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1. 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

2. 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

3. 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

4. The PARTNER trial of neoadjuvant olaparib with chemotherapy in triple-negative breast cancer.

Author

Abraham JE, Pinilla K, Dayimu A, Grybowicz L, Demiris N, Harvey C, Drewett LM, Lucey R, Fulton A, Roberts AN, Worley JR, Chhabra A, Qian W, Vallier AL, Hardy RM, Chan S, Hickish T, Tripathi D, Venkitaraman R, Persic M, Aslam S, Glassman D, Raj S, Borley A, Braybrooke JP, Sutherland S, Staples E, Scott LC, Davies M, Palmer CA, Moody M, Churn MJ, Newby JC, Mukesh MB, Chakrabarti A, Roylance RR, Schouten PC, Levitt NC, McAdam K, Armstrong AC, Copson ER, McMurtry E, Tischkowitz M, Provenzano E, and Earl HM

Subjects
Adult, Aged, Female, Humans, Middle Aged, Anthracyclines therapeutic use, Anthracyclines administration & dosage, Carboplatin administration & dosage, Carboplatin therapeutic use, Genes, BRCA1, Genes, BRCA2, Paclitaxel administration & dosage, Paclitaxel therapeutic use, Pathologic Complete Response, Progression-Free Survival, Prospective Studies, Survival Analysis, Time Factors, Adolescent, Young Adult, Antineoplastic Combined Chemotherapy Protocols administration & dosage, Antineoplastic Combined Chemotherapy Protocols therapeutic use, Neoadjuvant Therapy, Phthalazines administration & dosage, Phthalazines therapeutic use, Piperazines administration & dosage, Piperazines therapeutic use, Triple Negative Breast Neoplasms drug therapy, Triple Negative Breast Neoplasms metabolism, Triple Negative Breast Neoplasms surgery
Abstract

PARTNER is a prospective, phase II-III, randomized controlled clinical trial that recruited patients with triple-negative breast cancer 1,2 , who were germline BRCA1 and BRCA2 wild type 3 . Here we report the results of the trial. Patients (n = 559) were randomized on a 1:1 basis to receive neoadjuvant carboplatin-paclitaxel with or without 150 mg olaparib twice daily, on days 3 to 14, of each of four cycles (gap schedule olaparib, research arm) followed by three cycles of anthracycline-based chemotherapy before surgery. The primary end point was pathologic complete response (pCR) 4 , and secondary end points included event-free survival (EFS) and overall survival (OS) 5 . pCR was achieved in 51% of patients in the research arm and 52% in the control arm (P = 0.753). Estimated EFS at 36 months in the research and control arms was 80% and 79% (log-rank P > 0.9), respectively; OS was 90% and 87.2% (log-rank P = 0.8), respectively. In patients with pCR, estimated EFS at 36 months was 90%, and in those with non-pCR it was 70% (log-rank P < 0.001), and OS was 96% and 83% (log-rank P < 0.001), respectively. Neoadjuvant olaparib did not improve pCR rates, EFS or OS when added to carboplatin-paclitaxel and anthracycline-based chemotherapy in patients with triple-negative breast cancer who were germline BRCA1 and BRCA2 wild type. ClinicalTrials.gov ID: NCT03150576 ., (© 2024. The Author(s).)

Published
2024
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5. Multi-omic machine learning predictor of breast cancer therapy response.

Author

Sammut SJ, Crispin-Ortuzar M, Chin SF, Provenzano E, Bardwell HA, Ma W, Cope W, Dariush A, Dawson SJ, 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

Subjects
Female, Genomics, Humans, Machine Learning, Neoadjuvant Therapy, Tumor Microenvironment, Breast Neoplasms drug therapy, Breast Neoplasms genetics, Ecosystem
Abstract

Breast cancers are complex ecosystems of malignant cells and the tumour microenvironment 1 . The composition of these tumour ecosystems and interactions within them contribute to responses to cytotoxic therapy 2 . 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 surgery 3 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., (© 2021. The Author(s).)

Published
2022
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6. Diffusion kurtosis MRI as a predictive biomarker of response to neoadjuvant chemotherapy in high grade serous ovarian cancer.

Author

Deen SS, Priest AN, McLean MA, Gill AB, Brodie C, Crawford R, Latimer J, Baldwin P, Earl HM, Parkinson C, Smith S, Hodgkin C, Patterson I, Addley H, Freeman S, Moyle P, Jimenez-Linan M, Graves MJ, Sala E, Brenton JD, and Gallagher FA

Subjects
Adult, Aged, Aged, 80 and over, Cystadenocarcinoma, Serous drug therapy, Cystadenocarcinoma, Serous pathology, Environmental Biomarkers, Female, Humans, Middle Aged, Neoadjuvant Therapy methods, Ovarian Neoplasms drug therapy, Ovarian Neoplasms pathology, Ovary diagnostic imaging, Ovary pathology, Treatment Outcome, Cystadenocarcinoma, Serous diagnostic imaging, Diffusion Magnetic Resonance Imaging methods, Ovarian Neoplasms diagnostic imaging
Abstract

This study assessed the feasibility of using diffusion kurtosis imaging (DKI) as a measure of tissue heterogeneity and proliferation to predict the response of high grade serous ovarian cancer (HGSOC) to neoadjuvant chemotherapy (NACT). Seventeen patients with HGSOC were imaged at 3 T and had biopsy samples taken prior to any treatment. The patients were divided into two groups: responders and non-responders based on Response Evaluation Criteria In Solid Tumours (RECIST) criteria. The following imaging metrics were calculated: apparent diffusion coefficient (ADC), apparent diffusion (D app ) and apparent kurtosis (K app ). Tumour cellularity and proliferation were quantified using histology and Ki-67 immunohistochemistry. Mean K app before therapy was higher in responders compared to non-responders: 0.69 ± 0.13 versus 0.51 ± 0.11 respectively, P = 0.02. Tumour cellularity correlated positively with K app (rho = 0.50, P = 0.04) and negatively with both ADC (rho = -0.72, P = 0.001) and D app (rho = -0.80, P < 0.001). Ki-67 expression correlated with K app (rho = 0.53, P = 0.03) but not with ADC or D app . In conclusion, K app was found to be a potential predictive biomarker of NACT response in HGSOC, which suggests that DKI is a promising clinical tool for use oncology and radiology that should be evaluated further in future larger studies.

Published
2019
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