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Radiation induced gene signature predicts pathologic complete response to neoadjuvant chemotherapy in breast cancer patients

ABSTRACT: Purpose:The identification of biomarkers predictive of neoadjuvant chemotherapy response in breast cancer patients would be an important advancement in personalized cancer therapy. We hypothesized that due to similarities between radiation and chemotherapy induced cellular response mechanisms, radiation responsive genes may be useful in predicting response to neoadjuvant chemotherapy. Materials and Methods: Murine p53 null breast cancer cell lines representative of the luminal, basal-like and claudin-low human breast cancer subtypes were irradiated to identify radiation responsive genes. These murine radiation induced genes were then converted to their human orthologs. These genes were then used to develop a predictor of pathologic complete response (pCR) that was validated on two independent published neoadjuvant chemotherapy data sets of genomic data with response. Results: A radiation induced gene signature consisting of 30 genes was identified on a training set of 337 human primary breast cancer tumor samples that was prognostic for survival. Mean expression of this signature was calculated for individual samples in two independent published datasets and was found to be significantly predictive of pathologic complete response. Multivariate logistic regression analysis in both independent datasets showed that this 30 gene signature added significant predictive information independent of that provided by standard clinical predictors and other gene expression based predictors of pathologic complete response. Conclusion: This study provides new biologic information regarding response to neoadjuvant chemotherapy and a means of possibly improving the prediction of pathologic complete response.

ORGANISM(S): Mus musculus

PROVIDER: GSE48073 | GEO | 2015/01/21

SECONDARY ACCESSION(S): PRJNA208896

REPOSITORIES: GEO

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Project description:A gene expression signature characterizes expression data from breast cancer samples of patients with pathological complete response (pCR) or residual disease (RD) following the neoadjuvant trial. Several gene expression profiles have been reported to predict breast cancer response to neoadjuvant chemotherapy. These studies often consider breast cancer as a homogeneous entity, although higher rates of pathologic complete response (pCR) are known to occur within the basal-like subclass. We postulated that profiles with higher predictive accuracy could be derived from a subset analysis of basal-like tumors in isolation. Using a previously described ‘‘intrinsic’’ signature to differentiate breast tumor subclasses, we identified 50 basal-like tumors from two independent clinical trials associated with gene expression profile data. 24 tumor data sets (included in this GEO submission) were derived from a 119-patient neoadjuvant trial at our institution and an additional 26 tumor data sets were identified from a published data set (Hess et al. J Clin Oncol 24:4236–4244, 2006). The combined 50 basal-like tumors were partitioned to form a 37 sample training set with 13 sequestered for validation. Clinical surveillance occurred for a mean of 26 months. We identified a 23-gene profile which predicted pCR in basal-like breast cancers with 92% predictive accuracy in the sequestered validation data set. Furthermore, distinct cluster of patients with high rates of cancer recurrence was observed based on cluster analysis with the 23-gene signature. Disease-free survival analysis of these three clusters revealed significantly reduced survival in the patients of this high recurrence cluster. We identified a 23- gene signature which predicts response of basal-like breast cancer to neoadjuvant chemotherapy as well as disease-free survival. This signature is independent of tissue collection method and chemotherapeutic regimen. Keywords: Disease state analysis

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Radiation induced gene signature predicts pathologic complete response to neoadjuvant chemotherapy in breast cancer patients

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