The value of adjuvant radiotherapy on survival and recurrence in triple-negative breast cancer: a systematic review and meta-analysis of 5,507 patients

BACKGROUND: The value of adjuvant radiotherapy in triple negative breast cancer (TNBC) remains unclear. A systematic review and meta-analysis was conducted in TNBC patients to assess survival and recurrence outcomes associated with radiotherapy following either breast conserving therapy (BCT) or post-mastectomy radiotherapy (PMRT). METHODS: Four electronic databases were searched from January 2000 to November 2015 (PubMed, MEDLINE, EMBASE and Web of Science). Studies investigating overall survival and/or recurrence in TNBC patients according to radiotherapy administration were included. A random effects meta-analysis was conducted using mastectomy only patients as the reference.  RESULTS: Twelve studies were included. The pooled hazard ratio (HR) for locoregional recurrence comparing BCT and PMRT to mastectomy only was 0.61 (95% confidence interval [CI] 0.41-0.90) and 0.62 (95% CI 0.44-0.86), respectively. Adjuvant radiotherapy was not significantly associated with distant recurrence. The pooled HR for overall survival comparing BCT and PMRT to mastectomy only was 0.57 (95% CI 0.36-0.88) and HR 1.12 (95% CI 0.75, 1.69). Comparing PMRT to mastectomy only, tests for interaction were not significant for stage (p=0.98) or age at diagnosis (p=0.85). However, overall survival was improved in patients with late-stage disease (T3-4, N2-3) pooled HR 0.53 (95% CI 0.32-0.86), and women <40 years, pooled HR 0.30 (95% CI 0.11-0.82). CONCLUSIONS: Adjuvant radiotherapy was associated with a significantly lower risk of locoregional recurrence in TNBC patients, irrespective of the type of surgery. While radiotherapy was not consistently associated with an overall survival gain, benefits may be obtained in women with late-stage disease and younger patients. 

Network signatures based on gene pair expression ratios improve classification and the analysis of muscle-invasive urothelial cancer

Urothelial cancer (UC) is highly recurrent and can progress from non-invasive (NMIUC) to a more aggressive muscle-invasive (MIUC) subtype that invades the muscle tissue layer of the bladder. We present a proof of principle study that network-based features of gene pairs can be used to improve classifier performance and the functional analysis of urothelial cancer gene expression data. In the first step of our procedure each individual sample of a UC gene expression dataset is inflated by gene pair expression ratios that are defined based on a given network structure. In the second step an elastic net feature selection procedure for network-based signatures is applied to discriminate between NMIUC and MIUC samples. We performed a repeated random subsampling cross validation in three independent datasets. The network signatures were characterized by a functional enrichment analysis and studied for the enrichment of known cancer genes. We observed that the network-based gene signatures from meta collections of proteinprotein interaction (PPI) databases such as CPDB and the PPI databases HPRD and BioGrid improved the classification performance compared to single gene based signatures. The network based signatures that were derived from PPI databases showed a prominent enrichment of cancer genes (e.g., TP53, TRIM27 and HNRNPA2Bl). We provide a novel integrative approach for large-scale gene expression analysis for the identification and development of novel diagnostical targets in bladder cancer. Further, our method allowed to link cancer gene associations to network-based expression signatures that are not observed in gene-based expression signatures.