Differential effects of ELX/TEZ/IVA on organ-specific CFTR function in two patients with the rare CFTR splice mutations c.273+1G>A and c.165-2A>G.

Introduction: Evidence for the efficiency of highly-effective triple-CFTR-modulatory therapy with elexacaftor/tezacaftor/ivacaftor (ETI), either demonstrated in clinical trials or by in vitro testing, is lacking for about 10% of people with cystic fibrosis (pwCF) with rare mutations. Comprehensive assessment of CFTR function can provide critical information on the impact of ETI on CFTR function gains for such rare mutations, lending argument of the prescription of ETI. The mutation c.165-2A>G is a rare acceptor splice mutation that has not yet been functionally characterized. We here describe the functional changes induced by ETI in two brothers who are compound heterozygous for the splice mutations c.273+1G>C and c.165-2A>G. Methods: We assessed the effects of ETI on CFTR function by quantitative pilocarpine iontophoresis (QPIT), nasal potential difference measurements (nPD), intestinal current measurements (ICM), ß-adrenergic sweat secretion tests (SST) and multiple breath washout (MBW) prior to and 4 months after the initiation of ETI. Results: Functional CFTR analysis prior to ETI showed no CFTR function in the respiratory and intestinal epithelia and in the sweat gland reabsorptive duct in either brother. In contrast, ß-adrenergic stimulated, CFTR-mediated sweat secretion was detectable in the CF range. Under ETI, both brothers continued to exhibit high sweat chloride concentration in QPIT, evidence of low residual CFTR function in the respiratory epithelia, but normalized ß-adrenergically stimulated production of primary sweat. Discussion: Our results are the first to demonstrate that the c.165-2A>G/c.273+1G>C mutation genotype permits mutant CFTR protein expression. We showed organ-specific differences in the expression of CFTR and consecutive responses to ETI of the c.165-2A>G/c.273+1G>C CFTR mutants that are probably accomplished by non-canonical CFTR mRNA isoforms. This showcase tells us that the individual response of rare CFTR mutations to highly-effective CFTR modulation cannot be predicted from assays in standard cell cultures, but requires the personalized multi-organ assessment by CFTR biomarkers.

Discriminating malignant and benign clinical T1 renal masses on computed tomography: A pragmatic radiomics and machine learning approach.

The aim of this study was to discriminate malignant and benign clinical T1 renal masses on routinely acquired computed tomography (CT) images using radiomics and machine learning techniques.Adult patients undergoing surgical resection and histopathological analysis of clinical T1 renal masses were included. Preoperative CT studies in venous phase from multiple referring centers were included, without restriction to specific CT scanners, slice thickness, or degrees of artifacts. Renal masses were segmented and 120 standardized radiomic features extracted. Machine learning algorithms were used to predict malignancy of renal masses using radiomics features and cross-validation. Diagnostic accuracy of machine learning models and assessment by independent blinded radiologists were compared based on the gold standard of histopathologic diagnosis.A total of 94 patients met inclusion criteria (benign renal masses: n = 18; malignant: n = 76). CT studies from 18 different scanners were assessed with median slice thickness of 2.5 mm and artifacts in 15 cases (15.9%).Area under the receiver-operating-characteristics curve (AUC) of random forest (random forest [RF], AUC = 0.83) was significantly higher compared to the radiologists (AUC = 0.68, P = .047). Sensitivity was significantly higher for RF versus radiologists (0.88 vs 0.80, P = .045), whereas specificity was numerically higher for RF (0.67 vs 0.50, P = .083).Although limited by an overall small sample size and few benign renal tumors, a radiomic features and machine learning approach suggests a high diagnostic accuracy for discrimination of malignant and benign clinical T1 renal masses on venous phase CT. The presented algorithm robustly outperforms human readers in a real-life scenario with nonstandardized imaging studies from various referring centers.