Longitudinal studies of development and disease in the human brain have motivated the acquisition of large neuroimaging data sets and the concomitant development of robust methodological and statistical tools for quantifying neurostructural changes. Longitudinal-specific strategies for acquisition and processing have potentially significant benefits including more consistent estimates of intra-subject measurements while retaining predictive power. Using the first phase of the Alzheimer's Disease Neuroimaging Initiative (ADNI-1) data, comprising over 600 subjects with multiple time points from baseline to 36 months, we evaluate the utility of longitudinal FreeSurfer and Advanced Normalization Tools (ANTs) surrogate thickness values in the context of a linear mixed-effects (LME) modeling strategy. Specifically, we estimate the residual variability and between-subject variability associated with each processing stream as it is known from the statistical literature that minimizing the former while simultaneously maximizing the latter leads to greater scientific interpretability in terms of tighter confidence intervals in calculated mean trends, smaller prediction intervals, and narrower confidence intervals for determining cross-sectional effects. This strategy is evaluated over the entire cortex, as defined by the Desikan-Killiany-Tourville labeling protocol, where comparisons are made with the cross-sectional and longitudinal FreeSurfer processing streams. Subsequent linear mixed effects modeling for identifying diagnostic groupings within the ADNI cohort is provided as supporting evidence for the utility of the proposed ANTs longitudinal framework which provides unbiased structural neuroimage processing and competitive to superior power for longitudinal structural change detection.

FGFR inhibitors are approved for the treatment of advanced cholangiocarcinoma harboring FGFR2 fusions. However, the response rate is moderate, and resistance emerges rapidly due to acquired secondary FGFR2 mutations or due to other less-defined mechanisms. Here, we conducted high-throughput combination drug screens, biochemical analysis, and therapeutic studies using patient-derived models of FGFR2 fusion-positive cholangiocarcinoma to gain insight into these clinical profiles and uncover improved treatment strategies. We found that feedback activation of EGFR signaling limits FGFR inhibitor efficacy, restricting cell death induction in sensitive models and causing resistance in insensitive models lacking secondary FGFR2 mutations. Inhibition of wild-type EGFR potentiated responses to FGFR inhibitors in both contexts, durably suppressing MEK/ERK and mTOR signaling, increasing apoptosis, and causing marked tumor regressions in vivo. Our findings reveal EGFR-dependent adaptive signaling as an important mechanism limiting FGFR inhibitor efficacy and driving resistance and support clinical testing of FGFR/EGFR inhibitor therapy for FGFR2 fusion-positive cholangiocarcinoma.

Significance

We demonstrate that feedback activation of EGFR signaling limits the effectiveness of FGFR inhibitor therapy and drives adaptive resistance in patient-derived models of FGFR2 fusion-positive cholangiocarcinoma. These studies support the potential of combination treatment with FGFR and EGFR inhibitors as an improved treatment for patients with FGFR2-driven cholangiocarcinoma. This article is highlighted in the In This Issue feature, p. 1171.