A Qualitative Transcriptional Signature for Predicting Extreme Resistance of ER-Negative Breast Cancer to Paclitaxel, Doxorubicin, and Cyclophosphamide Neoadjuvant Chemotherapy

For estrogen receptor (ER)-negative breast cancer patients, paclitaxel (P), doxorubicin (A) and cyclophosphamide (C) neoadjuvant chemotherapy (NAC) is the standard therapeutic regimen. Pathologic complete response (pCR) and residual disease (RD) are common surrogate measures of chemosensitivity. Aft...

Full description

Bibliographic Details
Main Authors: Yanhua Chen, Hao Cai, Wannan Chen, Qingzhou Guan, Jun He, Zheng Guo, Jing Li
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-03-01
Series:Frontiers in Molecular Biosciences
Subjects:
Online Access:https://www.frontiersin.org/article/10.3389/fmolb.2020.00034/full
id doaj-ecd7c950521d411085b53a6181cb1c9b
record_format Article
collection DOAJ
language English
format Article
sources DOAJ
author Yanhua Chen
Yanhua Chen
Hao Cai
Wannan Chen
Wannan Chen
Qingzhou Guan
Qingzhou Guan
Qingzhou Guan
Jun He
Jun He
Zheng Guo
Zheng Guo
Jing Li
Jing Li
spellingShingle Yanhua Chen
Yanhua Chen
Hao Cai
Wannan Chen
Wannan Chen
Qingzhou Guan
Qingzhou Guan
Qingzhou Guan
Jun He
Jun He
Zheng Guo
Zheng Guo
Jing Li
Jing Li
A Qualitative Transcriptional Signature for Predicting Extreme Resistance of ER-Negative Breast Cancer to Paclitaxel, Doxorubicin, and Cyclophosphamide Neoadjuvant Chemotherapy
Frontiers in Molecular Biosciences
breast cancer
neoadjuvant chemotherapy
pathological complete response
extreme resistance
relative expression ordering
author_facet Yanhua Chen
Yanhua Chen
Hao Cai
Wannan Chen
Wannan Chen
Qingzhou Guan
Qingzhou Guan
Qingzhou Guan
Jun He
Jun He
Zheng Guo
Zheng Guo
Jing Li
Jing Li
author_sort Yanhua Chen
title A Qualitative Transcriptional Signature for Predicting Extreme Resistance of ER-Negative Breast Cancer to Paclitaxel, Doxorubicin, and Cyclophosphamide Neoadjuvant Chemotherapy
title_short A Qualitative Transcriptional Signature for Predicting Extreme Resistance of ER-Negative Breast Cancer to Paclitaxel, Doxorubicin, and Cyclophosphamide Neoadjuvant Chemotherapy
title_full A Qualitative Transcriptional Signature for Predicting Extreme Resistance of ER-Negative Breast Cancer to Paclitaxel, Doxorubicin, and Cyclophosphamide Neoadjuvant Chemotherapy
title_fullStr A Qualitative Transcriptional Signature for Predicting Extreme Resistance of ER-Negative Breast Cancer to Paclitaxel, Doxorubicin, and Cyclophosphamide Neoadjuvant Chemotherapy
title_full_unstemmed A Qualitative Transcriptional Signature for Predicting Extreme Resistance of ER-Negative Breast Cancer to Paclitaxel, Doxorubicin, and Cyclophosphamide Neoadjuvant Chemotherapy
title_sort qualitative transcriptional signature for predicting extreme resistance of er-negative breast cancer to paclitaxel, doxorubicin, and cyclophosphamide neoadjuvant chemotherapy
publisher Frontiers Media S.A.
series Frontiers in Molecular Biosciences
issn 2296-889X
publishDate 2020-03-01
description For estrogen receptor (ER)-negative breast cancer patients, paclitaxel (P), doxorubicin (A) and cyclophosphamide (C) neoadjuvant chemotherapy (NAC) is the standard therapeutic regimen. Pathologic complete response (pCR) and residual disease (RD) are common surrogate measures of chemosensitivity. After NAC, most patients still have RD; of these, some partially respond to NAC, whereas others show extreme resistance and cannot benefit from NAC but only suffer complications resulting from drug toxicity. Here we developed a qualitative transcriptional signature, based on the within-sample relative expression ordering (REO) of gene pairs, to identify extremely resistant samples to PAC NAC. Using gene expression data for ER-negative breast cancer patients including 113 pCR samples and 137 RD samples from four datasets, we selected 61 gene pairs with reversal REO patterns between the two groups as the resistance signature, denoted as NR61. Samples with more than 37 signature gene pairs that had the same REO patterns within the extremely resistant group were defined as having extreme resistance; otherwise, they were considered responders. In the GSE25055 and GSE25065 dataset, the NR61 signature could correctly identify 44 (97.8%) of the 45 pCR samples and 22 (95.7%) of the 23 pCR samples as responder samples, respectively; it also identified 13 (16.9%) of 77 RD samples and 8 (21.1%) of 38 RD samples as extremely resistant samples, respectively. Survival analysis showed that the distant relapse-free survival (DRFS) time of the 14 extremely resistant cases was significantly shorter than that of the 108 responders (P < 0.01; HR = 3.84; 95% CI = 1.91–7.70) in GSE25055. Similar results were obtained in GSE25065. Moreover, in the integrated data of the two datasets with 94 responders and 21 extremely resistant samples identified from RD patients, the former had significantly longer DRFS than the latter (P < 0.01; HR = 2.22; 95% CI = 1.26–3.90). In summary, our signature could effectively identify patients who completely respond to PAC NAC, as well as cases of extreme resistance, which can assist decision-making on the clinical therapy for these patients.
topic breast cancer
neoadjuvant chemotherapy
pathological complete response
extreme resistance
relative expression ordering
url https://www.frontiersin.org/article/10.3389/fmolb.2020.00034/full
work_keys_str_mv AT yanhuachen aqualitativetranscriptionalsignatureforpredictingextremeresistanceofernegativebreastcancertopaclitaxeldoxorubicinandcyclophosphamideneoadjuvantchemotherapy
AT yanhuachen aqualitativetranscriptionalsignatureforpredictingextremeresistanceofernegativebreastcancertopaclitaxeldoxorubicinandcyclophosphamideneoadjuvantchemotherapy
AT haocai aqualitativetranscriptionalsignatureforpredictingextremeresistanceofernegativebreastcancertopaclitaxeldoxorubicinandcyclophosphamideneoadjuvantchemotherapy
AT wannanchen aqualitativetranscriptionalsignatureforpredictingextremeresistanceofernegativebreastcancertopaclitaxeldoxorubicinandcyclophosphamideneoadjuvantchemotherapy
AT wannanchen aqualitativetranscriptionalsignatureforpredictingextremeresistanceofernegativebreastcancertopaclitaxeldoxorubicinandcyclophosphamideneoadjuvantchemotherapy
AT qingzhouguan aqualitativetranscriptionalsignatureforpredictingextremeresistanceofernegativebreastcancertopaclitaxeldoxorubicinandcyclophosphamideneoadjuvantchemotherapy
AT qingzhouguan aqualitativetranscriptionalsignatureforpredictingextremeresistanceofernegativebreastcancertopaclitaxeldoxorubicinandcyclophosphamideneoadjuvantchemotherapy
AT qingzhouguan aqualitativetranscriptionalsignatureforpredictingextremeresistanceofernegativebreastcancertopaclitaxeldoxorubicinandcyclophosphamideneoadjuvantchemotherapy
AT junhe aqualitativetranscriptionalsignatureforpredictingextremeresistanceofernegativebreastcancertopaclitaxeldoxorubicinandcyclophosphamideneoadjuvantchemotherapy
AT junhe aqualitativetranscriptionalsignatureforpredictingextremeresistanceofernegativebreastcancertopaclitaxeldoxorubicinandcyclophosphamideneoadjuvantchemotherapy
AT zhengguo aqualitativetranscriptionalsignatureforpredictingextremeresistanceofernegativebreastcancertopaclitaxeldoxorubicinandcyclophosphamideneoadjuvantchemotherapy
AT zhengguo aqualitativetranscriptionalsignatureforpredictingextremeresistanceofernegativebreastcancertopaclitaxeldoxorubicinandcyclophosphamideneoadjuvantchemotherapy
AT jingli aqualitativetranscriptionalsignatureforpredictingextremeresistanceofernegativebreastcancertopaclitaxeldoxorubicinandcyclophosphamideneoadjuvantchemotherapy
AT jingli aqualitativetranscriptionalsignatureforpredictingextremeresistanceofernegativebreastcancertopaclitaxeldoxorubicinandcyclophosphamideneoadjuvantchemotherapy
AT yanhuachen qualitativetranscriptionalsignatureforpredictingextremeresistanceofernegativebreastcancertopaclitaxeldoxorubicinandcyclophosphamideneoadjuvantchemotherapy
AT yanhuachen qualitativetranscriptionalsignatureforpredictingextremeresistanceofernegativebreastcancertopaclitaxeldoxorubicinandcyclophosphamideneoadjuvantchemotherapy
AT haocai qualitativetranscriptionalsignatureforpredictingextremeresistanceofernegativebreastcancertopaclitaxeldoxorubicinandcyclophosphamideneoadjuvantchemotherapy
AT wannanchen qualitativetranscriptionalsignatureforpredictingextremeresistanceofernegativebreastcancertopaclitaxeldoxorubicinandcyclophosphamideneoadjuvantchemotherapy
AT wannanchen qualitativetranscriptionalsignatureforpredictingextremeresistanceofernegativebreastcancertopaclitaxeldoxorubicinandcyclophosphamideneoadjuvantchemotherapy
AT qingzhouguan qualitativetranscriptionalsignatureforpredictingextremeresistanceofernegativebreastcancertopaclitaxeldoxorubicinandcyclophosphamideneoadjuvantchemotherapy
AT qingzhouguan qualitativetranscriptionalsignatureforpredictingextremeresistanceofernegativebreastcancertopaclitaxeldoxorubicinandcyclophosphamideneoadjuvantchemotherapy
AT qingzhouguan qualitativetranscriptionalsignatureforpredictingextremeresistanceofernegativebreastcancertopaclitaxeldoxorubicinandcyclophosphamideneoadjuvantchemotherapy
AT junhe qualitativetranscriptionalsignatureforpredictingextremeresistanceofernegativebreastcancertopaclitaxeldoxorubicinandcyclophosphamideneoadjuvantchemotherapy
AT junhe qualitativetranscriptionalsignatureforpredictingextremeresistanceofernegativebreastcancertopaclitaxeldoxorubicinandcyclophosphamideneoadjuvantchemotherapy
AT zhengguo qualitativetranscriptionalsignatureforpredictingextremeresistanceofernegativebreastcancertopaclitaxeldoxorubicinandcyclophosphamideneoadjuvantchemotherapy
AT zhengguo qualitativetranscriptionalsignatureforpredictingextremeresistanceofernegativebreastcancertopaclitaxeldoxorubicinandcyclophosphamideneoadjuvantchemotherapy
AT jingli qualitativetranscriptionalsignatureforpredictingextremeresistanceofernegativebreastcancertopaclitaxeldoxorubicinandcyclophosphamideneoadjuvantchemotherapy
AT jingli qualitativetranscriptionalsignatureforpredictingextremeresistanceofernegativebreastcancertopaclitaxeldoxorubicinandcyclophosphamideneoadjuvantchemotherapy
_version_ 1724790666601431040
spelling doaj-ecd7c950521d411085b53a6181cb1c9b2020-11-25T02:38:29ZengFrontiers Media S.A.Frontiers in Molecular Biosciences2296-889X2020-03-01710.3389/fmolb.2020.00034509622A Qualitative Transcriptional Signature for Predicting Extreme Resistance of ER-Negative Breast Cancer to Paclitaxel, Doxorubicin, and Cyclophosphamide Neoadjuvant ChemotherapyYanhua Chen0Yanhua Chen1Hao Cai2Wannan Chen3Wannan Chen4Qingzhou Guan5Qingzhou Guan6Qingzhou Guan7Jun He8Jun He9Zheng Guo10Zheng Guo11Jing Li12Jing Li13Fujian Key Laboratory of Medical Bioinformatics, Department of Bioinformatics, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, ChinaKey Laboratory of Gastrointestinal Cancer (Fujian Medical University), Ministry of Education, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, ChinaMedical Big Data and Bioinformatics Research Center, First Affiliated Hospital of Gannan Medical University, Ganzhou, ChinaKey Laboratory of Gastrointestinal Cancer (Fujian Medical University), Ministry of Education, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, ChinaFujian Key Laboratory of Tumor Microbiology, Department of Medical Microbiology, Fujian Medical University, Fuzhou, ChinaHenan Key Laboratory of Chinese Medicine for Respiratory Disease, Henan University of Chinese Medicine, Zhengzhou, ChinaCo-construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan & Education Ministry of P.R. China, Henan University of Chinese Medicine, Zhengzhou, ChinaAcademy of Sciences of Chinese Medicine, Henan University of Chinese Medicine, Zhengzhou, ChinaFujian Key Laboratory of Medical Bioinformatics, Department of Bioinformatics, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, ChinaKey Laboratory of Gastrointestinal Cancer (Fujian Medical University), Ministry of Education, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, ChinaFujian Key Laboratory of Medical Bioinformatics, Department of Bioinformatics, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, ChinaKey Laboratory of Gastrointestinal Cancer (Fujian Medical University), Ministry of Education, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, ChinaFujian Key Laboratory of Medical Bioinformatics, Department of Bioinformatics, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, ChinaKey Laboratory of Gastrointestinal Cancer (Fujian Medical University), Ministry of Education, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, ChinaFor estrogen receptor (ER)-negative breast cancer patients, paclitaxel (P), doxorubicin (A) and cyclophosphamide (C) neoadjuvant chemotherapy (NAC) is the standard therapeutic regimen. Pathologic complete response (pCR) and residual disease (RD) are common surrogate measures of chemosensitivity. After NAC, most patients still have RD; of these, some partially respond to NAC, whereas others show extreme resistance and cannot benefit from NAC but only suffer complications resulting from drug toxicity. Here we developed a qualitative transcriptional signature, based on the within-sample relative expression ordering (REO) of gene pairs, to identify extremely resistant samples to PAC NAC. Using gene expression data for ER-negative breast cancer patients including 113 pCR samples and 137 RD samples from four datasets, we selected 61 gene pairs with reversal REO patterns between the two groups as the resistance signature, denoted as NR61. Samples with more than 37 signature gene pairs that had the same REO patterns within the extremely resistant group were defined as having extreme resistance; otherwise, they were considered responders. In the GSE25055 and GSE25065 dataset, the NR61 signature could correctly identify 44 (97.8%) of the 45 pCR samples and 22 (95.7%) of the 23 pCR samples as responder samples, respectively; it also identified 13 (16.9%) of 77 RD samples and 8 (21.1%) of 38 RD samples as extremely resistant samples, respectively. Survival analysis showed that the distant relapse-free survival (DRFS) time of the 14 extremely resistant cases was significantly shorter than that of the 108 responders (P < 0.01; HR = 3.84; 95% CI = 1.91–7.70) in GSE25055. Similar results were obtained in GSE25065. Moreover, in the integrated data of the two datasets with 94 responders and 21 extremely resistant samples identified from RD patients, the former had significantly longer DRFS than the latter (P < 0.01; HR = 2.22; 95% CI = 1.26–3.90). In summary, our signature could effectively identify patients who completely respond to PAC NAC, as well as cases of extreme resistance, which can assist decision-making on the clinical therapy for these patients.https://www.frontiersin.org/article/10.3389/fmolb.2020.00034/fullbreast cancerneoadjuvant chemotherapypathological complete responseextreme resistancerelative expression ordering