Analysis of Clinical Characteristics and Influencing Factors of Early Neurological Deterioration in Patients With Mild Stroke by Intravenous Alteplase Therapy.

OBJECTIVES: Thrombolysis treatment for patients with mild stroke is controversial. The aim of our study was to investigate the clinical characteristics and influencing factors of early neurological deterioration (END) in this group of patients. METHODS: A retrospective analysis was performed on ischemic stroke patients with intravenous thrombolysis (IVT) in Wenzhou Central Hospital. Subgroup analyses were performed for the mild stroke group and nonmild stroke group, END group, and non-early neurological deterioration group in mild stroke patients, respectively. RESULTS: A total of 498 patients were included in this study. Compared with the control group, the mild stroke group was younger age, less atrial fibrillation, previous history of stroke and less use of antithrombotic drugs, more dyslipidemia, smoking, and drinking. Small artery occlusion type was more common in mild stroke, cardioembolism and stroke of undetermined etiology type were less. In the mild stroke group, the symptomatic intracerebral hemorrhage (sICH) rate was 2.54%, and the END rate was 16.1%. Predictors of END included systolic blood pressure, blood glucose, cardioembolism subtype, sICH, and large vessel occlusion. In END patients, the sICH rate was 10.53%, and 84.21% of cases started to worsen within 12 hours after IVT. There was no statistically significant difference in the time to exacerbation among different subtypes. CONCLUSIONS: The occurrence of mild stroke in young patients was largely related to unhealthy lifestyles. The incidence of END in mild stroke IVT patients was low, with most occurring within 12 hours of IVT. There were many risk factors for END: large vessel occlusion and hyperglycemia were independent risk factors for END after IVT. sICH was an important but rare risk factor for END.

Definition of a new blood cell count score for early survival prediction for non-small cell lung cancer patients treated with atezolizumab: Integrated analysis of four multicenter clinical trials.

Importance: Blood cell count test (BCT) is a robust method that provides direct quantification of various types of immune cells to reveal the immune landscape to predict atezolizumab treatment outcomes for clinicians to decide the next phase of treatment. Objective: This study aims to define a new BCTscore model to predict atezolizumab treatment benefits in non-small lung cell cancer (NSCLC) patients. Design Setting and Participants: This study analyzed four international, multicenter clinical trials (OAK, BIRCH, POPLAR, and FIR trials) to conduct post-hoc analyses of NSCLC patients undergoing atezolizumab (anti-PD-L1) single-agent treatment (n = 1,479) or docetaxel single-agent treatment (n = 707). BCT was conducted at three time points: pre-treatment (T1), the first day of treatment cycle 3 (T2), and first day of treatment cycle 5 (T3). Univariate and multivariate Cox regression analyses were conducted to identify early BCT biomarkers to predict atezolizumab treatment outcomes in NSCLC patients. Main Outcomes and Measures: Overall survival (OS) was used as the primary end point, whereas progression-free survival (PFS) according to Response Evaluation Criteria in Solid Tumors (RECIST), clinical benefit (CB), and objective response rate (ORR) were used as secondary end points. Results: The BCT biomarkers of neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) at time point T3 and neutrophil-to-monocyte ratio (NMR) at time point T2 with absolute cutoff values of NLR_T3 = 5, PLR_T3 = 180, and NMR_T2 = 6 were identified as strong predictive biomarkers for atezolizumab (Ate)-treated NSCLC patients in comparison with docetaxel (Dtx)-treated patients regarding OS (BCTscore low risk: HR Ate vs. Dtx = 1.54 (95% CI: 1.04-2.27), P = 0.031; high risk: HR Ate vs. Dtx = 0.84 (95% CI: 0.62-1.12), P = 0.235). The identified BCTscore model showed better OS AUC in the OAK (AUC12month = 0.696), BIRCH (AUC12month = 0.672) and POPLAR+FIR studies (AUC12month = 0.727) than that of each of the three single BCT biomarkers. Conclusion and Relevance: The BCTscore model is a valid predictive and prognostic biomarker for early survival prediction in atezolizumab-treated NSCLC patients.

Elucidation of the Application of Blood Test Biomarkers to Predict Immune-Related Adverse Events in Atezolizumab-Treated NSCLC Patients Using Machine Learning Methods.

Background: Development of severe immune-related adverse events (irAEs) is a major predicament to stop treatment with immune checkpoint inhibitors, even though tumor progression is suppressed. However, no effective early phase biomarker has been established to predict irAE until now. Method: This study retrospectively used the data of four international, multi-center clinical trials to investigate the application of blood test biomarkers to predict irAEs in atezolizumab-treated advanced non-small cell lung cancer (NSCLC) patients. Seven machine learning methods were exploited to dissect the importance score of 21 blood test biomarkers after 1,000 simulations by the training cohort consisting of 80%, 70%, and 60% of the combined cohort with 1,320 eligible patients. Results: XGBoost and LASSO exhibited the best performance in this study with relatively higher consistency between the training and test cohorts. The best area under the curve (AUC) was obtained by a 10-biomarker panel using the XGBoost method for the 8:2 training:test cohort ratio (training cohort AUC = 0.692, test cohort AUC = 0.681). This panel could be further narrowed down to a three-biomarker panel consisting of C-reactive protein (CRP), platelet-to-lymphocyte ratio (PLR), and thyroid-stimulating hormone (TSH) with a small median AUC difference using the XGBoost method [for the 8:2 training:test cohort ratio, training cohort AUC difference = -0.035 (p < 0.0001), and test cohort AUC difference = 0.001 (p=0.965)]. Conclusion: Blood test biomarkers currently do not have sufficient predictive power to predict irAE development in atezolizumab-treated advanced NSCLC patients. Nevertheless, biomarkers related to adaptive immunity and liver or thyroid dysfunction warrant further investigation.

Identification of 15 lncRNAs Signature for Predicting Survival Benefit of Advanced Melanoma Patients Treated with Anti-PD-1 Monotherapy.

The blockade of programmed cell death protein 1 (PD-1) as monotherapy has been widely used in melanoma, but to identify melanoma patients with survival benefit from anti-PD-1 monotherapy is still a big challenge. There is an urgent need for prognostic signatures improving the prediction of immunotherapy responses of these patients. We analyzed transcriptomic data of pre-treatment tumor biopsies and clinical profiles in advanced melanoma patients receiving only anti-PD-1 monotherapy (nivolumab or pembrolizumab) from the PRJNA356761 and PRJEB23709 data sets as the training and validation cohort, respectively. Weighted gene co-expression network analysis was used to identify the key module, then least absolute shrinkage and selection operator was conducted to determine prognostic-related long noncoding RNAs (lncRNAs). Subsequently, the differentially expressed genes between different clusters were identified, and their function and pathway annotation were performed. In this investigation, 92 melanoma patients with complete survival information (51 from training cohort and 41 from validation cohort) were included in our analyses. We initiallyidentified the key module (skyblue) by weighted gene co-expression network analysis, and then identified a 15 predictive lncRNAs (AC010904.2, LINC01126, AC012360.1, AC024933.1, AL442128.2, AC022211.4, AC022211.2, AC127496.5, NARF-AS1, AP000919.3, AP005329.2, AC023983.1, AC023983.2, AC139100.1, and AC012615.4) signature in melanoma patients treated with anti-PD-1 monotherapy by least absolute shrinkage and selection operator in the training cohort. These results were then validated in the validation cohort. Finally, enrichment analysis showed that the functions of differentially expressed genes between two consensus clusters were mainly related to the immune process and treatment. In summary, the 15 lncRNAs signature is a novel effective predictor for prognosis in advanced melanoma patients treated with anti-PD-1 monotherapy.