of protein polymorphism (MAPP) and PolyPhen-2. We also evaluated subjects by polygenotype from 18 breast cancer risk SNPs. From these analyses, we estimated the fraction of cases and controls that reach a breast cancer OR≥2.5 threshold. Results Analysis of mutation screening data from the nine genes revealed that 7.5% of cases and 2.4% of controls were carriers of at least one rare variant with an average OR≥2.5. 2.1% of cases and 1.2% of controls had a polygenotype with an average OR≥2.5. Conclusions Among early-onset breast cancer cases, 9.6% had a genotype associated with an increased risk sufficient to affect clinical management recommendations. Over two-thirds of variants conferring this level of risk were rare missense substitutions in moderate-risk genes. Placement in the estimated OR≥2.5 group by at least two of these missense analysis programs should be used to prioritise variants for further study. Panel testing often creates more heat than light; quantitative approaches to variant prioritisation and classification may facilitate more efficient clinical classification of variants.
UR - http://www.scopus.com/inward/record.url?scp=84957927611&partnerID=8YFLogxK
U2 - 10.1136/jmedgenet-2015-103398
DO - 10.1136/jmedgenet-2015-103398
M3 - Article
C2 - 26787654
AN - SCOPUS:84957927611
SN - 0022-2593
VL - 53
SP - 366
EP - 376
JO - Journal of Medical Genetics
JF - Journal of Medical Genetics
IS - 6
ER -