Machine-learning-aided prediction of brain metastases development in non-small-cell lung cancers

Purpose Non–small-cell lung cancer (NSCLC) shows a high incidence of brain metastases (BM). Early detection is crucial to improve clinical prospects. We trained and validated classifier models to identify patients with a high risk of developing BM, as they could potentially benefit from surveillance brain MRI. Methods Consecutive patients with an initial diagnosis of NSCLC from January 2011 to April 2019 and an in-house chest-CT scan (staging) were retrospectively recruited at a German lung cancer center. Brain imaging was performed at initial diagnosis and in case of neurological symptoms (follow-up). Subjects lost to follow-up or still alive without BM at the data cut-off point (12/2020) were excluded. Covariates included clinical and/or 3D-radiomics-features of the primary tumor from staging chest-CT. Four machine learning models for prediction (80/20 training) were compared. Gini Importance and SHAP were used as measures of importance; sensitivity, specificity, area under the precision-recall curve, and Matthew's Correlation Coefficient as evaluation metrics. Results Three hundred and ninety-five patients compromised the clinical cohort. Predictive models based on clinical features offered the best performance (tuned to maximize recall: sensitivity∼70%, specificity∼60%). Radiomics features failed to provide sufficient information, likely due to the heterogeneity of imaging data. Adenocarcinoma histology, lymph node invasion, and histological tumor grade were positively correlated with the prediction of BM, age, and squamous cell carcinoma histology were negatively correlated. A subgroup discovery analysis identified 2 candidate patient subpopulations appearing to present a higher risk of BM (female patients + adenocarcinoma histology, adenocarcinoma patients + no other distant metastases). Conclusion Analysis of the importance of input features suggests that the models are learning the relevant relationships between clinical features/development of BM. A higher number of samples is to be prioritized to improve performance. Employed prospectively at initial diagnosis, such models can help select high-risk subgroups for surveillance brain MRI

Модель професійної культури юриста: критерії та підходи

В статті визначається модель професійної культури

Analysis of Binding Sites on Complement Factor I That Are Required for Its Activity

The central complement inhibitor factor I (FI) degrades activated complement factors C4b and C3b in the presence of cofactors such as C4b-binding protein, factor H, complement receptor 1, and membrane cofactor protein. FI is a serine protease composed of two chains. The light chain comprises the serine protease domain, whereas the heavy chain contains several domains; that is, the FI and membrane attack complex domain (FIMAC), CD5, low density lipoprotein receptor 1 (LDLr1) and LDLr2 domains. To understand better how FI acts as a complement inhibitor, we used homology-based models of FI domains to predict potential binding sites. Specific amino acids were then mutated to yield 16 well expressed mutants, which were then purified from media of eukaryotic cells for functional analyses. The Michaelis constant (K-m) of all FI mutants toward a small substrate was not altered, whereas some mutants showed increased maximum initial velocity (V-max). All the mutations in the FIMAC domain affected the ability of FI to degrade C4b and C3b irrespective of the cofactor used, whereas only some mutations in the CD5 and LDLr1/2 domains had a similar effect. These same mutants also showed impaired binding to C3met. In conclusion, the FIMAC domain appears to harbor the main binding sites important for the ability of FI to degrade C4b and C3b.Pathophysiology and treatment of rheumatic disease

Circulating Metabolic Biomarkers of Screen-Detected Prostate Cancer in the ProtecT Study

BACKGROUND: Whether associations between circulating metabolites and prostate cancer are causal is unknown. We report on the largest study of metabolites and prostate cancer (2,291 cases and 2,661 controls) and appraise causality for a subset of the prostate cancer-metabolite associations using two-sample Mendelian randomization (MR). METHODS: The case-control portion of the study was conducted in nine UK centers with men ages 50-69 years who underwent prostate-specific antigen screening for prostate cancer within the Prostate Testing for Cancer and Treatment (ProtecT) trial. Two data sources were used to appraise causality: a genome-wide association study (GWAS) of metabolites in 24,925 participants and a GWAS of prostate cancer in 44,825 cases and 27,904 controls within the Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium. RESULTS: Thirty-five metabolites were strongly associated with prostate cancer (P < 0.0014, multiple-testing threshold). These fell into four classes: (i) lipids and lipoprotein subclass characteristics (total cholesterol and ratios, cholesterol esters and ratios, free cholesterol and ratios, phospholipids and ratios, and triglyceride ratios); (ii) fatty acids and ratios; (iii) amino acids; (iv) and fluid balance. Fourteen top metabolites were proxied by genetic variables, but MR indicated these were not causal. CONCLUSIONS: We identified 35 circulating metabolites associated with prostate cancer presence, but found no evidence of causality for those 14 testable with MR. Thus, the 14 MR-tested metabolites are unlikely to be mechanistically important in prostate cancer risk. IMPACT: The metabolome provides a promising set of biomarkers that may aid prostate cancer classification.status: publishe