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Correlating transcriptional networks with pathological complete response following neoadjuvant chemotherapy for breast cancer

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Abstract

We aimed to investigate the association between gene co-expression modules and responses to neoadjuvant chemotherapy in breast cancer by using a systematic biological approach. The gene expression profiles and clinico-pathological data of 508 (discovery set) and 740 (validation set) patients with breast cancer who received neoadjuvant chemotherapy were analyzed. Weighted gene co-expression network analysis was performed and identified seven co-regulated gene modules. Each module and gene signature were evaluated with logistic regression models for pathological complete response (pCR). The association between modules and pCR in each intrinsic molecular subtype was also investigated. Two transcriptional modules were correlated with tumor grade, estrogen receptor status, progesterone receptor status, and chemotherapy response in breast cancer. One module that constitutes upregulated cell proliferation genes was associated with a high probability for pCR in the whole (odds ratio (OR) = 5.20 and 3.45 in the discovery and validation datasets, respectively), luminal B, and basal-like subtypes. The prognostic potentials of novel genes, such as MELK, and pCR-related genes, such as ESR1 and TOP2A, were identified. The upregulation of another gene co-expression module was associated with weak chemotherapy responses (OR = 0.19 and 0.33 in the discovery and validation datasets, respectively). The novel gene CA12 was identified as a potential prognostic indicator in this module. A systems biology network-based approach may facilitate the discovery of biomarkers for predicting chemotherapy responses in breast cancer and contribute in developing personalized medicines.

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Abbreviations

WGCNA:

Weighted gene co-expression network analysis

TOM:

Topological overlap measure

k.total:

Network connectivity

k.in:

Intramodular connectivity

ME:

Module eigengene

ER:

Estrogen receptor

PR:

Progesterone receptor

HER2:

Human epidermal growth factor 2

PCC:

Pearson’s correlation coefficient

CI:

Confidence interval

GS:

Gene significance

AUC:

Area under the receiver operating characteristic curve

OR:

Odds ratio

pCR:

Pathological complete response

RD:

Residual disease

GGI:

Gene expression grade index

GEO:

Gene expression omnibus

LN:

Lymph node

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Conflict of interest

None of the authors has any conflict of interest regarding this study.

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Authors and Affiliations

  1. Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, 410008, People’s Republic of China

    Rong Liu, Qiao-Li Lv, Jing Yu, Lei Hu, Li-Hua Zhang, Yu Cheng & Hong-Hao Zhou

  2. Hunan Key Laboratory of Pharmacogenetics, Institute of Clinical Pharmacology, Central South University, Changsha, 410078, People’s Republic of China

    Rong Liu, Qiao-Li Lv, Jing Yu, Lei Hu, Li-Hua Zhang, Yu Cheng & Hong-Hao Zhou

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  1. Rong Liu
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  2. Qiao-Li Lv
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  3. Jing Yu
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  4. Lei Hu
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  5. Li-Hua Zhang
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  7. Hong-Hao Zhou
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Correspondence to Hong-Hao Zhou.

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Liu, R., Lv, QL., Yu, J. et al. Correlating transcriptional networks with pathological complete response following neoadjuvant chemotherapy for breast cancer. Breast Cancer Res Treat 151, 607–618 (2015). https://doi.org/10.1007/s10549-015-3428-x

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  • DOI: https://doi.org/10.1007/s10549-015-3428-x

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