An 11-Gene Signature Based on Treatment Responsiveness Predicts Radiation Therapy Survival Benefit Among Breast Cancer Patients

Abstract

PurposeWe developed a strategy of building prognosis gene signature based on clinical treatment responsiveness to predict radiotherapy survival benefit in breast cancer patients.Methods and MaterialsAnalyzed data came from the public database. PFS was used as an indicator of clinical treatment responsiveness. WGCNA was used to identify the most relevant modules to radiotherapy response. Based on the module genes, Cox regression model was used to build survival prognosis signature to distinguish the benefit group of radiotherapy. An external validation was also performed.ResultsIn the developed dataset, MEbrown module with 534 genes was identified by WGCNA, which was most correlated to the radiotherapy response of patients. A number of 11 hub genes were selected to build the survival prognosis signature. Patients that were divided into radio-sensitivity group and radio-resistant group based on the signature risk score had varied survival benefit. In developed dataset, the 3-, 5-, and 10-year AUC of the signature were 0.814 (CI95%: 0.742–0.905), 0.781 (CI95%: 0.682–0.880), and 0.762 (CI95%: 0.626–0.897), respectively. In validation dataset, the 3- and 5-year AUC of the signature were 0.706 (CI95%: 0.523–0.889) and 0.743 (CI95%: 0.595–0.891). The signature had higher predictive power than clinical factors alone and had more clinical prognosis efficiency. Functional enrichment analysis revealed that the identified genes were mainly enriched in immune-related processes. Further immune estimated analysis showed the difference in distribution of immune micro-environment between radio-sensitivity group and radio-resistant group.ConclusionsThe 11-gene signature may reflect differences in tumor immune micro-environment that underlie the differential response to radiation therapy and could guide clinical-decision making related to radiation in breast cancer patients.

Clinical Biomarkers of Tumour Radiosensitivity and Predicting Benefit from Radiotherapy: A Systematic Review

“…Seven studies used TCGA data only to generate signatures for soft tissue sarcoma (STS) (n = 2) [17,18], gastric cancer (n = 1) [19], HNSCC (n = 2) [20,33], breast cancer (n = 1) [24] and cervix cancer (n = 1) [35]; four of these used a cross-validated adaptive signature design (CVASD) approach to derive and validate the signature within the same dataset, two split the TCGA cohort into training and validation and one used the TCGA to validate a signature derived in a very small independent cohort. The remaining five studies used multiple publicly available microarray/RNA-Seq cohorts to derive signatures for breast cancer (n = 4) [14,25,32,36] and glioma/glioblastoma (n = 1) [39]. None of these have been externally validated.…”