Abstract
BackgroundIndividual variability in prognosis of esophageal cancer highlights the need for advances in personalized therapy. This systematic review aimed at elucidating the prognostic role of gene expression profiles and at identifying gene signatures to predict clinical outcome.MethodsA systematic search of the Medline, Embase and the Cochrane library databases (2000-2015) was performed. Articles associating gene expression profiles in patients with esophageal adenocarcinoma or squamous cell carcinoma to survival, response to chemo(radio)therapy and/or lymph node metastasis were identified. Differentially expressed genes and gene signatures were extracted from each study and combined to construct a list of prognostic genes per outcome and histological tumor type.ResultsThis review includes a total of 22 studies. Gene expression profiles were related to survival in 9 studies, to response to chemo(radio)therapy in 7 studies, and to lymph node metastasis in 9 studies. The studies proposed many differentially expressed genes. However, the findings were heterogeneous and only 12 (ALDH1A3, ATR, BIN1, CSPG2, DOK1, IFIT1, IFIT3, MAL, PCP4, PHB, SPP1) of the 1.112 reported genes were identified in more than 1 study. Overall, 16 studies reported a prognostic gene signature, which was externally validated in 10 studies.ConclusionThis systematic review shows heterogeneous findings in associating gene expression with clinical outcome in esophageal cancer. Larger validated studies employing RNA next-generation sequencing are required to establish gene expression profiles to predict clinical outcome and to select optimal personalized therapy.
Highlights
Multimodality treatment, combining esophagectomy with perioperative chemotherapy or neoadjuvant chemoradiotherapy, has been shown to improve patients’ survival and is the standard treatment for curable esophageal cancer. [5,6,7] due to the aggressive character of the tumor and the lack of effective individualized treatment, the survival remains poor with 5-year survival rates of merely 36-47%. [5,6,7,8] large individual differences in survival, treatment response and metastasis emphasize the need for more personalized therapy
The results demonstrate a large heterogeneity in gene expression profiles and gene signatures predicting survival, response to chemo(radio)therapy, and lymph node metastasis
In consistence with the distinct epidemiology and tumor biology of AC and squamous cell carcinoma (SCC) [4, 11], most studies conducted in East Asia included exclusively SCC and most studies in western countries focused on AC only
Summary
Esophageal cancer is the eight most common cancer worldwide, with 450.000 new cases and 400.000 estimated deaths per year. [1, 2] The two main types of esophageal cancer, squamous cell carcinoma (SCC) and adenocarcinoma (AC), differ in pathogenesis, epidemiology, tumor biology, prognosis and treatment strategies. [3, 4]Multimodality treatment, combining esophagectomy with perioperative chemotherapy or neoadjuvant chemoradiotherapy, has been shown to improve patients’ survival and is the standard treatment for curable esophageal cancer. [5,6,7] due to the aggressive character of the tumor and the lack of effective individualized treatment, the survival remains poor with 5-year survival rates of merely 36-47%. [5,6,7,8] large individual differences in survival, treatment response and metastasis emphasize the need for more personalized therapy. [5,6,7,8] large individual differences in survival, treatment response and metastasis emphasize the need for more personalized therapy. Existing histopathological terms, such as the pathologic TNM classification, are insufficient to accurately predict these individual differences in outcome and to inform personalized treatment. Gene signatures may find clinical application in predicting survival, response to neoadjuvant treatment and metastatic potential. This would enable individualized targeted therapy in order to www.impactjournals.com/oncotarget Studies Tool Study. This systematic review aimed at elucidating the prognostic role of gene expression profiles and at identifying gene signatures to predict clinical outcome
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