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1. Multi-omic machine learning predictor of breast cancer therapy response

Author

Sammut, S-J, Crispin-Ortuzar, M, Chin, S-F, Provenzano, E, Bardwell, HA, Ma, W, Cope, W, Dariush, A, Dawson, S-J, Abraham, JE, Dunn, J, Hiller, L, Thomas, J, Cameron, DA, Bartlett, JMS, Hayward, L, Pharoah, PD, Markowetz, F, Rueda, OM, Earl, HM, Caldas, C, Sammut, S-J, Crispin-Ortuzar, M, Chin, S-F, Provenzano, E, Bardwell, HA, Ma, W, Cope, W, Dariush, A, Dawson, S-J, Abraham, JE, Dunn, J, Hiller, L, Thomas, J, Cameron, DA, Bartlett, JMS, Hayward, L, Pharoah, PD, Markowetz, F, Rueda, OM, Earl, HM, and Caldas, C

Abstract

Breast cancers are complex ecosystems of malignant cells and the tumour microenvironment1. The composition of these tumour ecosystems and interactions within them contribute to responses to cytotoxic therapy2. Efforts to build response predictors have not incorporated this knowledge. We collected clinical, digital pathology, genomic and transcriptomic profiles of pre-treatment biopsies of breast tumours from 168 patients treated with chemotherapy with or without HER2 (encoded by ERBB2)-targeted therapy before surgery. Pathology end points (complete response or residual disease) at surgery3 were then correlated with multi-omic features in these diagnostic biopsies. Here we show that response to treatment is modulated by the pre-treated tumour ecosystem, and its multi-omics landscape can be integrated in predictive models using machine learning. The degree of residual disease following therapy is monotonically associated with pre-therapy features, including tumour mutational and copy number landscapes, tumour proliferation, immune infiltration and T cell dysfunction and exclusion. Combining these features into a multi-omic machine learning model predicted a pathological complete response in an external validation cohort (75 patients) with an area under the curve of 0.87. In conclusion, response to therapy is determined by the baseline characteristics of the totality of the tumour ecosystem captured through data integration and machine learning. This approach could be used to develop predictors for other cancers.

Published
2022

2. A network analysis to identify mediators of germline-driven differences in breast cancer prognosis

Author

Escala-Garcia, M, Abraham, J, Andrulis, IL, Anton-Culver, H, Arndt, V, Ashworth, A, Auer, PL, Auvinen, P, Beckmann, MW, Beesley, J, Behrens, S, Benitez, J, Bermisheva, M, Blomqvist, C, Blot, W, Bogdanova, NV, Bojesen, SE, Bolla, MK, Borresen-Dale, A-L, Brauch, H, Brenner, H, Brucker, SY, Burwinkel, B, Caldas, C, Canzian, F, Chang-Claude, J, Chanock, SJ, Chin, S-F, Clarke, CL, Couch, FJ, Cox, A, Cross, SS, Czene, K, Daly, MB, Dennis, J, Devilee, P, Dunn, JA, Dunning, AM, Dwek, M, Earl, HM, Eccles, DM, Eliassen, AH, Ellberg, C, Evans, DG, Fasching, PA, Figueroa, J, Flyger, H, Gago-Dominguez, M, Gapstur, SM, Garcia-Closas, M, Garcia-Saenz, JA, Gaudet, MM, George, A, Giles, GG, Goldgar, DE, Gonzalez-Neira, A, Grip, M, Guenel, P, Guo, Q, Haiman, CA, Hakansson, N, Hamann, U, Harrington, PA, Hiller, L, Hooning, MJ, Hopper, JL, Howell, A, Huang, C-S, Huang, G, Hunter, DJ, Jakubowska, A, John, EM, Kaaks, R, Kapoor, PM, Keeman, R, Kitahara, CM, Koppert, LB, Kraft, P, Kristensen, VN, Lambrechts, D, Le Marchand, L, Lejbkowicz, F, Lindblom, A, Lubinski, J, Mannermaa, A, Manoochehri, M, Manoukian, S, Margolin, S, Martinez, ME, Maurer, T, Mavroudis, D, Meindl, A, Milne, RL, Mulligan, AM, Neuhausen, SL, Nevanlinna, H, Newman, WG, Olshan, AF, Olson, JE, Olsson, H, Orr, N, Peterlongo, P, Petridis, C, Prentice, RL, Presneau, N, Punie, K, Ramachandran, D, Rennert, G, Romero, A, Sachchithananthan, M, Saloustros, E, Sawyer, EJ, Schmutzler, RK, Schwentner, L, Scott, C, Simard, J, Sohn, C, Southey, MC, Swerdlow, AJ, Tamimi, RM, Tapper, WJ, Teixeira, MR, Terry, MB, Thorne, H, Tollenaar, RAEM, Tomlinson, I, Troester, MA, Truong, T, Turnbull, C, Vachon, CM, van der Kolk, LE, Wang, Q, Winqvist, R, Wolk, A, Yang, XR, Ziogas, A, Pharoah, PDP, Hall, P, Wessels, LFA, Chenevix-Trench, G, Bader, GD, Doerk, T, Easton, DF, Canisius, S, Schmidt, MK, Escala-Garcia, M, Abraham, J, Andrulis, IL, Anton-Culver, H, Arndt, V, Ashworth, A, Auer, PL, Auvinen, P, Beckmann, MW, Beesley, J, Behrens, S, Benitez, J, Bermisheva, M, Blomqvist, C, Blot, W, Bogdanova, NV, Bojesen, SE, Bolla, MK, Borresen-Dale, A-L, Brauch, H, Brenner, H, Brucker, SY, Burwinkel, B, Caldas, C, Canzian, F, Chang-Claude, J, Chanock, SJ, Chin, S-F, Clarke, CL, Couch, FJ, Cox, A, Cross, SS, Czene, K, Daly, MB, Dennis, J, Devilee, P, Dunn, JA, Dunning, AM, Dwek, M, Earl, HM, Eccles, DM, Eliassen, AH, Ellberg, C, Evans, DG, Fasching, PA, Figueroa, J, Flyger, H, Gago-Dominguez, M, Gapstur, SM, Garcia-Closas, M, Garcia-Saenz, JA, Gaudet, MM, George, A, Giles, GG, Goldgar, DE, Gonzalez-Neira, A, Grip, M, Guenel, P, Guo, Q, Haiman, CA, Hakansson, N, Hamann, U, Harrington, PA, Hiller, L, Hooning, MJ, Hopper, JL, Howell, A, Huang, C-S, Huang, G, Hunter, DJ, Jakubowska, A, John, EM, Kaaks, R, Kapoor, PM, Keeman, R, Kitahara, CM, Koppert, LB, Kraft, P, Kristensen, VN, Lambrechts, D, Le Marchand, L, Lejbkowicz, F, Lindblom, A, Lubinski, J, Mannermaa, A, Manoochehri, M, Manoukian, S, Margolin, S, Martinez, ME, Maurer, T, Mavroudis, D, Meindl, A, Milne, RL, Mulligan, AM, Neuhausen, SL, Nevanlinna, H, Newman, WG, Olshan, AF, Olson, JE, Olsson, H, Orr, N, Peterlongo, P, Petridis, C, Prentice, RL, Presneau, N, Punie, K, Ramachandran, D, Rennert, G, Romero, A, Sachchithananthan, M, Saloustros, E, Sawyer, EJ, Schmutzler, RK, Schwentner, L, Scott, C, Simard, J, Sohn, C, Southey, MC, Swerdlow, AJ, Tamimi, RM, Tapper, WJ, Teixeira, MR, Terry, MB, Thorne, H, Tollenaar, RAEM, Tomlinson, I, Troester, MA, Truong, T, Turnbull, C, Vachon, CM, van der Kolk, LE, Wang, Q, Winqvist, R, Wolk, A, Yang, XR, Ziogas, A, Pharoah, PDP, Hall, P, Wessels, LFA, Chenevix-Trench, G, Bader, GD, Doerk, T, Easton, DF, Canisius, S, and Schmidt, MK

Abstract

Identifying the underlying genetic drivers of the heritability of breast cancer prognosis remains elusive. We adapt a network-based approach to handle underpowered complex datasets to provide new insights into the potential function of germline variants in breast cancer prognosis. This network-based analysis studies ~7.3 million variants in 84,457 breast cancer patients in relation to breast cancer survival and confirms the results on 12,381 independent patients. Aggregating the prognostic effects of genetic variants across multiple genes, we identify four gene modules associated with survival in estrogen receptor (ER)-negative and one in ER-positive disease. The modules show biological enrichment for cancer-related processes such as G-alpha signaling, circadian clock, angiogenesis, and Rho-GTPases in apoptosis.

Published
2020

3. 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

4. Body mass index and breast cancer survival: a Mendelian randomization analysis

Author

Guo, Q, Burgess, S, Turman, C, Bolla, MK, Wang, Q, Lush, M, Abraham, J, Aittomaki, K, Andrulis, IL, Apicella, C, Arndt, V, Barrdahl, M, Benitez, J, Berg, CD, Blomqvist, C, Bojesen, SE, Bonanni, B, Brand, JS, Brenner, H, Broeks, A, Burwinkel, B, Caldas, C, Campa, D, Canzian, F, Chang-Claude, J, Chanock, SJ, Chin, S-F, Couch, FJ, Cox, A, Cross, SS, Cybulski, C, Czene, K, Darabi, H, Devilee, P, Diver, WR, Dunning, AM, Earl, HM, Eccles, DM, Ekici, AB, Eriksson, M, Evans, DG, Fasching, PA, Figueroa, J, Flesch-Janys, D, Flyger, H, Gapstur, SM, Gaudet, MM, Giles, GG, Glendon, G, Grip, M, Gronwald, J, Haeberle, L, Haiman, CA, Hall, P, Hamann, U, Hankinson, S, Hartikainen, JM, Hein, A, Hiller, L, Hogervorst, FB, Holleczek, B, Hooning, MJ, Hoover, RN, Humphreys, K, Hunter, DJ, Husing, A, Jakubowska, A, Jukkola-Vuorinen, A, Kaaks, R, Kabisch, M, Kataja, V, Knight, JA, Koppert, LB, Kosma, V-M, Kristensen, VN, Lambrechts, D, Le Marchand, L, Li, J, Lindblom, A, Lindstrom, S, Lissowska, J, Lubinski, J, Machiela, MJ, Mannermaa, A, Manoukian, S, Margolin, S, Marme, F, Martens, JWM, McLean, C, Menendez, P, Milne, RL, Mulligan, AM, Muranen, TA, Nevanlinna, H, Neven, P, Nielsen, SF, Nordestgaard, BG, Olson, JE, Perez, JIA, Peterlongo, P, Phillips, K-A, Poole, CJ, Pylkas, K, Radice, P, Rahman, N, Rudiger, T, Rudolph, A, Sawyer, EJ, Schumacher, F, Seibold, P, Seynaeve, C, Shah, M, Smeets, A, Southey, MC, Tollenaar, RAEM, Tomlinson, I, Tsimiklis, H, Ulmer, H-U, Vachon, C, van den Ouweland, AMW, Van't Veer, LJ, Wildiers, H, Willett, W, Winqvist, R, Zamora, MP, Chenevix-Trench, G, Dork, T, Easton, DF, Garcia-Closas, M, Kraft, P, Hopper, JL, Zheng, W, Schmidt, MK, Pharoah, PDP, Guo, Q, Burgess, S, Turman, C, Bolla, MK, Wang, Q, Lush, M, Abraham, J, Aittomaki, K, Andrulis, IL, Apicella, C, Arndt, V, Barrdahl, M, Benitez, J, Berg, CD, Blomqvist, C, Bojesen, SE, Bonanni, B, Brand, JS, Brenner, H, Broeks, A, Burwinkel, B, Caldas, C, Campa, D, Canzian, F, Chang-Claude, J, Chanock, SJ, Chin, S-F, Couch, FJ, Cox, A, Cross, SS, Cybulski, C, Czene, K, Darabi, H, Devilee, P, Diver, WR, Dunning, AM, Earl, HM, Eccles, DM, Ekici, AB, Eriksson, M, Evans, DG, Fasching, PA, Figueroa, J, Flesch-Janys, D, Flyger, H, Gapstur, SM, Gaudet, MM, Giles, GG, Glendon, G, Grip, M, Gronwald, J, Haeberle, L, Haiman, CA, Hall, P, Hamann, U, Hankinson, S, Hartikainen, JM, Hein, A, Hiller, L, Hogervorst, FB, Holleczek, B, Hooning, MJ, Hoover, RN, Humphreys, K, Hunter, DJ, Husing, A, Jakubowska, A, Jukkola-Vuorinen, A, Kaaks, R, Kabisch, M, Kataja, V, Knight, JA, Koppert, LB, Kosma, V-M, Kristensen, VN, Lambrechts, D, Le Marchand, L, Li, J, Lindblom, A, Lindstrom, S, Lissowska, J, Lubinski, J, Machiela, MJ, Mannermaa, A, Manoukian, S, Margolin, S, Marme, F, Martens, JWM, McLean, C, Menendez, P, Milne, RL, Mulligan, AM, Muranen, TA, Nevanlinna, H, Neven, P, Nielsen, SF, Nordestgaard, BG, Olson, JE, Perez, JIA, Peterlongo, P, Phillips, K-A, Poole, CJ, Pylkas, K, Radice, P, Rahman, N, Rudiger, T, Rudolph, A, Sawyer, EJ, Schumacher, F, Seibold, P, Seynaeve, C, Shah, M, Smeets, A, Southey, MC, Tollenaar, RAEM, Tomlinson, I, Tsimiklis, H, Ulmer, H-U, Vachon, C, van den Ouweland, AMW, Van't Veer, LJ, Wildiers, H, Willett, W, Winqvist, R, Zamora, MP, Chenevix-Trench, G, Dork, T, Easton, DF, Garcia-Closas, M, Kraft, P, Hopper, JL, Zheng, W, Schmidt, MK, and Pharoah, PDP

Abstract

BACKGROUND: There is increasing evidence that elevated body mass index (BMI) is associated with reduced survival for women with breast cancer. However, the underlying reasons remain unclear. We conducted a Mendelian randomization analysis to investigate a possible causal role of BMI in survival from breast cancer. METHODS: We used individual-level data from six large breast cancer case-cohorts including a total of 36 210 individuals (2475 events) of European ancestry. We created a BMI genetic risk score (GRS) based on genotypes at 94 known BMI-associated genetic variants. Association between the BMI genetic score and breast cancer survival was analysed by Cox regression for each study separately. Study-specific hazard ratios were pooled using fixed-effect meta-analysis. RESULTS: BMI genetic score was found to be associated with reduced breast cancer-specific survival for estrogen receptor (ER)-positive cases [hazard ratio (HR) = 1.11, per one-unit increment of GRS, 95% confidence interval (CI) 1.01-1.22, P = 0.03). We observed no association for ER-negative cases (HR = 1.00, per one-unit increment of GRS, 95% CI 0.89-1.13, P = 0.95). CONCLUSIONS: Our findings suggest a causal effect of increased BMI on reduced breast cancer survival for ER-positive breast cancer. There is no evidence of a causal effect of higher BMI on survival for ER-negative breast cancer cases.

Published
2017

5. The somatic mutation profiles of 2,433 breast cancers refines their genomic and transcriptomic landscapes

Author

Pereira, B, Chin, S-F, Rueda, OM, Vollan, H-KM, Provenzano, E, Bardwell, HA, Pugh, M, Jones, L, Russell, R, Sammut, S-J, Tsui, DWY, Liu, B, Dawson, S-J, Abraham, J, Northen, H, Peden, JF, Mukherjee, A, Turashvili, G, Green, AR, McKinney, S, Oloumi, A, Shah, S, Rosenfeld, N, Murphy, L, Bentley, DR, Ellis, IO, Purushotham, A, Pinder, SE, Borresen-Dale, A-L, Earl, HM, Pharoah, PD, Ross, MT, Aparicio, S, Caldas, C, Pereira, B, Chin, S-F, Rueda, OM, Vollan, H-KM, Provenzano, E, Bardwell, HA, Pugh, M, Jones, L, Russell, R, Sammut, S-J, Tsui, DWY, Liu, B, Dawson, S-J, Abraham, J, Northen, H, Peden, JF, Mukherjee, A, Turashvili, G, Green, AR, McKinney, S, Oloumi, A, Shah, S, Rosenfeld, N, Murphy, L, Bentley, DR, Ellis, IO, Purushotham, A, Pinder, SE, Borresen-Dale, A-L, Earl, HM, Pharoah, PD, Ross, MT, Aparicio, S, and Caldas, C

Abstract

The genomic landscape of breast cancer is complex, and inter- and intra-tumour heterogeneity are important challenges in treating the disease. In this study, we sequence 173 genes in 2,433 primary breast tumours that have copy number aberration (CNA), gene expression and long-term clinical follow-up data. We identify 40 mutation-driver (Mut-driver) genes, and determine associations between mutations, driver CNA profiles, clinical-pathological parameters and survival. We assess the clonal states of Mut-driver mutations, and estimate levels of intra-tumour heterogeneity using mutant-allele fractions. Associations between PIK3CA mutations and reduced survival are identified in three subgroups of ER-positive cancer (defined by amplification of 17q23, 11q13-14 or 8q24). High levels of intra-tumour heterogeneity are in general associated with a worse outcome, but highly aggressive tumours with 11q13-14 amplification have low levels of intra-tumour heterogeneity. These results emphasize the importance of genome-based stratification of breast cancer, and have important implications for designing therapeutic strategies.

Published
2016

6. Identification of Novel Genetic Markers of Breast Cancer Survival

Author

Guo, Q, Schmidt, MK, Kraft, P, Canisius, S, Chen, C, Khan, S, Tyrer, J, Bolla, MK, Wang, Q, Dennis, J, Michailidou, K, Lush, M, Kar, S, Beesley, J, Dunning, AM, Shah, M, Czene, K, Darabi, H, Eriksson, M, Lambrechts, D, Weltens, C, Leunen, K, Bojesen, SE, Nordestgaard, BG, Nielsen, SF, Flyger, H, Chang-Claude, J, Rudolph, A, Seibold, P, Flesch-Janys, D, Blomqvist, C, Aittomaeki, K, Fagerholm, R, Muranen, TA, Couch, FJ, Olson, JE, Vachon, C, Andrulis, IL, Knight, JA, Glendon, G, Mulligan, AM, Broeks, A, Hogervorst, FB, Haiman, CA, Henderson, BE, Schumacher, F, Le Marchand, L, Hopper, JL, Tsimiklis, H, Apicella, C, Southey, MC, Cox, A, Cross, SS, Reed, MWR, Giles, GG, Milne, RL, McLean, C, Winqvist, R, Pylkaes, K, Jukkola-Vuorinen, A, Grip, M, Hooning, MJ, Hollestelle, A, Martens, JWM, Van den Ouweland, AMW, Marme, F, Schneeweiss, A, Yang, R, Burwinkel, B, Figueroa, J, Chanock, SJ, Lissowska, J, Sawyer, EJ, Tomlinson, I, Kerin, MJ, Miller, N, Brenner, H, Dieffenbach, AK, Arndt, V, Holleczek, B, Mannermaa, A, Kataja, V, Kosma, V-M, Hartikainen, JM, Li, J, Brand, JS, Humphreys, K, Devilee, P, Tollenaar, RAEM, Seynaeve, C, Radice, P, Peterlongo, P, Bonanni, B, Mariani, P, Fasching, PA, Beckmann, MW, Hein, A, Ekici, AB, Chenevix-Trench, G, Balleine, R, Phillips, K-A, Benitez, J, Zamora, MP, Perez, JIA, Menendez, P, Jakubowska, A, Lubinski, J, Jaworska-Bieniek, K, Durda, K, Hamann, U, Kabisch, M, Ulmer, HU, Ruediger, T, Margolin, S, Kristensen, V, Nord, S, Evans, DG, Abraham, JE, Earl, HM, Hiller, L, Dunn, JA, Bowden, S, Berg, C, Campa, D, Diver, WR, Gapstur, SM, Gaudet, MM, Hankinson, SE, Hoover, RN, Huesing, A, Kaaks, R, Machiela, MJ, Willett, W, Barrdahl, M, Canzian, F, Chin, S-F, Caldas, C, Hunter, DJ, Lindstrom, S, Garcia-Closas, M, Hall, P, Easton, DF, Eccles, DM, Rahman, N, Nevanlinna, H, Pharoah, PDP, Guo, Q, Schmidt, MK, Kraft, P, Canisius, S, Chen, C, Khan, S, Tyrer, J, Bolla, MK, Wang, Q, Dennis, J, Michailidou, K, Lush, M, Kar, S, Beesley, J, Dunning, AM, Shah, M, Czene, K, Darabi, H, Eriksson, M, Lambrechts, D, Weltens, C, Leunen, K, Bojesen, SE, Nordestgaard, BG, Nielsen, SF, Flyger, H, Chang-Claude, J, Rudolph, A, Seibold, P, Flesch-Janys, D, Blomqvist, C, Aittomaeki, K, Fagerholm, R, Muranen, TA, Couch, FJ, Olson, JE, Vachon, C, Andrulis, IL, Knight, JA, Glendon, G, Mulligan, AM, Broeks, A, Hogervorst, FB, Haiman, CA, Henderson, BE, Schumacher, F, Le Marchand, L, Hopper, JL, Tsimiklis, H, Apicella, C, Southey, MC, Cox, A, Cross, SS, Reed, MWR, Giles, GG, Milne, RL, McLean, C, Winqvist, R, Pylkaes, K, Jukkola-Vuorinen, A, Grip, M, Hooning, MJ, Hollestelle, A, Martens, JWM, Van den Ouweland, AMW, Marme, F, Schneeweiss, A, Yang, R, Burwinkel, B, Figueroa, J, Chanock, SJ, Lissowska, J, Sawyer, EJ, Tomlinson, I, Kerin, MJ, Miller, N, Brenner, H, Dieffenbach, AK, Arndt, V, Holleczek, B, Mannermaa, A, Kataja, V, Kosma, V-M, Hartikainen, JM, Li, J, Brand, JS, Humphreys, K, Devilee, P, Tollenaar, RAEM, Seynaeve, C, Radice, P, Peterlongo, P, Bonanni, B, Mariani, P, Fasching, PA, Beckmann, MW, Hein, A, Ekici, AB, Chenevix-Trench, G, Balleine, R, Phillips, K-A, Benitez, J, Zamora, MP, Perez, JIA, Menendez, P, Jakubowska, A, Lubinski, J, Jaworska-Bieniek, K, Durda, K, Hamann, U, Kabisch, M, Ulmer, HU, Ruediger, T, Margolin, S, Kristensen, V, Nord, S, Evans, DG, Abraham, JE, Earl, HM, Hiller, L, Dunn, JA, Bowden, S, Berg, C, Campa, D, Diver, WR, Gapstur, SM, Gaudet, MM, Hankinson, SE, Hoover, RN, Huesing, A, Kaaks, R, Machiela, MJ, Willett, W, Barrdahl, M, Canzian, F, Chin, S-F, Caldas, C, Hunter, DJ, Lindstrom, S, Garcia-Closas, M, Hall, P, Easton, DF, Eccles, DM, Rahman, N, Nevanlinna, H, and Pharoah, PDP

Abstract

BACKGROUND: Survival after a diagnosis of breast cancer varies considerably between patients, and some of this variation may be because of germline genetic variation. We aimed to identify genetic markers associated with breast cancer-specific survival. METHODS: We conducted a large meta-analysis of studies in populations of European ancestry, including 37954 patients with 2900 deaths from breast cancer. Each study had been genotyped for between 200000 and 900000 single nucleotide polymorphisms (SNPs) across the genome; genotypes for nine million common variants were imputed using a common reference panel from the 1000 Genomes Project. We also carried out subtype-specific analyses based on 6881 estrogen receptor (ER)-negative patients (920 events) and 23059 ER-positive patients (1333 events). All statistical tests were two-sided. RESULTS: We identified one new locus (rs2059614 at 11q24.2) associated with survival in ER-negative breast cancer cases (hazard ratio [HR] = 1.95, 95% confidence interval [CI] = 1.55 to 2.47, P = 1.91 x 10(-8)). Genotyping a subset of 2113 case patients, of which 300 were ER negative, provided supporting evidence for the quality of the imputation. The association in this set of case patients was stronger for the observed genotypes than for the imputed genotypes. A second locus (rs148760487 at 2q24.2) was associated at genome-wide statistical significance in initial analyses; the association was similar in ER-positive and ER-negative case patients. Here the results of genotyping suggested that the finding was less robust. CONCLUSIONS: This is currently the largest study investigating genetic variation associated with breast cancer survival. Our results have potential clinical implications, as they confirm that germline genotype can provide prognostic information in addition to standard tumor prognostic factors.

Published
2015

7. Identification of Novel Genetic Markers of Breast Cancer Survival

Author

Guo, Q, Schmidt, MK (Marjanka), Kraft, P, Canisius, S, Chen, C (Christopher Li Hsian), Khan, Salima, Tyrer, J, Bolla, MK, Wang, Q (Qing), Dennis, J, Michailidou, K, Lush, M, Kar, S, Beesley, J, Dunning, AM, Shah, M, Czene, K, Darabi, H, Eriksson, M, Lambrechts, D, Weltens, C, Leunen, K, Bojesen, SE, Nordestgaard, BG, Nielsen, SF, Flyger, H, Chang-Claude, J, Rudolph, A, Seibold, P, Flesch-Janys, D, Blomqvist, C, Aittomaki, K, Fagerholm, R, Muranen, TA, Couch, FJ, Olson, JE, Vachon, C, Andrulis, IL, Knight, JA, Glendon, G, Mulligan, AM, Broeks, A, Hogervorst, FB, Haiman, CA, Henderson, BE, Schumacher, F, Le Marchand, L, Hopper, JL, Tsimiklis, H, Apicella, C, Southey, MC, Cox, A, Cross, SS, Reed, MWR, Giles, GG, Milne, RL, McLean, C, Winqvist, R, Pylkas, K, Jukkola-Vuorinen, A, Grip, M, Hooning, Maartje, Hollestelle, Antoinette, Martens, John, van den Ouweland, Ans, Marme, F, Schneeweiss, A, Yang, RX, Burwinkel, B, Figueroa, J, Chanock, SJ, Lissowska, J, Sawyer, EJ, Tomlinson, I, Kerin, MJ, Miller, N, Brenner, H, Dieffenbach, AK, Arndt, V, Holleczek, B, Mannermaa, A, Kataja, V, Kosma, VM, Hartikainen, JM, Li, JM, Brand, JS, Humphreys, K, Devilee, P, Tollenaar, RAEM, Seynaeve, Caroline, Radice, P, Peterlongo, P, Bonanni, B, Mariani, P, Fasching, PA, Beckmann, MW, Hein, A, Ekici, AB, Chenevix-Trench, G, Balleine, R, Phillips, KA, Benitez, J, Zamora, MP, Perez, JIA, Menendez, P, Jakubowska, A, Lubinski, J, Jaworska-Bieniek, K, Durda, K, Hamann, U, Kabisch, M, Ulmer, HU, Rudiger, T, Margolin, S, Kristensen, V, Nord, S, Evans, DG, Abraham, JE, Earl, HM, Hiller, L, Dunn, JA, Bowden, S, Berg, Conny, Campa, D, Diver, WR, Gapstur, SM, Gaudet, MM, Hankinson, SE, Hoover, RN, Husing, A, Kaaks, R, Machiela, MJ, Willett, W, Barrdahl, M, Canzian, F, Chin, SF, Caldas, C, Hunter, DJ, Lindstrom, S, Garcia-Closas, M, Hall, P, Easton, DF, Eccles, DM, Rahman, N, Nevanlinna, H, Pharoah, PDP, Guo, Q, Schmidt, MK (Marjanka), Kraft, P, Canisius, S, Chen, C (Christopher Li Hsian), Khan, Salima, Tyrer, J, Bolla, MK, Wang, Q (Qing), Dennis, J, Michailidou, K, Lush, M, Kar, S, Beesley, J, Dunning, AM, Shah, M, Czene, K, Darabi, H, Eriksson, M, Lambrechts, D, Weltens, C, Leunen, K, Bojesen, SE, Nordestgaard, BG, Nielsen, SF, Flyger, H, Chang-Claude, J, Rudolph, A, Seibold, P, Flesch-Janys, D, Blomqvist, C, Aittomaki, K, Fagerholm, R, Muranen, TA, Couch, FJ, Olson, JE, Vachon, C, Andrulis, IL, Knight, JA, Glendon, G, Mulligan, AM, Broeks, A, Hogervorst, FB, Haiman, CA, Henderson, BE, Schumacher, F, Le Marchand, L, Hopper, JL, Tsimiklis, H, Apicella, C, Southey, MC, Cox, A, Cross, SS, Reed, MWR, Giles, GG, Milne, RL, McLean, C, Winqvist, R, Pylkas, K, Jukkola-Vuorinen, A, Grip, M, Hooning, Maartje, Hollestelle, Antoinette, Martens, John, van den Ouweland, Ans, Marme, F, Schneeweiss, A, Yang, RX, Burwinkel, B, Figueroa, J, Chanock, SJ, Lissowska, J, Sawyer, EJ, Tomlinson, I, Kerin, MJ, Miller, N, Brenner, H, Dieffenbach, AK, Arndt, V, Holleczek, B, Mannermaa, A, Kataja, V, Kosma, VM, Hartikainen, JM, Li, JM, Brand, JS, Humphreys, K, Devilee, P, Tollenaar, RAEM, Seynaeve, Caroline, Radice, P, Peterlongo, P, Bonanni, B, Mariani, P, Fasching, PA, Beckmann, MW, Hein, A, Ekici, AB, Chenevix-Trench, G, Balleine, R, Phillips, KA, Benitez, J, Zamora, MP, Perez, JIA, Menendez, P, Jakubowska, A, Lubinski, J, Jaworska-Bieniek, K, Durda, K, Hamann, U, Kabisch, M, Ulmer, HU, Rudiger, T, Margolin, S, Kristensen, V, Nord, S, Evans, DG, Abraham, JE, Earl, HM, Hiller, L, Dunn, JA, Bowden, S, Berg, Conny, Campa, D, Diver, WR, Gapstur, SM, Gaudet, MM, Hankinson, SE, Hoover, RN, Husing, A, Kaaks, R, Machiela, MJ, Willett, W, Barrdahl, M, Canzian, F, Chin, SF, Caldas, C, Hunter, DJ, Lindstrom, S, Garcia-Closas, M, Hall, P, Easton, DF, Eccles, DM, Rahman, N, Nevanlinna, H, and Pharoah, PDP

Abstract

Background: Survival after a diagnosis of breast cancer varies considerably between patients, and some of this variation may be because of germline genetic variation. We aimed to identify genetic markers associated with breast cancer-specific survival. Methods: We conducted a large meta-analysis of studies in populations of European ancestry, including 37 954 patients with 2900 deaths from breast cancer. Each study had been genotyped for between 200 000 and 900 000 single nucleotide polymorphisms (SNPs) across the genome; genotypes for nine million common variants were imputed using a common reference panel from the 1000 Genomes Project. We also carried out subtype-specific analyses based on 6881 estrogen receptor (ER)-negative patients (920 events) and 23 059 ER-positive patients (1333 events). All statistical tests were two-sided. Results: We identified one new locus (rs2059614 at 11q24.2) associated with survival in ER-negative breast cancer cases (hazard ratio [HR] = 1.95, 95% confidence interval [CI] = 1.55 to 2.47, P = 1.91 x 10(-8)). Genotyping a subset of 2113 case patients, of which 300 were ER negative, provided supporting evidence for the quality of the imputation. The association in this set of case patients was stronger for the observed genotypes than for the imputed genotypes. A second locus (rs148760487 at 2q24.2) was associated at genome-wide statistical significance in initial analyses; the association was similar in ER-positive and ER-negative case patients. Here the results of genotyping suggested that the finding was less robust. Conclusions: This is currently the largest study investigating genetic variation associated with breast cancer survival. Our results have potential clinical implications, as they confirm that germline genotype can provide prognostic information in addition to standard tumor prognostic factors.

Published
2015

8. A central review of histopathology reports after breast cancer neoadjuvant chemotherapy in the neo-tango trial

Author

Provenzano, E, Vallier, A-L, Champ, R, Walland, K, Bowden, S, Grier, A, Fenwick, N, Abraham, J, Iddawela, M, Caldas, C, Hiller, L, Dunn, J, Earl, HM, Provenzano, E, Vallier, A-L, Champ, R, Walland, K, Bowden, S, Grier, A, Fenwick, N, Abraham, J, Iddawela, M, Caldas, C, Hiller, L, Dunn, J, and Earl, HM

Abstract

BACKGROUND: Neo-tAnGo, a National Cancer Research Network (NCRN) multicentre randomised neoadjuvant chemotherapy trial in early breast cancer, enroled 831 patients in the United Kingdom. We report a central review of post-chemotherapy histopathology reports on the surgical specimens, to assess the presence and degree of response. METHODS: A central independent two-reader review (EP and HME) of histopathology reports from post-treatment surgical specimens was performed. The quality and completeness of pathology reporting across all centres was assessed. The reviews included pathological response to chemotherapy (pathological complete response (pCR); minimal residual disease (MRD); and lesser degrees of response), laterality, the number of axillary metastases and axillary nodes, and the type of surgery. A consensus was reached after discussion. RESULTS: In all, 825 surgical reports from 816 patients were available for review. Out of 4125 data items there were 347 discrepant results (8.4% of classifications), which involved 281 patients. These involved grading of breast response (169 but only 9 involving pCR vs MRD); laterality (6); presence of axillary metastasis (35); lymph node counts (108); and type of axillary surgery (29). Excluding cases with pCR, only 45% of reports included any comment regarding response in the breast and 30% in the axillary lymph nodes. CONCLUSION: We found considerable variability in the completeness of reporting of surgical specimens within this national neoadjuvant breast cancer trial. This highlights the need for consensus guidelines among trial groups on histopathology reporting, and the participation of histopathologists throughout the development and analysis of neoadjuvant trials.

Published
2013

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