Self-efficacy mediates the effect of professional identity on learning engagement for nursing students in higher vocational colleges: A cross-sectional study.

BACKGROUND: Learning engagement is a crucial predictor of academic achievement. It is essential to understand the factors influencing learning engagement among nursing students, especially from the learner's perspective, which is notably scarce but vital for designing effective educational interventions. OBJECTIVES: This study aims to investigate the mediating effect of self-efficacy on the relationship between professional identity and learning engagement for nursing students in higher vocational colleges. DESIGN: A cross-sectional electronic survey was conducted. SETTING: The study was conducted in four higher vocational colleges located in Guangdong Province, China. PARTICIPANTS: A total of 944 first- and second-year nursing students participated in the study between October and November 2022. METHODS: Data were collected with questionnaires on general information, professional identity, self-efficacy, and learning engagement and analyzed with SPSS 26.0 and PROCESS v4.1 (Model 4), exploring relationships among professional identity, self-efficacy, and learning engagement through Pearson correlations, multivariate regression, and mediation analysis with 5000 bootstrap samples. RESULTS: The participants exhibited moderate levels of professional identity (85.37 ± 13.52), self-efficacy (25.58 ± 5.74), and learning engagement (71.26 ± 16.17), which were all significantly correlated with each other (P < 0.01). In the model of the mediating effect, professional identity directly (ß = 0.811, t = 27.484, P < 0.001) and indirectly [ß = 0.112,95%CI (0.074-0.154)] significantly predicts college students' learning engagement; professional identity has a significant positive predictive effect on self-efficacy (ß = 0.182, t = 14.459, P < 0.001) and self-efficacy significantly predicts learning engagement (ß = 0.614, t = 8.292, P < 0.001). Furthermore, the direct effect of professional identity on learning engagement (0.699) and its mediating effect (0.112) account for 86.19 % and 13.81 % of the total effect (0.811), respectively. CONCLUSION: Participants exhibited moderate levels of professional identity, self-efficacy, and learning engagement. Professional identity and self-efficacy are interconnected and positively correlated, influencing learning engagement among nursing students, which highlights the need to foster these qualities to enhance education and future practice.

A Comprehensive Analysis of Programmed Cell Death-Associated Genes for Tumor Microenvironment Evaluation Promotes Precise Immunotherapy in Patients with Lung Adenocarcinoma.

Immune checkpoint inhibitors (ICIs) represent a new hot spot in tumor therapy. Programmed cell death has an important role in the prognosis. We explore a programmed cell death gene prognostic model associated with survival and immunotherapy prediction via computational algorithms. Patient details were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. We used LASSO algorithm and multiple-cox regression to establish a programmed cell death-associated gene prognostic model. Further, we explored whether this model could evaluate the sensitivity of patients to anti-PD-1/PD-L1. In total, 1342 patients were included. We constructed a programmed cell death model in TCGA cohorts, and the overall survival (OS) was significantly different between the high- and low-risk score groups (HR 2.70; 95% CI 1.94-3.75; p < 0.0001; 3-year OS AUC 0.71). Specifically, this model was associated with immunotherapy progression-free survival benefit in the validation cohort (HR 2.42; 95% CI 1.59-3.68; p = 0.015; 12-month AUC 0.87). We suggest that the programmed cell death model could provide guidance for immunotherapy in LUAD patients.

Clinical Evidence of Chemotherapy or Endocrine Therapy Maintenance in Patients with Metastatic Breast Cancer: Meta-Analysis of Randomized Clinical Trials and Propensity Score Matching of Multicenter Cohort Study.

PURPOSE: This study aims to comprehensively evaluate the clinical efficacy of chemotherapy or endocrine therapy maintenance in metastatic breast cancer (MBC) patients. MATERIALS AND METHODS: The meta-analysis of randomized clinical trials (RCTs) and propensity score matching of multicenter cohort study evaluated MBC patients who underwent first-line chemotherapy or endocrine therapy maintenance. This study is registered with PROSPERO: CRD42017071858 and ClinicalTrials.gov: NCT04258163. RESULTS: A total of 2,867 patients from 15 RCTs and 760 patients from multicenter cohort were included. The results from meta-analysis showed that chemotherapy maintenance improved progression-free survival (PFS) (hazard ratio [HR], 0.63; 95% confidence interval [CI], 0.54 to 0.73; p < 0.001; moderate-quality evidence) and overall survival (OS) (HR, 0.87; 95% CI 0.78 to 0.97; p=0.016; high-quality evidence) than observation. In the cohort study, for hormone receptor-positive MBC patients, chemotherapy maintenance improved PFS (HR, 0.67; 95% CI, 0.52 to 0.85; p < 0.001) and OS (HR, 0.55; 95% CI 0.42 to 0.73; p < 0.001) compared with observation, and endocrine therapy maintenance also improved PFS (HR, 0.65; 95% CI, 0.53 to 0.80; p < 0.001) and OS (HR, 0.55; 95% CI, 0.44 to 0.69; p < 0.001). There were no differences between chemotherapy and endocrine therapy maintenance in PFS and OS (all p > 0.05). Regardless of the continuum or switch maintenance therapy, showed prolonged survival in MBC patients who were response to first-line treatment. CONCLUSION: This study provided evidences for survival benefits of chemotherapy and endocrine therapy maintenance in MBC patients, and there was no difference efficacy between chemotherapy and endocrine therapy maintenance for hormone receptor-positive patients.

Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: A machine learning, multicenter study.

BACKGROUND: in current clinical practice, the standard evaluation for axillary lymph node (ALN) status in breast cancer has a low efficiency and is based on an invasive procedure that causes operative-associated complications in many patients. Therefore, we aimed to use machine learning techniques to develop an efficient preoperative magnetic resonance imaging (MRI) radiomics evaluation approach of ALN status and explore the association between radiomics and the tumor microenvironment in patients with early-stage invasive breast cancer. METHODS: in this retrospective multicenter study, three independent cohorts of patients with breast cancer (n = 1,088) were used to develop and validate signatures predictive of ALN status. After applying the machine learning random forest algorithm to select the key preoperative MRI radiomic features, we used ALN and tumor radiomic features to develop the ALN-tumor radiomic signature for ALN status prediction by the support vector machine algorithm in 803 patients with breast cancer from Sun Yat-sen Memorial Hospital and Sun Yat-sen University Cancer Center (training cohort). By combining ALN and tumor radiomic features with corresponding clinicopathologic information, the multiomic signature was constructed in the training cohort. Next, the external validation cohort (n = 179) of patients from Shunde Hospital of Southern Medical University and Tungwah Hospital of Sun Yat-Sen University, and the prospective-retrospective validation cohort (n = 106) of patients treated with neoadjuvant chemotherapy in prospective phase 3 trials [NCT01503905], were included to evaluate the predictive value of the two signatures, and their predictive performance was assessed by the area under operating characteristic curve (AUC). This study was registered with ClinicalTrials.gov, number NCT04003558. FINDINGS: the ALN-tumor radiomic signature for ALN status prediction comprising ALN and tumor radiomic features showed a high prediction quality with AUC of 0·88 in the training cohort, 0·87 in the external validation cohort, and 0·87 in the prospective-retrospective validation cohort. The multiomic signature incorporating tumor and lymph node MRI radiomics, clinical and pathologic characteristics, and molecular subtypes achieved better performance for ALN status prediction with AUCs of 0·90, 0·91, and 0·93 in the training cohort, the external validation cohort, and the prospective-retrospective validation cohort, respectively. Among patients who underwent neoadjuvant chemotherapy in the prospective-retrospective validation cohort, there were significant differences in the key radiomic features before and after neoadjuvant chemotherapy, especially in the gray-level dependence matrix features. Furthermore, there was an association between MRI radiomics and tumor microenvironment features including immune cells, long non-coding RNAs, and types of methylated sites. Interpretation this study presented a multiomic signature that could be preoperatively and conveniently used for identifying patients with ALN metastasis in early-stage invasive breast cancer. The multiomic signature exhibited powerful predictive ability and showed the prospect of extended application to tailor surgical management. Besides, significant changes in key radiomic features after neoadjuvant chemotherapy may be explained by changes in the tumor microenvironment, and the association between MRI radiomic features and tumor microenvironment features may reveal the potential biological underpinning of MRI radiomics. FUNDING: No funding.

A Prognostic Risk Score Based on Hypoxia-, Immunity-, and Epithelialto-Mesenchymal Transition-Related Genes for the Prognosis and Immunotherapy Response of Lung Adenocarcinoma.

Background: Lung adenocarcinoma (LUAD), the most common subtype of non-small cell lung cancer (NSCLC), is associated with poor prognosis. However, current stage-based clinical methods are insufficient for survival prediction and decision-making. This study aimed to establish a novel model for evaluating the risk of LUAD based on hypoxia, immunity, and epithelial-mesenchymal transition (EMT) gene signatures. Methods: In this study, we used data from TCGA-LUAD for the training cohort and GSE68465 and GSE72094 for the validation cohorts. Immunotherapy datasets GSE135222, GSE126044, and IMvigor210 were obtained from a previous study. Using bioinformatic and machine algorithms, we established a risk model based on hypoxia, immune, and EMT gene signatures, which was then used to divide patients into the high and low risk groups. We analyzed differences in enriched pathways between the two groups, following which we investigated whether the risk score was correlated with stemness scores, genes related to m6A, m5C, m1A and m7G modification, the immune microenvironment, immunotherapy response, and multiple anti-cancer drug sensitivity. Results: Overall survival differed significantly between the high-risk and low-risk groups (HR = 4.26). The AUCs for predicting 1-, 3-, and 5-year survival were 0.763, 0.766, and 0.728, respectively. In the GSE68465 dataset, the HR was 2.03, while the AUCs for predicting 1-, 3-, and 5-year survival were 0.69, 0.651, and 0.618, respectively. The corresponding values in the GSE72094 dataset were an HR of 2.36 and AUCs of 0.653, 0.662, and 0.749, respectively. The risk score model could independently predict OS in patients with LUAD, and highly correlated with stemness scores and numerous m6A, m5C, m1A and m7G modification-related genes. Furthermore, the risk model was significantly correlated with multiple immune microenvironment characteristics. In the GSE135222 dataset, the HR was 4.26 and the AUC was 0.702. Evaluation of the GSE126044 and IMvigor210 cohorts indicated that PD-1/PD-LI inhibitor treatment may be indicated in patients with low risk scores, while anti-cancer therapy with various drugs may be indicated in patients with high risk scores. Conclusion: Our novel risk model developed based on hypoxia, immune, and EMT gene signatures can aid in predicting clinical prognosis and guiding treatment in patients with LUAD.

Development and Validation of a Preoperative Magnetic Resonance Imaging Radiomics-Based Signature to Predict Axillary Lymph Node Metastasis and Disease-Free Survival in Patients With Early-Stage Breast Cancer.

Importance: Axillary lymph node metastasis (ALNM) status, typically estimated using an invasive procedure with a high false-negative rate, strongly affects the prognosis of recurrence in breast cancer. However, preoperative noninvasive tools to accurately predict ALNM status and disease-free survival (DFS) are lacking. Objective: To develop and validate dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomic signatures for preoperative identification of ALNM and to assess individual DFS in patients with early-stage breast cancer. Design, Setting, and Participants: This retrospective prognostic study included patients with histologically confirmed early-stage breast cancer diagnosed at 4 hospitals in China from July 3, 2007, to September 21, 2019, randomly divided (7:3) into development and vaidation cohorts. All patients underwent preoperative MRI scans, were treated with surgery and sentinel lymph node biopsy or ALN dissection, and were pathologically examined to determine the ALNM status. Data analysis was conducted from February 15, 2019, to March 20, 2020. Exposure: Clinical and DCE-MRI radiomic signatures. Main Outcomes and Measures: The primary end points were ALNM and DFS. Results: This study included 1214 women (median [IQR] age, 47 [42-55] years), split into development (849 [69.9%]) and validation (365 [30.1%]) cohorts. The radiomic signature identified ALNM in the development and validation cohorts with areas under the curve (AUCs) of 0.88 and 0.85, respectively, and the clinical-radiomic nomogram accurately predicted ALNM in the development and validation cohorts (AUC, 0.92 and 0.90, respectively) based on a least absolute shrinkage and selection operator (LASSO)-logistic regression model. The radiomic signature predicted 3-year DFS in the development and validation cohorts (AUC, 0.81 and 0.73, respectively), and the clinical-radiomic nomogram could discriminate high-risk from low-risk patients in the development cohort (hazard ratio [HR], 0.04; 95% CI, 0.01-0.11; P < .001) and the validation cohort (HR, 0.04; 95% CI, 0.004-0.32; P < .001) based on a random forest-Cox regression model. The clinical-radiomic nomogram was associated with 3-year DFS in the development and validation cohorts (AUC, 0.89 and 0.90, respectively). The decision curve analysis demonstrated that the clinical-radiomic nomogram displayed better clinical predictive usefulness than the clinical or radiomic signature alone. Conclusions and Relevance: This study described the application of MRI-based machine learning in patients with breast cancer, presenting novel individualized clinical decision nomograms that could be used to predict ALNM status and DFS. The clinical-radiomic nomograms were useful in clinical decision-making associated with personalized selection of surgical interventions and therapeutic regimens for patients with early-stage breast cancer.

Clinical evidence for association of neoadjuvant chemotherapy or chemoradiotherapy with efficacy and safety in patients with resectable esophageal carcinoma (NewEC study).

BACKGROUND: The efficacy and safety of neoadjuvant treatment over surgery alone and that of neoadjuvant chemoradiotherapy (NCRT) over neoadjuvant chemotherapy (NCT) in resectable esophageal carcinoma remains inconclusive. This study (NewEC) used global data to comprehensively evaluate these comparisons and to provide a preferable strategy for patient subsets. METHODS: This study included a meta-analysis of randomized controlled trials (RCTs) identified from inception to May 2019 from PubMed, EMBASE, the Cochrane Central Register of Controlled Trials, ClinicalTrials.gov, and congresses and a registry-based cohort study with patients from Massachusetts General Hospital (Massachusetts, USA) and Guangdong Provincial People's Hospital (Guangzhou, China) recruited from November 2000 and June 2017, to cross-validate the comparisons among NCRT versus NCT versus surgery. The GRADE approach was used to assessed quality of evidence in meta-analysis. Neural network machine learning propensity score-matched analysis was used to account for confounding by patient-level characteristics in the cohort study. The primary endpoint was overall survival (OS). The study was registered with PROSPERO CRD42017072242 and ClinicalTrials.gov NCT04027543. FINDINGS: Of 22,070 studies assessed, there were 38 (n = 6,993 patients) eligible RCTs. Additionally, 423 out of 467 screened patients were included in the cohort study. The results from trials showed that NCT had a better OS than surgery alone (hazard ratio [HR] 0·88, 95% confidence interval [CI] 0·79-0·98; high quality) and was only favorable for adenocarcinoma (HR 0·83, 95% CI 0·72-0·96; moderate quality). High-quality evidence showed a significantly better OS for NCRT than surgery alone (HR 0·74, 95% CI 0·66-0·82) for both adenocarcinoma (HR 0·73, 95% CI 0·62-0·86) and squamous cell carcinoma (SCC) (HR 0·73, 95% CI 0·65-0·83). The OS benefit of NCRT over NCT was seen in the pairwise (HR 0·78, 95% CI 0·62-0·99; high quality) and network (HR 0·82, 95% CI 0·72-0·93; high quality) meta-analyses, with similar results before (HR 0·60, 95% CI 0·40-0·91) and after (HR 0·44, 95% CI 0·25-0·77) matching in the cohort study, leading to a significantly increased 5-year OS rate in both adenocarcinoma and SCC before and after matching. The increased benefits from NCT or NCRT were not associated with the risk of 30-day or in-hospital mortality. INTERPRETATION: NewEC Study provided high-quality evidence supporting the survival benefits of NCRT or NCT over surgery alone, with NCRT presenting the greatest benefit for resectable esophageal carcinoma. FUNDING: National Science and Technology Major Project, the National Natural Science Foundation of China, the Natural Science Foundation of Guangdong Province, the Guangzhou Science and Technology Major Program, the Medical artificial intelligence project of Sun Yat-Sen Memorial Hospital, the Guangdong Science and Technology Department, the Guangdong Province Medical Scientific Research Foundation, and Guangdong Provincial People's Hospital Intermural Program.

Association of Long Noncoding RNA Biomarkers With Clinical Immune Subtype and Prediction of Immunotherapy Response in Patients With Cancer.

Importance: Long noncoding RNAs (lncRNAs) are involved in innate and adaptive immunity in cancer by mediating the functional state of immunologic cells, pathways, and genes. However, whether lncRNAs are associated with immune molecular classification and clinical outcomes of cancer immunotherapy is largely unknown. Objectives: To explore lncRNA-based immune subtypes associated with survival and response to cancer immunotherapy and to present a novel lncRNA score for immunotherapy prediction using computational algorithms. Design, Setting, and Participants: In this cohort study, an individual patient analysis based on a phase 2, single-arm clinical trial and multicohort was performed from June 25 through September 30, 2019. Data are from the phase 2 IMvigor210 trial and from The Cancer Genome Atlas (TCGA). The study analyzed lncRNA and genomic data of 348 patients with bladder cancer from the IMvigor210 trial and 71 patients with melanoma from TCGA who were treated with immunotherapy. In addition, a pancancer multicohort that included 2951 patients was obtained from TCGA. Main Outcomes and Measures: The primary end point was overall survival (OS). Results: Among 348 patients from the IMvigor210 trial (272 [78.2%] male) and 71 patients with melanoma from TCGA (mean [SD] age, 58.3 [13.4] years; 37 [52.1%] female), 4 distinct classes with statistically significant differences in OS (median months, not reached vs 9.6 vs 8.1 vs 6.7 months; P = .002) were identified. The greatest OS benefit was obtained in the immune-active class, as characterized by the immune-functional lncRNA signature and high CTL infiltration. Patients with low vs high lncRNA scores had statistically significantly longer OS (hazard ratio, 0.32; 95% CI, 0.24-0.42; P < .001) in the IMvigor210 trial and across various cancer types. The lncRNA score was associated with immunotherapeutic OS benefit in the IMvigor210 trial cohort (area under the curve [AUC], 0.79 at 12 months and 0.77 at 20 months) and in TCGA melanoma cohort (AUC, 0.87 at 24 months), superior tumor alteration burden, programmed cell death ligand 1 (PD-L1) expression, and cytotoxic T-lymphocyte (CTL) infiltration. Addition of the lncRNA score to the combination of tumor alteration burden, PD-L1 expression, and CTL infiltration to build a novel multiomics algorithm correlated more strongly with OS in the IMvigor210 trial cohort (AUC, 0.81 at 12 months and 0.80 at 20 months). Conclusions and Relevance: This study identifies novel lncRNA-based immune classes in cancer immunotherapy and recommends immunotherapy for patients in the immune-active class. In addition, the study recommends that the lncRNA score should be integrated into multiomic panels for precision immunotherapy.

Association of survival and genomic mutation signature with immunotherapy in patients with hepatocellular carcinoma.

BACKGROUND: Current guidelines lack recommendations for the use of immunotherapy and immune-related biomarkers for hepatocellular carcinoma (HCC). We aim to provide reliable evidence of the association of survival with HCC immunotherapy and to demonstrate that genomic mutation signature could be an effective biomarker to predict immunotherapy efficacy of HCC patients. METHODS: We conducted a meta-analysis of 17 randomized trials with 2055 patients and an individual patient-level analysis of 31 patients. Trial data were identified in PubMed, EMBASE and Cochrane Central library, and individual patient data were obtained from the cBioPortal database. Overall survival (OS) and progression-free survival (PFS) were assessed with the hazard ratio (HR) and 95% CI. This study is registered with PROSPERO, number CRD42018083991. RESULTS: The meta-analysis showed that compared to conventional therapy, immunotherapy resulted in prolonged OS (HR =0.65, P<0.0001, high quality) and PFS (HR =0.81, P<0.0001, high quality); the benefits were observed for cellular immunotherapy, tumor vaccine, and cytokine immunotherapy. Findings were robust to subgroup and trial sequential analyses. In the individual patient-level analysis of patients treated with immune checkpoint inhibitor, mutations in TERT, CTNNB1, BRD4, or MLL, and co-mutations in TP53 and TERT or BRD4 were associated with significantly worse survival. These oncogenes were used to develop a novel integrated mutation risk score, which exhibited better utility in predicting survival than the tumor mutation burden (TMB). Patients with low- versus high- mutation risk score had longer OS (HR =0.18, P=0.02) and PFS (HR =0.33, P=0.018). A nomogram comprising the mutation risk score and essential clinical factors further improved the predictive accuracy (AUC =0.840 for both 1- and 2-year OS). CONCLUSIONS: Immunotherapy showed longer OS and PFS than conventional therapy among HCC patients, especially patients with a low mutation risk score. The nomogram based on genomic and clinical characteristics is effective in predicting survival of HCC patients undergoing immune checkpoint inhibitor.

Association of Survival and Immune-Related Biomarkers With Immunotherapy in Patients With Non-Small Cell Lung Cancer: A Meta-analysis and Individual Patient-Level Analysis.

Importance: The beneficial role of immunotherapy and the clinical relevance of current biomarkers in non-small cell lung cancer (NSCLC) remain inconclusive; thus, appropriate strategies and reliable predictors need further definition. Objectives: To evaluate the association of clinical outcomes with immune checkpoint inhibitors, tumor vaccines, and cellular immunotherapy in patients with advanced NSCLC and to explore appropriate strategies, candidates, and predictors. Data Sources: The PubMed, EMBASE, and Cochrane Central Register of Controlled Trials databases were searched from inception to June 2018, using relevant search keywords and Medical Subject Headings (MeSH) terms, including tumor vaccine, cellular immunotherapy, immune checkpoint inhibitor, cytotoxic T-lymphocyte-associated protein 4, programmed death-ligand 1, programmed death receptor 1, and non-small cell lung carcinoma. Systematic reviews, meta-analyses, references, and conference proceedings were manually searched. Study Selection: English-language randomized clinical trials with available data that measured overall survival (OS), progression-free survival (PFS), or objective response rate comparing immune checkpoint inhibitors, tumor vaccines, or cellular immunotherapy with conventional therapy for patients with advanced or metastatic NSCLC were included. Thirty-one immunotherapy randomized clinical trials were included, and multicohort data included next-generation sequencing data from patients with advanced NCSLC. Data Extraction and Synthesis: Hazard ratios and 95% CIs were pooled to estimate the survival increases in OS and PFS. Dichotomous data, such as object response rate data, were analyzed using the risk ratio. Mantel-Haenszel random-effects model was used. I2 was used to assess the heterogeneity between trials; an I2 value exceeding 50% indicated the existence of substantial heterogeneity. Analyses took place from February 1, 2018, to August 31, 2018. Main Outcomes and Measures: Primary outcomes were OS and PFS. Results: In total, 14 395 patients (9500 [66.0%] men) were included in the meta-analysis, and 1833 patients (mean [SD], 65.2 [9.9] years; 1063 [58.0%] men) were included in the individual patient-level study. Compared with conventional therapy, immunotherapy was associated with significantly longer OS (hazard ratio, 0.76; 95% CI, 0.71-0.82; P < .001) and PFS (hazard ratio, 0.76; 95% CI, 0.70-0.83; P < .001). The best checkpoint blockade strategy was first-line pembrolizumab with platinum-based chemotherapy. The combined predictive utility of programmed cell death ligand 1 (PD-L1) expression and tumor mutation burden (TMB) was associated with predictive prognosis (whole-exome sequencing: 1-year PFS area under the receiver operating characteristic curve [AUC], 0.829; 3-year PFS AUC, 0.839; targeted next-generation sequencing: 1-year PFS AUC, 0.826; 3-year PFS AUC, 0.948). Moreover, the addition of CD8+ T-cell tumor-infiltrating lymphocytes was associated with improved prognosis predictions for OS (3-year OS AUC, 0.659; 5-year OS AUC, 0.665). RYR1 or MGAM mutations were significantly associated with concomitantly increased durable clinical benefits (RYR1: durable clinical benefit [DCB], 12 of 51 patients [24%]; no durable benefit [NDB], 2 of 55 patients [4%]; P < .001; MGAM: DCB, 12 of 51 patients [24%]; NDB, 0 patients; P < .001), a higher TMB (RYRI: high TMB, 12 of 53 patients [23%]; low TMB, 2 of 53 patients [38%]; P < .001; MGAM: high TMB, 9 of 53 patients [17%]; low TMB, 0 patients; P < .001), and higher PD-L1 expression (RYRI: high PD-L1 expression, 8 of 30 patients [27%]; low PD-L1 expression, 6 of 85 [7.1%]; P < .001; MGAM: high PD-L1 expression, 6 of 30 patients [20%]; low PD-L1 expression, 5 of 85 patients [6%]; P < .001). Conclusions and Relevance: Immunotherapies showed promising clinical outcomes for patients with NSCLC. Pembrolizumab with platinum-based chemotherapy was found to be the most appropriate first-line immune checkpoint inhibitor regimen for advanced NSCLC, and the combined use of PD-L1 expression and TMB was found to be a promising biomarker to evaluate patients' survival and response to precision immunotherapy. The further combination of CD8+ T-cell tumor-infiltrating lymphocytes, PD-L1 expression, and TMB was associated with reliable prognosis. The predictive value of that combination needs to be prospectively validated in large-scale studies.

Corticosteroids combined with doublet or single-agent immunosuppressive therapy for active proliferative lupus nephritis.

OBJECTIVES: We performed a meta-analysis to assess whether corticosteroids (C) plus (+) doublet immunosuppressive therapy (IT) is superior to the classical combination of C with single-agent IT in active proliferative lupus nephritis (LN). METHOD: Randomized trials evaluating the benefits and risks of C+doublet versus single-agent IT in active proliferative LN were obtained by searching PubMed, EMBASE, and Cochrane Central Register. The primary outcome was overall response rate (ORR). The secondary outcomes were the change from baseline in Systemic Lupus Erythematosus Disease Activity Index (SLE-DAI) score, negative conversion ratio of anti-double-stranded DNA (anti-dsDNA), and adverse events. The PROSPERO registry number is CRD42017068491. RESULTS: Eleven trials with 1855 patients were included. Compared with C+single-agent IT, C+doublet IT had a significantly higher ORR (relative risk [RR], 1.22; 95% confidence interval [CI], 1.09 to 1.35; P < 0.01). In a subgroup analysis, C+doublet IT without biologics had a significantly higher ORR than C+single-agent IT (RR, 1.30; 95% CI, 1.13 to 1.50; P < 0.01), while C+doublet IT including biologics improved ORR only for refractory severe LN (RR, 1.46; 95% CI, 1.09 to 1.96; P = 0.012). A larger change from baseline in SLE-DAI scores (standardized mean difference, - 0.49; 95% CI, - 0.68 to - 0.30; P < 0.01) and a higher negative conversion ratio of anti-dsDNA (RR, 1.34; 95% CI, 1.06 to 1.69; P = 0.014) were observed with C+doublet IT than with C+single-agent IT. The rates of adverse events were similar between the two regimens. CONCLUSIONS: Compared with single-agent IT, the combination of C and doublet IT without biologics improved clinical outcomes in active proliferative LN. Key Points • Compared with corticosteroids + single-agent immunosuppressive therapy, corticosteroids + doublet immunosuppressive therapy without biologics had a significantly higher overall response rate in active proliferative lupus nephritis. • Compared with corticosteroids + single-agent immunosuppressive therapy, corticosteroids + doublet immunosuppressive therapy including biologics improved overall response rate only for refractory severe lupus nephritis. • A larger change from baseline in SLE-DAI scores and a higher negative conversion ratio of anti-dsDNA were observed with corticosteroids + doublet immunosuppressive therapy than with corticosteroids + single-agent immunosuppressive therapy.

Prognostic and predictive value of tumor-infiltrating lymphocytes for clinical therapeutic research in patients with non-small cell lung cancer.

BACKGROUND: Previous preclinical and clinical studies have shown that levels of tumor-infiltrating lymphocytes (TILs) significantly correlated with prognosis in non-small cell lung cancer (NSCLC), and survival after therapy; however, this finding remains controversial. We performed a meta-analysis, to evaluate, systematically, the clinical utilization of TIL subtypes in patients with NSCLC. METHODS: The PubMed, ISI Web of Science, EMBASE, and Cochrane Library databases were searched to identify relevant studies. We pooled estimates of treatment effects, and hazards were summarized using random or fixed effects models to evaluate survival outcomes. RESULTS: A total of 24 relevant studies involving 7,006 patients were eligible. The median percentage of lymph node positivity was 45.7% (95% confidence interval [CI], 37.1-56.4%). Pooled analysis shows that high levels of CD8+ TILs had a good prognostic effect on survival with a hazard ratio (HR) of 0.91 (P = 0.013) for death and 0.74 (P = 0.001) for recurrence, as did high levels of CD3+ and CD4+ TILs, with HRs of 0.77 (P = 0.009) and 0.78 (P = 0.005) for death, respectively. By contrast, high levels of FoxP3+ regulatory TILs had a worse prognostic effect for overall and recurrence-free survival, with HRs of 1.69 (P = 0.042) and 1.79 (P = 0.001), respectively. No individual study affected the results, and no publication bias was found. CONCLUSIONS: Our findings support the hypothesis that TILs could be a prognostic marker in NSCLC. High-quality randomized studies are needed to verify statistically the effect of TILs on prognosis in future research.