In vitro modulator responsiveness of 655 CFTR variants found in people with cystic fibrosis.

BACKGROUND: In 2017, the US Food and Drug Administration initiated expansion of drug labels for the treatment of cystic fibrosis (CF) to include CF transmembrane conductance regulator (CFTR) gene variants based on in vitro functional studies. This study aims to identify CFTR variants that result in increased chloride (Cl-) transport function by the CFTR protein after treatment with the CFTR modulator combination elexacaftor/tezacaftor/ivacaftor (ELX/TEZ/IVA). These data may benefit people with CF (pwCF) who are not currently eligible for modulator therapies. METHODS: Plasmid DNA encoding 655 CFTR variants and wild-type (WT) CFTR were transfected into Fisher Rat Thyroid cells that do not natively express CFTR. After 24 h of incubation with control or TEZ and ELX, and acute addition of IVA, CFTR function was assessed using the transepithelial current clamp conductance assay. Each variant's forskolin/cAMP-induced baseline Cl- transport activity, responsiveness to IVA alone, and responsiveness to the TEZ/ELX/IVA combination were measured in three different laboratories. Western blots were conducted to evaluate CFTR protein maturation and complement the functional data. RESULTS AND CONCLUSIONS: 253 variants not currently approved for CFTR modulator therapy showed low baseline activity (<10 % of normal CFTR Cl- transport activity). For 152 of these variants, treatment with ELX/TEZ/IVA improved the Cl- transport activity by ≥10 % of normal CFTR function, which is suggestive of clinical benefit. ELX/TEZ/IVA increased CFTR function by ≥10 percentage points for an additional 140 unapproved variants with ≥10 % but <50 % of normal CFTR function at baseline. These findings significantly expand the number of rare CFTR variants for which ELX/TEZ/IVA treatment should result in clinical benefit.

A general framework for inference on algorithm-agnostic variable importance.

In many applications, it is of interest to assess the relative contribution of features (or subsets of features) toward the goal of predicting a response - in other words, to gauge the variable importance of features. Most recent work on variable importance assessment has focused on describing the importance of features within the confines of a given prediction algorithm. However, such assessment does not necessarily characterize the prediction potential of features, and may provide a misleading reflection of the intrinsic value of these features. To address this limitation, we propose a general framework for nonparametric inference on interpretable algorithm-agnostic variable importance. We define variable importance as a population-level contrast between the oracle predictiveness of all available features versus all features except those under consideration. We propose a nonparametric efficient estimation procedure that allows the construction of valid confidence intervals, even when machine learning techniques are used. We also outline a valid strategy for testing the null importance hypothesis. Through simulations, we show that our proposal has good operating characteristics, and we illustrate its use with data from a study of an antibody against HIV-1 infection.