HER2-HER3 Heterodimer Quantification by FRET-FLIM and Patient Subclass Analysis of the COIN Colorectal Trial.

Background: The phase III MRC COIN trial showed no statistically significant benefit from adding the EGFR-target cetuximab to oxaliplatin-based chemotherapy in first-line treatment of advanced colorectal cancer. This study exploits additional information on HER2-HER3 dimerization to achieve patient stratification and reveal previously hidden subgroups of patients who had differing disease progression and treatment response.
Methods: HER2-HER3 dimerization was quantified by fluorescence lifetime imaging microscopy in primary tumor samples from 550 COIN trial patients receiving oxaliplatin and fluoropyrimidine chemotherapy with or without cetuximab. Bayesian latent class analysis and covariate reduction was performed to analyze the effects of HER2-HER3 dimer, RAS mutation, and cetuximab on progression-free survival and overall survival (OS). All statistical tests were two-sided.
Results: Latent class analysis on a cohort of 398 patients revealed two patient subclasses with differing prognoses (median OS = 1624 days [95% confidence interval [CI] = 1466 to 1816 days] vs 461 days [95% CI = 431 to 504 days]): Class 1 (15.6%) showed a benefit from cetuximab in OS (hazard ratio = 0.43, 95% CI = 0.25 to 0.76, P = .004). Class 2 showed an association of increased HER2-HER3 with better OS (hazard ratio = 0.64, 95% CI = 0.44 to 0.94, P = .02). A class prediction signature was formed and tested on an independent validation cohort (n = 152) validating the prognostic utility of the dimer assay. Similar subclasses were also discovered in full trial dataset (n = 1630) based on 10 baseline clinicopathological and genetic covariates.
Conclusions: Our work suggests that the combined use of HER dimer imaging and conventional mutation analyses will be able to identify a small subclass of patients (>10%) who will have better prognosis following chemotherapy. A larger prospective cohort will be required to confirm its utility in predicting the outcome of anti-EGFR treatment.
(© The Author(s) 2019. Published by Oxford University Press.)