Neadjuvant DCF therapy, metabolic response and molecular biomarkers in esophageal squamous cell carcinoma

Aim/Background

In Japan, neoadjuvant chemotherapy (NAC) followed by radical esophagectomy has been accepted as the standard therapeutic approach to resectable cStage II/III esophageal squamous cell carcinoma (ESCC).There are conflicting data regarding positron emission tomography computed tomography (PET-CT) to predict pathologic response or clinical outcome following NAC in esophageal cancers. The identification of new predictive molecular markers for ESCC could improve the risk-adapted treatment strategies.

Methods

Two courses of docetaxel/cisplatin/5-FU (DCF) have been administered as NAC in 85 patients with node-positive ESCC. The DCF regimen consisted of 60 mg/m² of docetaxel on day 1, and 350 mg/m² of 5-FU and 6 mg/m² of cisplatin on days 1-5. Response was evaluated by RECIST v1.0 and metabolic response in standardized uptake value by FDG PET. Agilent microarray (SurePrint G3 Gene Expression 8x60K) was utilized for identifying predictive biomarkers.

Results

Complete response, partial response, stable disease, and progressive disease were observed in 4, 49, 28, and 4 patients, respectively. Pathologic complete response was achieved in 8 cases (9.4 %), and the overall pathologic response rate was 36 %. SUV reduction rate ranged from -98% to +98% (median 54%) and was significantly associated with clinical response status and pathological response status (P < 0.01). Metabolic non-responders (n = 19) with tumors showing less than 35% decrease experienced lower overall survival compared with metabolic responders (n = 66) with those showing more than 35% decrease (log-rank p = 0.012; univariate HR = 2.58, 95% CI 1.15-5.41). In addition, we investigated tumors of metabolic responders and those of non-responder by microarray and found the promising candidate genes as a predictive biomarker.

Conclusions

PET-based metabolic response assessment to NAC in ESCC patients may correlate with clinical outcome and survival. In addition, we will demonstrate promising roles of candidate genes as a predictive biomarker with data validation.

Clinical trial identification

N/A

Disclosure

All authors have declared no conflicts of interest.