Cargando…
Identifying Circulating Tumor DNA Mutation Profiles in Metastatic Breast Cancer Patients with Multiline Resistance
PURPOSE: In cancer patients, tumor gene mutations contribute to drug resistance and treatment failure. In patients with metastatic breast cancer (MBC), these mutations increase after multiline treatment, thereby decreasing treatment efficiency. The aim of this study was to evaluate gene mutation pat...
Autores principales: | , , , , , , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6020712/ https://www.ncbi.nlm.nih.gov/pubmed/29807833 http://dx.doi.org/10.1016/j.ebiom.2018.05.015 |
_version_ | 1783335351713005568 |
---|---|
author | Hu, Zhe-Yu Xie, Ning Tian, Can Yang, Xiaohong Liu, Liping Li, Jing Xiao, Huawu Wu, Hui Lu, Jun Gao, Jianxiang Hu, Xuming Cao, Min Shui, Zhengrong Xiao, Mengjia Tang, Yu He, Qiongzhi Chang, Lianpeng Xia, Xuefeng Yi, Xin Liao, Qianjin Ouyang, Quchang |
author_facet | Hu, Zhe-Yu Xie, Ning Tian, Can Yang, Xiaohong Liu, Liping Li, Jing Xiao, Huawu Wu, Hui Lu, Jun Gao, Jianxiang Hu, Xuming Cao, Min Shui, Zhengrong Xiao, Mengjia Tang, Yu He, Qiongzhi Chang, Lianpeng Xia, Xuefeng Yi, Xin Liao, Qianjin Ouyang, Quchang |
author_sort | Hu, Zhe-Yu |
collection | PubMed |
description | PURPOSE: In cancer patients, tumor gene mutations contribute to drug resistance and treatment failure. In patients with metastatic breast cancer (MBC), these mutations increase after multiline treatment, thereby decreasing treatment efficiency. The aim of this study was to evaluate gene mutation patterns in MBC patients to predict drug resistance and disease progression. METHOD: A total of 68 MBC patients who had received multiline treatment were recruited. Circulating tumor DNA (ctDNA) mutations were evaluated and compared among hormone receptor (HR)/human epidermal growth factor receptor 2 (HER2) subgroups. RESULTS: The baseline gene mutation pattern (at the time of recruitment) varied among HR/HER2 subtypes. BRCA1 and MED12 were frequently mutated in triple negative breast cancer (TNBC) patients, PIK3CA and FAT1 mutations were frequent in HR+ patients, and PIK3CA and ERBB2 mutations were frequent in HER2+ patients. Gene mutation patterns also varied in patients who progressed within either 3 months or 3–6 months of chemotherapy treatment. For example, in HR+ patients who progressed within 3 months of treatment, the frequency of TERT mutations significantly increased. Other related mutations included FAT1 and NOTCH4. In HR+ patients who progressed within 3–6 months, PIK3CA, TP53, MLL3, ERBB2, NOTCH2, and ERS1 were the candidate mutations. This suggests that different mechanisms underlie disease progression at different times after treatment initiation. In the COX model, the ctDNA TP53 + PIK3CA gene mutation pattern successfully predicted progression within 6 months. CONCLUSION: ctDNA gene mutation profiles differed among HR/HER2 subtypes of MBC patients. By identifying mutations associated with treatment resistance, we hope to improve therapy selection for MBC patients who received multiline treatment. |
format | Online Article Text |
id | pubmed-6020712 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-60207122018-06-28 Identifying Circulating Tumor DNA Mutation Profiles in Metastatic Breast Cancer Patients with Multiline Resistance Hu, Zhe-Yu Xie, Ning Tian, Can Yang, Xiaohong Liu, Liping Li, Jing Xiao, Huawu Wu, Hui Lu, Jun Gao, Jianxiang Hu, Xuming Cao, Min Shui, Zhengrong Xiao, Mengjia Tang, Yu He, Qiongzhi Chang, Lianpeng Xia, Xuefeng Yi, Xin Liao, Qianjin Ouyang, Quchang EBioMedicine Research Paper PURPOSE: In cancer patients, tumor gene mutations contribute to drug resistance and treatment failure. In patients with metastatic breast cancer (MBC), these mutations increase after multiline treatment, thereby decreasing treatment efficiency. The aim of this study was to evaluate gene mutation patterns in MBC patients to predict drug resistance and disease progression. METHOD: A total of 68 MBC patients who had received multiline treatment were recruited. Circulating tumor DNA (ctDNA) mutations were evaluated and compared among hormone receptor (HR)/human epidermal growth factor receptor 2 (HER2) subgroups. RESULTS: The baseline gene mutation pattern (at the time of recruitment) varied among HR/HER2 subtypes. BRCA1 and MED12 were frequently mutated in triple negative breast cancer (TNBC) patients, PIK3CA and FAT1 mutations were frequent in HR+ patients, and PIK3CA and ERBB2 mutations were frequent in HER2+ patients. Gene mutation patterns also varied in patients who progressed within either 3 months or 3–6 months of chemotherapy treatment. For example, in HR+ patients who progressed within 3 months of treatment, the frequency of TERT mutations significantly increased. Other related mutations included FAT1 and NOTCH4. In HR+ patients who progressed within 3–6 months, PIK3CA, TP53, MLL3, ERBB2, NOTCH2, and ERS1 were the candidate mutations. This suggests that different mechanisms underlie disease progression at different times after treatment initiation. In the COX model, the ctDNA TP53 + PIK3CA gene mutation pattern successfully predicted progression within 6 months. CONCLUSION: ctDNA gene mutation profiles differed among HR/HER2 subtypes of MBC patients. By identifying mutations associated with treatment resistance, we hope to improve therapy selection for MBC patients who received multiline treatment. Elsevier 2018-05-26 /pmc/articles/PMC6020712/ /pubmed/29807833 http://dx.doi.org/10.1016/j.ebiom.2018.05.015 Text en © 2018 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Paper Hu, Zhe-Yu Xie, Ning Tian, Can Yang, Xiaohong Liu, Liping Li, Jing Xiao, Huawu Wu, Hui Lu, Jun Gao, Jianxiang Hu, Xuming Cao, Min Shui, Zhengrong Xiao, Mengjia Tang, Yu He, Qiongzhi Chang, Lianpeng Xia, Xuefeng Yi, Xin Liao, Qianjin Ouyang, Quchang Identifying Circulating Tumor DNA Mutation Profiles in Metastatic Breast Cancer Patients with Multiline Resistance |
title | Identifying Circulating Tumor DNA Mutation Profiles in Metastatic Breast Cancer Patients with Multiline Resistance |
title_full | Identifying Circulating Tumor DNA Mutation Profiles in Metastatic Breast Cancer Patients with Multiline Resistance |
title_fullStr | Identifying Circulating Tumor DNA Mutation Profiles in Metastatic Breast Cancer Patients with Multiline Resistance |
title_full_unstemmed | Identifying Circulating Tumor DNA Mutation Profiles in Metastatic Breast Cancer Patients with Multiline Resistance |
title_short | Identifying Circulating Tumor DNA Mutation Profiles in Metastatic Breast Cancer Patients with Multiline Resistance |
title_sort | identifying circulating tumor dna mutation profiles in metastatic breast cancer patients with multiline resistance |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6020712/ https://www.ncbi.nlm.nih.gov/pubmed/29807833 http://dx.doi.org/10.1016/j.ebiom.2018.05.015 |
work_keys_str_mv | AT huzheyu identifyingcirculatingtumordnamutationprofilesinmetastaticbreastcancerpatientswithmultilineresistance AT xiening identifyingcirculatingtumordnamutationprofilesinmetastaticbreastcancerpatientswithmultilineresistance AT tiancan identifyingcirculatingtumordnamutationprofilesinmetastaticbreastcancerpatientswithmultilineresistance AT yangxiaohong identifyingcirculatingtumordnamutationprofilesinmetastaticbreastcancerpatientswithmultilineresistance AT liuliping identifyingcirculatingtumordnamutationprofilesinmetastaticbreastcancerpatientswithmultilineresistance AT lijing identifyingcirculatingtumordnamutationprofilesinmetastaticbreastcancerpatientswithmultilineresistance AT xiaohuawu identifyingcirculatingtumordnamutationprofilesinmetastaticbreastcancerpatientswithmultilineresistance AT wuhui identifyingcirculatingtumordnamutationprofilesinmetastaticbreastcancerpatientswithmultilineresistance AT lujun identifyingcirculatingtumordnamutationprofilesinmetastaticbreastcancerpatientswithmultilineresistance AT gaojianxiang identifyingcirculatingtumordnamutationprofilesinmetastaticbreastcancerpatientswithmultilineresistance AT huxuming identifyingcirculatingtumordnamutationprofilesinmetastaticbreastcancerpatientswithmultilineresistance AT caomin identifyingcirculatingtumordnamutationprofilesinmetastaticbreastcancerpatientswithmultilineresistance AT shuizhengrong identifyingcirculatingtumordnamutationprofilesinmetastaticbreastcancerpatientswithmultilineresistance AT xiaomengjia identifyingcirculatingtumordnamutationprofilesinmetastaticbreastcancerpatientswithmultilineresistance AT tangyu identifyingcirculatingtumordnamutationprofilesinmetastaticbreastcancerpatientswithmultilineresistance AT heqiongzhi identifyingcirculatingtumordnamutationprofilesinmetastaticbreastcancerpatientswithmultilineresistance AT changlianpeng identifyingcirculatingtumordnamutationprofilesinmetastaticbreastcancerpatientswithmultilineresistance AT xiaxuefeng identifyingcirculatingtumordnamutationprofilesinmetastaticbreastcancerpatientswithmultilineresistance AT yixin identifyingcirculatingtumordnamutationprofilesinmetastaticbreastcancerpatientswithmultilineresistance AT liaoqianjin identifyingcirculatingtumordnamutationprofilesinmetastaticbreastcancerpatientswithmultilineresistance AT ouyangquchang identifyingcirculatingtumordnamutationprofilesinmetastaticbreastcancerpatientswithmultilineresistance |