Abstract P6-08-28: Prognostic and predictive value of an integrated mRNA-lncRNA signature in triple-negative breast cancer: A comprehensive transcriptome analysis
Authors: Jiang Yizhou , Liu Yi-Rong , Yu Ke-Da , Hu Xin , Xu Xiao-En , Yao Ling , Shao Zhi-Ming 
Published: May 2015

Purpose

Triple-negative breast cancer (TNBC) is a highly diverse group of disease, and clinical outcome of patients with TNBC is highly variable. Due to the heterogeneity of TNBCs, there is limited molecular signature routinely used for predicting the risk of disease recurrence and benefit of adjuvant chemotherapy. Our study aims to develop and validate a RNA signature, integrating messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs) together, for TNBC patients to improve risk stratification and avoid unnecessary adjuvant therapy.

Methods

Using transcriptome microarrays, we analyzed 33 paired TNBC and adjacent normal breast tissues, and identified 1,644 mRNAs and 1,047 lncRNAs which were differentially expressed between tumors and normal tissues. We further determined the expression of these mRNAs and lncRNAs in an additional 134 TNBC samples using transcriptome microarrays, and confirmed their associations with patients' recurrence-free survival (RFS). Using the LASSO Cox regression model, we built an integrated mRNA-lncRNA signature incorporating seven mRNAs and three lncRNAs. Prognostic and predictive accuracy of the signature was tested in the training set of 167 TNBC patients and further validated in an independent validation set of 143 TNBC patients.

Results

In the training set, we identified 36 mRNAs and 32 lncRNAs which were tumor-specific and significantly associated with patients' RFS. Using the LASSO Cox regression model, an integrated mRNA-lncRNA signature based on seven mRNAs (CCR4, CTSB, ERO1L, HIF1A, IGFR1R, SRD5A1 and TFF1) and three lncRNAs (n381928, n333541 and TCONS_I2_00013109-XLOC_I2_007048) was developed in the training set and subsequently validated in the validation set. Patients were classified to the high-risk (high risk of recurrence) and low-risk (low risk of recurrence) groups according to their scores in the signature. In the training set, multivariate analysis showed that the predicted high-risk group had higher risk of developing recurrent disease within five years of surgery than the low-risk patients (hazard ratio [HR] = 6.12, 95% confidence interval [CI] 2.76-13.56, P&lt;0.001). In the validation set, the predicted high-risk group also had poorer RFS in multivariate analysis (HR = 5.09, 95% CI 1.82-10.96, P&lt;0.001). Furthermore, analyzing the areas under the time-dependent receiver operating curve for five-year RFS, we proved the integrated mRNA-lncRNA signature had better prognostic value than the seven-mRNA-only signature and clinicopathological risk factors in both the training and validation sets. Finally, Cox proportional hazards models were utilized to test the interaction between adjuvant chemotherapy and different risk groups. Patients in the low-risk group had a more favorable response to adjuvant chemotherapy both in the training and validation sets.

Conclusion

The integrated mRNA-lncRNA signature is a reliable tool for predicting disease recurrence and benefit of adjuvant chemotherapy in TNBC patients. If further validated in larger population, it could facilitate patient counseling and individualize treatment of TNBC.

Citation Format: Yizhou Jiang, Yi-Rong Liu, Ke-Da Yu, Xin Hu, Xiao-En Xu, Ling Yao, Zhi-Ming Shao. Prognostic and predictive value of an integrated mRNA-lncRNA signature in triple-negative breast cancer: A comprehensive transcriptome analysis [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P6-08-28.