A mutual information-based in vivo monitoring of adaptive response to targeted therapies in melanoma

摘要

Highlights ? In vivo dependencies should be quantified in adaptive resistance mechanistic studies ? Mutual information (MI) quantifies any type rather than just linear dependencies ? MI outperforms classic expression correlation coefficients ? Adaptive response to small-molecules inhibitors can be monitored in vivo using MI ? Strategies that prevent adaptive resistance can be monitored in vivo using MI The mechanisms of adaptive resistance to genetic-based targeted therapies of solid malignancies have been the subject of intense research. These studies hold great promise for finding co-targetable hub/pathways which in turn would control the downstream non-genetic mechanisms of adaptive resistance. Many such mechanisms have been described in the paradigmatic BRAF-mutated melanoma model of adaptive response to BRAF inhibition. Currently, a major challenge for these mechanistic studies is to confirm in vivo , at the single-cell proteomic level, the existence of dependencies between the co-targeted hub/pathways and their downstream effectors. Moreover, the drug-induced in vivo modulation of these dependencies needs to be demonstrated. Here, we implement such single-cell-based in vivo expression dependency quantification using immunohistochemistry (IHC)-based analyses of sequential biopsies in two xenograft models. These mimic phase 2 and 3 trials in our own therapeutic strategy to prevent the adaptive response to BRAF inhibition. In this mechanistic model, the dependencies between the targeted Li _(2)CO _(3)-inducible hub HuR and the resistance effectors are more likely time-shifted and transient since the minority of HuR ~(Low) cells, which act as a reservoir of adaptive plasticity, switch to a HuR ~(High) state as they paradoxically proliferate under BRAF inhibition. Nevertheless, we show that a copula/kernel density estimator (KDE)-based quantification of mutual information (MI) efficiently captures, at the individual level, the dependencies between HuR and two relevant resistance markers pERK and EGFR, and outperforms classic expression correlation coefficients. Ultimately, the validation of MI as a predictive IHC-based metric of response to our therapeutic strategy will be carried in clinical trials.