Bayesian Ranking Research Articles

Comparison of treatment regimens for unresectable stage III epidermal growth factor receptor (EGFR)mutant non-small cell lung cancer.

Durvalumab after chemoradiotherapy (CRT) failed to bring survival benefits to patients with epidermal growth factor receptor (EGFR) mutations in PACIFIC study ( evaluating durvalumab in patients with stage III, unresectable NSCLC who did not have disease progression after concurrent chemoradiotherapy). We aimed to explore whether locally advanced inoperable patients with EGFR mutations benefit from tyrosine kinase inhibitors (TKIs) and the optimal treatment regimen. We screened the PubMed, Embase, the Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov databases from January 1, 2000 to December 31, 2022 and performed a meta-analysis based on a Bayesian framework, with progression-free survival (PFS) and overall survival (OS) as the primary endpoints. A total of 1156 patients were identified in 16studies that included 6 treatment measures, including CRT, CRT followed by durvalumab (CRT-Durva), TKI monotherapy, radiotherapy combined with TKI (RT-TKI), CRT combined with TKI (CRT-TKI), and TKI combined with durvalumab (TKI-Durva). The PFS after the TKI-containing treatments was significantly longer than after the TKI-free treatments (hazard ratio [HR]=0.37, 95% confidence interval [CI],0.20-0.66). The PFS of TKI monotherapy was significantly longer than CRT (HR=0.66, 95% CI, 0.50-0.87) but shorter than RT-TKI (HR=1.78, 95% CI,1.17-2.67). Furthermore, the PFS of RT-TKI or CRT-TKI were both significantly longer than that of CRT or CRT-Durva. RT-TKI ranked first in the Bayesian ranking, with the longest OS (60.8months, 95% CI=37.2-84.3 months) and the longest PFS (21.5months, 95% CI,15.4-27.5 months) in integrated analysis. For unresectable stage III EGFR mutant NSCLC, RT and TKI are both essential. Based on the current evidence, RT-TKI brings the best survival advantage, while CRT-TKI needs further estimation. Large randomized clinical trials are urgently needed to explore the appropriate application sequences of TKI, radiotherapy, and chemotherapy. PROSPERO; https://www.crd.york.ac.uk/PROSPERO/; No. CRD42022298490.

Which Is More Suitable for First-Line Treatment of Extensive-Stage Small Cell Lung Cancer, PD-L1 Inhibitors Versus PD-1 Inhibitors? A Systematic Review and Network Meta-Analysis.

In this network meta-analysis (NMA), the efficiency and safety of PD-1 inhibitors + chemotherapy and PD-L1 inhibitors + chemotherapy were compared in the first-line therapy of patients with extensive-stage small cell lung cancer (ES-SCLC). We searched research databases, conference abstracts, and trial registries and subsequently chose relevant studies and extracted dates. The NMA was conducted to estimate the efficiency and safety of the PD-1 inhibitors + chemotherapy and PD-L1 inhibitors + chemotherapy on overall survival (OS), progression-free survival (PFS), overall remission rate (ORR), and adverse events (AEs). Studies were assessed for quality. Subgroup analyses were used to evaluate study heterogeneity. We included six randomized trials with a total of 3163 patients. Direct comparisons showed that patients who received either PD-1 inhibitors + chemotherapy (HR: 0.71, 95% CI: 0.57-0.87) or PD-L1 inhibitors + chemotherapy (HR: 0.74, 0.61-0.89) demonstrated significantly longer OS than those who received placebo + chemotherapy. The results of the NMA showed that no significant differences in OS (HR 0.96 95% CI: 0.72-1.3), PFS (HR 0.83, 95% CI: 0.51-1.4), and ORR (OR 1.3 95% CI: 0.66-2.5) were observed for PD-1 inhibitors + chemotherapy compared with PD-L1 inhibitors + chemotherapy, but the Bayesian ranking revealed that patients receiving PD-1 inhibitors + chemotherapy tended to have longer OS, PFS benefit, and better treatment response than patients receiving PD-L1 inhibitors + chemotherapy. In terms of safety, no significant difference was observed in their safety profiles. In comparison to placebo + chemotherapy, PD-L1 inhibitors + chemotherapy and PD-1 inhibitors + chemotherapy significantly improved survival for ES-SCLC. According to the available data, PD-L1 inhibitors + chemotherapy and PD-1 inhibitors + chemotherapy had equivalent efficacy and safety; however, the level of evidence of this type of comparison is limited.

Sustainable transparency on recommender systems: Bayesian ranking of images for explainability

Recommender Systems have become crucial in the modern world, commonly guiding users towards relevant content or products, and having a large influence over the decisions of users and citizens. However, ensuring transparency and user trust in these systems remains a challenge; personalized explanations have emerged as a solution, offering justifications for recommendations. Among the existing approaches for generating personalized explanations, using existing visual content created by users is a promising option to maximize transparency and user trust. State-of-the-art models that follow this approach, despite leveraging highly optimized architectures, employ surrogate learning tasks that do not efficiently model the objective of ranking images as explanations for a given recommendation; this leads to a suboptimal training process with high computational costs that may not be reduced without affecting model performance. This work presents BRIE, a novel model where we leverage Bayesian Pairwise Ranking to enhance the training process, allowing us to consistently outperform state-of-the-art models in six real-world datasets while reducing its model size by up to 64 times and its CO2 emissions by up to 75% in training and inference.

A comparison of alternative ranking methods in two-stage clinical trials with multiple interventions: An application to the anxiolysis for laceration repair in children trial.

Multi-arm, multi-stage trials frequently include a standard care to which all interventions are compared. This may increase costs and hinders comparisons among the experimental arms. Furthermore, the standard care may not be evident, particularly when there is a large variation in standard practice. Thus, we aimed to develop an adaptive clinical trial that drops ineffective interventions following an interim analysis before selecting the best intervention at the final stage without requiring a standard care. We used Bayesian methods to develop a multi-arm, two-stage adaptive trial and evaluated two different methods for ranking interventions, the probability that each intervention was optimal (Pbest) and using the surface under the cumulative ranking curve (SUCRA), at both the interim and final analysis. The proposed trial design determines the maximum sample size for each intervention using the Average Length Criteria. The interim analysis takes place at approximately half the pre-specified maximum sample size and aims to drop interventions for futility if either Pbest or the SUCRA is below a pre-specified threshold. The final analysis compares all remaining interventions at the maximum sample size to conclude superiority based on either Pbest or the SUCRA. The two ranking methods were compared across 12 scenarios that vary the number of interventions and the assumed differences between the interventions. The thresholds for futility and superiority were chosen to control type 1 error, and then the predictive power and expected sample size were evaluated across scenarios. A trial comparing three interventions that aim to reduce anxiety for children undergoing a laceration repair in the emergency department was then designed, known as the Anxiolysis for Laceration Repair in Children Trial (ALICE) trial. As the number of interventions increases, the SUCRA results in a higher predictive power compared with Pbest. Using Pbest results in a lower expected sample size when there is an effective intervention. Using the Average Length Criterion, the ALICE trial has a maximum sample size for each arm of 100 patients. This sample size results in a 86% and 85% predictive power using Pbest and the SUCRA, respectively. Thus, we chose Pbest as the ranking method for the ALICE trial. Bayesian ranking methods can be used in multi-arm, multi-stage trials with no clear control intervention. When more interventions are included, the SUCRA results in a higher power than Pbest. Future work should consider whether other ranking methods may also be relevant for clinical trial design.

Drug combinations screening using a Bayesian ranking approach based on dose-response models.

Drug combinations have been of increasing interest in recent years for the treatment of complex diseases such as cancer, as they could reduce the risk of drug resistance. Moreover, in oncology, combining drugs may allow tackling tumor heterogeneity. Identifying potent combinations can be an arduous task since exploring the full dose-response matrix of candidate combinations over a large number of drugs is costly and sometimes unfeasible, as the quantity of available biological material is limited and may vary across patients. Our objective was to develop a rank-based screening approach for drug combinations in the setting of limited biological resources. A hierarchical Bayesian 4-parameter log-logistic (4PLL) model was used to estimate dose-response curves of dose-candidate combinations based on a parsimonious experimental design. We computed various activity ranking metrics, such as the area under the dose-response curve and Bliss synergy score, and we used the posterior distributions of ranks and the surface under the cumulative ranking curve to obtain a comprehensive final ranking of combinations. Based on simulations, our proposed method achieved good operating characteristics to identifying the most promising treatments in various scenarios with limited sample sizes and interpatient variability. We illustrate the proposed approach on real data from a combination screening experiment in acute myeloidleukemia.

Research on the structure of corporate financial management objective system based on BRP method

Abstract Exploring the architecture of corporate financial management objectives of the BRP method is to help companies achieve revenue growth. In this paper, we analyze the motivation for implementing business process reengineering and constructing an intelligent financial management system based on the BRP method and intelligent technology. The DSBPR data analysis and processing model is constructed by using Bayesian ranking model combined with similarity in heterogeneous information networks in the intelligent financial management system. The intelligent financial management system constructed in this paper is analyzed in terms of the operating capacity and profitability of the enterprise. From the viewpoint of operating capability, the turnover rate of receivables, total assets, and current assets decreased by 87.17%, 91.85% and 49.68%, respectively in ten years, and the change rate of inventory turnover was 7.91%. In terms of profitability, there is a difference of 166.12% between the maximum and minimum values of return on net assets. This shows that the use of an intelligent financial management system can provide intuitive data analysis for enterprise development, which helps enterprises to target the coordinated development of enterprise strategies and thus promote the improvement of economic returns.

A Bayesian network meta-analysis for acute thrombosis after lower extremity artery endovascular treatment.

Various endovascular treatment devices have been widely used in the lower extremity arterial disease (LEAD). Their patency efficiency for target lesions has been well studied and reported. Comparison of the risk of acute thrombosis events between the different endovascular treatment devices is unclear. To rank the risk of acute thrombosis events when bare metal stents (BMSs), covered stents (CSs), drug-eluting stents (DESs), drug-coated balloons (DCBs), and conventional percutaneous transluminal balloon angioplasty (PTA) are used to treat LEAD through Bayesian network meta-analysis. We performed a network meta-analysis of randomized controlled trials comparing the risk of 1-year postoperative acute thrombosis between BMSs, CSs, DESs, DCBs, and PTA for treating LEAD. Bayesian random models were used for pooled endovascular treatment modality comparisons. We ranked these treatment modalities via the Bayesian method according to their surface under the cumulative ranking curve (SUCRA) and estimated probabilities. Nineteen studies (38 study arms; 2758 patients) were included. The Bayesian network ranking of treatments indicated that DCB had the lowest risk of acute thrombosis, PTA had the second-lowest risk of thrombosis, and CS, BMS, and DES had the highest risk of thrombosis. Regarding the treatment efficacy, the OR values of the loss of primary patency were significantly lower for DCB (OR = 0.44, 95% CI: 0.30-0.62), DES (OR = 0.36, 95% CI: 0.14-0.94), and CS (OR = 0.31, 95% CI: 0.18,0.56) than for PTA. When BMS was used as a reference, only the OR for CS was significantly lower (OR = 0.41, 95% CI = 0.21-0.82). Correspondingly, the Bayesian ranking of treatments from better to worse target lesion primary patency was CS, DES, DCB, BMS, and PTA. With the available research evidence and according to the network analysis ranking, DES appears to have the highest risk of acute thrombosis and DCB appears to have the lowest risk.

Exploring the comparative cardiovascular death benefits of sodium-glucose cotransporter 2 inhibitors in type 2 diabetes: a frequentist and Bayesian network meta-analysis-based scoring.

Cardiovascular death (CV death) is the most objective component of the primary or secondary endpoint in cardiovascular outcome trials (CVOTs) conducted with sodium-glucose cotransporter 2 inhibitors (SGLT-2is). CV death is often incorporated into primary composite outcomes. It is combined with major adverse cardiovascular events (MACEs) in trials with atherosclerotic cardiovascular disease (ASCVD) at baseline and with hospitalization due to heart failure (hHF) in trials with heart failure at baseline. Unlike the primary composites, CV death reduction by itself demonstrated significant variations among the CVOTs with SGLT-2is. Moreover, the impact of the individual agents within the SGLT-2i group on the reduction in CV death has not been explored objectively. This network meta-analysis was undertaken to construct a hierarchy based on indirect pairwise comparisons and rankings among the individual agents within SGLT-2is. A Cochrane library-based web search yielded 13 randomized controlled trials for analysis. Stata/BE 17.0 and RStudio 2022.07.1 Build 554 software were used to conduct a frequentist and Bayesian network meta-analysis. The effect size was assessed based on the risk ratio (RR). Ranking of the individual agents was performed with a frequentist approach (P-score and a multidimensional scaling [MDS] rank system) and a Bayesian ranking (surface under the cumulative ranking [SUCRA]). Regarding the overall data, SGLT-2is reduced the CV death risk by 12% (RR: 0.88, 95% CI 0.80-0.96). All three scoring methods resulted in empagliflozin scoring the highest. There was a 15% RR reduction in CV death (95% CI 0.71-1.02) in the ASCVD and multiple cardiovascular risk factor (MRF) groups and an 11% RR reduction in the HF group, with empagliflozin ranking the highest in the former group and dapagliflozin in the latter. Empagliflozin ranked the highest compared to the other SGLT-2is in the overall population and the trials including type 2 diabetes (T2D) patients with ASCVD or MRF at baseline, while dapagliflozin ranked the highest in the trials of patients with HF at baseline. https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022381556, identifier CRD42022381556.

Permanent pacemaker implantation following transcatheter aortic valve implantation: a systematic review and Bayesian network meta-analysis

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Permanent pacemaker implantation (PPI) remains a common complication after transcatheter aortic valve implantation (TAVI). Different transcatheter heart valves (THVs) exist, and newly approved devices are growing. A comparison according to the mechanisms of TAVI prosthesis with longer follow-up needs to be made. We address this issue using a systematic review and a Bayesian network meta-analysis (NMA). Methods A systematic literature review was carried out up to Oct 31, 2022, to identify studies reporting the use of balloon-expandable valves (BEVs), self-expandable valves (SEVs), and mechanically expandable valves (MEVs). We compared the various THVs, or standard medical treatment (STD), with a common comparator of surgical aortic valve replacement (SAVR) concerning PPI. Data at the most extended follow-up for each study were extracted. A random-effects Bayesian NMA was used to calculate odds ratios (OR) and 95% credible intervals (CrI) using MetInsight V4.0.0. The surface under the cumulative ranking area (SUCRA) probabilities was selected to calculate the ranking and hierarchy of the different treatments. The larger SUCRA indicates the greater likelihood of becoming the best intervention. Results We included 15 RCTs comprising 12.783 patients, among whom 8.585 underwent TAVI using various commercially available THVs, including BEVs (Sapien & Sapien XT, Sapien 3), MEVs (Corevalve, Evolut & Pro/R, Acurate Neo, Portico), and MEVs (Lotus). The median follow-up was 2.5 years (IQR 1-5). No significant differences in the risk of PPI were observed between STD: 1.70 [0.556, 5.39], Sapien & Sapien XT: 1.39 [0.834, 2.41], Sapien 3: 1.70 [0.977, 2.88], and Acurate Neo: 1.68 [0.768, 3.789], compared with SARV. All the other THVs (Corevalve: 3.07 [1.99. 4.959]; Evolut & Pro/R: 3.19 [1.80, 6.03]; Lotus: 5.22 [2.08, 13.7]; Portico: 7.22 [2.85, 19.4]) showed significant differences in the risk of PPI compared with SARV. In Bayesian ranking results, Sapien & Sapien XT had the highest chance (77%) of providing a lower PPI rate. Meanwhile, Portico THV was ranked the worst (5%) regarding the likelihood of PPI rate (Figure 1). According to the mechanism, all the THVs showed a higher risk for PPI than SARV (BEVs: 1.60 [1.01, 2.54]; SEVs: 3.09 [1.97, 4.96]; and MEV: 5.30 [1.62, 17.7]). STD: 1.92 [0.518, 7.14] had a non-significant PPI rate compared with SARV. SUCRA plot showed that SARV was the intervention with the lowest (95%) probability of PPI, followed by BEVs (65%), STD (55%), SEVs (27%) and MEVs (7%) (Figure 2). In both analyses, the assessment of inconsistency between direct and indirect estimates was not significant (P > 0.05). Conclusions This NMA of RCTs includes a more significant number of patients with longer follow-up. We demonstrated that BEVs are associated with lower PPI rates than SEVs and MEVs. Some newer generations of THVs (Acurate Neo, Evolut R/Pro) performed better than the older ones, reducing the PPI rate.

Immune checkpoint inhibitors in first-line therapies of metastatic or early triple-negative breast cancer: a systematic review and network meta-analysis.

The optimal first-line immune checkpoint inhibitor (ICI) treatment strategy for metastatic or early triple-negative breast cancer (TNBC) has not yet been determined as a result of various randomized controlled trials (RCTs). The purpose of this study was to compare the efficacy and safety of ICIs in patients with metastatic or early TNBC. RCTs comparing the efficacy and safety of ICIs in patients with TNBC were included in the studies. Based on PRISMA guidelines, we estimated pooled hazard ratios (HRs) and odds ratios (ORs) using random-effects models of Bayesian network meta-analysis. Primary outcomes were progression-free survival (PFS) and overall survival (OS). Secondary outcomes included pathologic complete response rate (pCR), grade ≥ 3 treatment-related adverse events (trAEs), immune-related adverse events (irAEs), and grade ≥ 3 irAEs. The criteria for eligibility were met by a total of eight RCTs involving 4,589 patients with TNBC. When ICIs were used in patients without programmed death-ligand 1 (PD-L1) selection, there was a trend toward improved PFS, OS, and pCR, without significant differences. Pembrolizumab plus chemotherapy is superior to other treatment regimens in terms of survival for TNBC patients based on Bayesian ranking profiles. Subgroup analysis by PD-L1 positive population indicated similar results, and atezolizumab plus chemotherapy provided better survival outcomes. Among grade ≥ 3 trAEs and any grade irAEs, there was no statistically significant difference among different ICI agents. The combination of ICIs with chemotherapy was associated with a higher incidence of grade ≥ 3 irAEs. Based on rank probability, the ICI plus chemotherapy group was more likely to be associated with grade ≥ 3 trAEs, any grade irAEs, and grade ≥ 3 irAEs. Hypothyroidism and hyperthyroidism were the most frequent irAEs in patients receiving ICI. ICI regimens had relatively greater efficacy and safety profile. Pembrolizumab plus chemotherapy and atezolizumab plus chemotherapy seem to be superior first-line treatments for intention-to-treat and PD-L1-positive TNBC patients, respectively. It may be useful for making clinical decisions to evaluate the efficacy and safety of different ICIs based on our study. https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022354643.

Comparison of energy-efficiency benchmarking methodologies for residential buildings

Benchmarking with defined metrics is an excellent tool to track progress and obtain the desired goals. Benchmarking end-use energy in the residential sector has been a topic of great interest among researchers in recent times. This research analyses the buildings’ energy performance based on a devised benchmarking procedure for residential buildings in the Indian city of Jaipur. Creation of benchmarking system involves collection, analysis and verification of the results obtained from a similar set of data into consideration. This study explores the relationship between the Energy Performance Index (EPI) and with other influencing factors like; Area, end-use appliances (Fridge, AC, Cooler, etc.) to ensure the contribution of each variable towards the buildings’ end-use energy consumption. In this study, information has been collected and analysed from over 2700 houses of Jaipur City. The independent and dependent variables have been identified, and a five-star benchmarking framework has been developed. There are very few studies available investigating the effectiveness of various benchmarking techniques, the domain still lacks a comparative performance study of black-box and gray-box benchmarking techniques. This study has selected two black box approaches and one gray box approach to design a benchmarking model and has compared the energy performance of sample buildings. According to the Pearson and Spearman correlation coefficients, Multiple Linear Regression (MLR) and Bayesian ranking scores are the most consistent for energy benchmarking of the residential building sector. This study proposes a novel implementation of the composite indicator (C.I.) as a platform for designing energy benchmarking tables. Finally, the study concludes with recommendations for future work to employ numerous or hybrid combinations of benchmarking methodologies that are expected to provide a more accurate depiction of the energy performance of buildings.

Rethinking the Funding Line at the Swiss National Science Foundation: Bayesian Ranking and Lottery

Funding agencies rely on peer review and expert panels to select the research deserving funding. Peer review has limitations, including bias against risky proposals or interdisciplinary research. The inter-rater reliability between reviewers and panels is low, particularly for proposals near the funding line. Funding agencies are also increasingly acknowledging the role of chance. The Swiss National Science Foundation (SNSF) introduced a lottery for proposals in the middle group of good but not excellent proposals. In this article, we introduce a Bayesian hierarchical model for the evaluation process. To rank the proposals, we estimate their expected ranks (ER), which incorporates both the magnitude and uncertainty of the estimated differences between proposals. A provisional funding line is defined based on ER and budget. The ER and its credible interval are used to identify proposals with similar quality and credible intervals that overlap with the provisional funding line. These proposals are entered into a lottery. We illustrate the approach for two SNSF grant schemes in career and project funding. We argue that the method could reduce bias in the evaluation process. R code, data and other materials for this article are available online.

Optimization of treatment options for EGFR-mutant, stage III, unresectable NSCLC: A systematic review and meta-analysis.

8548 Background: The PACIFIC study established a new treatment standard for Stage III unresectable NSCLC, but CRT followed by durvalumab failed to bring survival benefit to patients with EGFR mutations. EGFR-TKIs have been successful in the setting of first-line treatment of advanced NSCLC and postoperative adjuvant treatment of NSCLC. We explored whether locally advanced inoperable patients with EGFR mutations can benefit from EGFR-TKIs and the possibly best treatment regimen through meta-analysis. Methods: Studies involving unresectable stage III NSCLC with EGFR mutations published in PubMed, Embase, Cochrane, ClinicalTrials.gov, and abstracts of important international conferences (ASCO, ESMO, WCLC) from January 1, 2000 to September 30, 2021 were screened. An integrative analysis was performed using STATA (version 16.0), and a network meta-analysis based on a Bayesian framework was performed using R (version 3.6.1) for the studies with a control group. The primary endpoints were progression-free survival (PFS) and overall survival (OS). Results: A total of 3291 patients were identified in 17 studies, including 5 treatment measures including concurrent chemoradiotherapy (CRT), CRT followed by durvalumab (CRT+Durva), TKI monotherapy, radiotherapy combined with TKI (RT+TKI), and CRT combined with TKI (CRT+TKI). PFS with TKI-free treatments (CRT, CRT+Durva) was significantly shorter than TKI-containing ones (TKI, RT+TKI, CRT+TKI) (HR 2.17, 95%CI 1.47-3.19), but its advantage only translated into borderline OS benefit (1.27, 0.99-1.63). In detail, the PFS with TKI-containing measures, including TKI monotherapy (0.66, 0.50-0.87), RT+TKI (0.37, 0.28-0.50), or CRT+TKI (0.14, 0.03-0.75) were all significantly longer than CRT. Furthermore, the PFS with both RT+TKI (0.40, 0.21-0.76) and CRT+TKI (0.15, 0.03-0.74) were significantly longer than CRT+Durva, while no statistical difference existed in PFS between RT+TKI and CRT+TKI. However, TKI alone had significantly shorter PFS than RT+TKI (1.78, 1.17-2.67). There was no statistical difference in OS among all the treatments, with RT+TKI ranking first in the Bayesian ranking. The integrated analysis found that RT+TKI had the longest OS (65.7 months, 55.5-76.0 months) and PFS (21.8 months, 18.0-25.7 months) and the highest response rate (77.7%, 68.8%-86.6%). Severe neutropenia was the most common with CRT+TKI, while RT+TKI brought the highest incidences of radiation pneumonitis and esophagitis. Conclusions: For EGFR mutant Stage III unresectable NSCLC, RT and TKI are both essential. RT+TKI and CRT+TKI have significantly longer PFS than CRT±immunotherapy, and RT+TKI has OS benefit trends. Due to the lack of sufficient studies on CRT+TKI, it is urgent to conduct large randomized clinical trials to explore the optimal treatment for these patients.

Efficacy of first-line treatments in the elderly and non-elderly patients with advanced epidermal growth factor receptor mutated, non-small cell lung cancer: a network meta-analysis

ObjectiveEpidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) are the current standard of care for advanced or metastatic non-small cell lung cancer (NSCLC) patients harboring EGFR activating mutations. However, the optimal strategy for elderly NSCLC patients is still under debate. This study was designed to explore the optimal first-line regimens by comparing diverse strategies for elderly and non-elderly EGFR-mutated NSCLC patients.MethodsA systematic review was conducted to summarize all available randomized controlled trials (RCTs) from PubMed, EMBASE, Cochrane Central Register of Controlled Trials databases, and international conferences before September 30, 2020. The primary outcome was progression free survival (PFS), and the secondary outcome was overall survival (OS). A network meta-analysis (NMA) was constructed using the Bayesian statistical model to synthesize the survival outcomes of all the treatments.ResultsIn total, 12 RCTs were deemed eligible for inclusion with 3779 patients who have received 10 diverse treatments including EGFR-TKIs. Results from the Bayesian ranking suggested that osimertinib was most likely to rank the first in overall population and in elderly patients in PFS, with the cumulative probabilities of 42.20% and 31.46%, respectively. In non-elderly group (younger than 65 years old), standard of care (SoC, representing first-generation EGFR-TKIs in this NMA) + chemotherapy ranked the first (31.66%). As for OS, SoC + chemotherapy ranked first in all patients (64.33%), patients younger than 65 years old (61.98%), or older than 65 years old (34.45%).ConclusionThe regimen of osimertinib is associated with the most favorable PFS in elderly advanced EGFR-mutated NSCLC patients, while SoC + chemotherapy is the optimal strategy in PFS for non-elderly NSCLC patients harboring EGFR activating mutations, and in OS for both elderly and non-elderly EGFR-mutated advanced NSCLC patients.Trial registrationINPLASY protocol 2020100061 https://doi.org/10.37766/inplasy2020.20.0061.

Front-Line Therapy in EGFR Exon 19 Deletion and 21 Leu858Arg Mutations in Advanced Non-Small Cell Lung Cancer: A Network Meta-Analysis

Objective This study aimed to compare the efficacy of different first-line strategies based on different EGFR mutation types (19 deletion and 21 Leu858Arg mutations). Methods We conducted a systematic review and network meta-analysis (NMA) by searching and analyzing RCTs on PubMed, Embase, Cochrane Library, ASCO.org, and ESMO.org, from inception to September 30th, 2020. Results Nineteen RCTs involving 5450 patients were finally included in this study, covering 10 different treatment strategies. The Bayesian ranking results suggested that, in terms of PFS, in the overall population and in patients with 19del mutation, osimertinib was most likely to rank the first, with the cumulative probabilities of 41.89% and 45.73%, respectively, while for patients with 21 Leu858Arg mutation, standard of care (SoC, represents first-generation EGFR-TKIs in this NMA) + chemotherapy was most likely to rank the first, with the cumulative probabilities of 30.81% in PFS. Moreover, SoC + chemotherapy provided the best overall survival benefit for the overall population and patients with 19del, with the cumulative probabilities of 57.85% and 33.51%, respectively. In contrast, for patients with 21 Leu858Arg mutation, dacomitinib showed the most favorable overall survival, with the cumulative probabilities of 36.73%. Conclusions In this NMA, osimertinib and SoC combined with chemotherapy would be the optimal first-line treatment options for advanced NSCLC patients harboring EGFR 19 deletion mutation and 21 Leu858Arg mutation, respectively. This finding is likely to be adopted in clinical practice and provide guidance for future clinical study design. Systematic review registration: INPLASY2020100059.

Comparative efficacy and safety of first-line treatments for advanced non-small cell lung cancer with ALK-rearranged: a meta-analysis of clinical trials

BackgroundWhereas there are many pharmacological interventions prescribed for patients with advanced anaplastic lymphoma kinase (ALK)- rearranged non-small cell lung cancer (NSCLC), comparative data between novel generation ALK-tyrosine kinase inhibitors (TKIs) remain scant. Here, we indirectly compared the efficacy and safety of first-line systemic therapeutic options used for the treatment of ALK-rearranged NSCLC.MethodsWe included all phase 2 and 3 randomised controlled trials (RCTs) comparing any two or three treatment options. Eligible studies reported at least one of the following outcomes: progression free survival (PFS), overall survival (OS), objective response rate (ORR), or adverse events of grade 3 or higher (Grade ≥ 3 AEs). Subgroup analysis was conducted according to central nervous system (CNS) metastases.ResultsA total of 9 RCTs consisting of 2484 patients with 8 treatment options were included in the systematic review. Our analysis showed that alectinib (300 mg and 600 mg), brigatinib, lorlatinib and ensartinib yielded the most favorable PFS. Whereas there was no significant OS or ORR difference among the ALK-TKIs. According to Bayesian ranking profiles, lorlatinib, alectinib 600 mg and alectinib 300 mg had the best PFS (63.7%), OS (35.9%) and ORR (37%), respectively. On the other hand, ceritinib showed the highest rate of severe adverse events (60%).ConclusionOur analysis indicated that alectinib and lorlatinib might be associated with the best therapeutic efficacy in first-line treatment for major population of advanced NSCLC patients with ALK-rearrangement. However, since there is little comparative evidence on the treatment options, there is need for relative trials to fully determine the best treatment options as well as the rapidly evolving treatment landscape.

Identification of miRNA-disease associations via multiple information integration with Bayesian ranking.

In recent years, increasing microRNA (miRNA)-disease associations were identified through traditionally biological experiments. These associations contribute to revealing molecular mechanism of diseases and preventing and curing diseases. To improve the efficiency of miRNA-disease association discovery, some calculation methods were developed as auxiliary tools for researchers. In the current study, we raised a novel model named Bayesian Ranking for MiRNA-Disease Association prediction (BRMDA) by improving Bayesian Personalized Ranking from three aspects: (i) taking advantage of similarity of diseases and miRNAs; (ii) incorporating miRNA bias for miRNAs associated with different number of diseases; and (iii) implementing neighborhood-based approach for new miRNAs and diseases. For each investigated disease, BRMDA used the set of triples (i.e. disease, labeled miRNA, unlabeled miRNA) that reflected association preference of the disease to miRNAs as training set, which made full use of unknown samples rather than simply considering them as negative samples. To investigate the predictive performance of BRMDA, we employed leave-one-out cross-validation and obtained Area Under the Curve of 0.8697, which outperformed many classical methods. Besides, we further implemented three distinct classes of case studies for three common Neoplasms. As a result, there are 44 (Colon Neoplasms), 49 (Esophageal Neoplasms) and 49 (Lung Neoplasms) among the top 50 predicted miRNAs validated through experiments. In short, BRMDA would be a trustable tool for inferring valuable associations.

GBDR: a Bayesian model for precise prediction of pathogenic microorganisms using 16S rRNA gene sequences

BackgroundRecent evidences have suggested that human microorganisms participate in important biological activities in the human body. The dysfunction of host-microbiota interactions could lead to complex human disorders. The knowledge on host-microbiota interactions can provide valuable insights into understanding the pathological mechanism of diseases. However, it is time-consuming and costly to identify the disorder-specific microbes from the biological “haystack” merely by routine wet-lab experiments. With the developments in next-generation sequencing and omics-based trials, it is imperative to develop computational prediction models for predicting microbe-disease associations on a large scale.ResultsBased on the known microbe-disease associations derived from the Human Microbe-Disease Association Database (HMDAD), the proposed model shows reliable performance with high values of the area under ROC curve (AUC) of 0.9456 and 0.8866 in leave-one-out cross validations and five-fold cross validations, respectively. In case studies of colorectal carcinoma, 80% out of the top-20 predicted microbes have been experimentally confirmed via published literatures.ConclusionBased on the assumption that functionally similar microbes tend to share the similar interaction patterns with human diseases, we here propose a group based computational model of Bayesian disease-oriented ranking to prioritize the most potential microbes associating with various human diseases. Based on the sequence information of genes, two computational approaches (BLAST+ and MEGA 7) are leveraged to measure the microbe-microbe similarity from different perspectives. The disease-disease similarity is calculated by capturing the hierarchy information from the Medical Subject Headings (MeSH) data. The experimental results illustrate the accuracy and effectiveness of the proposed model. This work is expected to facilitate the characterization and identification of promising microbial biomarkers.

A multi-task CNN learning model for taxonomic assignment of human viruses

BackgroundTaxonomic assignment is a key step in the identification of human viral pathogens. Current tools for taxonomic assignment from sequencing reads based on alignment or alignment-free k-mer approaches may not perform optimally in cases where the sequences diverge significantly from the reference sequences. Furthermore, many tools may not incorporate the genomic coverage of assigned reads as part of overall likelihood of a correct taxonomic assignment for a sample.ResultsIn this paper, we describe the development of a pipeline that incorporates a multi-task learning model based on convolutional neural network (MT-CNN) and a Bayesian ranking approach to identify and rank the most likely human virus from sequence reads. For taxonomic assignment of reads, the MT-CNN model outperformed Kraken 2, Centrifuge, and Bowtie 2 on reads generated from simulated divergent HIV-1 genomes and was more sensitive in identifying SARS as the closest relation in four RNA sequencing datasets for SARS-CoV-2 virus. For genomic region assignment of assigned reads, the MT-CNN model performed competitively compared with Bowtie 2 and the region assignments were used for estimation of genomic coverage that was incorporated into a naïve Bayesian network together with the proportion of taxonomic assignments to rank the likelihood of candidate human viruses from sequence data.ConclusionsWe have developed a pipeline that combines a novel MT-CNN model that is able to identify viruses with divergent sequences together with assignment of the genomic region, with a Bayesian approach to ranking of taxonomic assignments by taking into account both the number of assigned reads and genomic coverage. The pipeline is available at GitHub via https://github.com/MaHaoran627/CNN_Virus.

P48.16 Network Meta-Analysis of ICI for Extensive Small-Cell Lung Cancer (ESCLC) as Upfront Therapy

Adding ICI to EP is a breakthrough strategy for treating ESCLC in the first-line setting. However, there is no head to head comparison between these agents to guide the clinical decisions. Thus, we performed a systematic review and network meta-analysis (NMA) to evaluate the effectiveness and safety of ICIs in combination with EP for this target population. According to PRISMA-NMA statement, a systematic search of literature was carried out in PUBMED, Cochrane Central Register of Controlled Trials, and Clinicaltrial.gov database up to Jun 31, 2020. Only randomized clinical trials (RCTs) comparing ICIs+EP vs. EP were considered. We performed an NMA using MetaInsight V 3.13 web-based tool, with a Bayesian framework to estimates risk ratios (RRs) and Odds ratios (OR) with their 95% credible intervals (CrIs). Ranking tables with the probability of best treatment choice for each outcomes, derivated from the model and network estimates were obtained. OS and PFS rate at 12 months, ORR, AEs G3-4 , and immune-related AEs were the selected outcomes. Five RCTs that involved more than 2000 pts and 7 interventions met the inclusion criteria. All selected trials had EP as a control arm. No significant differences in the risk of mortality, risk of progression, and risk of immune-related AEs were observed between Atezolizumab (A), Durvalumab+Tremelimumab (DT), Durvalumab (D), Pembrolizumab (P), or Ipilimumab (I) (4 RCTs, n 2615) in association with EP, neither between these interventions and Nivolumab (5 RCTs, n 2759) for ORR and EAs G3-4. In Bayesian ranking results, A and D were most likely to be ranked first for OS (cumulative probability 40%). Meanwhile, P and D were ranked first for ORR (36%) and PFS (30%). In the safety profile, D was ranked with the less probability of AEs G3-4 (30%), but P and I were first for immune-related AEs (15%). In the comparison of anti PD-1 vs. PD-L1 agents, therapy with anti PD-1 had the highest chance to provide better ORR (70%) and less immune-related AEs (66%), but anti PDL-1 were best regarding AEs G3-4 (42%) and PFS rate at 12 months (72%). Due to the comparisons between experimental treatments proceeded only from indirect evidence, inconsistency between direct and indirect estimates was not feasible. This indirect comparison did not find evidence suggesting survival differences between these agents. All treatments showed better efficacy results over EP, except A for ORR. For safety outcomes in PD-1 vs. PD-L1 comparison; even there was a not significant difference in producing AEs G 3-4 or immuno-related AEs, they did not rank similarly. Therefore this distinct pattern of AEs could to be considered for choosing an agent. Producing data related to the quality of life, economic burden, and patient´s preferences may help to define what would the best option for this population.