A systematic review and meta-analysis of somatic and germline DNA sequence biomarkers of esophageal cancer survival, therapy response and stage

作者：Findlay J M^*; Middleton M R; Tomlinson I

来源：Annals of Oncology, 2015, 26(4): 624-644.

DOI：10.1093/annonc/mdu449

• biomarkers
• genomic
• cancer
• esophageal
• prognosis
• cancer staging

Introduction: There is an urgent need for biomarkers to help predict prognosis and guide management of esophageal cancer. This review identifies, evaluates and meta-analyses the evidence for reported somatic and germline DNA sequence biomarkers of outcome and stage.
Methods: A systematic review was carried out of the PubMed, EMBASE and Cochrane databases (20 August 2014), in conjunction with the ASCO Level of Evidence scale for biomarker research. Meta-analyses were carried out for all reported markers associated with outcome measures by more than one study.
Results: Four thousand and four articles were identified, 762 retrieved and 182 studies included. There were 65 reported markers of survival or recurrence 12 (18.5%) were excluded due to multiple comparisons. Following meta-analysis, significant associations were seen for six tumor variants (mutant TP53 and PIK3CA, copy number gain of ERBB2/HER2, CCND1 and FGF3, and chromosomal instability/ploidy) and seven germline polymorphisms: ERCC1 rs3212986, ERCC2 rs1799793, TP53 rs1042522, MDM2 rs2279744, TYMS rs34743033, ABCB1 rs1045642 and MTHFR rs1801133. Twelve germline markers of treatment complications were reported; 10 were excluded. Two tumor and 15 germline markers (11 excluded) of chemo (radio) therapy response were reported. Following meta-analysis, associations were demonstrated for mutant TP53, ERCC1 rs11615 and XRCC1 rs25487. There were 41 tumor/germline reported markers of stage; 27 (65.9%) were excluded.
Conclusions: Numerous DNA markers of outcome and stage have been reported, yet few are backed by high-quality evidence. Despite this, a small number of variants appear reliable. These merit evaluation in prospective trials, within the context of high-throughput sequencing and gene expression.

• 出版日期2015-4