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Construction of novel immune-related signature for prediction of pathological complete response to neoadjuvant chemotherapy in human breast cancer.

BACKGROUND
The aim of this study was to construct a novel prediction model for the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) using immune-related gene expression data.


PATIENTS AND METHODS
DNA microarray data were used to perform a gene expression analysis of tumor samples obtained before NAC from 117 primary breast cancer patients. The samples were randomly divided into the training (n = 58) and the internal validation (n = 59) sets that were used to construct the prediction model for pCR. The model was further validated using an external validation set consisting of 901 patients treated with NAC from six public datasets.


RESULTS
The training set was used to construct an immune-related 23-gene signature for NAC (IRSN-23) that is capable of classifying the patients as either genomically predicted responders (Gp-R) or non-responders (Gp-NR). IRSN-23 was first validated using an internal validation set, and the results showed that the pCR rate for Gp-R was significantly higher than that obtained for Gp-NR (38 versus 0%, P = 1.04E-04). The model was then tested using an external validation set, and this analysis showed that the pCR rate for Gp-R was also significantly higher (40 versus 11%, P = 4.98E-23). IRSN-23 predicted pCR regardless of the intrinsic subtypes (PAM50) and chemotherapeutic regimens, and a multivariate analysis showed that IRSN-23 was the most important predictor of pCR (odds ratio = 4.6; 95% confidence interval = 2.7-7.7; P = 8.25E-09).


CONCLUSION
The novel prediction model (IRSN-23) constructed with immune-related genes can predict pCR independently of the intrinsic subtypes and chemotherapeutic regimens.