A panel of tumor biomarkers to predict complete pathological response to neo-adjuvant treatment in Locally Advanced Rectal Cancer

Pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer patients is related to a favorable prognosis. The identification of early biomarkers predictive of pathological complete response would help to optimize the multimodality management of the patients. A panel of 11 tumor-related proteins was investigated by immunohistochemistry in the pre-treatment biopsy of a group of locally advanced rectal cancer patients, to identify early biomarkers of pathological complete response to neoadjuvant chemoradiotherapy. A mono-institutional retrospective cohort of 95 stage II/III locally advanced rectal cancer patients treated with neoadjuvant chemoradiotherapy and surgery was selected based on clinical-pathological characteristics and the availability of a pre-treatment tumor biopsy. Eleven selected protein markers expression (MLH1, GLUT1, Ki67, CA-IX, CXCR4, COX2, CXCL12, HIF1, VEGF, CD44, and RAD51) was investigated. The optimal cut-off values were calculated by receiver operating characteristic curve analysis. Classification and regression tree analysis was performed to investigate the biomarkers interaction. Patients presenting either Ki67, HIF1 or RAD51 below the cut-off value, or CXCR4 or COX2 above the cut-off value, were more likely to get a pathological complete response. Classification and regression tree analysis identified three groups of patients resulting from the combination of Ki67 and CXCR4 expression. Patients with high expression of Ki67 had the lowest chance to get a pathological complete response (18%), as compared to patients with low expression of both Ki67 and CXCR4 (29%), and patients with low Ki67 and high CXCR4 expression (70%). Pre-treatment Ki67, CXCR4, COX2, HIF1, RAD51 in tumor biopsies are associated with pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer. A combined evaluation of Ki67 and CXCR4 would increase their predictive potential. If validated, their optimal cut-off could be used to select patients for a tailored multi-modality treatment.