Nijole C Pollock1, Johnny R Ramroop1, Heather Hampel2,3, Melissa A Troester4, Kathleen Conway4, Jennifer J Hu5, Jo L Freudenheim6, Olufunmilayo I Olopade7, Dezheng Huo8, Elad Ziv9,10,11, Susan L Neuhausen12, Patrick Stevens13, Joseph Paul McElroy14, Amanda Ewart Toland15,16,17. 1. Department of Cancer Biology and Genetics, The Ohio State University, Columbus, OH, USA. 2. OSU Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA. 3. Division of Human Genetics, Department of Internal Medicine, The Ohio State University Wexner Medical Center, Columbus, OH, USA. 4. Department of Epidemiology and the Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. 5. Department of Public Health Sciences, Sylvester Comprehensive Cancer Center, University of Miami School of Medicine, Miami, FL, USA. 6. Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, Buffalo, NY, USA. 7. Section of Hematology and Oncology, Department of Medicine, University of Chicago, Chicago, IL, USA. 8. Department of Public Health Sciences, University of Chicago, Chicago, IL, USA. 9. Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA. 10. Department of Medicine, University of California, San Francisco, San Francisco, CA, USA. 11. Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA. 12. Department of Population Sciences, Beckman Research Institute of City of Hope, Duarte, CA, USA. 13. Bioinformatics Shared Resource, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA. 14. Center for Biostatistics, Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA. 15. Department of Cancer Biology and Genetics, The Ohio State University, Columbus, OH, USA. Amanda.toland@osumc.edu. 16. OSU Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA. Amanda.toland@osumc.edu. 17. Division of Human Genetics, Department of Internal Medicine, The Ohio State University Wexner Medical Center, Columbus, OH, USA. Amanda.toland@osumc.edu.
Abstract
PURPOSE: Somatic driver mutations in TP53 are associated with triple-negative breast cancer (TNBC) and poorer outcomes. Breast cancers in women of African ancestry (AA) are more likely to be TNBC and have somatic TP53 mutations than cancers in non-Hispanic White (NHW) women. Missense driver mutations in TP53 have varied functional impact including loss-of-function (LOF) or gain-of-function (GOF) activity, and dominant negative (DNE) effects. We aimed to determine if there were differences in somatic TP53 mutation types by patient ancestry or TNBC status. METHODS: We identified breast cancer datasets with somatic TP53 mutation data, ancestry, age, and hormone receptor status. Mutations were classified for functional impact using published data and type of mutation. We assessed differences using Fisher's exact test. RESULTS: From 96 breast cancer studies, we identified 2964 women with somatic TP53 mutations: 715 (24.1%) Asian, 258 (8.7%) AA, 1931 (65.2%) NHW, and 60 (2%) Latina. The distribution of TP53 mutation type was similar by ancestry. However, 35.8% of tumors from NHW individuals had GOF mutations compared to 29% from AA individuals (p = 0.04). Mutations with DNE activity were positively associated with TNBC (OR 1.37, p = 0.03) and estrogen receptor (ER) negative status (OR 1.38; p = 0.005). CONCLUSIONS: Somatic TP53 mutation types did not differ by ancestry overall, but GOF mutations were more common in NHW women than AA women. ER-negative and TNBC tumors are less likely to have DNE+ TP53 mutations which could reflect biological processes. Larger cohorts and functional studies are needed to further elucidate these findings.
PURPOSE: Somatic driver mutations in TP53 are associated with triple-negative breast cancer (TNBC) and poorer outcomes. Breast cancers in women of African ancestry (AA) are more likely to be TNBC and have somatic TP53 mutations than cancers in non-Hispanic White (NHW) women. Missense driver mutations in TP53 have varied functional impact including loss-of-function (LOF) or gain-of-function (GOF) activity, and dominant negative (DNE) effects. We aimed to determine if there were differences in somatic TP53 mutation types by patient ancestry or TNBC status. METHODS: We identified breast cancer datasets with somatic TP53 mutation data, ancestry, age, and hormone receptor status. Mutations were classified for functional impact using published data and type of mutation. We assessed differences using Fisher's exact test. RESULTS: From 96 breast cancer studies, we identified 2964 women with somatic TP53 mutations: 715 (24.1%) Asian, 258 (8.7%) AA, 1931 (65.2%) NHW, and 60 (2%) Latina. The distribution of TP53 mutation type was similar by ancestry. However, 35.8% of tumors from NHW individuals had GOF mutations compared to 29% from AA individuals (p = 0.04). Mutations with DNE activity were positively associated with TNBC (OR 1.37, p = 0.03) and estrogen receptor (ER) negative status (OR 1.38; p = 0.005). CONCLUSIONS: Somatic TP53 mutation types did not differ by ancestry overall, but GOF mutations were more common in NHW women than AA women. ER-negative and TNBC tumors are less likely to have DNE+ TP53 mutations which could reflect biological processes. Larger cohorts and functional studies are needed to further elucidate these findings.
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