To identify predictive biomarkers for the efficacy of cetuximab.
247 patients were recruited, including ITT patients of previous RCT (NCT01564810) (primary group) and a following cohort with same inclusion criteria of previous RCT (validation group). The DNA samples were sequenced for single nucleotide polymorphisms (SNPs) of biomarkers including the well-known "new" RAS (KRAS exons 3 and 4; NRAS exons 2, 3 and 4) and other 108 genes, using Ion Torrent Personal Genome Machine (PGM) sequencing. A 5% cutoff value was used to determine mutations.
In primary group, interaction tests between biomarker status and treatment effect were performed for objective response rate, progression-free survival and overall survival. Nine potential predictive biomarkers were identified, including FGFR3, GNAS, FLT3, NOTCH1, RBMXL3, ERBB2, MDN1, PTCH1 and LY6G6D. With RAS wild-type patients in primary group, we built a predictive model for the objective response to cetuximab plus chemotherapy with Logistic regression, employing 9 identified biomarkers (wild-type vs. mutant) and treatment (cetuximab plus chemotherapy vs. chemotherapy) as variables. Our predictive model classified patients in primary group very well (AUC = 0.81, 95%CI 0.72-0.90, P
Apart from RAS mutations, several new biomarkers were firstly correlated with efficacy of cetuximab. and our predictive model including these biomarkers were useful to further tailor the administration of cetuximab, but need further validation.
Clinical trial identification
Legal entity responsible for the study
Zhongshan Hospital, Fudan University
All authors have declared no conflicts of interest.