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4 Publications visible to you, out of a total of 4
Transcriptome-wide association study of breast cancer risk by estrogen-receptor status
GC-HBOC - German Consortium for Hereditary Breast and Ovarian Cancer

Abstract (Expand)

Previous transcriptome-wide association studies (TWAS) have identified breast cancer risk genes by integrating data from expression quantitative loci and genome-wide association studies (GWAS), but … analyses of breast cancer subtype-specific associations have been limited. In this study, we conducted a TWAS using gene expression data from GTEx and summary statistics from the hitherto largest GWAS meta-analysis conducted for breast cancer overall, and by estrogen receptor subtypes (ER+ and ER-). We further compared associations with ER+ and ER- subtypes, using a case-only TWAS approach. We also conducted multigene conditional analyses in regions with multiple TWAS associations. Two genes, STXBP4 and HIST2H2BA, were specifically associated with ER+ but not with ER- breast cancer. We further identified 30 TWAS-significant genes associated with overall breast cancer risk, including four that were not identified in previous studies. Conditional analyses identified single independent breast-cancer gene in three of six regions harboring multiple TWAS-significant genes. Our study provides new information on breast cancer genetics and biology, particularly about genomic differences between ER+ and ER- breast cancer.

Authors: Helian Feng, Alexander Gusev, Bogdan Pasaniuc, Lang Wu, Jirong Long, Zomoroda Abu-Full, Kristiina Aittomäki, Irene L. Andrulis, Hoda Anton-Culver, Antonis C. Antoniou, Adalgeir Arason, Volker Arndt, Kristan J. Aronson, Banu K. Arun, Ella Asseryanis, Paul L. Auer, Jacopo Azzollini, Judith Balmaña, Rosa B. Barkardottir, Daniel R. Barnes, Daniel Barrowdale, Matthias W. Beckmann, Sabine Behrens, Javier Benitez, Marina Bermisheva, Katarzyna Białkowska, Ana Blanco, Carl Blomqvist, Bram Boeckx, Natalia V. Bogdanova, Stig E. Bojesen, Manjeet K. Bolla, Bernardo Bonanni, Ake Borg, Hiltrud Brauch, Hermann Brenner, Ignacio Briceno, Annegien Broeks, Thomas Brüning, Barbara Burwinkel, Qiuyin Cai, Trinidad Caldés, Maria A. Caligo, Ian Campbell, Sander Canisius, Daniele Campa, Brian D. Carter, Jonathan Carter, Jose E. Castelao, Jenny Chang-Claude, Stephen J. Chanock, Hans Christiansen, Wendy K. Chung, Kathleen B. M. Claes, Christine L. Clarke, Fergus J. Couch, Angela Cox, Simon S. Cross, Cezary Cybulski, Kamila Czene, Mary B. Daly, Miguel de La Hoya, Kim de Leeneer, Joe Dennis, Peter Devilee, Orland Diez, Susan M. Domchek, Thilo Dörk, Isabel Dos-Santos-Silva, Alison M. Dunning, Miriam Dwek, Diana M. Eccles, Bent Ejlertsen, Carolina Ellberg, Christoph Engel, Mikael Eriksson, Peter A. Fasching, Olivia Fletcher, Henrik Flyger, Florentia Fostira, Eitan Friedman, Lin Fritschi, Debra Frost, Marike Gabrielson, Patricia A. Ganz, Susan M. Gapstur, Judy Garber, Montserrat García-Closas, José A. García-Sáenz, Mia M. Gaudet, Graham G. Giles, Gord Glendon, Andrew K. Godwin, Mark S. Goldberg, David E. Goldgar, Anna González-Neira, Mark H. Greene, Jacek Gronwald, Pascal Guénel, Christopher A. Haiman, Per Hall, Ute Hamann, Christopher Hake, Wei He, Jane Heyworth, Frans B. L. Hogervorst, Antoinette Hollestelle, Maartje J. Hooning, Robert N. Hoover, John L. Hopper, Guanmengqian Huang, Peter J. Hulick, Keith Humphreys, Evgeny N. Imyanitov, Claudine Isaacs, Milena Jakimovska, Anna Jakubowska, Paul James, Ramunas Janavicius, Rachel C. Jankowitz, Esther M. John, Nichola Johnson, Vijai Joseph, Audrey Jung, Beth Y. Karlan, Elza Khusnutdinova, Johanna I. Kiiski, Irene Konstantopoulou, Vessela N. Kristensen, Yael Laitman, Diether Lambrechts, Conxi Lazaro, Dominique Leroux, Goska Leslie, Jenny Lester, Fabienne Lesueur, Noralane Lindor, Sara Lindström, Wing-Yee Lo, Jennifer T. Loud, Jan Lubiński, Enes Makalic, Arto Mannermaa, Mehdi Manoochehri, Siranoush Manoukian, Sara Margolin, John W. M. Martens, Maria E. Martinez, Laura Matricardi, Tabea Maurer, Dimitrios Mavroudis, Lesley McGuffog, Alfons Meindl, Usha Menon, Kyriaki Michailidou, Pooja M. Kapoor, Austin Miller, Marco Montagna, Fernando Moreno, Lidia Moserle, Anna M. Mulligan, Taru A. Muranen, Katherine L. Nathanson, Susan L. Neuhausen, Heli Nevanlinna, Ines Nevelsteen, Finn C. Nielsen, Liene Nikitina-Zake, Kenneth Offit, Edith Olah, Olufunmilayo I. Olopade, Håkan Olsson, Ana Osorio, Janos Papp, Tjoung-Won Park-Simon, Michael T. Parsons, Inge S. Pedersen, Ana Peixoto, Paolo Peterlongo, Julian Peto, Paul D. P. Pharoah, Kelly-Anne Phillips, Dijana Plaseska-Karanfilska, Bruce Poppe, Nisha Pradhan, Karolina Prajzendanc, Nadege Presneau, Kevin Punie, Katri Pylkäs, Paolo Radice, Johanna Rantala, Muhammad Usman Rashid, Gad Rennert, Harvey A. Risch, Mark Robson, Atocha Romero, Emmanouil Saloustros, Dale P. Sandler, Catarina Santos, Elinor J. Sawyer, Marjanka K. Schmidt, Daniel F. Schmidt, Rita K. Schmutzler, Minouk J. Schoemaker, Rodney J. Scott, Priyanka Sharma, Xiao-Ou Shu, Jacques Simard, Christian F. Singer, Anne-Bine Skytte, Penny Soucy, Melissa C. Southey, John J. Spinelli, Amanda B. Spurdle, Jennifer Stone, Anthony J. Swerdlow, William J. Tapper, Jack A. Taylor, Manuel R. Teixeira, Mary Beth Terry, Alex Teulé, Mads Thomassen, Kathrin Thöne, Darcy L. Thull, Marc Tischkowitz, Amanda E. Toland, Rob A. E. M. Tollenaar, Diana Torres, Thérèse Truong, Nadine Tung, Celine M. Vachon, Christi J. van Asperen, Ans M. W. van den Ouweland, Elizabeth J. van Rensburg, Ana Vega, Alessandra Viel, Paula Vieiro-Balo, Qin Wang, Barbara Wappenschmidt, Clarice R. Weinberg, Jeffrey N. Weitzel, Camilla Wendt, Robert Winqvist, Xiaohong R. Yang, Drakoulis Yannoukakos, Argyrios Ziogas, Roger L. Milne, Douglas F. Easton, Georgia Chenevix-Trench, Wei Zheng, Peter Kraft, Xia Jiang

Date Published: 1st Jul 2020

Publication Type: Journal article

Human Diseases: breast cancer

Towards Phenotyping of Clinical Trial Eligibility Criteria.
NLP4CR - Natural Language Processing for Clinical Research

Abstract (Expand)

BACKGROUND: Medical plaintext documents contain important facts about patients, but they are rarely available for structured queries. The provision of structured information from natural language texts … in addition to the existing structured data can significantly speed up the search for fulfilled inclusion criteria and thus improve the recruitment rate. OBJECTIVES: This work is aimed at supporting clinical trial recruitment with text mining techniques to identify suitable subjects in hospitals. METHOD: Based on the inclusion/exclusion criteria of 5 sample studies and a text corpus consisting of 212 doctor's letters and medical follow-up documentation from a university cancer center, a prototype was developed and technically evaluated using NLP procedures (UIMA) for the extraction of facts from medical free texts. RESULTS: It was found that although the extracted entities are not always correct (precision between 23% and 96%), they provide a decisive indication as to which patient file should be read preferentially. CONCLUSION: The prototype presented here demonstrates the technical feasibility. In order to find available, lucrative phenotypes, an in-depth evaluation is required.

Authors: M. Lobe, S. Staubert, C. Goldberg, I. Haffner, A. Winter

Date Published: 5th May 2018

Publication Type: Journal article

Human Diseases: breast cancer

Prevalence of BRCA1/2 germline mutations in 21 401 families with breast and ovarian cancer.
GC-HBOC - German Consortium for Hereditary Breast and Ovarian Cancer

Abstract (Expand)

PURPOSE: To characterise the prevalence of pathogenic germline mutations in BRCA1 and BRCA2 in families with breast cancer (BC) and ovarian cancer (OC) history. PATIENTS AND METHODS: Data from 21 401 … families were gathered between 1996 and 2014 in a clinical setting in the German Consortium for Hereditary Breast and Ovarian Cancer, comprising full pedigrees with cancer status of all individual members at the time of first counselling, and BRCA1/2 mutation status of the index patient. RESULTS: The overall BRCA1/2 mutation prevalence was 24.0% (95% CI 23.4% to 24.6%). Highest mutation frequencies were observed in families with at least two OCs (41.9%, 95% CI 36.1% to 48.0%) and families with at least one breast and one OC (41.6%, 95% CI 40.3% to 43.0%), followed by male BC with at least one female BC or OC (35.8%; 95% CI 32.2% to 39.6%). In families with a single case of early BC (<36 years), mutations were found in 13.7% (95% CI 11.9% to 15.7%). Postmenopausal unilateral or bilateral BC did not increase the probability of mutation detection. Occurrence of premenopausal BC and OC in the same woman led to higher mutation frequencies compared with the occurrence of these two cancers in different individuals (49.0%; 95% CI 41.0% to 57.0% vs 31.5%; 95% CI 28.0% to 35.2%). CONCLUSIONS: Our data provide guidance for healthcare professionals and decision-makers to identify individuals who should undergo genetic testing for hereditary breast and ovarian cancer. Moreover, it supports informed decision-making of counselees on the uptake of genetic testing.

Authors: K. Kast, K. Rhiem, B. Wappenschmidt, E. Hahnen, J. Hauke, B. Bluemcke, V. Zarghooni, N. Herold, N. Ditsch, M. Kiechle, M. Braun, C. Fischer, N. Dikow, S. Schott, N. Rahner, D. Niederacher, T. Fehm, A. Gehrig, C. Mueller-Reible, N. Arnold, N. Maass, G. Borck, N. de Gregorio, C. Scholz, B. Auber, R. Varon-Manteeva, D. Speiser, J. Horvath, N. Lichey, P. Wimberger, S. Stark, U. Faust, B. H. Weber, G. Emons, S. Zachariae, A. Meindl, R. K. Schmutzler, C. Engel

Date Published: 2nd Mar 2016

Publication Type: Journal article

Human Diseases: breast cancer, ovarian cancer

Validation of the Manchester scoring system for predicting BRCA1/2 mutations in 9,390 families suspected of having hereditary breast and ovarian cancer.
GC-HBOC - German Consortium for Hereditary Breast and Ovarian Cancer

Abstract (Expand)

The Manchester scoring system (MSS) allows the calculation of the probability for the presence of mutations in BRCA1 or BRCA2 genes in families suspected of having hereditary breast and ovarian cancer. … In 9,390 families, we determined the predictive performance of the MSS without (MSS-2004) and with (MSS-2009) consideration of pathology parameters. Moreover, we validated a recalibrated version of the MSS-2009 (MSS-recal). Families were included in the registry of the German Consortium for Hereditary Breast and Ovarian Cancer, using defined clinical criteria. Receiver operating characteristics (ROC) analysis was used to determine the predictive performance. The recalibrated model was developed using logistic regression analysis and tested using an independent random validation sample. The area under the ROC curves regarding a mutation in any of the two BRCA genes was 0.77 (95%CI 0.75-0.79) for MSS-2004, 0.80 (95%CI 0.78-0.82) for MSS-2009, and 0.82 (95%CI 0.80-0.83) for MSS-recal. Sensitivity at the 10% mutation probability cutoff was similar for all three models (MSS-2004 92.2%, MSS-2009 92.2%, and MSS-recal 90.3%), but specificity of MSS-recal (46.0%) was considerably higher than that of MSS-2004 (25.4%) and MSS-2009 (32.3%). In the MSS-recal model, almost all predictors of the original MSS were significantly predictive. However, the score values of some predictors, for example, high grade triple negative breast cancers, differed considerably from the originally proposed score values. The original MSS performed well in our sample of high risk families. The use of pathological parameters increased the predictive performance significantly. Recalibration improved the specificity considerably without losing much sensitivity.

Authors: K. Kast, R. K. Schmutzler, K. Rhiem, M. Kiechle, C. Fischer, D. Niederacher, N. Arnold, T. Grimm, D. Speiser, B. Schlegelberger, D. Varga, J. Horvath, M. Beer, S. Briest, A. Meindl, C. Engel

Date Published: 15th Nov 2014

Publication Type: Not specified

Human Diseases: breast cancer, ovarian cancer

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