Synergic Interactions Between Hepatic Stellate Cells and Uveal Melanoma in Metastatic Growth

Uveal melanoma (UM) is a malignant intraocular tumor that spreads to the liver in half of the cases. Since hepatic cells could play a role in the therapeutic resistance of metastatic UM, the purpose of our study was to investigate the pro-invasive role of hepatic stellate cells (HSteCs) in metastatic UM at the micro- and macro-metastatic stages. We first performed an immunostaining with the alpha-smooth muscle actin (αSMA) to localize activated HSteCs in UM liver macro-metastases from four patients. Their accumulation of collagen was assessed with Masson’s Trichrome stain. Next, we inoculated metastatic UM cells alone or with human HSteCs in triple-immunodeficient mice, in order to determine if HSteCs are recruited as early as the micro-metastatic stage. The growth of metastatic foci was imaged in the liver by ex vivo fluorescence imaging. Histological analyses were performed with Masson’s Trichrome and Picrosirius Red stains, and antibodies against Melan-A and αSMA. The collagen content was measured in xenografts by quantitative polarization microscopy. In patient hepatectomy samples, activated HSteCs and their pathological matrix were localized surrounding the malignant lesions. In the mouse xenograft model, the number of hepatic metastases was increased when human HSteCs were co-inoculated. Histological analyses revealed a significant recruitment of HSteCs near the micro/macrolesions, and an increase in fibrillar collagen production. Our results show that HSteCs can provide a permissive microenvironment and might increase the therapeutic resistance of metastatic UM

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers

Population pharmacokinetics and pharmacogenetics of ritonavir-boosted darunavir in the presence of raltegravir or tenofovir disoproxil fumarate/emtricitabine in HIV-infected adults and the relationship with virological response : a sub-study of the NEAT001/ANRS143 randomized trial

OBJECTIVES: NEAT001/ANRS143 demonstrated non-inferiority of once-daily darunavir/ritonavir (800/100 mg) + twice-daily raltegravir (400 mg) versus darunavir/ritonavir + tenofovir disoproxil fumarate/emtricitabine (245/200 mg once daily) in treatment-naive patients. We investigated the population pharmacokinetics of darunavir, ritonavir, tenofovir and emtricitabine and relationships with demographics, genetic polymorphisms and virological failure. METHODS: Non-linear mixed-effects models (NONMEM v. 7.3) were applied to determine pharmacokinetic parameters and assess demographic covariates and relationships with SNPs (SLCO3A1, SLCO1B1, NR1I2, NR1I3, CYP3A5*3, CYP3A4*22, ABCC2, ABCC10, ABCG2 and SCL47A1). The relationship between model-predicted darunavir AUC0-24 and C24 with time to virological failure was evaluated by Cox regression. RESULTS: Of 805 enrolled, 716, 720, 347 and 361 were included in the darunavir, ritonavir, tenofovir and emtricitabine models, respectively (11% female, 83% Caucasian). No significant effect of patient demographics or SNPs was observed for darunavir or tenofovir apparent oral clearance (CL/F); coadministration of raltegravir did not influence darunavir or ritonavir CL/F. Ritonavir CL/F decreased by 23% in NR1I2 63396C>T carriers and emtricitabine CL/F was linearly associated with creatinine clearance (P < 0.001). No significant relationship was demonstrated between darunavir AUC0-24 or C24 and time to virological failure [HR (95% CI): 2.28 (0.53-9.80), P = 0.269; and 1.82 (0.61-5.41), P = 0.279, respectively]. CONCLUSIONS: Darunavir concentrations were unaltered in the presence of raltegravir and not associated with virological failure. Polymorphisms investigated had little impact on study-drug pharmacokinetics. Darunavir/ritonavir + raltegravir may be an appropriate option for patients experiencing NRTI-associated toxicity