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1. A Likelihood Ratio Approach for Utilizing Case-Control Data in the Clinical Classification of Rare Sequence Variants: Application to BRCA1 and BRCA2

Author

Cutting, G, Zanti, M, O'Mahony, DG, Parsons, MT, Li, H, Dennis, J, Aittomakkiki, K, Andrulis, IL, Anton-Culver, H, Aronson, KJ, Augustinsson, A, Becher, H, Bojesen, SE, Bolla, MK, Brenner, H, Brown, MA, Buys, SS, Canzian, F, Caputo, SM, Castelao, JE, Chang-Claude, J, Czene, K, Daly, MB, De Nicolo, A, Devilee, P, Dork, T, Dunning, AM, Dwek, M, Eccles, DM, Engel, C, Evans, DG, Fasching, PA, Gago-Dominguez, M, Garcia-Closas, M, Garcia-Saenz, JA, Gentry-Maharaj, A, Geurts-Giele, WRR, Giles, GG, Glendon, G, Goldberg, MS, Garcia, EBG, Guendert, M, Guenel, P, Hahnen, E, Haiman, CA, Hall, P, Hamann, U, Harkness, EF, Hogervorst, FBL, Hollestelle, A, Hoppe, R, Hopper, JL, Houdayer, C, Houlston, RS, Howell, A, Investigators, A, Jakimovska, M, Jakubowska, A, Jernstrom, H, John, EM, Kaaks, R, Kitahara, CM, Koutros, S, Kraft, P, Kristensen, VN, Lacey, J, Lambrechts, D, Leone, M, Lindblom, A, Lush, M, Mannermaa, A, Manoochehri, M, Manoukian, S, Margolin, S, Martinez, ME, Menon, U, Milne, RL, Monteiro, AN, Murphy, RA, Neuhausen, SL, Nevanlinna, H, Newman, WG, Offit, K, Park, SK, James, P, Peterlongo, P, Peto, J, Plaseska-Karanfilska, D, Punie, K, Radice, P, Rashid, MU, Rennert, G, Romero, A, Rosenberg, EH, Saloustros, E, Sandler, DP, Schmidt, MK, Schmutzler, RK, Shu, X-O, Simard, J, Southey, MC, Stone, J, Stoppa-Lyonnet, D, Tamimi, RM, Tapper, WJ, Taylor, JA, Teo, SH, Teras, LR, Terry, MB, Thomassen, M, Troester, MA, Vachon, CM, Vega, A, Vreeswijk, MPG, Wang, Q, Wappenschmidt, B, Weinberg, CR, Wolk, A, Zheng, W, Feng, B, Couch, FJ, Spurdle, AB, Easton, DF, Goldgar, DE, Michailidou, K, Cutting, G, Zanti, M, O'Mahony, DG, Parsons, MT, Li, H, Dennis, J, Aittomakkiki, K, Andrulis, IL, Anton-Culver, H, Aronson, KJ, Augustinsson, A, Becher, H, Bojesen, SE, Bolla, MK, Brenner, H, Brown, MA, Buys, SS, Canzian, F, Caputo, SM, Castelao, JE, Chang-Claude, J, Czene, K, Daly, MB, De Nicolo, A, Devilee, P, Dork, T, Dunning, AM, Dwek, M, Eccles, DM, Engel, C, Evans, DG, Fasching, PA, Gago-Dominguez, M, Garcia-Closas, M, Garcia-Saenz, JA, Gentry-Maharaj, A, Geurts-Giele, WRR, Giles, GG, Glendon, G, Goldberg, MS, Garcia, EBG, Guendert, M, Guenel, P, Hahnen, E, Haiman, CA, Hall, P, Hamann, U, Harkness, EF, Hogervorst, FBL, Hollestelle, A, Hoppe, R, Hopper, JL, Houdayer, C, Houlston, RS, Howell, A, Investigators, A, Jakimovska, M, Jakubowska, A, Jernstrom, H, John, EM, Kaaks, R, Kitahara, CM, Koutros, S, Kraft, P, Kristensen, VN, Lacey, J, Lambrechts, D, Leone, M, Lindblom, A, Lush, M, Mannermaa, A, Manoochehri, M, Manoukian, S, Margolin, S, Martinez, ME, Menon, U, Milne, RL, Monteiro, AN, Murphy, RA, Neuhausen, SL, Nevanlinna, H, Newman, WG, Offit, K, Park, SK, James, P, Peterlongo, P, Peto, J, Plaseska-Karanfilska, D, Punie, K, Radice, P, Rashid, MU, Rennert, G, Romero, A, Rosenberg, EH, Saloustros, E, Sandler, DP, Schmidt, MK, Schmutzler, RK, Shu, X-O, Simard, J, Southey, MC, Stone, J, Stoppa-Lyonnet, D, Tamimi, RM, Tapper, WJ, Taylor, JA, Teo, SH, Teras, LR, Terry, MB, Thomassen, M, Troester, MA, Vachon, CM, Vega, A, Vreeswijk, MPG, Wang, Q, Wappenschmidt, B, Weinberg, CR, Wolk, A, Zheng, W, Feng, B, Couch, FJ, Spurdle, AB, Easton, DF, Goldgar, DE, and Michailidou, K

Abstract

A large number of variants identified through clinical genetic testing in disease susceptibility genes, are of uncertain significance (VUS). Following the recommendations of the American College of Medical Genetics and Genomics (ACMG) and Association for Molecular Pathology (AMP), the frequency in case-control datasets (PS4 criterion), can inform their interpretation. We present a novel case-control likelihood ratio-based method that incorporates gene-specific age-related penetrance. We demonstrate the utility of this method in the analysis of simulated and real datasets. In the analyses of simulated data, the likelihood ratio method was more powerful compared to other methods. Likelihood ratios were calculated for a case-control dataset of BRCA1 and BRCA2 variants from the Breast Cancer Association Consortium (BCAC), and compared with logistic regression results. A larger number of variants reached evidence in favor of pathogenicity, and a substantial number of variants had evidence against pathogenicity - findings that would not have been reached using other case-control analysis methods. Our novel method provides greater power to classify rare variants compared to classical case-control methods. As an initiative from the ENIGMA Analytical Working Group, we provide user-friendly scripts and pre-formatted excel calculators for implementation of the method for rare variants in BRCA1, BRCA2 and other high-risk genes with known penetrance.

Published
2023

2. Aggregation tests identify new gene associations with breast cancer in populations with diverse ancestry

Author

Mueller, SH, Lai, AG, Valkovskaya, M, Michailidou, K, Bolla, MK, Wang, Q, Dennis, J, Lush, M, Abu-Ful, Z, Ahearn, TU, Andrulis, IL, Anton-Culver, H, Antonenkova, NN, Arndt, V, Aronson, KJ, Augustinsson, A, Baert, T, Freeman, LEB, Beckmann, MW, Behrens, S, Benitez, J, Bermisheva, M, Blomqvist, C, Bogdanova, N, Bojesen, SE, Bonanni, B, Brenner, H, Brucker, SY, Buys, SS, Castelao, JE, Chan, TL, Chang-Claude, J, Chanock, SJ, Choi, J-Y, Chung, WK, Colonna, S, Cornelissen, S, Couch, FJ, Czene, K, Daly, MB, Devilee, P, Dork, T, Dossus, L, Dwek, M, Eccles, DM, Ekici, AB, Eliassen, AH, Engel, C, Evans, DG, Fasching, PA, Fletcher, O, Flyger, H, Gago-Dominguez, M, Gao, Y-T, Garcia-Closas, M, Garcia-Saenz, JA, Genkinger, J, Gentry-Maharaj, A, Grassmann, F, Guenel, P, Gundert, M, Haeberle, L, Hahnen, E, Haiman, CA, Hakansson, N, Hall, P, Harkness, EF, Harrington, PA, Hartikainen, JM, Hartman, M, Hein, A, Ho, W-K, Hooning, MJ, Hoppe, R, Hopper, JL, Houlston, RS, Howell, A, Hunter, DJ, Huo, D, Investigators, A, Ito, H, Iwasaki, M, Jakubowska, A, Janni, W, John, EM, Jones, ME, Jung, A, Kaaks, R, Kang, D, Khusnutdinova, EK, Kim, S-W, Kitahara, CM, Koutros, S, Kraft, P, Kristensen, VN, Kubelka-Sabit, K, Kurian, AW, Kwong, A, Lacey, J, Lambrechts, D, Le Marchand, L, Li, J, Linet, M, Lo, W-Y, Long, J, Lophatananon, A, Mannermaa, A, Manoochehri, M, Margolin, S, Matsuo, K, Mavroudis, D, Menon, U, Muir, K, Murphy, RA, Nevanlinna, H, Newman, WG, Niederacher, D, O'Brien, KM, Obi, N, Offit, K, Olopade, O, Olshan, AF, Olsson, H, Park, SK, Patel, A, Perou, CM, Peto, J, Pharoah, PDP, Plaseska-Karanfilska, D, Presneau, N, Rack, B, Radice, P, Ramachandran, D, Rashid, MU, Rennert, G, Romero, A, Ruddy, KJ, Ruebner, M, Saloustros, E, Sandler, DP, Sawyer, EJ, Schmidt, MK, Schmutzler, RK, Schneider, MO, Scott, C, Shah, M, Sharma, P, Shen, C-Y, Shu, X-O, Simard, J, Surowy, H, Tamimi, RM, Tapper, WJ, Taylor, JA, Teo, SH, Teras, LR, Toland, AE, Tollenaar, RAEM, Torres, D, Torres-Mejia, G, Troester, MA, Truong, T, Vachon, CM, Vijai, J, Weinberg, CR, Wendt, C, Winqvist, R, Wolk, A, Wu, AH, Yamaji, T, Yang, XR, Yu, J-C, Zheng, W, Ziogas, A, Ziv, E, Dunning, AM, Easton, DF, Hemingway, H, Hamann, U, Kuchenbaecker, KB, Mueller, SH, Lai, AG, Valkovskaya, M, Michailidou, K, Bolla, MK, Wang, Q, Dennis, J, Lush, M, Abu-Ful, Z, Ahearn, TU, Andrulis, IL, Anton-Culver, H, Antonenkova, NN, Arndt, V, Aronson, KJ, Augustinsson, A, Baert, T, Freeman, LEB, Beckmann, MW, Behrens, S, Benitez, J, Bermisheva, M, Blomqvist, C, Bogdanova, N, Bojesen, SE, Bonanni, B, Brenner, H, Brucker, SY, Buys, SS, Castelao, JE, Chan, TL, Chang-Claude, J, Chanock, SJ, Choi, J-Y, Chung, WK, Colonna, S, Cornelissen, S, Couch, FJ, Czene, K, Daly, MB, Devilee, P, Dork, T, Dossus, L, Dwek, M, Eccles, DM, Ekici, AB, Eliassen, AH, Engel, C, Evans, DG, Fasching, PA, Fletcher, O, Flyger, H, Gago-Dominguez, M, Gao, Y-T, Garcia-Closas, M, Garcia-Saenz, JA, Genkinger, J, Gentry-Maharaj, A, Grassmann, F, Guenel, P, Gundert, M, Haeberle, L, Hahnen, E, Haiman, CA, Hakansson, N, Hall, P, Harkness, EF, Harrington, PA, Hartikainen, JM, Hartman, M, Hein, A, Ho, W-K, Hooning, MJ, Hoppe, R, Hopper, JL, Houlston, RS, Howell, A, Hunter, DJ, Huo, D, Investigators, A, Ito, H, Iwasaki, M, Jakubowska, A, Janni, W, John, EM, Jones, ME, Jung, A, Kaaks, R, Kang, D, Khusnutdinova, EK, Kim, S-W, Kitahara, CM, Koutros, S, Kraft, P, Kristensen, VN, Kubelka-Sabit, K, Kurian, AW, Kwong, A, Lacey, J, Lambrechts, D, Le Marchand, L, Li, J, Linet, M, Lo, W-Y, Long, J, Lophatananon, A, Mannermaa, A, Manoochehri, M, Margolin, S, Matsuo, K, Mavroudis, D, Menon, U, Muir, K, Murphy, RA, Nevanlinna, H, Newman, WG, Niederacher, D, O'Brien, KM, Obi, N, Offit, K, Olopade, O, Olshan, AF, Olsson, H, Park, SK, Patel, A, Perou, CM, Peto, J, Pharoah, PDP, Plaseska-Karanfilska, D, Presneau, N, Rack, B, Radice, P, Ramachandran, D, Rashid, MU, Rennert, G, Romero, A, Ruddy, KJ, Ruebner, M, Saloustros, E, Sandler, DP, Sawyer, EJ, Schmidt, MK, Schmutzler, RK, Schneider, MO, Scott, C, Shah, M, Sharma, P, Shen, C-Y, Shu, X-O, Simard, J, Surowy, H, Tamimi, RM, Tapper, WJ, Taylor, JA, Teo, SH, Teras, LR, Toland, AE, Tollenaar, RAEM, Torres, D, Torres-Mejia, G, Troester, MA, Truong, T, Vachon, CM, Vijai, J, Weinberg, CR, Wendt, C, Winqvist, R, Wolk, A, Wu, AH, Yamaji, T, Yang, XR, Yu, J-C, Zheng, W, Ziogas, A, Ziv, E, Dunning, AM, Easton, DF, Hemingway, H, Hamann, U, and Kuchenbaecker, KB

Abstract

BACKGROUND: Low-frequency variants play an important role in breast cancer (BC) susceptibility. Gene-based methods can increase power by combining multiple variants in the same gene and help identify target genes. METHODS: We evaluated the potential of gene-based aggregation in the Breast Cancer Association Consortium cohorts including 83,471 cases and 59,199 controls. Low-frequency variants were aggregated for individual genes' coding and regulatory regions. Association results in European ancestry samples were compared to single-marker association results in the same cohort. Gene-based associations were also combined in meta-analysis across individuals with European, Asian, African, and Latin American and Hispanic ancestry. RESULTS: In European ancestry samples, 14 genes were significantly associated (q < 0.05) with BC. Of those, two genes, FMNL3 (P = 6.11 × 10-6) and AC058822.1 (P = 1.47 × 10-4), represent new associations. High FMNL3 expression has previously been linked to poor prognosis in several other cancers. Meta-analysis of samples with diverse ancestry discovered further associations including established candidate genes ESR1 and CBLB. Furthermore, literature review and database query found further support for a biologically plausible link with cancer for genes CBLB, FMNL3, FGFR2, LSP1, MAP3K1, and SRGAP2C. CONCLUSIONS: Using extended gene-based aggregation tests including coding and regulatory variation, we report identification of plausible target genes for previously identified single-marker associations with BC as well as the discovery of novel genes implicated in BC development. Including multi ancestral cohorts in this study enabled the identification of otherwise missed disease associations as ESR1 (P = 1.31 × 10-5), demonstrating the importance of diversifying study cohorts.

Published
2023

3. A genome-wide gene-environment interaction study of breast cancer risk for women of European ancestry

Author

Middha, PK, Wang, X, Behrens, S, Bolla, MK, Wang, Q, Dennis, J, Michailidou, K, Ahearn, TU, Andrulis, IL, Anton-Culver, H, Arndt, V, Aronson, KJ, Auer, PL, Augustinsson, A, Baert, T, Freeman, LEB, Becher, H, Beckmann, MW, Benitez, J, Bojesen, SE, Brauch, H, Brenner, H, Brooks-Wilson, A, Campa, D, Canzian, F, Carracedo, A, Castelao, JE, Chanock, SJ, Chenevix-Trench, G, Cordina-Duverger, E, Couch, FJ, Cox, A, Cross, SS, Czene, K, Dossus, L, Dugue, P-A, Eliassen, AH, Eriksson, M, Evans, DG, Fasching, PA, Figueroa, J, Fletcher, O, Flyger, H, Gabrielson, M, Gago-Dominguez, M, Giles, GG, Gonzalez-Neira, A, Grassmann, F, Grundy, A, Guenel, P, Haiman, CA, Hakansson, N, Hall, P, Hamann, U, Hankinson, SE, Harkness, EF, Holleczek, B, Hoppe, R, Hopper, JL, Houlston, RS, Howell, A, Hunter, DJ, Ingvar, C, Isaksson, K, Jernstroem, H, John, EM, Jones, ME, Kaaks, R, Keeman, R, Kitahara, CM, Ko, Y-D, Koutros, S, Kurian, AW, Lacey, JV, Lambrechts, D, Larson, NL, Larsson, S, Le Marchand, L, Lejbkowicz, F, Li, S, Linet, M, Lissowska, J, Martinez, ME, Maurer, T, Mulligan, AM, Mulot, C, Murphy, RA, Newman, WG, Nielsen, SF, Nordestgaard, BG, Norman, A, O'Brien, KM, Olson, JE, Patel, AV, Prentice, R, Rees-Punia, E, Rennert, G, Rhenius, V, Ruddy, KJ, Sandler, DP, Scott, CG, Shah, MT, Shu, X-O, Smeets, A, Southey, MC, Stone, J, Tamimi, RM, Taylor, JA, Teras, LR, Tomczyk, K, Troester, MA, Truong, T, Vachon, CM, Wang, SS, Weinberg, CR, Wildiers, H, Willett, W, Winham, SJ, Wolk, A, Yang, X, Zamora, MP, Zheng, W, Ziogas, A, Dunning, AM, Pharoah, PDP, Garcia-Closas, M, Schmidt, MK, Kraft, P, Milne, RL, Lindstroem, S, Easton, DF, Chang-Claude, J, Middha, PK, Wang, X, Behrens, S, Bolla, MK, Wang, Q, Dennis, J, Michailidou, K, Ahearn, TU, Andrulis, IL, Anton-Culver, H, Arndt, V, Aronson, KJ, Auer, PL, Augustinsson, A, Baert, T, Freeman, LEB, Becher, H, Beckmann, MW, Benitez, J, Bojesen, SE, Brauch, H, Brenner, H, Brooks-Wilson, A, Campa, D, Canzian, F, Carracedo, A, Castelao, JE, Chanock, SJ, Chenevix-Trench, G, Cordina-Duverger, E, Couch, FJ, Cox, A, Cross, SS, Czene, K, Dossus, L, Dugue, P-A, Eliassen, AH, Eriksson, M, Evans, DG, Fasching, PA, Figueroa, J, Fletcher, O, Flyger, H, Gabrielson, M, Gago-Dominguez, M, Giles, GG, Gonzalez-Neira, A, Grassmann, F, Grundy, A, Guenel, P, Haiman, CA, Hakansson, N, Hall, P, Hamann, U, Hankinson, SE, Harkness, EF, Holleczek, B, Hoppe, R, Hopper, JL, Houlston, RS, Howell, A, Hunter, DJ, Ingvar, C, Isaksson, K, Jernstroem, H, John, EM, Jones, ME, Kaaks, R, Keeman, R, Kitahara, CM, Ko, Y-D, Koutros, S, Kurian, AW, Lacey, JV, Lambrechts, D, Larson, NL, Larsson, S, Le Marchand, L, Lejbkowicz, F, Li, S, Linet, M, Lissowska, J, Martinez, ME, Maurer, T, Mulligan, AM, Mulot, C, Murphy, RA, Newman, WG, Nielsen, SF, Nordestgaard, BG, Norman, A, O'Brien, KM, Olson, JE, Patel, AV, Prentice, R, Rees-Punia, E, Rennert, G, Rhenius, V, Ruddy, KJ, Sandler, DP, Scott, CG, Shah, MT, Shu, X-O, Smeets, A, Southey, MC, Stone, J, Tamimi, RM, Taylor, JA, Teras, LR, Tomczyk, K, Troester, MA, Truong, T, Vachon, CM, Wang, SS, Weinberg, CR, Wildiers, H, Willett, W, Winham, SJ, Wolk, A, Yang, X, Zamora, MP, Zheng, W, Ziogas, A, Dunning, AM, Pharoah, PDP, Garcia-Closas, M, Schmidt, MK, Kraft, P, Milne, RL, Lindstroem, S, Easton, DF, and Chang-Claude, J

Abstract

BACKGROUND: Genome-wide studies of gene-environment interactions (G×E) may identify variants associated with disease risk in conjunction with lifestyle/environmental exposures. We conducted a genome-wide G×E analysis of ~ 7.6 million common variants and seven lifestyle/environmental risk factors for breast cancer risk overall and for estrogen receptor positive (ER +) breast cancer. METHODS: Analyses were conducted using 72,285 breast cancer cases and 80,354 controls of European ancestry from the Breast Cancer Association Consortium. Gene-environment interactions were evaluated using standard unconditional logistic regression models and likelihood ratio tests for breast cancer risk overall and for ER + breast cancer. Bayesian False Discovery Probability was employed to assess the noteworthiness of each SNP-risk factor pairs. RESULTS: Assuming a 1 × 10-5 prior probability of a true association for each SNP-risk factor pairs and a Bayesian False Discovery Probability < 15%, we identified two independent SNP-risk factor pairs: rs80018847(9p13)-LINGO2 and adult height in association with overall breast cancer risk (ORint = 0.94, 95% CI 0.92-0.96), and rs4770552(13q12)-SPATA13 and age at menarche for ER + breast cancer risk (ORint = 0.91, 95% CI 0.88-0.94). CONCLUSIONS: Overall, the contribution of G×E interactions to the heritability of breast cancer is very small. At the population level, multiplicative G×E interactions do not make an important contribution to risk prediction in breast cancer.

Published
2023

5. Common variants in breast cancer risk loci predispose to distinct tumor subtypes

Author

Ahearn, TU, Zhang, H, Michailidou, K, Milne, RL, Bolla, MK, Dennis, J, Dunning, AM, Lush, M, Wang, Q, Andrulis, IL, Anton-Culver, H, Arndt, V, Aronson, KJ, Auer, PL, Augustinsson, A, Baten, A, Becher, H, Behrens, S, Benitez, J, Bermisheva, M, Blomqvist, C, Bojesen, SE, Bonanni, B, Borresen-Dale, A-L, Brauch, H, Brenner, H, Brooks-Wilson, A, Bruening, T, Burwinkel, B, Buys, SS, Canzian, F, Castelao, JE, Chang-Claude, J, Chanock, SJ, Chenevix-Trench, G, Clarke, CL, Collee, JM, Cox, A, Cross, SS, Czene, K, Daly, MB, Devilee, P, Dork, T, Dwek, M, Eccles, DM, Evans, DG, Fasching, PA, Figueroa, J, Floris, G, Gago-Dominguez, M, Gapstur, SM, Garcia-Saenz, JA, Gaudet, MM, Giles, GG, Goldberg, MS, Gonzalez-Neira, A, Alnaes, GIG, Grip, M, Guenel, P, Haiman, CA, Hall, P, Hamann, U, Harkness, EF, Heemskerk-Gerritsen, BAM, Holleczek, B, Hollestelle, A, Hooning, MJ, Hoover, RN, Hopper, JL, Howell, A, Jakimovska, M, Jakubowska, A, John, EM, Jones, ME, Jung, A, Kaaks, R, Kauppila, S, Keeman, R, Khusnutdinova, E, Kitahara, CM, Ko, Y-D, Koutros, S, Kristensen, VN, Kruger, U, Kubelka-Sabit, K, Kurian, AW, Kyriacou, K, Lambrechts, D, Lee, DG, Lindblom, A, Linet, M, Lissowska, J, Llaneza, A, Lo, W-Y, MacInnis, RJ, Mannermaa, A, Manoochehri, M, Margolin, S, Martinez, ME, McLean, C, Meindl, A, Menon, U, Nevanlinna, H, Newman, WG, Nodora, J, Offit, K, Olsson, H, Orr, N, Park-Simon, T-W, Patel, A, Peto, J, Pita, G, Plaseska-Karanfilska, D, Prentice, R, Punie, K, Pylkas, K, Radice, P, Rennert, G, Romero, A, Ruediger, T, Saloustros, E, Sampson, S, Sandler, DP, Sawyer, EJ, Schmutzler, RK, Schoemaker, MJ, Schottker, B, Sherman, ME, Shu, X-O, Smichkoska, S, Southey, MC, Spinelli, JJ, Swerdlow, AJ, Tamimi, RM, Tapper, WJ, Taylor, JA, Teras, LR, Terry, MB, Torres, D, Troester, MA, Vachon, CM, van Deurzen, CHM, van Veen, EM, Wagner, P, Weinberg, CR, Wendt, C, Wesseling, J, Winqvist, R, Wolk, A, Yang, XR, Zheng, W, Couch, FJ, Simard, J, Kraft, P, Easton, DF, Pharoah, PDP, Schmidt, MK, Garcia-Closas, M, Chatterjee, N, Ahearn, TU, Zhang, H, Michailidou, K, Milne, RL, Bolla, MK, Dennis, J, Dunning, AM, Lush, M, Wang, Q, Andrulis, IL, Anton-Culver, H, Arndt, V, Aronson, KJ, Auer, PL, Augustinsson, A, Baten, A, Becher, H, Behrens, S, Benitez, J, Bermisheva, M, Blomqvist, C, Bojesen, SE, Bonanni, B, Borresen-Dale, A-L, Brauch, H, Brenner, H, Brooks-Wilson, A, Bruening, T, Burwinkel, B, Buys, SS, Canzian, F, Castelao, JE, Chang-Claude, J, Chanock, SJ, Chenevix-Trench, G, Clarke, CL, Collee, JM, Cox, A, Cross, SS, Czene, K, Daly, MB, Devilee, P, Dork, T, Dwek, M, Eccles, DM, Evans, DG, Fasching, PA, Figueroa, J, Floris, G, Gago-Dominguez, M, Gapstur, SM, Garcia-Saenz, JA, Gaudet, MM, Giles, GG, Goldberg, MS, Gonzalez-Neira, A, Alnaes, GIG, Grip, M, Guenel, P, Haiman, CA, Hall, P, Hamann, U, Harkness, EF, Heemskerk-Gerritsen, BAM, Holleczek, B, Hollestelle, A, Hooning, MJ, Hoover, RN, Hopper, JL, Howell, A, Jakimovska, M, Jakubowska, A, John, EM, Jones, ME, Jung, A, Kaaks, R, Kauppila, S, Keeman, R, Khusnutdinova, E, Kitahara, CM, Ko, Y-D, Koutros, S, Kristensen, VN, Kruger, U, Kubelka-Sabit, K, Kurian, AW, Kyriacou, K, Lambrechts, D, Lee, DG, Lindblom, A, Linet, M, Lissowska, J, Llaneza, A, Lo, W-Y, MacInnis, RJ, Mannermaa, A, Manoochehri, M, Margolin, S, Martinez, ME, McLean, C, Meindl, A, Menon, U, Nevanlinna, H, Newman, WG, Nodora, J, Offit, K, Olsson, H, Orr, N, Park-Simon, T-W, Patel, A, Peto, J, Pita, G, Plaseska-Karanfilska, D, Prentice, R, Punie, K, Pylkas, K, Radice, P, Rennert, G, Romero, A, Ruediger, T, Saloustros, E, Sampson, S, Sandler, DP, Sawyer, EJ, Schmutzler, RK, Schoemaker, MJ, Schottker, B, Sherman, ME, Shu, X-O, Smichkoska, S, Southey, MC, Spinelli, JJ, Swerdlow, AJ, Tamimi, RM, Tapper, WJ, Taylor, JA, Teras, LR, Terry, MB, Torres, D, Troester, MA, Vachon, CM, van Deurzen, CHM, van Veen, EM, Wagner, P, Weinberg, CR, Wendt, C, Wesseling, J, Winqvist, R, Wolk, A, Yang, XR, Zheng, W, Couch, FJ, Simard, J, Kraft, P, Easton, DF, Pharoah, PDP, Schmidt, MK, Garcia-Closas, M, and Chatterjee, N

Abstract

BACKGROUND: Genome-wide association studies (GWAS) have identified multiple common breast cancer susceptibility variants. Many of these variants have differential associations by estrogen receptor (ER) status, but how these variants relate with other tumor features and intrinsic molecular subtypes is unclear. METHODS: Among 106,571 invasive breast cancer cases and 95,762 controls of European ancestry with data on 173 breast cancer variants identified in previous GWAS, we used novel two-stage polytomous logistic regression models to evaluate variants in relation to multiple tumor features (ER, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and grade) adjusting for each other, and to intrinsic-like subtypes. RESULTS: Eighty-five of 173 variants were associated with at least one tumor feature (false discovery rate < 5%), most commonly ER and grade, followed by PR and HER2. Models for intrinsic-like subtypes found nearly all of these variants (83 of 85) associated at p < 0.05 with risk for at least one luminal-like subtype, and approximately half (41 of 85) of the variants were associated with risk of at least one non-luminal subtype, including 32 variants associated with triple-negative (TN) disease. Ten variants were associated with risk of all subtypes in different magnitude. Five variants were associated with risk of luminal A-like and TN subtypes in opposite directions. CONCLUSION: This report demonstrates a high level of complexity in the etiology heterogeneity of breast cancer susceptibility variants and can inform investigations of subtype-specific risk prediction.

Published
2022

6. Incorporating progesterone receptor expression into the PREDICT breast prognostic model

Author

Grootes, I, Keeman, R, Blows, FM, Milne, RL, Giles, GG, Swerdlow, AJ, Fasching, PA, Abubakar, M, Andrulis, IL, Anton-Culver, H, Beckmann, MW, Blomqvist, C, Bojesen, SE, Bolla, MK, Bonanni, B, Briceno, I, Burwinkel, B, Camp, NJ, Castelao, JE, Choi, J-Y, Clarke, CL, Couch, FJ, Cox, A, Cross, SS, Czene, K, Devilee, P, Dork, T, Dunning, AM, Dwek, M, Easton, DF, Eccles, DM, Eriksson, M, Ernst, K, Evans, DG, Figueroa, JD, Fink, V, Floris, G, Fox, S, Gabrielson, M, Gago-Dominguez, M, Garcia-Saenz, JA, Gonzalez-Neira, A, Haeberle, L, Haiman, CA, Hall, P, Hamann, U, Harkness, EF, Hartman, M, Hein, A, Hooning, MJ, Hou, M-F, Howell, SJ, Ito, H, Jakubowska, A, Janni, W, John, EM, Jung, A, Kang, D, Kristensen, VN, Kwong, A, Lambrechts, D, Li, J, Manoochehri, M, Margolin, S, Matsuo, K, Taib, NAM, Mulligan, AM, Nevanlinna, H, Newman, WG, Offit, K, Osorio, A, Park, SK, Park-Simon, T-W, Patel, A, Presneau, N, Pylkas, K, Rack, B, Radice, P, Rennert, G, Romero, A, Saloustros, E, Sawyer, EJ, Schneeweiss, A, Schochter, F, Schoemaker, MJ, Shen, C-Y, Shibli, R, Sinn, P, Tapper, WJ, Tawfiq, E, Teo, SH, Teras, LR, Torres, D, Vachon, CM, van Deurzen, CHM, Wendt, C, Williams, JA, Winqvist, R, Elwood, M, Schmidt, MK, Pharoah, PDP, Grootes, I, Keeman, R, Blows, FM, Milne, RL, Giles, GG, Swerdlow, AJ, Fasching, PA, Abubakar, M, Andrulis, IL, Anton-Culver, H, Beckmann, MW, Blomqvist, C, Bojesen, SE, Bolla, MK, Bonanni, B, Briceno, I, Burwinkel, B, Camp, NJ, Castelao, JE, Choi, J-Y, Clarke, CL, Couch, FJ, Cox, A, Cross, SS, Czene, K, Devilee, P, Dork, T, Dunning, AM, Dwek, M, Easton, DF, Eccles, DM, Eriksson, M, Ernst, K, Evans, DG, Figueroa, JD, Fink, V, Floris, G, Fox, S, Gabrielson, M, Gago-Dominguez, M, Garcia-Saenz, JA, Gonzalez-Neira, A, Haeberle, L, Haiman, CA, Hall, P, Hamann, U, Harkness, EF, Hartman, M, Hein, A, Hooning, MJ, Hou, M-F, Howell, SJ, Ito, H, Jakubowska, A, Janni, W, John, EM, Jung, A, Kang, D, Kristensen, VN, Kwong, A, Lambrechts, D, Li, J, Manoochehri, M, Margolin, S, Matsuo, K, Taib, NAM, Mulligan, AM, Nevanlinna, H, Newman, WG, Offit, K, Osorio, A, Park, SK, Park-Simon, T-W, Patel, A, Presneau, N, Pylkas, K, Rack, B, Radice, P, Rennert, G, Romero, A, Saloustros, E, Sawyer, EJ, Schneeweiss, A, Schochter, F, Schoemaker, MJ, Shen, C-Y, Shibli, R, Sinn, P, Tapper, WJ, Tawfiq, E, Teo, SH, Teras, LR, Torres, D, Vachon, CM, van Deurzen, CHM, Wendt, C, Williams, JA, Winqvist, R, Elwood, M, Schmidt, MK, and Pharoah, PDP

Abstract

BACKGROUND: Predict Breast (www.predict.nhs.uk) is an online prognostication and treatment benefit tool for early invasive breast cancer. The aim of this study was to incorporate the prognostic effect of progesterone receptor (PR) status into a new version of PREDICT and to compare its performance to the current version (2.2). METHOD: The prognostic effect of PR status was based on the analysis of data from 45,088 European patients with breast cancer from 49 studies in the Breast Cancer Association Consortium. Cox proportional hazard models were used to estimate the hazard ratio for PR status. Data from a New Zealand study of 11,365 patients with early invasive breast cancer were used for external validation. Model calibration and discrimination were used to test the model performance. RESULTS: Having a PR-positive tumour was associated with a 23% and 28% lower risk of dying from breast cancer for women with oestrogen receptor (ER)-negative and ER-positive breast cancer, respectively. The area under the ROC curve increased with the addition of PR status from 0.807 to 0.809 for patients with ER-negative tumours (p = 0.023) and from 0.898 to 0.902 for patients with ER-positive tumours (p = 2.3 × 10-6) in the New Zealand cohort. Model calibration was modest with 940 observed deaths compared to 1151 predicted. CONCLUSION: The inclusion of the prognostic effect of PR status to PREDICT Breast has led to an improvement of model performance and more accurate absolute treatment benefit predictions for individual patients. Further studies should determine whether the baseline hazard function requires recalibration.

Published
2022

7. Breast cancer risks associated with missense variants in breast cancer susceptibility genes

Author

Dorling, L, Carvalho, S, Allen, J, Parsons, MT, Fortuno, C, Gonzalez-Neira, A, Heijl, SM, Adank, MA, Ahearn, TU, Andrulis, IL, Auvinen, P, Becher, H, Beckmann, MW, Behrens, S, Bermisheva, M, Bogdanova, NV, Bojesen, SE, Bolla, MK, Bremer, M, Briceno, I, Camp, NJ, Campbell, A, Castelao, JE, Chang-Claude, J, Chanock, SJ, Chenevix-Trench, G, Collee, JM, Czene, K, Dennis, J, Dork, T, Eriksson, M, Evans, DG, Fasching, PA, Figueroa, J, Flyger, H, Gabrielson, M, Gago-Dominguez, M, Garcia-Closas, M, Giles, GG, Glendon, G, Guenel, P, Gundert, M, Hadjisavvas, A, Hahnen, E, Hall, P, Hamann, U, Harkness, EF, Hartman, M, Hogervorst, FBL, Hollestelle, A, Hoppe, R, Howell, A, Jakubowska, A, Jung, A, Khusnutdinova, E, Kim, S-W, Ko, Y-D, Kristensen, VN, Lakeman, IMM, Li, J, Lindblom, A, Loizidou, MA, Lophatananon, A, Lubinski, J, Luccarini, C, Madsen, MJ, Mannermaa, A, Manoochehri, M, Margolin, S, Mavroudis, D, Milne, RL, Mohd Taib, NA, Muir, K, Nevanlinna, H, Newman, WG, Oosterwijk, JC, Park, SK, Peterlongo, P, Radice, P, Saloustros, E, Sawyer, EJ, Schmutzler, RK, Shah, M, Sim, X, Southey, MC, Surowy, H, Suvanto, M, Tomlinson, I, Torres, D, Truong, T, van Asperen, CJ, Waltes, R, Wang, Q, Yang, XR, Pharoah, PDP, Schmidt, MK, Benitez, J, Vroling, B, Dunning, AM, Teo, SH, Kvist, A, de la Hoya, M, Devilee, P, Spurdle, AB, Vreeswijk, MPG, Easton, DF, Dorling, L, Carvalho, S, Allen, J, Parsons, MT, Fortuno, C, Gonzalez-Neira, A, Heijl, SM, Adank, MA, Ahearn, TU, Andrulis, IL, Auvinen, P, Becher, H, Beckmann, MW, Behrens, S, Bermisheva, M, Bogdanova, NV, Bojesen, SE, Bolla, MK, Bremer, M, Briceno, I, Camp, NJ, Campbell, A, Castelao, JE, Chang-Claude, J, Chanock, SJ, Chenevix-Trench, G, Collee, JM, Czene, K, Dennis, J, Dork, T, Eriksson, M, Evans, DG, Fasching, PA, Figueroa, J, Flyger, H, Gabrielson, M, Gago-Dominguez, M, Garcia-Closas, M, Giles, GG, Glendon, G, Guenel, P, Gundert, M, Hadjisavvas, A, Hahnen, E, Hall, P, Hamann, U, Harkness, EF, Hartman, M, Hogervorst, FBL, Hollestelle, A, Hoppe, R, Howell, A, Jakubowska, A, Jung, A, Khusnutdinova, E, Kim, S-W, Ko, Y-D, Kristensen, VN, Lakeman, IMM, Li, J, Lindblom, A, Loizidou, MA, Lophatananon, A, Lubinski, J, Luccarini, C, Madsen, MJ, Mannermaa, A, Manoochehri, M, Margolin, S, Mavroudis, D, Milne, RL, Mohd Taib, NA, Muir, K, Nevanlinna, H, Newman, WG, Oosterwijk, JC, Park, SK, Peterlongo, P, Radice, P, Saloustros, E, Sawyer, EJ, Schmutzler, RK, Shah, M, Sim, X, Southey, MC, Surowy, H, Suvanto, M, Tomlinson, I, Torres, D, Truong, T, van Asperen, CJ, Waltes, R, Wang, Q, Yang, XR, Pharoah, PDP, Schmidt, MK, Benitez, J, Vroling, B, Dunning, AM, Teo, SH, Kvist, A, de la Hoya, M, Devilee, P, Spurdle, AB, Vreeswijk, MPG, and Easton, DF

Abstract

BACKGROUND: Protein truncating variants in ATM, BRCA1, BRCA2, CHEK2, and PALB2 are associated with increased breast cancer risk, but risks associated with missense variants in these genes are uncertain. METHODS: We analyzed data on 59,639 breast cancer cases and 53,165 controls from studies participating in the Breast Cancer Association Consortium BRIDGES project. We sampled training (80%) and validation (20%) sets to analyze rare missense variants in ATM (1146 training variants), BRCA1 (644), BRCA2 (1425), CHEK2 (325), and PALB2 (472). We evaluated breast cancer risks according to five in silico prediction-of-deleteriousness algorithms, functional protein domain, and frequency, using logistic regression models and also mixture models in which a subset of variants was assumed to be risk-associated. RESULTS: The most predictive in silico algorithms were Helix (BRCA1, BRCA2 and CHEK2) and CADD (ATM). Increased risks appeared restricted to functional protein domains for ATM (FAT and PIK domains) and BRCA1 (RING and BRCT domains). For ATM, BRCA1, and BRCA2, data were compatible with small subsets (approximately 7%, 2%, and 0.6%, respectively) of rare missense variants giving similar risk to those of protein truncating variants in the same gene. For CHEK2, data were more consistent with a large fraction (approximately 60%) of rare missense variants giving a lower risk (OR 1.75, 95% CI (1.47-2.08)) than CHEK2 protein truncating variants. There was little evidence for an association with risk for missense variants in PALB2. The best fitting models were well calibrated in the validation set. CONCLUSIONS: These results will inform risk prediction models and the selection of candidate variants for functional assays and could contribute to the clinical reporting of gene panel testing for breast cancer susceptibility.

Published
2022

8. Mendelian randomisation study of smoking exposure in relation to breast cancer risk

Author

Park, HA, Neumeyer, S, Michailidou, K, Bolla, MK, Wang, Q, Dennis, J, Ahearn, TU, Andrulis, IL, Anton-Culver, H, Antonenkova, NN, Arndt, V, Aronson, KJ, Augustinsson, A, Baten, A, Beane Freeman, LE, Becher, H, Beckmann, MW, Behrens, S, Benitez, J, Bermisheva, M, Bogdanova, NV, Bojesen, SE, Brauch, H, Brenner, H, Brucker, SY, Burwinkel, B, Campa, D, Canzian, F, Castelao, JE, Chanock, SJ, Chenevix-Trench, G, Clarke, CL, Børresen-Dale, A-L, Grenaker Alnæs, GI, Sahlberg, KK, Ottestad, L, Kåresen, R, Schlichting, E, Holmen, MM, Sauer, T, Haakensen, V, Engebråten, O, Naume, B, Fosså, A, Kiserud, CE, Reinertsen, KV, Helland, Å, Riis, M, Geisler, J, Conroy, DM, Couch, FJ, Cox, A, Cross, SS, Czene, K, Daly, MB, Devilee, P, Dörk, T, dos-Santos-Silva, I, Dwek, M, Eccles, DM, Eliassen, AH, Engel, C, Eriksson, M, Evans, DG, Fasching, PA, Flyger, H, Fritschi, L, García-Closas, M, García-Sáenz, JA, Gaudet, MM, Giles, GG, Glendon, G, Goldberg, MS, Goldgar, DE, González-Neira, A, Grip, M, Guénel, P, Hahnen, E, Haiman, CA, Håkansson, N, Hall, P, Hamann, U, Han, S, Harkness, EF, Hart, SN, He, W, Heemskerk-Gerritsen, BAM, Hopper, JL, Hunter, DJ, Clarke, C, Marsh, D, Scott, R, Baxter, R, Yip, D, Carpenter, J, Davis, A, Pathmanathan, N, Simpson, P, Graham, D, Sachchithananthan, M, Amor, D, Andrews, L, Antill, Y, Balleine, R, Beesley, J, Bennett, I, Bogwitz, M, Botes, L, Brennan, M, Brown, M, Buckley, M, Burke, J, Butow, P, Caldon, L, Campbell, I, Chauhan, D, Chauhan, M, Christian, A, Cohen, P, Colley, A, Crook, A, Cui, J, Cummings, M, Dawson, S-J, DeFazio, A, Delatycki, M, Dickson, R, Dixon, J, Edkins, T, Edwards, S, Farshid, G, Fellows, A, Fenton, G, Field, M, Flanagan, J, Fong, P, Forrest, L, Fox, S, French, J, Friedlander, M, Gaff, C, Gattas, M, George, P, Greening, S, Harris, M, Hart, S, Hayward, N, Hopper, J, Hoskins, C, Hunt, C, James, P, Jenkins, M, Kidd, A, Kirk, J, Koehler, J, Kollias, J, Lakhani, S, Lawrence, M, Lindeman, G, Lipton, L, Lobb, L, Mann, G, McLachlan, SA, Meiser, B, Milne, R, Nightingale, S, O’Connell, S, O’Sullivan, S, Ortega, DG, Pachter, N, Patterson, B, Pearn, A, Phillips, K, Pieper, E, Rickard, E, Robinson, B, Saleh, M, Salisbury, E, Saunders, C, Saunus, J, Scott, C, Sexton, A, Shelling, A, Southey, M, Spurdle, A, Taylor, J, Taylor, R, Thorne, H, Trainer, A, Tucker, K, Visvader, J, Walker, L, Williams, R, Winship, I, Young, MA, Jager, A, Jakubowska, A, John, EM, Jung, A, Kaaks, R, Kapoor, PM, Keeman, R, Khusnutdinova, E, Kitahara, CM, Koppert, LB, Koutros, S, Kristensen, VN, Kurian, AW, Lacey, J, Lambrechts, D, Le Marchand, L, Lo, W-Y, Lubiński, J, Mannermaa, A, Manoochehri, M, Margolin, S, Martinez, ME, Mavroudis, D, Meindl, A, Menon, U, Milne, RL, Muranen, TA, Nevanlinna, H, Newman, WG, Nordestgaard, BG, Offit, K, Olshan, AF, Olsson, H, Park-Simon, T-W, Peterlongo, P, Peto, J, Plaseska-Karanfilska, D, Presneau, N, Radice, P, Rennert, G, Rennert, HS, Romero, A, Saloustros, E, Sawyer, EJ, Schmidt, MK, Schmutzler, RK, Schoemaker, MJ, Schwentner, L, Shah, M, Shu, X-O, Simard, J, Smeets, A, Southey, MC, Spinelli, JJ, Stevens, V, Swerdlow, AJ, Tamimi, RM, Tapper, WJ, Taylor, JA, Terry, MB, Tomlinson, I, Troester, MA, Truong, T, Vachon, CM, van Veen, EM, Vijai, J, Wang, S, Wendt, C, Winqvist, R, Wolk, A, Ziogas, A, Dunning, AM, Pharoah, PDP, Easton, DF, Zheng, W, Kraft, P, Chang-Claude, J, HUS Gynecology and Obstetrics, Department of Obstetrics and Gynecology, Park, Hanla A. [0000-0001-8055-3729], Dennis, Joe [0000-0003-4591-1214], Augustinsson, Annelie [0000-0003-3415-0536], Brenner, Hermann [0000-0002-6129-1572], Canzian, Federico [0000-0002-4261-4583], Cox, Angela [0000-0002-5138-1099], Devilee, Peter [0000-0002-8023-2009], Fasching, Peter A. [0000-0003-4885-8471], Harkness, Elaine F. [0000-0001-6625-7739], Hart, Steven N. [0000-0001-7714-2734], Heemskerk-Gerritsen, Bernadette A. M. [0000-0002-9724-6693], Jakubowska, Anna [0000-0002-5650-0501], Kapoor, Pooja Middha [0000-0001-5503-8215], Kurian, Allison W. [0000-0002-6175-9470], Newman, William G. [0000-0002-6382-4678], Peterlongo, Paolo [0000-0001-6951-6855], Peto, Julian [0000-0002-1685-8912], Sawyer, Elinor J. [0000-0001-8285-4111], Scott, Christopher [0000-0003-1340-0647], Smeets, Ann [0000-0002-5091-6602], Tomlinson, Ian [0000-0003-3037-1470], Truong, Thérèse [0000-0002-2943-6786], Pharoah, Paul D. P. [0000-0001-8494-732X], and Apollo - University of Cambridge Repository

Subjects
0301 basic medicine, Oncology, Medicin och hälsovetenskap, Cancer Research, Genotyping Techniques, Breast Neoplasms, Case-Control Studies, Cigarette Smoking, Female, Genetic Pleiotropy, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Mendelian Randomization Analysis, Polymorphism, Single Nucleotide, ALCOHOL, Medical and Health Sciences, 0302 clinical medicine, Breast cancer, Pleiotropy, Epidemiology, Medicine, TOBACCO, Breast Neoplasms/epidemiology, Cigarette Smoking/adverse effects, WOMEN, ASSOCIATION, Single Nucleotide, 3. Good health, Substance abuse, 692/699/67/1347, 030220 oncology & carcinogenesis, Life Sciences & Biomedicine, 692/499, medicine.medical_specialty, 3122 Cancers, Single-nucleotide polymorphism, Article, 03 medical and health sciences, Internal medicine, ddc:610, Polymorphism, Genetic association, Science & Technology, business.industry, Cancer, medicine.disease, 030104 developmental biology, Clinical research, Risk factors, TISSUE, INFERENCE, CIGARETTE-SMOKING, business
Abstract

Background Despite a modest association between tobacco smoking and breast cancer risk reported by recent epidemiological studies, it is still equivocal whether smoking is causally related to breast cancer risk. Methods We applied Mendelian randomisation (MR) to evaluate a potential causal effect of cigarette smoking on breast cancer risk. Both individual-level data as well as summary statistics for 164 single-nucleotide polymorphisms (SNPs) reported in genome-wide association studies of lifetime smoking index (LSI) or cigarette per day (CPD) were used to obtain MR effect estimates. Data from 108,420 invasive breast cancer cases and 87,681 controls were used for the LSI analysis and for the CPD analysis conducted among ever-smokers from 26,147 cancer cases and 26,072 controls. Sensitivity analyses were conducted to address pleiotropy. Results Genetically predicted LSI was associated with increased breast cancer risk (OR 1.18 per SD, 95% CI: 1.07–1.30, P = 0.11 × 10–2), but there was no evidence of association for genetically predicted CPD (OR 1.02, 95% CI: 0.78–1.19, P = 0.85). The sensitivity analyses yielded similar results and showed no strong evidence of pleiotropic effect. Conclusion Our MR study provides supportive evidence for a potential causal association with breast cancer risk for lifetime smoking exposure but not cigarettes per day among smokers.

Published
2021

9. Common variants in breast cancer risk loci predispose to distinct tumor subtypes

Author

Ahearn, TU, Zhang, H, Michailidou, K, Milne, RL, Bolla, MK, Dennis, J, Dunning, AM, Lush, M, Wang, Q, Andrulis, IL, Anton-Culver, H, Arndt, V, Aronson, KJ, Auer, PL, Augustinsson, A, Baten, A, Becher, H, Behrens, S, Benitez, J, Bermisheva, M, Blomqvist, C, Bojesen, SE, Bonanni, B, Børresen-Dale, A-L, Brauch, H, Brenner, H, Brooks-Wilson, A, Brüning, T, Burwinkel, B, Buys, SS, Canzian, F, Castelao, JE, Chang-Claude, J, Chanock, SJ, Chenevix-Trench, G, Clarke, CL, Sahlberg, KK, Ottestad, L, Kåresen, R, Schlichting, E, Holmen, MM, Sauer, T, Haakensen, V, Engebråten, O, Naume, B, Fosså, A, Kiserud, CE, Reinertsen, KV, Helland, Å, Riis, M, Geisler, J, Collée, JM, Cox, A, Cross, SS, Czene, K, Daly, MB, Devilee, P, Dörk, T, Dwek, M, Eccles, DM, Evans, DG, Fasching, PA, Figueroa, J, Floris, G, Gago-Dominguez, M, Gapstur, SM, García-Sáenz, JA, Gaudet, MM, Giles, GG, Goldberg, MS, González-Neira, A, Alnæs, GIG, Grip, M, Guénel, P, Haiman, CA, Hall, P, Hamann, U, Harkness, EF, Heemskerk-Gerritsen, BAM, Holleczek, B, Hollestelle, A, Hooning, MJ, Hoover, RN, Hopper, JL, Howell, A, Clarke, C, Balleine, R, Baxter, R, Braye, S, Carpenter, J, Dahlstrom, J, Forbes, J, Lee, C, Marsh, D, Morey, A, Pathmanathan, N, Scott, R, Simpson, P, Spigelman, A, Wilcken, N, Yip, D, Zeps, N, Fox, S, Campbell, I, Bowtell, D, Spurdle, A, Webb, P, de Fazio, A, Tassell, M, Kirk, J, Lindeman, G, Price, M, Southey, M, Milne, R, Deb, S, Jakimovska, M, Jakubowska, A, John, EM, Jones, ME, Jung, A, Kaaks, R, Kauppila, S, Keeman, R, Khusnutdinova, E, Kitahara, CM, Ko, Y-D, Koutros, S, Kristensen, VN, Krüger, U, Kubelka-Sabit, K, Kurian, AW, Kyriacou, K, Lambrechts, D, Lee, DG, Lindblom, A, Linet, M, Lissowska, J, Llaneza, A, Lo, W-Y, MacInnis, RJ, Mannermaa, A, Manoochehri, M, Margolin, S, Martinez, ME, McLean, C, Meindl, A, Menon, U, Nevanlinna, H, Newman, WG, Nodora, J, Offit, K, Olsson, H, Orr, N, Park-Simon, T-W, Patel, AV, Peto, J, Pita, G, Plaseska-Karanfilska, D, Prentice, R, Punie, K, Pylkäs, K, Radice, P, Rennert, G, Romero, A, Rüdiger, T, Saloustros, E, Sampson, S, Sandler, DP, Sawyer, EJ, Schmutzler, RK, Schoemaker, MJ, Schöttker, B, Sherman, ME, Shu, X-O, Smichkoska, S, Southey, MC, Spinelli, JJ, Swerdlow, AJ, Tamimi, RM, Tapper, WJ, Taylor, JA, Teras, LR, Terry, MB, Torres, D, Troester, MA, Vachon, CM, van Deurzen, CHM, van Veen, EM, Wagner, P, Weinberg, CR, Wendt, C, Wesseling, J, Winqvist, R, Wolk, A, Yang, XR, Zheng, W, Couch, FJ, Simard, J, Kraft, P, Easton, DF, Pharoah, PDP, Schmidt, MK, García-Closas, M, Chatterjee, N, Ahearn, Thomas U [0000-0003-0771-7752], Easton, Douglas [0000-0003-2444-3247], Pharoah, Paul [0000-0001-8494-732X], Apollo - University of Cambridge Repository, Medicum, HUS Comprehensive Cancer Center, Department of Oncology, Clinicum, Department of Obstetrics and Gynecology, Biosciences, HUS Gynecology and Obstetrics, Clinical Genetics, Medical Oncology, and Pathology

Subjects
False discovery rate, Oncology, Common breast cancer susceptibility variants, Receptor, ErbB-2, Estrogen receptor, PROGRESSION, Etiologic heterogeneity, Logistic regression, Basic medicine, Breast cancer, 0302 clinical medicine, PRIMARY THERAPY, HETEROGENEITY, RC254-282, HISTOLOGICAL GRADE, 0303 health sciences, Breast Neoplasms/epidemiology, Receptors, Estrogen/genetics, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, INTERNATIONAL EXPERT CONSENSUS, humanities, Receptor, ErbB-2/genetics, 3. Good health, Receptors, Estrogen, Receptors, Progesterone/genetics, 030220 oncology & carcinogenesis, Female, Biomarkers, Tumor/genetics, Receptors, Progesterone, Medical Genetics, Research Article, Risk, medicine.medical_specialty, SUSCEPTIBILITY LOCI, 3122 Cancers, Breast Neoplasms, Single-nucleotide polymorphism, Biology, 03 medical and health sciences, SDG 3 - Good Health and Well-being, Internal medicine, Progesterone receptor, Biomarkers, Tumor, medicine, Genetic predisposition, Humans, ddc:610, GENOME-WIDE ASSOCIATION, Genetic association, Medicinsk genetik, 030304 developmental biology, Cancer och onkologi, medicine.disease, Cancer and Oncology, Clinical medicine, Genome-Wide Association Study
Abstract

Background Genome-wide association studies (GWAS) have identified multiple common breast cancer susceptibility variants. Many of these variants have differential associations by estrogen receptor (ER) status, but how these variants relate with other tumor features and intrinsic molecular subtypes is unclear. Methods Among 106,571 invasive breast cancer cases and 95,762 controls of European ancestry with data on 173 breast cancer variants identified in previous GWAS, we used novel two-stage polytomous logistic regression models to evaluate variants in relation to multiple tumor features (ER, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and grade) adjusting for each other, and to intrinsic-like subtypes. Results Eighty-five of 173 variants were associated with at least one tumor feature (false discovery rate p Conclusion This report demonstrates a high level of complexity in the etiology heterogeneity of breast cancer susceptibility variants and can inform investigations of subtype-specific risk prediction.

Published
2020

10. Genome-wide association study identifies 32 novel breast cancer susceptibility loci from overall and subtype-specific analyses

Author

Zhang, H, Ahearn, TU, Lecarpentier, J, Barnes, D, Beesley, J, Qi, G, Jiang, X, O’Mara, TA, Zhao, N, Bolla, MK, Dunning, AM, Dennis, J, Wang, Q, Ful, ZA, Aittomäki, K, Andrulis, IL, Anton-Culver, H, Arndt, V, Aronson, KJ, Arun, BK, Auer, PL, Azzollini, J, Barrowdale, D, Becher, H, Beckmann, MW, Behrens, S, Benitez, J, Bermisheva, M, Bialkowska, K, Blanco, A, Blomqvist, C, Bogdanova, NV, Bojesen, SE, Bonanni, B, Bondavalli, D, Borg, A, Brauch, H, Brenner, H, Briceno, I, Broeks, A, Brucker, SY, Brüning, T, Burwinkel, B, Buys, SS, Byers, H, Caldés, T, Caligo, MA, Calvello, M, Campa, D, Castelao, JE, Chang-Claude, J, Chanock, SJ, Christiaens, M, Christiansen, H, Chung, WK, Claes, KBM, Clarke, CL, Cornelissen, S, Couch, FJ, Cox, A, Cross, SS, Czene, K, Daly, MB, Devilee, P, Diez, O, Domchek, SM, Dörk, T, Dwek, M, Eccles, DM, Ekici, AB, Evans, DG, Fasching, PA, Figueroa, J, Foretova, L, Fostira, F, Friedman, E, Frost, D, Gago-Dominguez, M, Gapstur, SM, Garber, J, García-Sáenz, JA, Gaudet, MM, Gayther, SA, Giles, GG, Godwin, AK, Goldberg, MS, Goldgar, DE, González-Neira, A, Greene, MH, Gronwald, J, Guénel, P, Häberle, L, Hahnen, E, Haiman, CA, Hake, CR, Hall, P, Hamann, U, Harkness, EF, Heemskerk-Gerritsen, BAM, Hillemanns, P, Hogervorst, FBL, Holleczek, B, Hollestelle, A, Hooning, MJ, Hoover, RN, Hopper, JL, Howell, A, Huebner, H, Hulick, PJ, Imyanitov, EN, Isaacs, C, Izatt, L, Jager, A, Jakimovska, M, Jakubowska, A, James, P, Janavicius, R, Janni, W, John, EM, Jones, ME, Jung, A, Kaaks, R, Kapoor, PM, Karlan, BY, Keeman, R, Khan, S, Khusnutdinova, E, Kitahara, CM, Ko, Y-D, Konstantopoulou, I, Koppert, LB, Koutros, S, Kristensen, VN, Laenkholm, A-V, Lambrechts, D, Larsson, SC, Laurent-Puig, P, Lazaro, C, Lazarova, E, Lejbkowicz, F, Leslie, G, Lesueur, F, Lindblom, A, Lissowska, J, Lo, W-Y, Loud, JT, Lubinski, J, Lukomska, A, MacInnis, RJ, Mannermaa, A, Manoochehri, M, Manoukian, S, Margolin, S, Martinez, ME, Matricardi, L, McGuffog, L, McLean, C, Mebirouk, N, Meindl, A, Menon, U, Miller, A, Mingazheva, E, Montagna, M, Mulligan, AM, Mulot, C, Muranen, TA, Nathanson, KL, Neuhausen, SL, Nevanlinna, H, Neven, P, Newman, WG, Nielsen, FC, Nikitina-Zake, L, Nodora, J, Offit, K, Olah, E, Olopade, OI, Olsson, H, Orr, N, Papi, L, Papp, J, Park-Simon, T-W, Parsons, MT, Peissel, B, Peixoto, A, Peshkin, B, Peterlongo, P, Peto, J, Phillips, K-A, Piedmonte, M, Plaseska-Karanfilska, D, Prajzendanc, K, Prentice, R, Prokofyeva, D, Rack, B, Radice, P, Ramus, SJ, Rantala, J, Rashid, MU, Rennert, G, Rennert, HS, Risch, HA, Romero, A, Rookus, MA, Rübner, M, Rüdiger, T, Saloustros, E, Sampson, S, Sandler, DP, Sawyer, EJ, Scheuner, MT, Schmutzler, RK, Schneeweiss, A, Schoemaker, MJ, Schöttker, B, Schürmann, P, Senter, L, Sharma, P, Sherman, ME, Shu, X-O, Singer, CF, Smichkoska, S, Soucy, P, Southey, MC, Spinelli, JJ, Stone, J, Stoppa-Lyonnet, D, Swerdlow, AJ, Szabo, CI, Tamimi, RM, Tapper, WJ, Taylor, JA, Teixeira, MR, Terry, M, Thomassen, M, Thull, DL, Tischkowitz, M, Toland, AE, Tollenaar, RAEM, Tomlinson, I, Torres, D, Troester, MA, Truong, T, Tung, N, Untch, M, Vachon, CM, van den Ouweland, AMW, van der Kolk, LE, van Veen, EM, vanRensburg, EJ, Vega, A, Wappenschmidt, B, Weinberg, CR, Weitzel, JN, Wildiers, H, Winqvist, R, Wolk, A, Yang, XR, Yannoukakos, D, Zheng, W, Zorn, KK, Milne, RL, Kraft, P, Simard, J, Pharoah, PDP, Michailidou, K, Antoniou, AC, Schmidt, MK, Chenevix-Trench, G, Easton, DF, Chatterjee, N, and García-Closas, M

Abstract

Breast cancer susceptibility variants frequently show heterogeneity in associations by tumor subtype1,2,3. To identify novel loci, we performed a genome-wide association study including 133,384 breast cancer cases and 113,789 controls, plus 18,908 BRCA1 mutation carriers (9,414 with breast cancer) of European ancestry, using both standard and novel methodologies that account for underlying tumor heterogeneity by estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 status and tumor grade. We identified 32 novel susceptibility loci (P

Published
2020

11. Assessment of interactions between 205 breast cancer susceptibility loci and 13 established risk factors in relation to breast cancer risk, in the Breast Cancer Association Consortium

Author

Kapoor, PM, Lindström, S, Behrens, S, Wang, X, Michailidou, K, Bolla, MK, Wang, Q, Dennis, J, Dunning, AM, Pharoah, PDP, Schmidt, MK, Kraft, P, García-Closas, M, Easton, DF, Milne, RL, Chang-Claude, J, Ahearn, T, Andrulis, IL, Anton-Culver, H, Arndt, V, Aronson, KJ, Auer, PL, Augustinsson, A, Freeman, LEB, Beckmann, MW, Benitez, J, Bernstein, L, Berrandou, T, Bojesen, SE, Brauch, H, Brenner, H, Brock, IW, Broeks, A, Brooks-Wilson, A, Butterbach, K, Cai, Q, Campa, D, Canzian, F, Carter, BD, Castelao, JE, Chanock, SJ, Chenevix-Trench, G, Cheng, T-YD, Clarke, CL, Cordina-Duverger, E, Couch, FJ, Cox, A, Cross, SS, Czene, K, Dai, JY, Dite, GS, Earp, HS, Eliassen, AH, Eriksson, M, Evans, DG, Fasching, PA, Figueroa, J, Flyger, H, Fritschi, L, Gabrielson, M, Gago-Dominguez, M, Gapstur, SM, Gaudet, MM, Giles, GG, González-Neira, A, Grundy, A, Guénel, P, Haeberle, L, Haiman, CA, Håkansson, N, Hall, P, Hamann, U, Hankinson, SE, Harkness, EF, Harstad, T, He, W, Heyworth, J, Hoover, RN, Hopper, JL, Humphreys, K, Hunter, DJ, Marrón, PI, John, EM, Jones, ME, Jung, A, Kaaks, R, Keeman, R, Kitahara, CM, Ko, Y-D, Koutros, S, Krüger, U, Lambrechts, D, Marchand, LL, Lee, E, Lejbkowicz, F, Linet, M, Lissowska, J, Llaneza, A, Lo, W-Y, Makalic, E, Martinez, ME, Maurer, T, Muñoz-Garzon, VM, Neuhausen, SL, Neven, P, Newman, WG, Nielsen, SF, Nordestgaard, BG, Norman, A, O'Brien, KM, Olshan, AF, Olson, JE, Olsson, H, Orr, N, Perou, CM, Pinchev, M, Prentice, R, Rennert, G, Rennert, HS, Ruddy, KJ, Sandler, DP, Schneider, MO, Schoemaker, MJ, Schöttker, B, Scott, RJ, Scott, C, Sherman, ME, Shrubsole, MJ, Shu, X-O, Southey, MC, Spinelli, JJ, Stone, J, Swerdlow, AJ, Tamimi, RM, Taylor, JA, Thöne, K, Troester, MA, Truong, T, Vachon, CM, van Ongeval, C, van Veen, EM, Wagner, P, Weinberg, CR, Wildiers, H, Willett, W, Winham, SJ, Wolk, A, Yang, XR, Zheng, W, and Ziogas, A

Subjects
0301 basic medicine, Oncology, medicine.medical_specialty, Genotype, Genome-wide association study, Single-nucleotide polymorphism, Breast Neoplasms, Polymorphism, Single Nucleotide, White People, 03 medical and health sciences, 0302 clinical medicine, Breast cancer, breast cancer, Risk Factors, single nucleotide polymorphism, Internal medicine, medicine, SNP, Humans, risk factors, Genetic Predisposition to Disease, Breast, Alleles, Cancer och onkologi, Factor XIII, Europeans, business.industry, Gene-environment interaction, epidemiology, Case-control study, Cancer, General Medicine, Odds ratio, medicine.disease, Europe, Genetics and Environment, 030104 developmental biology, Receptors, Estrogen, 030220 oncology & carcinogenesis, Case-Control Studies, Cancer and Oncology, Female, Gene-Environment Interaction, business, Genome-Wide Association Study
Abstract

Background Previous gene-environment interaction studies of breast cancer risk have provided sparse evidence of interactions. Using the largest available dataset to date, we performed a comprehensive assessment of potential effect modification of 205 common susceptibility variants by 13 established breast cancer risk factors, including replication of previously reported interactions. Methods Analyses were performed using 28 176 cases and 32 209 controls genotyped with iCOGS array and 44 109 cases and 48 145 controls genotyped using OncoArray from the Breast Cancer Association Consortium (BCAC). Gene-environment interactions were assessed using unconditional logistic regression and likelihood ratio tests for breast cancer risk overall and by estrogen-receptor (ER) status. Bayesian false discovery probability was used to assess the noteworthiness of the meta-analysed array-specific interactions. Results Noteworthy evidence of interaction at ≤1% prior probability was observed for three single nucleotide polymorphism (SNP)-risk factor pairs. SNP rs4442975 was associated with a greater reduction of risk of ER-positive breast cancer [odds ratio (OR)int = 0.85 (0.78-0.93), Pint = 2.8 x 10–4] and overall breast cancer [ORint = 0.85 (0.78-0.92), Pint = 7.4 x 10–5) in current users of estrogen-progesterone therapy compared with non-users. This finding was supported by replication using OncoArray data of the previously reported interaction between rs13387042 (r2 = 0.93 with rs4442975) and current estrogen-progesterone therapy for overall disease (Pint = 0.004). The two other interactions suggested stronger associations between SNP rs6596100 and ER-negative breast cancer with increasing parity and younger age at first birth. Conclusions Overall, our study does not suggest strong effect modification of common breast cancer susceptibility variants by established risk factors.

Published
2020

12. Genome-wide association study identifies 32 novel breast cancer susceptibility loci from overall and subtype-specific analyses

Author

Zhan, H, Ahearn, TU, Lecarpentier, J, Barnes, D, Beesley, J, Qi, G, Jiang, X, O'Mara, TA, Zhao, N, Bolla, MK, Dunning, AM, Dennis, J, Wang, Q, Abu Ful, Z, Aittomaki, K, Andrulis, IL, Anton-Culver, H, Arndt, V, Aronson, KJ, Arun, BK, Auer, PL, Azzollini, J, Barrowdale, D, Becher, H, Beckmann, MW, Behrens, S, Benitez, J, Bermisheva, M, Bialkowska, K, Blanco, A, Blomqvist, C, Bogdanova, N, Bojesen, SE, Bonanni, B, Bondavalli, D, Borg, A, Brauch, H, Brenner, H, Briceno, I, Broeks, A, Brucker, SY, Bruening, T, Burwinkel, B, Buys, SS, Byers, H, Caldes, T, Caligo, MA, Calvello, M, Campa, D, Castelao, JE, Chang-Claude, J, Chanock, SJ, Christiaens, M, Christiansen, H, Chung, WK, Claes, KBM, Clarke, CL, Cornelissen, S, Couch, FJ, Cox, A, Cross, SS, Czene, K, Daly, MB, Devilee, P, Diez, O, Domchek, SM, Doerk, T, Dwek, M, Eccles, DM, Ekici, AB, Evans, DG, Fasching, PA, Figueroa, J, Foretova, L, Fostira, F, Friedman, E, Frost, D, Gago-Dominguez, M, Gapstur, SM, Garber, J, Garcia-Saenz, JA, Gaudet, MM, Gayther, SA, Giles, GG, Godwin, AK, Goldberg, MS, Goldgar, DE, Gonzalez-Neira, A, Greene, MH, Gronwald, J, Guenel, P, Haeberle, L, Hahnen, E, Haiman, CA, Hake, CR, Hall, P, Hamann, U, Harkness, EF, Heemskerk-Gerritsen, BAM, Hillemanns, P, Hogervorst, FBL, Holleczek, B, Hollestelle, A, Hooning, MJ, Hoover, RN, Hopper, JL, Howell, A, Huebner, H, Hulick, PJ, Imyanitov, EN, Isaacs, C, Izatt, L, Jager, A, Jakimovska, M, Jakubowska, A, James, P, Janavicius, R, Janni, W, John, EM, Jones, ME, Jung, A, Kaaks, R, Kapoor, PM, Karlan, BY, Keeman, R, Khan, S, Khusnutdinova, E, Kitahara, CM, Ko, Y-D, Konstantopoulou, I, Koppert, LB, Koutros, S, Kristensen, VN, Laenkholm, A-V, Lambrechts, D, Larsson, SC, Laurent-Puig, P, Lazaro, C, Lazarova, E, Lejbkowicz, F, Leslie, G, Lesueur, F, Lindblom, A, Lissowska, J, Lo, W-Y, Loud, JT, Lubinski, J, Lukomska, A, MacInnis, RJ, Mannermaa, A, Manoochehri, M, Manoukian, S, Margolin, S, Martinez, ME, Matricardi, L, McGuffog, L, McLean, C, Mebirouk, N, Meindl, A, Menon, U, Miller, A, Mingazheva, E, Montagna, M, Mulligan, AM, Mulot, C, Muranen, TA, Nathanson, KL, Neuhausen, SL, Nevanlinna, H, Neven, P, Newman, WG, Nielsens, FC, Nikitina-Zake, L, Nodora, J, Offit, K, Olah, E, Olopade, O, Olsson, H, Orr, N, Papi, L, Papp, J, Park-Simon, T-W, Parsons, MT, Peissel, B, Peixoto, A, Peshkin, B, Peterlongo, P, Peto, J, Phillips, K-A, Piedmonte, M, Plaseska-Karanfilska, D, Prajzendanc, K, Prentice, R, Prokofyeva, D, Rack, B, Radice, P, Ramus, SJ, Rantala, J, Rashid, MU, Rennert, G, Rennert, HS, Risch, HA, Romero, A, Rookus, MA, Ruebner, M, Ruediger, T, Saloustros, E, Sampson, S, Sandler, DP, Sawyer, EJ, Scheuner, MT, Schmutzler, RK, Schneeweiss, A, Schoemaker, MJ, Schoettker, B, Schuermann, P, Senter, L, Sharma, P, Sherman, ME, Shu, X-O, Singer, CF, Smichkoska, S, Soucy, P, Southey, MC, Spinelli, JJ, Stone, J, Stoppa-Lyonnet, D, Swerdlow, AJ, Szabo, C, Tamimi, RM, Tapper, WJ, Taylor, JA, Teixeira, MR, Terry, M, Thomassen, M, Thull, DL, Tischkowitz, M, Toland, AE, Tollenaar, RAEM, Tomlinson, I, Torres, D, Troester, MA, Truong, T, Tung, N, Untch, M, Vachon, CM, van den Ouweland, AMW, van der Kolk, LE, van Veen, EM, vanRensburg, EJ, Vega, A, Wappenschmidt, B, Weinberg, CR, Weitzel, JN, Wildiers, H, Winqvist, R, Wolk, A, Yang, XR, Yannoukakos, D, Zheng, W, Zorn, KK, Milne, RL, Kraft, P, Simard, J, Pharoah, PDP, Michailidou, K, Antoniou, AC, Schmidt, MK, Chenevix-Trench, G, Easton, DF, Chatterjee, N, Garcia-Closas, M, Zhan, H, Ahearn, TU, Lecarpentier, J, Barnes, D, Beesley, J, Qi, G, Jiang, X, O'Mara, TA, Zhao, N, Bolla, MK, Dunning, AM, Dennis, J, Wang, Q, Abu Ful, Z, Aittomaki, K, Andrulis, IL, Anton-Culver, H, Arndt, V, Aronson, KJ, Arun, BK, Auer, PL, Azzollini, J, Barrowdale, D, Becher, H, Beckmann, MW, Behrens, S, Benitez, J, Bermisheva, M, Bialkowska, K, Blanco, A, Blomqvist, C, Bogdanova, N, Bojesen, SE, Bonanni, B, Bondavalli, D, Borg, A, Brauch, H, Brenner, H, Briceno, I, Broeks, A, Brucker, SY, Bruening, T, Burwinkel, B, Buys, SS, Byers, H, Caldes, T, Caligo, MA, Calvello, M, Campa, D, Castelao, JE, Chang-Claude, J, Chanock, SJ, Christiaens, M, Christiansen, H, Chung, WK, Claes, KBM, Clarke, CL, Cornelissen, S, Couch, FJ, Cox, A, Cross, SS, Czene, K, Daly, MB, Devilee, P, Diez, O, Domchek, SM, Doerk, T, Dwek, M, Eccles, DM, Ekici, AB, Evans, DG, Fasching, PA, Figueroa, J, Foretova, L, Fostira, F, Friedman, E, Frost, D, Gago-Dominguez, M, Gapstur, SM, Garber, J, Garcia-Saenz, JA, Gaudet, MM, Gayther, SA, Giles, GG, Godwin, AK, Goldberg, MS, Goldgar, DE, Gonzalez-Neira, A, Greene, MH, Gronwald, J, Guenel, P, Haeberle, L, Hahnen, E, Haiman, CA, Hake, CR, Hall, P, Hamann, U, Harkness, EF, Heemskerk-Gerritsen, BAM, Hillemanns, P, Hogervorst, FBL, Holleczek, B, Hollestelle, A, Hooning, MJ, Hoover, RN, Hopper, JL, Howell, A, Huebner, H, Hulick, PJ, Imyanitov, EN, Isaacs, C, Izatt, L, Jager, A, Jakimovska, M, Jakubowska, A, James, P, Janavicius, R, Janni, W, John, EM, Jones, ME, Jung, A, Kaaks, R, Kapoor, PM, Karlan, BY, Keeman, R, Khan, S, Khusnutdinova, E, Kitahara, CM, Ko, Y-D, Konstantopoulou, I, Koppert, LB, Koutros, S, Kristensen, VN, Laenkholm, A-V, Lambrechts, D, Larsson, SC, Laurent-Puig, P, Lazaro, C, Lazarova, E, Lejbkowicz, F, Leslie, G, Lesueur, F, Lindblom, A, Lissowska, J, Lo, W-Y, Loud, JT, Lubinski, J, Lukomska, A, MacInnis, RJ, Mannermaa, A, Manoochehri, M, Manoukian, S, Margolin, S, Martinez, ME, Matricardi, L, McGuffog, L, McLean, C, Mebirouk, N, Meindl, A, Menon, U, Miller, A, Mingazheva, E, Montagna, M, Mulligan, AM, Mulot, C, Muranen, TA, Nathanson, KL, Neuhausen, SL, Nevanlinna, H, Neven, P, Newman, WG, Nielsens, FC, Nikitina-Zake, L, Nodora, J, Offit, K, Olah, E, Olopade, O, Olsson, H, Orr, N, Papi, L, Papp, J, Park-Simon, T-W, Parsons, MT, Peissel, B, Peixoto, A, Peshkin, B, Peterlongo, P, Peto, J, Phillips, K-A, Piedmonte, M, Plaseska-Karanfilska, D, Prajzendanc, K, Prentice, R, Prokofyeva, D, Rack, B, Radice, P, Ramus, SJ, Rantala, J, Rashid, MU, Rennert, G, Rennert, HS, Risch, HA, Romero, A, Rookus, MA, Ruebner, M, Ruediger, T, Saloustros, E, Sampson, S, Sandler, DP, Sawyer, EJ, Scheuner, MT, Schmutzler, RK, Schneeweiss, A, Schoemaker, MJ, Schoettker, B, Schuermann, P, Senter, L, Sharma, P, Sherman, ME, Shu, X-O, Singer, CF, Smichkoska, S, Soucy, P, Southey, MC, Spinelli, JJ, Stone, J, Stoppa-Lyonnet, D, Swerdlow, AJ, Szabo, C, Tamimi, RM, Tapper, WJ, Taylor, JA, Teixeira, MR, Terry, M, Thomassen, M, Thull, DL, Tischkowitz, M, Toland, AE, Tollenaar, RAEM, Tomlinson, I, Torres, D, Troester, MA, Truong, T, Tung, N, Untch, M, Vachon, CM, van den Ouweland, AMW, van der Kolk, LE, van Veen, EM, vanRensburg, EJ, Vega, A, Wappenschmidt, B, Weinberg, CR, Weitzel, JN, Wildiers, H, Winqvist, R, Wolk, A, Yang, XR, Yannoukakos, D, Zheng, W, Zorn, KK, Milne, RL, Kraft, P, Simard, J, Pharoah, PDP, Michailidou, K, Antoniou, AC, Schmidt, MK, Chenevix-Trench, G, Easton, DF, Chatterjee, N, and Garcia-Closas, M

Abstract

Breast cancer susceptibility variants frequently show heterogeneity in associations by tumor subtype1-3. To identify novel loci, we performed a genome-wide association study including 133,384 breast cancer cases and 113,789 controls, plus 18,908 BRCA1 mutation carriers (9,414 with breast cancer) of European ancestry, using both standard and novel methodologies that account for underlying tumor heterogeneity by estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 status and tumor grade. We identified 32 novel susceptibility loci (P < 5.0 × 10-8), 15 of which showed evidence for associations with at least one tumor feature (false discovery rate < 0.05). Five loci showed associations (P < 0.05) in opposite directions between luminal and non-luminal subtypes. In silico analyses showed that these five loci contained cell-specific enhancers that differed between normal luminal and basal mammary cells. The genetic correlations between five intrinsic-like subtypes ranged from 0.35 to 0.80. The proportion of genome-wide chip heritability explained by all known susceptibility loci was 54.2% for luminal A-like disease and 37.6% for triple-negative disease. The odds ratios of polygenic risk scores, which included 330 variants, for the highest 1% of quantiles compared with middle quantiles were 5.63 and 3.02 for luminal A-like and triple-negative disease, respectively. These findings provide an improved understanding of genetic predisposition to breast cancer subtypes and will inform the development of subtype-specific polygenic risk scores.

Published
2020

13. Genome-wide association study of germline variants and breast cancer-specific mortality

Author

Escala-Garcia, M, Guo, Q, Doerk, T, Canisius, S, Keeman, R, Dennis, J, Beesley, J, Lecarpentier, J, Bolla, MK, Wang, Q, Abraham, J, Andrulis, IL, Anton-Culver, H, Arndt, V, Auer, PL, Beckmann, MW, Behrens, S, Benitez, J, Bermisheva, M, Bernstein, L, Blomqvist, C, Boeckx, B, Bojesen, SE, Bonanni, B, Borresen-Dale, A-L, Brauch, H, Brenner, H, Brentnall, A, Brinton, L, Broberg, P, Brock, IW, Brucker, SY, Burwinkel, B, Caldas, C, Caldes, T, Campa, D, Canzian, F, Carracedo, A, Carter, BD, Castelao, JE, Chang-Claude, J, Chanock, SJ, Chenevix-Trench, G, Cheng, T-YD, Chin, S-F, Clarke, CL, Cordina-Duverger, E, Couch, FJ, Cox, DG, Cox, A, Cross, SS, Czene, K, Daly, MB, Devilee, P, Dunn, JA, Dunning, AM, Durcan, L, Dwek, M, Earl, HM, Ekici, AB, Eliassen, AH, Ellberg, C, Engel, C, Eriksson, M, Evans, DG, Figueroa, J, Flesch-Janys, D, Flyger, H, Gabrielson, M, Gago-Dominguez, M, Galle, E, Gapstur, SM, Garcia-Closas, M, Garcia-Saenz, JA, Gaudet, MM, George, A, Georgoulias, V, Giles, GG, Glendon, G, Goldgar, DE, Gonzalez-Neira, A, Alnaes, GIG, Grip, M, Guenel, P, Haeberle, L, Hahnen, E, Haiman, CA, Hakansson, N, Hall, P, Hamann, U, Hankinson, S, Harkness, EF, Harrington, PA, Hart, SN, Hartikainen, JM, Hein, A, Hillemanns, P, Hiller, L, Holleczek, B, Hollestelle, A, Hooning, MJ, Hoover, RN, Hopper, JL, Howell, A, Huang, G, Humphreys, K, Hunter, DJ, Janni, W, John, EM, Jones, ME, Jukkola-Vuorinen, A, Jung, A, Kaaks, R, Kabisch, M, Kaczmarek, K, Kerin, MJ, Khan, S, Khusnutdinova, E, Kiiski, J, Kitahara, CM, Knight, JA, Ko, Y-D, Koppert, LB, Kosma, V-M, Kraft, P, Kristensen, VN, Kruger, U, Kuehl, T, Lambrechts, D, Le Marchand, L, Lee, E, Lejbkowicz, F, Li, L, Lindblom, A, Lindstrom, S, Linet, M, Lissowska, J, Lo, W-Y, Loibl, S, Lubinski, J, Lux, MP, MacInnis, RJ, Maierthaler, M, Maishman, T, Makalic, E, Mannermaa, A, Manoochehri, M, Manoukian, S, Margolin, S, Martinez, ME, Mavroudis, D, McLean, C, Meindl, A, Middha, P, Miller, N, Milne, RL, Moreno, F, Mulligan, AM, Mulot, C, Nassir, R, Neuhausen, SL, Newman, WT, Nielsen, SF, Nordestgaard, BG, Norman, A, Olsson, H, Orr, N, Pankratz, VS, Park-Simon, T-W, Perez, JIA, Perez-Barrios, C, Peterlongo, P, Petridis, C, Pinchev, M, Prajzendanc, K, Prentice, R, Presneau, N, Prokofieva, D, Pylkas, K, Rack, B, Radice, P, Ramachandran, D, Rennert, G, Rennert, HS, Rhenius, V, Romero, A, Roylance, R, Saloustros, E, Sawyer, EJ, Schmidt, DF, Schmutzler, RK, Schneeweiss, A, Schoemaker, MJ, Schumacher, F, Schwentner, L, Scott, RJ, Scott, C, Seynaeve, C, Shah, M, Simard, J, Smeets, A, Sohn, C, Southey, MC, Swerdlow, AJ, Talhouk, A, Tamimi, RM, Tapper, WJ, Teixeira, MR, Tengstrom, M, Terry, MB, Thoene, K, Tollenaar, RAEM, Tomlinson, I, Torres, D, Truong, T, Turman, C, Turnbull, C, Ulmer, H-U, Untch, M, Vachon, C, van Asperen, CJ, van den Ouweland, AMW, van Veen, EM, Wendt, C, Whittemore, AS, Willett, W, Winqvist, R, Wolk, A, Yang, XR, Zhang, Y, Easton, DF, Fasching, PA, Nevanlinna, H, Eccles, DM, Pharoah, PDP, Schmidt, MK, Escala-Garcia, M, Guo, Q, Doerk, T, Canisius, S, Keeman, R, Dennis, J, Beesley, J, Lecarpentier, J, Bolla, MK, Wang, Q, Abraham, J, Andrulis, IL, Anton-Culver, H, Arndt, V, Auer, PL, Beckmann, MW, Behrens, S, Benitez, J, Bermisheva, M, Bernstein, L, Blomqvist, C, Boeckx, B, Bojesen, SE, Bonanni, B, Borresen-Dale, A-L, Brauch, H, Brenner, H, Brentnall, A, Brinton, L, Broberg, P, Brock, IW, Brucker, SY, Burwinkel, B, Caldas, C, Caldes, T, Campa, D, Canzian, F, Carracedo, A, Carter, BD, Castelao, JE, Chang-Claude, J, Chanock, SJ, Chenevix-Trench, G, Cheng, T-YD, Chin, S-F, Clarke, CL, Cordina-Duverger, E, Couch, FJ, Cox, DG, Cox, A, Cross, SS, Czene, K, Daly, MB, Devilee, P, Dunn, JA, Dunning, AM, Durcan, L, Dwek, M, Earl, HM, Ekici, AB, Eliassen, AH, Ellberg, C, Engel, C, Eriksson, M, Evans, DG, Figueroa, J, Flesch-Janys, D, Flyger, H, Gabrielson, M, Gago-Dominguez, M, Galle, E, Gapstur, SM, Garcia-Closas, M, Garcia-Saenz, JA, Gaudet, MM, George, A, Georgoulias, V, Giles, GG, Glendon, G, Goldgar, DE, Gonzalez-Neira, A, Alnaes, GIG, Grip, M, Guenel, P, Haeberle, L, Hahnen, E, Haiman, CA, Hakansson, N, Hall, P, Hamann, U, Hankinson, S, Harkness, EF, Harrington, PA, Hart, SN, Hartikainen, JM, Hein, A, Hillemanns, P, Hiller, L, Holleczek, B, Hollestelle, A, Hooning, MJ, Hoover, RN, Hopper, JL, Howell, A, Huang, G, Humphreys, K, Hunter, DJ, Janni, W, John, EM, Jones, ME, Jukkola-Vuorinen, A, Jung, A, Kaaks, R, Kabisch, M, Kaczmarek, K, Kerin, MJ, Khan, S, Khusnutdinova, E, Kiiski, J, Kitahara, CM, Knight, JA, Ko, Y-D, Koppert, LB, Kosma, V-M, Kraft, P, Kristensen, VN, Kruger, U, Kuehl, T, Lambrechts, D, Le Marchand, L, Lee, E, Lejbkowicz, F, Li, L, Lindblom, A, Lindstrom, S, Linet, M, Lissowska, J, Lo, W-Y, Loibl, S, Lubinski, J, Lux, MP, MacInnis, RJ, Maierthaler, M, Maishman, T, Makalic, E, Mannermaa, A, Manoochehri, M, Manoukian, S, Margolin, S, Martinez, ME, Mavroudis, D, McLean, C, Meindl, A, Middha, P, Miller, N, Milne, RL, Moreno, F, Mulligan, AM, Mulot, C, Nassir, R, Neuhausen, SL, Newman, WT, Nielsen, SF, Nordestgaard, BG, Norman, A, Olsson, H, Orr, N, Pankratz, VS, Park-Simon, T-W, Perez, JIA, Perez-Barrios, C, Peterlongo, P, Petridis, C, Pinchev, M, Prajzendanc, K, Prentice, R, Presneau, N, Prokofieva, D, Pylkas, K, Rack, B, Radice, P, Ramachandran, D, Rennert, G, Rennert, HS, Rhenius, V, Romero, A, Roylance, R, Saloustros, E, Sawyer, EJ, Schmidt, DF, Schmutzler, RK, Schneeweiss, A, Schoemaker, MJ, Schumacher, F, Schwentner, L, Scott, RJ, Scott, C, Seynaeve, C, Shah, M, Simard, J, Smeets, A, Sohn, C, Southey, MC, Swerdlow, AJ, Talhouk, A, Tamimi, RM, Tapper, WJ, Teixeira, MR, Tengstrom, M, Terry, MB, Thoene, K, Tollenaar, RAEM, Tomlinson, I, Torres, D, Truong, T, Turman, C, Turnbull, C, Ulmer, H-U, Untch, M, Vachon, C, van Asperen, CJ, van den Ouweland, AMW, van Veen, EM, Wendt, C, Whittemore, AS, Willett, W, Winqvist, R, Wolk, A, Yang, XR, Zhang, Y, Easton, DF, Fasching, PA, Nevanlinna, H, Eccles, DM, Pharoah, PDP, and Schmidt, MK

Abstract

BACKGROUND: We examined the associations between germline variants and breast cancer mortality using a large meta-analysis of women of European ancestry. METHODS: Meta-analyses included summary estimates based on Cox models of twelve datasets using ~10.4 million variants for 96,661 women with breast cancer and 7697 events (breast cancer-specific deaths). Oestrogen receptor (ER)-specific analyses were based on 64,171 ER-positive (4116) and 16,172 ER-negative (2125) patients. We evaluated the probability of a signal to be a true positive using the Bayesian false discovery probability (BFDP). RESULTS: We did not find any variant associated with breast cancer-specific mortality at P < 5 × 10-8. For ER-positive disease, the most significantly associated variant was chr7:rs4717568 (BFDP = 7%, P = 1.28 × 10-7, hazard ratio [HR] = 0.88, 95% confidence interval [CI] = 0.84-0.92); the closest gene is AUTS2. For ER-negative disease, the most significant variant was chr7:rs67918676 (BFDP = 11%, P = 1.38 × 10-7, HR = 1.27, 95% CI = 1.16-1.39); located within a long intergenic non-coding RNA gene (AC004009.3), close to the HOXA gene cluster. CONCLUSIONS: We uncovered germline variants on chromosome 7 at BFDP < 15% close to genes for which there is biological evidence related to breast cancer outcome. However, the paucity of variants associated with mortality at genome-wide significance underpins the challenge in providing genetic-based individualised prognostic information for breast cancer patients.

Published
2019

14. Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes

Author

Mavaddat, N, Michailidou, K, Dennis, J, Lush, M, Fachal, L, Lee, A, Tyrer, JP, Chen, T-H, Wang, Q, Bolla, MK, Yang, X, Adank, MA, Ahearn, T, Aittomaki, K, Allen, J, Andrulis, IL, Anton-Culver, H, Antonenkova, NN, Arndt, V, Aronson, KJ, Auer, PL, Auvinen, P, Barrdahl, M, Freeman, LEB, Beckmann, MW, Behrens, S, Benitez, J, Bermisheva, M, Bernstein, L, Blomqvist, C, Bogdanova, N, Bojesen, SE, Bonanni, B, Borresen-Dale, A-L, Brauch, H, Bremer, M, Brenner, H, Brentnall, A, Brock, IW, Brooks-Wilson, A, Brucker, SY, Bruening, T, Burwinkel, B, Campa, D, Carter, BD, Castelao, JE, Chanock, SJ, Chlebowski, R, Christiansen, H, Clarke, CL, Collee, JM, Cordina-Duverger, E, Cornelissen, S, Couch, FJ, Cox, A, Cross, SS, Czene, K, Daly, MB, Devilee, P, Doerk, T, dos-Santos-Silva, I, Dumont, M, Durcan, L, Dwek, M, Eccles, DM, Ekici, AB, Eliassen, AH, Ellberg, C, Engel, C, Eriksson, M, Evans, DG, Fasching, PA, Figueroa, J, Fletcher, O, Flyger, H, Foersti, A, Fritschi, L, Gabrielson, M, Gago-Dominguez, M, Gapstur, SM, Garcia-Saenz, JA, Gaudet, MM, Georgoulias, V, Giles, GG, Gilyazova, IR, Glendon, G, Goldberg, MS, Goldgar, DE, Gonzalez-Neira, A, Alnaes, GIG, Grip, M, Gronwald, J, Grundy, A, Guenel, P, Haeberle, L, Hahnen, E, Haiman, CA, Hakansson, N, Hamann, U, Hankinson, SE, Harkness, EF, Hart, SN, He, W, Hein, A, Heyworth, J, Hillemanns, P, Hollestelle, A, Hooning, MJ, Hoover, RN, Hopper, JL, Howell, A, Huang, G, Humphreys, K, Hunter, DJ, Jakimovska, M, Jakubowska, A, Janni, W, John, EM, Johnson, N, Jones, ME, Jukkola-Vuorinen, A, Jung, A, Kaaks, R, Kaczmarek, K, Kataja, V, Keeman, R, Kerin, MJ, Khusnutdinova, E, Kiiski, J, Knight, JA, Ko, Y-D, Kosma, V-M, Koutros, S, Kristensen, VN, Kruger, U, Kuehl, T, Lambrechts, D, Le Marchand, L, Lee, E, Lejbkowicz, F, Lilyquist, J, Lindblom, A, Lindstrom, S, Lissowska, J, Lo, W-Y, Loibl, S, Long, J, Lubinski, J, Lux, MP, MacInnis, RJ, Maishman, T, Makalic, E, Kostovska, IM, Mannermaa, A, Manoukian, S, Margolin, S, Martens, JWM, Martinez, ME, Mavroudis, D, McLean, C, Meindl, A, Menon, U, Middha, P, Miller, N, Moreno, F, Mulligan, AM, Mulot, C, Munoz-Garzon, VM, Neuhausen, SL, Nevanlinna, H, Neven, P, Newman, WG, Nielsen, SF, Nordestgaard, BG, Norman, A, Offit, K, Olson, JE, Olsson, H, Orr, N, Pankratz, VS, Park-Simon, T-W, Perez, JIA, Perez-Barrios, C, Peterlongo, P, Peto, J, Pinchev, M, Plaseska-Karanfilska, D, Polley, EC, Prentice, R, Presneau, N, Prokofyeva, D, Purrington, K, Pylkas, K, Rack, B, Radice, P, Rau-Murthy, R, Rennert, G, Rennert, HS, Rhenius, V, Robson, M, Romero, A, Ruddy, KJ, Ruebner, M, Saloustros, E, Sandler, DP, Sawyer, EJ, Schmidt, DF, Schmutzler, RK, Schneeweiss, A, Schoemaker, MJ, Schumacher, F, Schuermann, P, Schwentner, L, Scott, C, Scott, RJ, Seynaeve, C, Shah, M, Sherman, ME, Shrubsole, MJ, Shu, X-O, Slager, S, Smeets, A, Sohn, C, Soucy, P, Southey, MC, Spinelli, JJ, Stegmaier, C, Stone, J, Swerdlow, AJ, Tamimi, RM, Tapper, WJ, Taylor, JA, Terry, MB, Thoene, K, Tollenaar, RAEM, Tomlinson, I, Truong, T, Tzardi, M, Ulmer, H-U, Untch, M, Vachon, CM, van Veen, EM, Vijai, J, Weinberg, CR, Wendt, C, Whittemore, AS, Wildiers, H, Willett, W, Winqvist, R, Wolk, A, Yang, XR, Yannoukakos, D, Zhang, Y, Zheng, W, Ziogas, A, Clarke, C, Balleine, R, Baxter, R, Braye, S, Carpenter, J, Dahlstrom, J, Forbes, J, Lee, CS, Marsh, D, Morey, A, Pathmanathan, N, Scott, R, Simpson, P, Spigelman, A, Wilcken, N, Yip, D, Zeps, N, Sexton, A, Dobrovic, A, Christian, A, Trainer, A, Fellows, A, Shelling, A, De Fazio, A, Blackburn, A, Crook, A, Meiser, B, Patterson, B, Saunders, C, Hunt, C, Amor, D, Ortega, DG, Edkins, E, Salisbury, E, Haan, E, Macrea, F, Farshid, G, Lindeman, G, Trench, G, Mann, G, Giles, G, Gill, G, Thorne, H, Campbell, I, Hickie, I, Caldon, L, Winship, I, Cui, J, Flanagan, J, Kollias, J, Visvader, J, Taylor, J, Burke, J, Saunus, J, Forbs, J, Hopper, J, Beesley, J, Kirk, J, French, J, Tucker, K, Wu, K, Phillips, K, Forrest, L, Lipton, L, Andrews, L, Lobb, L, Walker, L, Kentwell, M, Spurdle, M, Cummings, M, Gleeson, M, Harris, M, Jenkins, M, Young, MA, Delatycki, M, Wallis, M, Burgess, M, Brown, M, Southey, M, Bogwitz, M, Field, M, Friedlander, M, Gattas, M, Saleh, M, Aghmesheh, M, Hayward, N, Pachter, N, Cohen, P, Duijf, P, James, P, Fong, P, Butow, P, Williams, R, Kefford, R, Simard, J, Balleine, R-M, Dawson, S-J, Lok, S, O'connell, S, Greening, S, Nightingale, S, Edwards, S, Fox, S, McLachlan, S-A, Lakhani, S, Dudding, T, Antill, Y, Sahlberg, KK, Ottestad, L, Karesen, R, Schlichting, E, Holmen, MM, Sauer, T, Haakensen, V, Engebraten, O, Naume, B, Fossa, A, Kiserud, CE, Reinertsen, K, Helland, A, Riis, M, Geisler, J, Dunning, AM, Thompson, DJ, Chenevix-Trench, G, Chang-Claude, J, Schmidt, MK, Hall, P, Milne, RL, Pharoah, PDP, Antoniou, AC, Chatterjee, N, Kraft, P, Garcia-Closas, M, Easton, DF, Mavaddat, N, Michailidou, K, Dennis, J, Lush, M, Fachal, L, Lee, A, Tyrer, JP, Chen, T-H, Wang, Q, Bolla, MK, Yang, X, Adank, MA, Ahearn, T, Aittomaki, K, Allen, J, Andrulis, IL, Anton-Culver, H, Antonenkova, NN, Arndt, V, Aronson, KJ, Auer, PL, Auvinen, P, Barrdahl, M, Freeman, LEB, Beckmann, MW, Behrens, S, Benitez, J, Bermisheva, M, Bernstein, L, Blomqvist, C, Bogdanova, N, Bojesen, SE, Bonanni, B, Borresen-Dale, A-L, Brauch, H, Bremer, M, Brenner, H, Brentnall, A, Brock, IW, Brooks-Wilson, A, Brucker, SY, Bruening, T, Burwinkel, B, Campa, D, Carter, BD, Castelao, JE, Chanock, SJ, Chlebowski, R, Christiansen, H, Clarke, CL, Collee, JM, Cordina-Duverger, E, Cornelissen, S, Couch, FJ, Cox, A, Cross, SS, Czene, K, Daly, MB, Devilee, P, Doerk, T, dos-Santos-Silva, I, Dumont, M, Durcan, L, Dwek, M, Eccles, DM, Ekici, AB, Eliassen, AH, Ellberg, C, Engel, C, Eriksson, M, Evans, DG, Fasching, PA, Figueroa, J, Fletcher, O, Flyger, H, Foersti, A, Fritschi, L, Gabrielson, M, Gago-Dominguez, M, Gapstur, SM, Garcia-Saenz, JA, Gaudet, MM, Georgoulias, V, Giles, GG, Gilyazova, IR, Glendon, G, Goldberg, MS, Goldgar, DE, Gonzalez-Neira, A, Alnaes, GIG, Grip, M, Gronwald, J, Grundy, A, Guenel, P, Haeberle, L, Hahnen, E, Haiman, CA, Hakansson, N, Hamann, U, Hankinson, SE, Harkness, EF, Hart, SN, He, W, Hein, A, Heyworth, J, Hillemanns, P, Hollestelle, A, Hooning, MJ, Hoover, RN, Hopper, JL, Howell, A, Huang, G, Humphreys, K, Hunter, DJ, Jakimovska, M, Jakubowska, A, Janni, W, John, EM, Johnson, N, Jones, ME, Jukkola-Vuorinen, A, Jung, A, Kaaks, R, Kaczmarek, K, Kataja, V, Keeman, R, Kerin, MJ, Khusnutdinova, E, Kiiski, J, Knight, JA, Ko, Y-D, Kosma, V-M, Koutros, S, Kristensen, VN, Kruger, U, Kuehl, T, Lambrechts, D, Le Marchand, L, Lee, E, Lejbkowicz, F, Lilyquist, J, Lindblom, A, Lindstrom, S, Lissowska, J, Lo, W-Y, Loibl, S, Long, J, Lubinski, J, Lux, MP, MacInnis, RJ, Maishman, T, Makalic, E, Kostovska, IM, Mannermaa, A, Manoukian, S, Margolin, S, Martens, JWM, Martinez, ME, Mavroudis, D, McLean, C, Meindl, A, Menon, U, Middha, P, Miller, N, Moreno, F, Mulligan, AM, Mulot, C, Munoz-Garzon, VM, Neuhausen, SL, Nevanlinna, H, Neven, P, Newman, WG, Nielsen, SF, Nordestgaard, BG, Norman, A, Offit, K, Olson, JE, Olsson, H, Orr, N, Pankratz, VS, Park-Simon, T-W, Perez, JIA, Perez-Barrios, C, Peterlongo, P, Peto, J, Pinchev, M, Plaseska-Karanfilska, D, Polley, EC, Prentice, R, Presneau, N, Prokofyeva, D, Purrington, K, Pylkas, K, Rack, B, Radice, P, Rau-Murthy, R, Rennert, G, Rennert, HS, Rhenius, V, Robson, M, Romero, A, Ruddy, KJ, Ruebner, M, Saloustros, E, Sandler, DP, Sawyer, EJ, Schmidt, DF, Schmutzler, RK, Schneeweiss, A, Schoemaker, MJ, Schumacher, F, Schuermann, P, Schwentner, L, Scott, C, Scott, RJ, Seynaeve, C, Shah, M, Sherman, ME, Shrubsole, MJ, Shu, X-O, Slager, S, Smeets, A, Sohn, C, Soucy, P, Southey, MC, Spinelli, JJ, Stegmaier, C, Stone, J, Swerdlow, AJ, Tamimi, RM, Tapper, WJ, Taylor, JA, Terry, MB, Thoene, K, Tollenaar, RAEM, Tomlinson, I, Truong, T, Tzardi, M, Ulmer, H-U, Untch, M, Vachon, CM, van Veen, EM, Vijai, J, Weinberg, CR, Wendt, C, Whittemore, AS, Wildiers, H, Willett, W, Winqvist, R, Wolk, A, Yang, XR, Yannoukakos, D, Zhang, Y, Zheng, W, Ziogas, A, Clarke, C, Balleine, R, Baxter, R, Braye, S, Carpenter, J, Dahlstrom, J, Forbes, J, Lee, CS, Marsh, D, Morey, A, Pathmanathan, N, Scott, R, Simpson, P, Spigelman, A, Wilcken, N, Yip, D, Zeps, N, Sexton, A, Dobrovic, A, Christian, A, Trainer, A, Fellows, A, Shelling, A, De Fazio, A, Blackburn, A, Crook, A, Meiser, B, Patterson, B, Saunders, C, Hunt, C, Amor, D, Ortega, DG, Edkins, E, Salisbury, E, Haan, E, Macrea, F, Farshid, G, Lindeman, G, Trench, G, Mann, G, Giles, G, Gill, G, Thorne, H, Campbell, I, Hickie, I, Caldon, L, Winship, I, Cui, J, Flanagan, J, Kollias, J, Visvader, J, Taylor, J, Burke, J, Saunus, J, Forbs, J, Hopper, J, Beesley, J, Kirk, J, French, J, Tucker, K, Wu, K, Phillips, K, Forrest, L, Lipton, L, Andrews, L, Lobb, L, Walker, L, Kentwell, M, Spurdle, M, Cummings, M, Gleeson, M, Harris, M, Jenkins, M, Young, MA, Delatycki, M, Wallis, M, Burgess, M, Brown, M, Southey, M, Bogwitz, M, Field, M, Friedlander, M, Gattas, M, Saleh, M, Aghmesheh, M, Hayward, N, Pachter, N, Cohen, P, Duijf, P, James, P, Fong, P, Butow, P, Williams, R, Kefford, R, Simard, J, Balleine, R-M, Dawson, S-J, Lok, S, O'connell, S, Greening, S, Nightingale, S, Edwards, S, Fox, S, McLachlan, S-A, Lakhani, S, Dudding, T, Antill, Y, Sahlberg, KK, Ottestad, L, Karesen, R, Schlichting, E, Holmen, MM, Sauer, T, Haakensen, V, Engebraten, O, Naume, B, Fossa, A, Kiserud, CE, Reinertsen, K, Helland, A, Riis, M, Geisler, J, Dunning, AM, Thompson, DJ, Chenevix-Trench, G, Chang-Claude, J, Schmidt, MK, Hall, P, Milne, RL, Pharoah, PDP, Antoniou, AC, Chatterjee, N, Kraft, P, Garcia-Closas, M, and Easton, DF

Abstract

Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57-1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628-0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs.

Published
2019

16. Young age at first pregnancy does protect against early onset breast cancer in <italic>BRCA1</italic> and <italic>BRCA2</italic> mutation carriers.

Author

Evans, DG, Harkness, EF, Howel, S., Woodward, ER, Howell, A., and Lalloo, F.

Abstract

Purpose: Previous research assessing the impact of pregnancy and age at first pregnancy on breast cancer risk in BRCA1 and BRCA2 mutation carriers has produced conflicting results, with some studies showing an increased risk following early first pregnancy in contrast to the reduced risk in the general population of women. The present study addresses these inconsistencies.Methods: Female BRCA1 and BRCA2 carriers from North West England were assessed for breast cancer incidence prior to 50 years of age comparing those with an early first full-term pregnancy (< 21 years) to those without a full-term pregnancy. Breast cancer incidence per decade from 20 years and Kaplan–Meier analyses were performed.Results: 2424 female mutation carriers (1278 BRCA1; 1146 BRCA2) developed 990 breast cancers under the age of 50 years. Women who had their first term pregnancy prior to age 21 (n = 441) had a lower cancer incidence especially between age 30–39 years. Kaplan–Meier analysis showed an odds ratio of 0.78 for BRCA1 (p = 0.005) and 0.73 for BRCA2 (p = 0.002).Conclusions: The present study demonstrates a clear protective effect of early first pregnancy on breast cancer risk in both BRCA1 and BRCA2 mutation carriers. [ABSTRACT FROM AUTHOR]

Published
2018
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34. FACT: a new chapter.

Author

Ernst E, Barnes J, Armstrong NC, Harkness EF, Pittler MH, Stevinson C, and White AR

Published
1999

35. Breast density prediction from low and standard dose mammograms using deep learning: effect of image resolution and model training approach on prediction quality.

Author

Squires S, Harkness EF, Mackenzie A, Evans DG, Howell SJ, and Astley SM

Subjects
Humans, Female, Breast diagnostic imaging, Image Processing, Computer-Assisted methods, Reproducibility of Results, Algorithms, Radiographic Image Interpretation, Computer-Assisted methods, Mammography methods, Deep Learning, Breast Density, Breast Neoplasms diagnostic imaging, Radiation Dosage
Abstract

Purpose . To improve breast cancer risk prediction for young women, we have developed deep learning methods to estimate mammographic density from low dose mammograms taken at approximately 1/10th of the usual dose. We investigate the quality and reliability of the density scores produced on low dose mammograms focussing on how image resolution and levels of training affect the low dose predictions. Methods . Deep learning models are developed and tested, with two feature extraction methods and an end-to-end trained method, on five different resolutions of 15,290 standard dose and simulated low dose mammograms with known labels. The models are further tested on a dataset with 296 matching standard and real low dose images allowing performance on the low dose images to be ascertained. Result s. Prediction quality on standard and simulated low dose images compared to labels is similar for all equivalent model training and image resolution versions. Increasing resolution results in improved performance of both feature extraction methods for standard and simulated low dose images, while the trained models show high performance across the resolutions. For the trained models the Spearman rank correlation coefficient between predictions of standard and low dose images at low resolution is 0.951 (0.937 to 0.960) and at the highest resolution 0.956 (0.942 to 0.965). If pairs of model predictions are averaged, similarity increases. Conclusions . Deep learning mammographic density predictions on low dose mammograms are highly correlated with standard dose equivalents for feature extraction and end-to-end approaches across multiple image resolutions. Deep learning models can reliably make high quality mammographic density predictions on low dose mammograms., (Creative Commons Attribution license.)

Published
2024
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36. Germline testing of BRCA1 , BRCA2 , PALB2 and CHEK2 c.1100delC in 1514 triple negative familial and isolated breast cancers from a single centre, with extended testing of ATM , RAD51C and RAD51D in over 400.

Author

Woodward ER, Lalloo F, Forde C, Pugh S, Burghel GJ, Schlecht H, Harkness EF, Howell A, Howell SJ, Gandhi A, and Evans DG

Subjects
Female, Humans, Middle Aged, Genetic Predisposition to Disease, Genes, BRCA2, Genes, BRCA1, Germ Cells pathology, Germ-Line Mutation genetics, Checkpoint Kinase 2 genetics, DNA-Binding Proteins genetics, BRCA1 Protein genetics, Breast Neoplasms genetics, Triple Negative Breast Neoplasms genetics, Triple Negative Breast Neoplasms pathology, Fanconi Anemia Complementation Group N Protein, BRCA2 Protein, Ataxia Telangiectasia Mutated Proteins
Abstract

Background: The identification of germline pathogenic gene variants (PGVs) in triple negative breast cancer (TNBC) is important to inform further primary cancer risk reduction and TNBC treatment strategies. We therefore investigated the contribution of breast cancer associated PGVs to familial and isolated invasive TNBC., Methods: Outcomes of germline BRCA1 , BRCA2 and CHEK2 &#95;c.1100delC testing were recorded in 1514 women (743-isolated, 771-familial), and for PALB2 in 846 women (541-isolated, 305-familial), with TNBC and smaller numbers for additional genes. Breast cancer free controls were identified from Predicting Risk Of Cancer At Screening and BRIDGES (Breast cancer RIsk after Diagnostic GEne Sequencing) studies., Results: BRCA1 &#95;PGVs were detected in 52 isolated (7.0%) and 195 (25.3%) familial cases (isolated-OR=58.9, 95% CI: 16.6 to 247.0), BRCA2 &#95;PGVs in 21 (2.8%) isolated and 67 (8.7%) familial cases (isolated-OR=5.0, 95% CI: 2.3 to 11.2), PALB2&#95; PGVs in 9 (1.7%) isolated and 12 (3.9%) familial cases (isolated-OR=8.8, 95% CI: 2.5 to 30.4) and CHEK2&#95;c .1100delC in 0 isolated and 3 (0.45%) familial cases (isolated-OR=0.0, 95% CI: 0.00 to 2.11). BRCA1 &#95;PGV detection rate was >10% for all familial TNBC age groups and significantly higher for younger diagnoses (familial: <50 years, n=165/538 (30.7%); ≥50 years, n=30/233 (12.9%); p<0.0001). Women with a G3&#95;TNBC were more likely to have a BRCA1&#95; PGV as compared with a BRCA2 or PALB2 &#95;PGV (p<0.0001). 0/743 isolated TNBC had the CHEK2&#95; c.1100delC PGV and 0/305 any ATM &#95;PGV, but 2/240 (0.83%) had a RAD51D &#95;PGV., Conclusion: PGVs in BRCA1 are associated with G3&#95;TNBCs. Familial TNBCs and isolated TNBCs <30 years have a >10% likelihood of a PGV in BRCA1. BRCA1 &#95;PGVs are associated with younger age of familial TNBC. There was no evidence for any increased risk of TNBC with CHEK2 or ATM PGVs., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Published
2024
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37. TP53 c.455C>T p.(Pro152Leu) pathogenic variant is a lower risk allele with attenuated risks of breast cancer and sarcoma.

Author

Evans DG, Harkness EF, and Woodward ER

Abstract

Germline (likely) pathogenic TP53 variants cause Li-Fraumeni syndrome (LFS), typically associated with sarcoma, brain, breast and adrenal tumours. Although classical LFS is highly penetrant, the p.R337H variant, common in Brazil, is typically associated with childhood adrenal tumours and an older onset age of other LFS tumours. Previously, we reported the finding of p.P152L in 6 children from 5 families with adrenal tumours. We have now assessed cancer risks over the subsequent 23 years, and in one further family with p.P152L. Cancer risks were compared with those in the 11 families known to our service with classical dominant negative mutations affecting neighbouring codons 245 and 248 (codon 245/248).Compared with codon 245/248 families, we found lower age-related risks for all non-adrenal tumours in codon 152 families (p<0.0001) with an absence of breast cancer as compared with 100% penetrance by age 36 years in codon 245/248 families (p<0.0001), and lower rates of sarcoma in non-irradiated individuals (p=0.0001). Although there were more adrenal tumours in codon 152 families (6/26 individuals, 1/27 for codon 245/248), this was not significant (p=0.05).Understanding codon-specific cancer risks in LFS is important for accurate personalised cancer risk assessment, and subsequent prevention and early detection strategies., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. No commercial re-use. See rights and permissions. Published by BMJ.)

Published
2023
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38. Detection of pathogenic variants in breast cancer susceptibility genes in bilateral breast cancer.

Author

Evans DG, Burghel GJ, Schlecht H, Harkness EF, Gandhi A, Howell SJ, Howell A, Forde C, Lalloo F, Newman WG, Smith MJ, and Woodward ER

Subjects
Humans, Female, BRCA1 Protein genetics, BRCA2 Protein genetics, Genetic Predisposition to Disease, Breast Neoplasms diagnosis
Abstract

Purpose: To investigate the frequency of germline pathogenic variants (PVs) in women with bilateral breast cancer., Methods: We undertook BRCA1/2 and CHEK2 c.1100delC molecular analysis in 764 samples and a multigene panel in 156. Detection rates were assessed by age at first primary, Manchester Score, and breast pathology. Oestrogen receptor (ER) status of the contralateral versus first breast cancer was compared on 1081 patients with breast cancer with BRCA1 /B RCA2 PVs., Results: 764 women with bilateral breast cancer have undergone testing of BRCA1/2 and CHEK2 ; 407 were also tested for PALB2 and 177 for ATM . Detection rates were BRCA1 11.6%, BRCA2 14.0%, CHEK2 2.4%, PALB2 1.0%, ATM 1.1% and, for a subset of mainly very early onset tumours, TP53 4.6% (9 of 195). The highest PV detection rates were for triple negative cancers for BRCA1 (26.4%), grade 3 ER+HER2 for BRCA2 (27.9%) and HER2+ for CHEK2 (8.9%). ER status of the first primary in BRCA1 and BRCA2 PV heterozygotes was strongly predictive of the ER status of the second contralateral tumour since ~90% of second tumours were ER- in BRCA1 heterozygotes, and 50% were ER- in BRCA2 heterozygotes if the first was ER-., Conclusion: We have shown a high rate of detection of BRCA1 and BRCA2 PVs in triple negative and grade 3 ER+HER2- first primary diagnoses, respectively. High rates of HER2+ were associated with CHEK2 PVs, and women ≤30 years were associated with TP53 PVs. First primary ER status in BRCA1/2 strongly predicts the second tumour will be the same ER status even if unusual for PVs in that gene., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. No commercial re-use. See rights and permissions. Published by BMJ.)

Published
2023
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40. A genome-wide gene-environment interaction study of breast cancer risk for women of European ancestry.

Author

Middha P, Wang X, Behrens S, Bolla MK, Wang Q, Dennis J, Michailidou K, Ahearn TU, Andrulis IL, Anton-Culver H, Arndt V, Aronson KJ, Auer PL, Augustinsson A, Baert T, Freeman LEB, Becher H, Beckmann MW, Benitez J, Bojesen SE, Brauch H, Brenner H, Brooks-Wilson A, Campa D, Canzian F, Carracedo A, Castelao JE, Chanock SJ, Chenevix-Trench G, Cordina-Duverger E, Couch FJ, Cox A, Cross SS, Czene K, Dossus L, Dugué PA, Eliassen AH, Eriksson M, Evans DG, Fasching PA, Figueroa JD, Fletcher O, Flyger H, Gabrielson M, Gago-Dominguez M, Giles GG, González-Neira A, Grassmann F, Grundy A, Guénel P, Haiman CA, Håkansson N, Hall P, Hamann U, Hankinson SE, Harkness EF, Holleczek B, Hoppe R, Hopper JL, Houlston RS, Howell A, Hunter DJ, Ingvar C, Isaksson K, Jernström H, John EM, Jones ME, Kaaks R, Keeman R, Kitahara CM, Ko YD, Koutros S, Kurian AW, Lacey JV, Lambrechts D, Larson NL, Larsson S, Le Marchand L, Lejbkowicz F, Li S, Linet M, Lissowska J, Martinez ME, Maurer T, Mulligan AM, Mulot C, Murphy RA, Newman WG, Nielsen SF, Nordestgaard BG, Norman A, O'Brien KM, Olson JE, Patel AV, Prentice R, Rees-Punia E, Rennert G, Rhenius V, Ruddy KJ, Sandler DP, Scott CG, Shah M, Shu XO, Smeets A, Southey MC, Stone J, Tamimi RM, Taylor JA, Teras LR, Tomczyk K, Troester MA, Truong T, Vachon CM, Wang SS, Weinberg CR, Wildiers H, Willett W, Winham SJ, Wolk A, Yang XR, Zamora MP, Zheng W, Ziogas A, Dunning AM, Pharoah PDP, García-Closas M, Schmidt MK, Kraft P, Milne RL, Lindström S, Easton DF, and Chang-Claude J

Subjects
Adult, Female, Humans, Genetic Predisposition to Disease, Bayes Theorem, Genome-Wide Association Study, Risk Factors, Polymorphism, Single Nucleotide, Case-Control Studies, Gene-Environment Interaction, Breast Neoplasms etiology, Breast Neoplasms genetics
Abstract

Background: Genome-wide studies of gene-environment interactions (G×E) may identify variants associated with disease risk in conjunction with lifestyle/environmental exposures. We conducted a genome-wide G×E analysis of ~ 7.6 million common variants and seven lifestyle/environmental risk factors for breast cancer risk overall and for estrogen receptor positive (ER +) breast cancer., Methods: Analyses were conducted using 72,285 breast cancer cases and 80,354 controls of European ancestry from the Breast Cancer Association Consortium. Gene-environment interactions were evaluated using standard unconditional logistic regression models and likelihood ratio tests for breast cancer risk overall and for ER + breast cancer. Bayesian False Discovery Probability was employed to assess the noteworthiness of each SNP-risk factor pairs., Results: Assuming a 1 × 10 -5 prior probability of a true association for each SNP-risk factor pairs and a Bayesian False Discovery Probability < 15%, we identified two independent SNP-risk factor pairs: rs80018847(9p13)-LINGO2 and adult height in association with overall breast cancer risk (OR int  = 0.94, 95% CI 0.92-0.96), and rs4770552(13q12)-SPATA13 and age at menarche for ER + breast cancer risk (OR int  = 0.91, 95% CI 0.88-0.94)., Conclusions: Overall, the contribution of G×E interactions to the heritability of breast cancer is very small. At the population level, multiplicative G×E interactions do not make an important contribution to risk prediction in breast cancer., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Published
2023
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41. Differential involvement of germline pathogenic variants in breast cancer genes between DCIS and low-grade invasive cancers.

Author

Evans DG, Sithambaram S, van Veen EM, Burghel GJ, Schlecht H, Harkness EF, Byers H, Ellingford JM, Gandhi A, Howell SJ, Howell A, Forde C, Lalloo F, Newman WG, Smith MJ, and Woodward ER

Subjects
Female, Humans, BRCA1 Protein genetics, BRCA2 Protein genetics, Germ-Line Mutation genetics, Genes, BRCA2, Germ Cells pathology, Carcinoma, Intraductal, Noninfiltrating diagnosis, Carcinoma, Intraductal, Noninfiltrating genetics, Carcinoma, Intraductal, Noninfiltrating pathology, Breast Neoplasms diagnosis, Breast Neoplasms genetics, Breast Neoplasms epidemiology
Abstract

Purpose: To investigate frequency of germline pathogenic variants (PVs) in women with ductal carcinoma in situ (DCIS) and grade 1 invasive breast cancer (G1BC)., Methods: We undertook BRCA1/2 analysis in 311 women with DCIS and 392 with G1BC and extended panel testing (non- BRCA1 / 2 ) in 176/311 with DCIS and 156/392 with G1BC. We investigated PV detection by age at diagnosis, Manchester Score (MS), DCIS grade and receptor status., Results: 30/311 (9.6%) with DCIS and 16/392 with G1BC (4.1%) had a BRCA1 / 2 PV (p=0.003), and 24/176-(13.6%) and 7/156-(4.5%), respectively, a non- BRCA1 / 2 PV (p=0.004). Increasing MS was associated with increased likelihood of BRCA1/2 PV in both DCIS and G1BC, although the 10% threshold was not predictive for G1GB. 13/32 (40.6%) DCIS and 0/17 with G1BC <40 years had a non-BRCA1/2 PV (p<0.001). 0/16 DCIS G1 had a PV. For G2 and G3 DCIS, PV rates were 10/98 ( BRCA1 / 2 ) and 9/90 (non- BRCA1 / 2 ), and 8/47 ( BRCA1 / 2 ) and 8/45 (non- BRCA1 / 2 ), respectively. 6/9 BRCA1 and 3/26 BRCA2 -associated DCIS were oestrogen receptor negative-(p=0.003). G1BC population testing showed no increased PV rate (OR=1.16, 95% CI 0.28 to 4.80)., Conclusion: DCIS is more likely to be associated with both BRCA1/2 and non- BRCA1 / 2 PVs than G1BC. Extended panel testing ought to be offered in young-onset DCIS where PV detection rates are highest., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. No commercial re-use. See rights and permissions. Published by BMJ.)

Published
2023
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42. Quantifying the effects of risk-stratified breast cancer screening when delivered in real time as routine practice versus usual screening: the BC-Predict non-randomised controlled study (NCT04359420).

Author

Gareth Evans D, McWilliams L, Astley S, Brentnall AR, Cuzick J, Dobrashian R, Duffy SW, Gorman LS, Harkness EF, Harrison F, Harvie M, Jerrison A, Machin M, Maxwell AJ, Howell SJ, Wright SJ, Payne K, Qureshi N, Ruane H, Southworth J, Fox L, Bowers S, Hutchinson G, Thorpe E, Ulph F, Woof V, Howell A, and French DP

Subjects
Female, Humans, Mammography, Early Detection of Cancer, Breast Density, Risk Factors, Breast Neoplasms diagnosis
Abstract

Background: Risk stratification as a routine part of the NHS Breast Screening Programme (NHSBSP) could provide a better balance of benefits and harms. We developed BC-Predict, to offer women when invited to the NHSBSP, which collects standard risk factor information; mammographic density; and in a sub-sample, a Polygenic Risk Score (PRS)., Methods: Risk prediction was estimated primarily from self-reported questionnaires and mammographic density using the Tyrer-Cuzick risk model. Women eligible for NHSBSP were recruited. BC-Predict produced risk feedback letters, inviting women at high risk (≥8% 10-year) or moderate risk (≥5-<8% 10-year) to have appointments to discuss prevention and additional screening., Results: Overall uptake of BC-Predict in screening attendees was 16.9% with 2472 consenting to the study; 76.8% of those received risk feedback within the 8-week timeframe. Recruitment was 63.2% with an onsite recruiter and paper questionnaire compared to <10% with BC-Predict only (P < 0.0001). Risk appointment attendance was highest for those at high risk (40.6%); 77.5% of those opted for preventive medication., Discussion: We have shown that a real-time offer of breast cancer risk information (including both mammographic density and PRS) is feasible and can be delivered in reasonable time, although uptake requires personal contact. Preventive medication uptake in women newly identified at high risk is high and could improve the cost-effectiveness of risk stratification., Trial Registration: Retrospectively registered with clinicaltrials.gov (NCT04359420)., (© 2023. The Authors.)

Published
2023
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43. Correction To: Quantifying the effects of risk-stratified breast cancer screening when delivered in real time as routine practice versus usual screening: the BC-Predict non-randomised controlled study (NCT04359420).

Author

Gareth Evans D, McWilliams L, Astley S, Brentnall AR, Cuzick J, Dobrashian R, Duffy SW, Gorman LS, Harkness EF, Harrison F, Harvie M, Jerrison A, Machin M, Maxwell AJ, Howell SJ, Wright SJ, Payne K, Qureshi N, Ruane H, Southworth J, Fox L, Bowers S, Hutchinson G, Thorpe E, Ulph F, Woof V, Howell A, and French DP

Published
2023
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44. The effect of variable labels on deep learning models trained to predict breast density.

Author

Squires S, Harkness EF, Evans DG, and Astley SM

Subjects
Humans, Female, Breast Density, Mammography methods, Deep Learning, Breast Neoplasms diagnostic imaging
Abstract

Purpose . High breast density is associated with reduced efficacy of mammographic screening and increased risk of developing breast cancer. Accurate and reliable automated density estimates can be used for direct risk prediction and passing density related information to further predictive models. Expert reader assessments of density show a strong relationship to cancer risk but also inter-reader variation. The effect of label variability on model performance is important when considering how to utilise automated methods for both research and clinical purposes. Methods . We utilise subsets of images with density labels from the same 13 readers and 12 reader pairs, and train a deep transfer learning model which is used to assess how label variability affects the mapping from representation to prediction. We then create two end-to-end models: one that is trained on averaged labels across the reader pairs and the second that is trained using individual reader scores, with a novel alteration to the objective function. The combination of these two end-to-end models allows us to investigate the effect of label variability on the model representation formed. Results . We show that the trained mappings from representations to labels are altered considerably by the variability of reader scores. Training on labels with distribution variation removed causes the Spearman rank correlation coefficients to rise from 0.751 ± 0.002 to either 0.815 ± 0.026 when averaging across readers or 0.844 ± 0.002 when averaging across images. However, when we train different models to investigate the representation effect we see little difference, with Spearman rank correlation coefficients of 0.846 ± 0.006 and 0.850 ± 0.006 showing no statistically significant difference in the quality of the model representation with regard to density prediction. Conclusions . We show that the mapping between representation and mammographic density prediction is significantly affected by label variability. However, the effect of the label variability on the model representation is limited., (Creative Commons Attribution license.)

Published
2023
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45. Radiation treatment of benign tumors in NF2-related-schwannomatosis: A national study of 266 irradiated patients showing a significant increase in malignancy/malignant progression.

Author

Evans DG, Halliday D, Obholzer R, Afridi S, Forde C, Rutherford SA, Hammerbeck-Ward C, Lloyd SK, Freeman SM, Pathmanaban ON, Thomas OM, Laitt RD, Stivaros S, Kilday JP, Vassallo G, McBain C, Lavin T, Paterson C, Whitfield G, McCabe MG, Axon PR, Halliday J, Mackeith S, Parry A, Harkness EF, Buttimore J, and King AT

Abstract

Background: Radiation treatment of benign tumors in tumor predisposition syndromes is controversial, but short-term studies from treatment centers suggest safety despite apparent radiation-associated malignancy being reported. We determined whether radiation treatment in NF2-related schwannomatosis patients is associated with increased rates of subsequent malignancy (M)/malignant progression (MP)., Methods: All UK patients with NF2 were eligible if they had a clinical/molecular diagnosis. Cases were NF2 patients treated with radiation for benign tumors. Controls were matched for treatment location with surgical/medical treatments based on age and year of treatment. Prospective data collection began in 1990 with addition of retrospective cases in 1969. Kaplan-Meier analysis was performed for malignancy incidence and survival. Outcomes were central nervous system (CNS) M/MP (2cm annualized diameter growth) and survival from index tumor treatment., Results: In total, 1345 NF2 patients, 266 (133-Male) underwent radiation treatments between 1969 and 2021 with median first radiotherapy age of 32.9 (IQR = 22.4-46.0). Nine subsequent CNS malignancies/MPs were identified in cases with only 4 in 1079 untreated ( P < .001). Lifetime and 20-year CNS M/MP was ~6% in all irradiated patients-(4.9% for vestibular schwannomas [VS] radiotherapy) versus <1% in the non-irradiated population ( P < .001/.01). Controls were well matched for age at NF2 diagnosis and treatment (Males = 133%-50%) and had no M/MP in the CNS post-index tumor treatment ( P = .0016). Thirty-year survival from index tumor treatment was 45.62% (95% CI = 34.0-56.5) for cases and 66.4% (57.3-74.0) for controls ( P = .02), but was nonsignificantly worse for VS radiotherapy., Conclusion: NF2 patients should not be offered radiotherapy as first-line treatment of benign tumors and should be given a frank discussion of the potential 5% excess absolute risk of M/MP., Competing Interests: None other than many of the authors are surgeons, but 4 are oncologists., (© The Author(s) 2023. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.)

Published
2023
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46. Aggregation tests identify new gene associations with breast cancer in populations with diverse ancestry.

Author

Mueller SH, Lai AG, Valkovskaya M, Michailidou K, Bolla MK, Wang Q, Dennis J, Lush M, Abu-Ful Z, Ahearn TU, Andrulis IL, Anton-Culver H, Antonenkova NN, Arndt V, Aronson KJ, Augustinsson A, Baert T, Freeman LEB, Beckmann MW, Behrens S, Benitez J, Bermisheva M, Blomqvist C, Bogdanova NV, Bojesen SE, Bonanni B, Brenner H, Brucker SY, Buys SS, Castelao JE, Chan TL, Chang-Claude J, Chanock SJ, Choi JY, Chung WK, Colonna SV, Cornelissen S, Couch FJ, Czene K, Daly MB, Devilee P, Dörk T, Dossus L, Dwek M, Eccles DM, Ekici AB, Eliassen AH, Engel C, Evans DG, Fasching PA, Fletcher O, Flyger H, Gago-Dominguez M, Gao YT, García-Closas M, García-Sáenz JA, Genkinger J, Gentry-Maharaj A, Grassmann F, Guénel P, Gündert M, Haeberle L, Hahnen E, Haiman CA, Håkansson N, Hall P, Harkness EF, Harrington PA, Hartikainen JM, Hartman M, Hein A, Ho WK, Hooning MJ, Hoppe R, Hopper JL, Houlston RS, Howell A, Hunter DJ, Huo D, Ito H, Iwasaki M, Jakubowska A, Janni W, John EM, Jones ME, Jung A, Kaaks R, Kang D, Khusnutdinova EK, Kim SW, Kitahara CM, Koutros S, Kraft P, Kristensen VN, Kubelka-Sabit K, Kurian AW, Kwong A, Lacey JV, Lambrechts D, Le Marchand L, Li J, Linet M, Lo WY, Long J, Lophatananon A, Mannermaa A, Manoochehri M, Margolin S, Matsuo K, Mavroudis D, Menon U, Muir K, Murphy RA, Nevanlinna H, Newman WG, Niederacher D, O'Brien KM, Obi N, Offit K, Olopade OI, Olshan AF, Olsson H, Park SK, Patel AV, Patel A, Perou CM, Peto J, Pharoah PDP, Plaseska-Karanfilska D, Presneau N, Rack B, Radice P, Ramachandran D, Rashid MU, Rennert G, Romero A, Ruddy KJ, Ruebner M, Saloustros E, Sandler DP, Sawyer EJ, Schmidt MK, Schmutzler RK, Schneider MO, Scott C, Shah M, Sharma P, Shen CY, Shu XO, Simard J, Surowy H, Tamimi RM, Tapper WJ, Taylor JA, Teo SH, Teras LR, Toland AE, Tollenaar RAEM, Torres D, Torres-Mejía G, Troester MA, Truong T, Vachon CM, Vijai J, Weinberg CR, Wendt C, Winqvist R, Wolk A, Wu AH, Yamaji T, Yang XR, Yu JC, Zheng W, Ziogas A, Ziv E, Dunning AM, Easton DF, Hemingway H, Hamann U, and Kuchenbaecker KB

Subjects
Humans, Female, Genetic Predisposition to Disease, Black People, Genetic Testing, Genome-Wide Association Study methods, Polymorphism, Single Nucleotide, Formins genetics, Breast Neoplasms genetics
Abstract

Background: Low-frequency variants play an important role in breast cancer (BC) susceptibility. Gene-based methods can increase power by combining multiple variants in the same gene and help identify target genes., Methods: We evaluated the potential of gene-based aggregation in the Breast Cancer Association Consortium cohorts including 83,471 cases and 59,199 controls. Low-frequency variants were aggregated for individual genes' coding and regulatory regions. Association results in European ancestry samples were compared to single-marker association results in the same cohort. Gene-based associations were also combined in meta-analysis across individuals with European, Asian, African, and Latin American and Hispanic ancestry., Results: In European ancestry samples, 14 genes were significantly associated (q < 0.05) with BC. Of those, two genes, FMNL3 (P = 6.11 × 10 -6 ) and AC058822.1 (P = 1.47 × 10 -4 ), represent new associations. High FMNL3 expression has previously been linked to poor prognosis in several other cancers. Meta-analysis of samples with diverse ancestry discovered further associations including established candidate genes ESR1 and CBLB. Furthermore, literature review and database query found further support for a biologically plausible link with cancer for genes CBLB, FMNL3, FGFR2, LSP1, MAP3K1, and SRGAP2C., Conclusions: Using extended gene-based aggregation tests including coding and regulatory variation, we report identification of plausible target genes for previously identified single-marker associations with BC as well as the discovery of novel genes implicated in BC development. Including multi ancestral cohorts in this study enabled the identification of otherwise missed disease associations as ESR1 (P = 1.31 × 10 -5 ), demonstrating the importance of diversifying study cohorts., (© 2023. The Author(s).)

Published
2023
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47. Obesity at age 20 and weight gain during adulthood increase risk of total and premature all-cause mortality: findings from women attending breast screening in Manchester.

Author

Pegington M, Harvie M, Harkness EF, Brentnall A, Malcomson L, Southworth J, Fox J, Howell A, Cuzick J, and Evans DG

Subjects
Female, Humans, Middle Aged, Body Mass Index, Obesity epidemiology, Obesity complications, Proportional Hazards Models, Risk Factors, State Medicine, Thinness epidemiology, Weight Gain, Aged, Breast Neoplasms complications, Overweight epidemiology, Overweight complications
Abstract

Background: Obesity in early adulthood is associated with lower breast cancer rates in later life. This could be interpreted as a positive reinforcement of excess weight amongst younger women however, the wider implications of higher weights are less well known. This study examined the association between both obesity in early adulthood and body mass index (BMI) change through adulthood, and all-cause mortality., Methods: The Predicting Risk of Cancer At Screening (PROCAS) study recruited 57,902 women aged 46-73 years (median age 57.2, IQR 51.8-63.7 years) from the Greater Manchester National Health Service breast screening programme in North West England between 2009 and 2015. It was used to assess associations between BMI at 20 years and cohort entry with all-cause mortality ascertained via deaths recorded on the National Breast Screening System to June 2020. Hazard ratios were estimated using proportional hazards (Cox) regression adjusted for factors at entry to the cohort: age, deprivation, bilateral oophorectomy, hormone-replacement therapy, menopausal status, ethnicity, alcohol intake, physical activity, and BMI., Results: The prevalence of overweight (25-30 kg/m 2 ) and obesity (> 30 kg/m 2 ) were 10.4% and 2.5% respectively at 20 years, increasing to 35.2% and 25.9% respectively at cohort entry. After a mean 8.7 years follow-up we observed that overweight (HR = 1.27, 95%CI = 1.10-1.47) and obesity (HR = 2.11, 95%CI = 1.67-2.66) at 20 years had a higher mortality rate compared with healthy weight. Women who were underweight/healthy weight at 20 years and gained weight to obesity at entry had a slightly increased mortality rate compared with women who were underweight/healthy weight at both time points (HR 1.16, 95%CI = 1.02-1.32). Women with overweight (HR = 1.36, 95%CI = 1.06-1.75) or obesity (HR = 1.90, 95%CI = 1.45-2.48) at both 20 years and entry had a higher mortality rate than women who were underweight/healthy weight at both points., Conclusions: Women who self-reported overweight and obesity at 20 years had a shorter life expectancy in this cohort of women attending breast cancer screening. Weight gain from 20 years was common in this group. Girls and women should be supported to maintain a healthy weight throughout the lifespan to help increase life expectancy. Trial registration number NCT04359420, retrospectively registered 24/04/2020., (© 2023. The Author(s).)

Published
2023
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48. A likelihood ratio approach for utilizing case-control data in the clinical classification of rare sequence variants: application to BRCA1 and BRCA2 .

Author

Zanti M, O'Mahony DG, Parsons MT, Li H, Dennis J, Aittomäkkiki K, Andrulis IL, Anton-Culver H, Aronson KJ, Augustinsson A, Becher H, Bojesen SE, Bolla MK, Brenner H, Brown MA, Buys SS, Canzian F, Caputo SM, Castelao JE, Chang-Claude J, Czene K, Daly MB, De Nicolo A, Devilee P, Dörk T, Dunning AM, Dwek M, Eccles DM, Engel C, Evans DG, Fasching PA, Gago-Dominguez M, García-Closas M, García-Sáenz JA, Gentry-Maharaj A, Geurts-Giele WRR, Giles GG, Glendon G, Goldberg MS, Garcia EBG, Güendert M, Guénel P, Hahnen E, Haiman CA, Hall P, Hamann U, Harkness EF, Hogervorst FBL, Hollestelle A, Hoppe R, Hopper JL, Houdayer C, Houlston RS, Howell A, Jakimovska M, Jakubowska A, Jernström H, John EM, Kaaks R, Kitahara CM, Koutros S, Kraft P, Kristensen VN, Lacey JV, Lambrechts D, Léoné M, Lindblom A, Lubiński J, Lush M, Mannermaa A, Manoochehri M, Manoukian S, Margolin S, Martinez ME, Menon U, Milne RL, Monteiro AN, Murphy RA, Neuhausen SL, Nevanlinna H, Newman WG, Offit K, Park SK, James P, Peterlongo P, Peto J, Plaseska-Karanfilska D, Punie K, Radice P, Rashid MU, Rennert G, Romero A, Rosenberg EH, Saloustros E, Sandler DP, Schmidt MK, Schmutzler RK, Shu XO, Simard J, Southey MC, Stone J, Stoppa-Lyonnet D, Tamimi RM, Tapper WJ, Taylor JA, Teo SH, Teras LR, Terry MB, Thomassen M, Troester MA, Vachon CM, Vega A, Vreeswijk MPG, Wang Q, Wappenschmidt B, Weinberg CR, Wolk A, Zheng W, Feng B, Couch FJ, Spurdle AB, Easton DF, Goldgar DE, and Michailidou K

Subjects
Humans, Case-Control Studies, Female, Likelihood Functions, Genetic Variation, Penetrance, Genetic Testing methods, BRCA2 Protein genetics, Genetic Predisposition to Disease, BRCA1 Protein genetics, Breast Neoplasms genetics
Abstract

A large number of variants identified through clinical genetic testing in disease susceptibility genes, are of uncertain significance (VUS). Following the recommendations of the American College of Medical Genetics and Genomics (ACMG) and Association for Molecular Pathology (AMP), the frequency in case-control datasets (PS4 criterion), can inform their interpretation. We present a novel case-control likelihood ratio-based method that incorporates gene-specific age-related penetrance. We demonstrate the utility of this method in the analysis of simulated and real datasets. In the analyses of simulated data, the likelihood ratio method was more powerful compared to other methods. Likelihood ratios were calculated for a case-control dataset of BRCA1 and BRCA2 variants from the Breast Cancer Association Consortium (BCAC), and compared with logistic regression results. A larger number of variants reached evidence in favor of pathogenicity, and a substantial number of variants had evidence against pathogenicity - findings that would not have been reached using other case-control analysis methods. Our novel method provides greater power to classify rare variants compared to classical case-control methods. As an initiative from the ENIGMA Analytical Working Group, we provide user-friendly scripts and pre-formatted excel calculators for implementation of the method for rare variants in BRCA1 , BRCA2 and other high-risk genes with known penetrance.

Published
2023
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49. Magnitude and attributed reasons for adult weight gain amongst women at increased risk of breast cancer.

Author

Pegington M, Harkness EF, Howell A, Evans DG, and Harvie M

Subjects
Adult, Pregnancy, Child, Female, Humans, Young Adult, Middle Aged, Aged, Body Mass Index, Postmenopause, Obesity epidemiology, Risk Factors, Weight Gain, Weight Loss, Breast Neoplasms complications
Abstract

Background: Excess weight (BMI ≥25.0 kg/m 2 ) and weight gain during adult life increase the risk of postmenopausal breast cancer in women who are already at increased risk of the disease. Reasons for weight gain in this population can inform strategies for weight gain prevention., Methods: Baseline data from six weight loss studies for women at increased risk of breast cancer (age 31-74 years) were collated. Self-reported patterns of adult weight gain and attributed reasons for weight gain before joining the weight loss study were reported for the whole population and secondary analyses reported the different reasons given by women with/without children, pre-/peri- or postmenopausal, and moderate/high risk of breast cancer., Results: Five hundred and one women with a mean age of 47.6 (SD 8.4) years and median BMI of 29.9 (IQR 27.0-34.7) kg/m 2 were included in the analyses. The median weight gain since young adulthood (18-20 years) was 20.5 (IQR 14.0-29.7) kg or 33.7 (23.4-50.2) % and median annual weight gain was 0.73 (IQR 0.51-1.08) kg. Four hundred and one women were included in analysis of weight gain reasons. The main five self-reported reasons for weight gain were children / childcare / pregnancy (stated by 55.9% of participants), followed by inactivity (41.9%), comfort or boredom eating (38.2%), portion size (32.4%), and stress (27.4%). Reasons appeared broadly similar between the different groups in the secondary analyses., Conclusions: We have highlighted common reasons for weight gain in women at increased risk of breast cancer. This will inform future interventions to support women to avoid weight gain in adulthood which would reduce the burden of breast cancer., Trial Registration: NIHR NRR N0226132725, ISRCTN52913838, ISRCTN77916487, ISRCTN91372184, ISRCTN10803394 and ISRCTN16431108., (© 2022. The Author(s).)

Published
2022
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50. Incorporating progesterone receptor expression into the PREDICT breast prognostic model.

Author

Grootes I, Keeman R, Blows FM, Milne RL, Giles GG, Swerdlow AJ, Fasching PA, Abubakar M, Andrulis IL, Anton-Culver H, Beckmann MW, Blomqvist C, Bojesen SE, Bolla MK, Bonanni B, Briceno I, Burwinkel B, Camp NJ, Castelao JE, Choi JY, Clarke CL, Couch FJ, Cox A, Cross SS, Czene K, Devilee P, Dörk T, Dunning AM, Dwek M, Easton DF, Eccles DM, Eriksson M, Ernst K, Evans DG, Figueroa JD, Fink V, Floris G, Fox S, Gabrielson M, Gago-Dominguez M, García-Sáenz JA, González-Neira A, Haeberle L, Haiman CA, Hall P, Hamann U, Harkness EF, Hartman M, Hein A, Hooning MJ, Hou MF, Howell SJ, Ito H, Jakubowska A, Janni W, John EM, Jung A, Kang D, Kristensen VN, Kwong A, Lambrechts D, Li J, Lubiński J, Manoochehri M, Margolin S, Matsuo K, Taib NAM, Mulligan AM, Nevanlinna H, Newman WG, Offit K, Osorio A, Park SK, Park-Simon TW, Patel AV, Presneau N, Pylkäs K, Rack B, Radice P, Rennert G, Romero A, Saloustros E, Sawyer EJ, Schneeweiss A, Schochter F, Schoemaker MJ, Shen CY, Shibli R, Sinn P, Tapper WJ, Tawfiq E, Teo SH, Teras LR, Torres D, Vachon CM, van Deurzen CHM, Wendt C, Williams JA, Winqvist R, Elwood M, Schmidt MK, García-Closas M, and Pharoah PDP

Subjects
Female, Humans, Progesterone, Prognosis, Receptor, ErbB-2 metabolism, Breast Neoplasms pathology, Receptors, Progesterone metabolism
Abstract

Background: Predict Breast (www.predict.nhs.uk) is an online prognostication and treatment benefit tool for early invasive breast cancer. The aim of this study was to incorporate the prognostic effect of progesterone receptor (PR) status into a new version of PREDICT and to compare its performance to the current version (2.2)., Method: The prognostic effect of PR status was based on the analysis of data from 45,088 European patients with breast cancer from 49 studies in the Breast Cancer Association Consortium. Cox proportional hazard models were used to estimate the hazard ratio for PR status. Data from a New Zealand study of 11,365 patients with early invasive breast cancer were used for external validation. Model calibration and discrimination were used to test the model performance., Results: Having a PR-positive tumour was associated with a 23% and 28% lower risk of dying from breast cancer for women with oestrogen receptor (ER)-negative and ER-positive breast cancer, respectively. The area under the ROC curve increased with the addition of PR status from 0.807 to 0.809 for patients with ER-negative tumours (p = 0.023) and from 0.898 to 0.902 for patients with ER-positive tumours (p = 2.3 × 10 -6 ) in the New Zealand cohort. Model calibration was modest with 940 observed deaths compared to 1151 predicted., Conclusion: The inclusion of the prognostic effect of PR status to PREDICT Breast has led to an improvement of model performance and more accurate absolute treatment benefit predictions for individual patients. Further studies should determine whether the baseline hazard function requires recalibration., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Published
2022
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