PIPET: predicting relevant subpopulations in single-cell data using phenotypic information from bulk data.

Single-cell RNA sequencing has revealed cellular heterogeneity in complex tissues, notably benefiting research on diseases such as cancer. However, the integration of single-cell data from small samples with extensive clinical features in bulk data remains underexplored. In this study, we introduce PIPET, an algorithmic method for predicting relevant subpopulations in single-cell data based on multivariate phenotypic information from bulk data. PIPET generates feature vectors for each phenotype from differentially expressed genes in bulk data and then identifies relevant cellular subpopulations by assessing the similarity between single-cell data and these vectors. Subsequently, phenotype-related cell states can be analyzed based on these subpopulations. In simulated datasets, PIPET showed robust performance in predicting multiclassification cellular subpopulations. Application of PIPET to lung adenocarcinoma single-cell RNA sequencing data revealed cellular subpopulations with poor survival and associations with TP53 mutations. Similarly, in breast cancer single-cell data, PIPET identified cellular subpopulations associated with the PAM50 clinical subtypes and triple-negative breast cancer subtypes. Overall, PIPET effectively identified relevant cellular subpopulations in single-cell data, guided by phenotypic information from bulk data. This approach comprehensively delineates the molecular characteristics of each cellular subpopulation, offering insights into disease-related subpopulations and guiding personalized treatment strategies.

Adaptive Bayesian information borrowing methods for finding and optimizing subgroup-specific doses.

In precision oncology, integrating multiple cancer patient subgroups into a single master protocol allows for the simultaneous assessment of treatment effects in these subgroups and promotes the sharing of information between them, ultimately reducing sample sizes and costs and enhancing scientific validity. However, the safety and efficacy of these therapies may vary across different subgroups, resulting in heterogeneous outcomes. Therefore, identifying subgroup-specific optimal doses in early-phase clinical trials is crucial for the development of future trials. In this article, we review various innovative Bayesian information-borrowing strategies that aim to determine and optimize subgroup-specific doses. Specifically, we discuss Bayesian hierarchical modeling, Bayesian clustering, Bayesian model averaging or selection, pairwise borrowing, and other relevant approaches. By employing these Bayesian information-borrowing methods, investigators can gain a better understanding of the intricate relationships between dose, toxicity, and efficacy in each subgroup. This increased understanding significantly improves the chances of identifying an optimal dose tailored to each specific subgroup. Furthermore, we present several practical recommendations to guide the design of future early-phase oncology trials involving multiple subgroups when using the Bayesian information-borrowing methods.

Adaptive promising zone design for cancer immunotherapy with heterogeneous delayed treatment effect.

Indirect mechanisms of cancer immunotherapies result in delayed treatment effects that vary among patients. Consequently, the use of the log-rank test in trial design and analysis can lead to significant power loss and pose additional challenges for interim decisions in adaptive designs. In this paper, we describe patients' survival using a piecewise proportional hazard model with random lag time and propose an adaptive promising zone design for cancer immunotherapy with heterogeneous delayed effects. We provide solutions for calculating conditional power and adjusting the critical value for the log-rank test with interim data. We divide the sample space into three zones - unfavourable, promising, and favourable -based on re-estimations of the survival parameters, the log-rank test statistic at the interim analysis, and the initial and maximum sample sizes. If the interim results fall into the promising zone, the sample size is increased; otherwise, it remains unchanged. We show through simulations that our proposed approach has greater overall power than the fixed sample design and similar power to the matched group sequential trial. Furthermore, we confirm that critical value adjustment effectively controls the type I error rate inflation. Finally, we provide recommendations on the implementation of our proposed method in cancer immunotherapy trials.

Group sequential design with maximin efficiency robust test for immunotherapy with generalized delayed treatment effect.

The delayed treatment effect is a common feature of immunotherapy, characterized by a gradual onset of action ranging from no effect to full effect. In this study, we propose a generalized delayed treatment effect function to depict the delayed effective process precisely and flexibly. To reduce potential power loss caused by the delayed treatment effect in a group sequential trial, we employ the maximin efficiency robust test, which enhances power robustness across a range of possible delays. We present novel approaches based on the Markov chain method for determining group sequential boundaries, calculating the power function, and estimating the maximum sample size through iterative regressions between the square root of the maximum sample size and the normal quantile of power. Extensive simulation studies validate the effectiveness of our approaches, particularly in balanced trials, demonstrating the validity of group sequential boundaries and the accuracy of maximum sample size estimations. Additionally, we utilize a real trial as an example to compare our considered trial with group sequential trials using the log-rank and generalized piecewise weighted log-rank tests. The results show significantly reduced maximum sample sizes, highlighting the economic advantage of our approach.

Racial Disparities in MiT Family Translocation Renal Cell Carcinoma.

Racial disparities have been documented in the biology and outcome of certain renal cell carcinomas (RCCs) among Black patients. However, little is known about racial differences in MiT family translocation RCC (TRCC). To investigate this issue, we performed a case-control study using data from The Cancer Genome Atlas (TCGA) and the Chinese OrigiMed2020 cohort. A total of 676 patients with RCC (14 Asian, 113 Black, and 525 White) were identified in TCGA, and TRCC was defined as RCC with TFE3/TFEB translocation or TFEB amplification, leading to 21 patients with TRCC (2 Asian, 8 Black, 10 White, and 1 unknown). Asian (2 of 14 [14.3%] vs 10 of 525 [1.9%]; P = .036) and Black (8 of 113 [7.1%] vs 1.9%; P = .007) patients with RCC showed significantly higher prevalence of TRCC compared with White patients with RCC. The overall mortality rate of TRCC was slightly higher in Asian and Black patients compared with White patients (HR: 6.05, P = .069). OrigiMed2020 Chinese patients with RCC had a significantly higher proportion of TRCC with TFE3 fusions than TCGA White patients with RCC (13 of 250 [5.2%] vs 7 of 525 [1.3%]; P = .003). Black patients with TRCC were more likely to exhibit the proliferative subtype than White patients (6 of 8 [75%] vs 2 of 9 [22.2%]; P = .057) for those who had RNA-seq profiles. We present evidence of higher prevalence of TRCC in Asian and Black patients with RCC compared with White patients and show that these tumors in Asian and Black patients have distinct transcriptional signatures and are associated with poor outcomes.

IBIS: identify biomarker-based subgroups with a Bayesian enrichment design for targeted combination therapy.

BACKGROUND: Combination therapies directed at multiple targets have potentially improved treatment effects for cancer patients. Compared to monotherapy, targeted combination therapy leads to an increasing number of subgroups and complicated biomarker-based efficacy profiles, making it more difficult for efficacy evaluation in clinical trials. Therefore, it is necessary to develop innovative clinical trial designs to explore the efficacy of targeted combination therapy in different subgroups and identify patients who are more likely to benefit from the investigational combination therapy. METHODS: We propose a statistical tool called 'IBIS' to Identify BIomarker-based Subgroups and apply it to the enrichment design framework. The IBIS contains three main elements: subgroup division, efficacy evaluation and subgroup identification. We first enumerate all possible subgroup divisions based on biomarker levels. Then, Jensen-Shannon divergence is used to distinguish high-efficacy and low-efficacy subgroups, and Bayesian hierarchical model (BHM) is employed to borrow information within these two subsets for efficacy evaluation. Regarding subgroup identification, a hypothesis testing framework based on Bayes factors is constructed. This framework also plays a key role in go/no-go decisions and enriching specific population. Simulation studies are conducted to evaluate the proposed method. RESULTS: The accuracy and precision of IBIS could reach a desired level in terms of estimation performance. In regard to subgroup identification and population enrichment, the proposed IBIS has superior and robust characteristics compared with traditional methods. An example of how to obtain design parameters for an adaptive enrichment design under the IBIS framework is also provided. CONCLUSIONS: IBIS has the potential to be a useful tool for biomarker-based subgroup identification and population enrichment in clinical trials of targeted combination therapy.

SIRT6 regulates obesity-induced oxidative stress via ENDOG/SOD2 signaling in the heart.

The sirtuin 6 (SIRT6) participates in regulating glucose and lipid homeostasis. However, the function of SIRT6 in the process of cardiac pathogenesis caused by obesity-associated lipotoxicity remains to be unveiled. This study was designed to elucidate the role of SIRT6 in the pathogenesis of cardiac injury due to nutrition overload-induced obesity and explore the downstream signaling pathways affecting oxidative stress in the heart. In this study, we used Sirt6 cardiac-specific knockout murine models treated with a high-fat diet (HFD) feeding to explore the function and mechanism of SIRT6 in the heart tissue during HFD-induced obesity. We also took advantage of neonatal cardiomyocytes to study the role and downstream molecules of SIRT6 during HFD-induced injury in vitro, in which intracellular oxidative stress and mitochondrial content were assessed. We observed that during HFD-induced obesity, Sirt6 loss-of-function aggravated cardiac injury including left ventricular hypertrophy and lipid accumulation. Our results evidenced that upon increased fatty acid uptake, SIRT6 positively regulated the expression of endonuclease G (ENDOG), which is a mitochondrial-resident molecule that plays an important role in mitochondrial biogenesis and redox homeostasis. Our results also showed that SIRT6 positively regulated superoxide dismutase 2 (SOD2) expression post-transcriptionally via ENDOG. Our study gives a new sight into SIRT6 beneficial role in mitochondrial biogenesis of cardiomyocytes. Our data also show that SIRT6 is required to reduce intracellular oxidative stress in the heart triggered by high-fat diet-induced obesity, involving the control of ENDOG/SOD2.

BASIC: A Bayesian adaptive synthetic-control design for phase II clinical trials.

BACKGROUND: Randomized controlled trials are considered the gold standard for evaluating experimental treatments but often require large sample sizes. Single-arm trials require smaller sample sizes but are subject to bias when using historical control data for comparative inferences. This article presents a Bayesian adaptive synthetic-control design that exploits historical control data to create a hybrid of a single-arm trial and a randomized controlled trial. METHODS: The Bayesian adaptive synthetic control design has two stages. In stage 1, a prespecified number of patients are enrolled in a single arm given the experimental treatment. Based on the stage 1 data, applying propensity score matching and Bayesian posterior prediction methods, the usefulness of the historical control data for identifying a pseudo sample of matched synthetic-control patients for making comparative inferences is evaluated. If a sufficient number of synthetic controls can be identified, the single-arm trial is continued. If not, the trial is switched to a randomized controlled trial. The performance of The Bayesian adaptive synthetic control design is evaluated by computer simulation. RESULTS: The Bayesian adaptive synthetic control design achieves power and unbiasedness similar to a randomized controlled trial but on average requires a much smaller sample size, provided that the historical control data patients are sufficiently comparable to the trial patients so that a good number of matched controls can be identified in the historical control data. Compared to a single-arm trial, The Bayesian adaptive synthetic control design yields much higher power and much smaller bias. CONCLUSION: The Bayesian adaptive synthetic-control design provides a useful tool for exploiting historical control data to improve the efficiency of single-arm phase II clinical trials, while addressing the problem of bias when comparing trial results to historical control data. The proposed design achieves power similar to a randomized controlled trial but may require a substantially smaller sample size.

MIDAS-2: an enhanced Bayesian platform design for immunotherapy combinations with subgroup efficacy exploration.

Although immunotherapy combinations have revolutionised cancer treatment, the rapid screening of effective and optimal therapies from large numbers of candidate combinations, as well as exploring subgroup efficacy, remains challenging. This necessitates innovative, integrated, and efficient trial designs. In this study, we extend the MIDAS design to include subgroup exploration and propose an enhanced Bayesian information borrowing platform design called MIDAS-2. MIDAS-2 enables quick and continuous screening of promising combination strategies and exploration of their subgroup effects within a unified platform design framework. We use a regression model to characterize the efficacy pattern in subgroups. Information borrowing is applied through Bayesian hierarchical modelling to improve trial efficiency considering the limited sample size in subgroups. Time trend calibration is also employed to avoid potential baseline drifts. Simulation results demonstrate that MIDAS-2 yields high probabilities for identifying the effective drug combinations as well as promising subgroups, facilitating appropriate selection of the best treatments for each subgroup. The proposed design is robust against small time trend drifts, and the type I error is successfully controlled after calibration when a large drift is expected. Overall, MIDAS-2 provides an adaptive drug screening and subgroup exploring framework to accelerate immunotherapy development in an efficient, accurate, and integrated fashion.

A Bayesian phase I-II clinical trial design to find the biological optimal dose on drug combination.

In recent years, combined therapy shows expected treatment effect as they increase dose intensity, work on multiple targets and benefit more patients for antitumor treatment. However, dose -finding designs for combined therapy face a number of challenges. Therefore, under the framework of phase I-II, we propose a two-stage dose -finding design to identify the biologically optimal dose combination (BODC), defined as the one with the maximum posterior mean utility under acceptable safety. We model the probabilities of toxicity and efficacy by using linear logistic regression models and conduct Bayesian model selection (BMS) procedure to define the most likely pattern of dose-response surface. The BMS can adaptively select the most suitable model during the trial, making the results robust. We investigated the operating characteristics of the proposed design through simulation studies under various practical scenarios and showed that the proposed design is robust and performed well.

A comparative study of adaptive trial designs for dose optimization.

Recently, the US Food and Drug Administration Oncology Center of Excellence initiated Project Optimus to reform the dose optimization and dose selection paradigm in oncology drug development. The agency pointed out that the current paradigm for dose selection-based on the maximum tolerated dose (MTD)-is not sufficient for molecularly targeted therapies and immunotherapies, for which efficacy may not increase after the dose reaches a certain level. In these cases, it is more appropriate to identify the optimal biological dose (OBD) that optimizes the risk-benefit tradeoff of the drug. Project Optimus has spurred tremendous interest and urgent need for guidance on designing dose optimization trials. In this article, we review several representative dose optimization designs, including model-based and model-assisted designs, and compare their operating characteristics based on 10,000 randomly generated scenarios with various dose-toxicity and dose-efficacy curves and some fixed representative scenarios. The results show that, compared with model-based designs, model-assisted methods have advantages of easy-to-implement, robustness, and high accuracy to identify OBD. Some guidance is provided to help biostatisticians and clinicians to choose appropriate dose optimization methods in practice.

MOVICS: an R package for multi-omics integration and visualization in cancer subtyping.

SUMMARY: Stratification of cancer patients into distinct molecular subgroups based on multi-omics data is an important issue in the context of precision medicine. Here, we present MOVICS, an R package for multi-omics integration and visualization in cancer subtyping. MOVICS provides a unified interface for 10 state-of-the-art multi-omics integrative clustering algorithms, and incorporates the most commonly used downstream analyses in cancer subtyping researches, including characterization and comparison of identified subtypes from multiple perspectives, and verification of subtypes in external cohort using two model-free approaches for multiclass prediction. MOVICS also creates feature rich customizable visualizations with minimal effort. By analysing two published breast cancer cohort, we signifies that MOVICS can serve a wide range of users and assist cancer therapy by moving away from the 'one-size-fits-all' approach to patient care. AVAILABILITY AND IMPLEMENTATION: MOVICS package and online tutorial are freely available at https://github.com/xlucpu/MOVICS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Metformin alleviates HFD-induced oxidative stress in hepatocyte via activating SIRT6/PGC-1α/ENDOG signaling.

Metformin is accepted as a first-line drug for the therapy of Type 2 diabetes (T2D), while its mechanism is still controversial. In the present study, by taking advantage of mouse model of high-fat-diet (HFD)-induced obesity and primary mouse hepatocytes (PMHCs) as well as human hepatocyte L02 cell line, we aimed to investigate the involvement of SIRTs during the application of metformin for the therapy of T2D. Our data evidenced that during HFD-induced obesity, there was elevation of nucleus protein acetylation. Analysis of liver tissue showed that among all SIRT members, SIRT6 expression was significantly down-regulated during HFD feeding, which was sustained to regular level with metformin administration. Our result also showed that SIRT6 suppressed intracellular oxidative stress upon FAs stimulation in PMHCs and L02 cells. Mechanistically, SIRT6, but not SIRT1 promoted PGC-1α expression. We further prove that ENDOG is downstream of PGC-1α. In addition, we evidenced that ENDOG protects hepatocytes from lipid-induced oxidative stress, and down-regulation of Endog blunted the protective role of metformin in defending against FAs-induced oxidative stress. Our study established a novel mechanism of metformin in counteracting lipid-induced hepatic injury via activating SIRT6/PGC-1α/ENDOG signaling, thus providing novel targets of metformin in the therapy of T2D.

M&M: A maximum duration design with the Maxcombo test for a group sequential trial of an immunotherapy with a random delayed treatment effect.

A random delayed treatment effect is expected in a confirmatory clinical trial for an immunotherapy due to the individual heterogeneity of physiological conditions. For this reason, the delay time will be assumed to follow a continuous distribution that is difficult to estimate accurately based on the early-phase data, which hinders the specification of the most powerful weighted log-rank test. Therefore, we propose a simulation-based maximum duration design with a robustly powerful Maxcombo test for a group sequential trial for the immunotherapy with the random delayed treatment effect. The design obtains the group sequential boundaries by a simulation procedure and determines the required maximum sample size using a one-dimensional search in which another simulation procedure is used to calculate empirical power. The simulation researches proved the accuracy of the group sequential boundaries and their robustness against the misspecified maximum sample sizes for large samples and revealed their moderate sensitivity against the misspecified survival distributions under the null hypothesis of no difference. The studies investigated whether the type I error rate would inflate under the "inferior" null hypothesis and evaluated the robustness against different distributions of the delay time in terms of the empirical power among the Maxcombo tests and component weighted log-rank tests.

Association between serum 5-methyltetrahydrofolate and homocysteine in Chinese hypertensive participants with different MTHFR C677T polymorphisms: a cross-sectional study.

BACKGROUND AND AIMS: Clarifying the association between 5-methyltetrahydrofolate and homocysteine and the effect pattern of methylene tetrahydrofolate reductase (MTHFR C677T) may contribute to the management of homocysteine and may serve as a significant reference for a randomized controlled trial of 5-methyltetrahydrofolate intervention. This study aimed to reveal the association between these two biochemical indices. METHODS: Study population was drawn from the baseline data of the China Stroke Primary Prevention Trial (CSPPT), including 2328 hypertensive participants. 5-methyltetrahydrofolate and homocysteine were determined by stable-isotope dilution liquid chromatography-tandem mass spectrometry and automatic clinical analyzers, respectively. MTHFR C677T polymorphisms were detected using TaqMan assay. Multiple linear regression was performed to evaluate the association between serum 5-methyltetrahydrofolate and homocysteine. RESULTS: There was a significant inverse association between 5-methyltetrahydrofolate and homocysteine when 5-methyltetrahydrofolate was ≤ 10 ng/mL, and this association was modified by MTHFR C677T (per 1-ng/mL increment; All: ß = - 0.50, P <  0.001; CC: ß = - 0.14, P = 0.087; CT: ß = - 0.20, P = 0.011; TT: ß = - 1.19, P <  0.001). Moreover, the decline in trend in genotype TT participants was stronger than in genotype CC participants (P for difference <  0.001) and genotype CT participants (P for difference <  0.001), while there was no significant difference between genotype CC and genotype CT participants (P for difference = 0.757). CONCLUSIONS: Our data showed a non-linear association between serum homocysteine and 5-methyltetrahydrofolate among Chinese hypertensive adults, however, it could be inversely linearly fitted when serum 5-methyltetrahydrofolate was ≤ 10 ng/mL, and this association was modified by MTHFR C677T.

Borrowing historical information to improve phase I clinical trials using meta-analytic-predictive priors.

Multiple phase I clinical trials may be performed to determine specific maximum tolerated doses (MTD) for specific races or cancer types. In these situations, borrowing historical information has potential to improve the accuracy of estimating toxicity rate and increase the probability of correctly targeting MTD. To utilize historical information in phase I clinical trials, we proposed using the Meta-Analytic-Predictive (MAP) priors to automatically estimate the heterogeneity between historical trials and give a relatively reasonable amount of borrowed information. We then applied MAP priors in some famous phase I trial designs, such as the continual reassessment method (CRM), Keyboard design and Bayesian optimal interval design (BOIN), to accomplish the process of dose finding. A clinical trial example and extended simulation studies show that our proposed methods have robust and efficient statistical performance, compared with those designs which do not consider borrowing information.

Bayesian two-stage sequential enrichment design for biomarker-guided phase II trials for anticancer therapies.

Biomarker-guided phase II trials have become increasingly important for personalized cancer treatment. In this paper, we propose a Bayesian two-stage sequential enrichment design for such biomarker-guided trials. We assumed that all patients were dichotomized as marker positive or marker negative based on their biomarker status; the positive patients were considered more likely to respond to the targeted drug. Early stopping rules and adaptive randomization methods were embedded in the design to control the number of patients receiving inferior treatment. At the same time, a Bayesian hierarchical model was used to borrow information between the positive and negative control arms to improve efficiency. Simulation results showed that the proposed design achieved higher empirical power while controlling the type I error and assigned more patients to the superior treatment arms. The operating characteristics suggested that the design has good performance and may be useful for biomarker-guided phase II trials for evaluating anticancer therapies.

An Adaptive Information Borrowing Platform Design for Testing Drug Candidates of COVID-19.

Background: There have been thousands of clinical trials for COVID-19 to target effective treatments. However, quite a few of them are traditional randomized controlled trials with low efficiency. Considering the three particularities of pandemic disease: timeliness, repurposing, and case spike, new trial designs need to be developed to accelerate drug discovery. Methods: We propose an adaptive information borrowing platform design that can sequentially test drug candidates under a unified framework with early efficacy/futility stopping. Power prior is used to borrow information from previous stages and the time trend calibration method deals with the baseline effectiveness drift. Two drug development strategies are applied: the comprehensive screening strategy and the optimal screening strategy. At the same time, we adopt adaptive randomization to set a higher allocation ratio to the experimental arms for ethical considerations, which can help more patients to receive the latest treatments and shorten the trial duration. Results: Simulation shows that in general, our method has great operating characteristics with type I error controlled and power increased, which can select effective/optimal drugs with a high probability. The early stopping rules can be successfully triggered to stop the trial when drugs are either truly effective or not optimal, and the time trend calibration performs consistently well with regard to different baseline drifts. Compared with the nonborrowing method, borrowing information in the design substantially improves the probability of screening promising drugs and saves the sample size. Sensitivity analysis shows that our design is robust to different design parameters. Conclusions: Our proposed design achieves the goal of gaining efficiency, saving sample size, meeting ethical requirements, and speeding up the trial process and is suitable and well performed for COVID-19 clinical trials to screen promising treatments or target optimal therapies.

Multiomics data reveals the influences of myasthenia gravis on thymoma and its precision treatment.

Thymoma is a rare characterized by a unique association with autoimmune diseases, especially myasthenia gravis (MG). However, little is known about the molecular characteristics of MG-associated thymoma individuals. We aim to examine the influences of MG on thymoma by analyzing multiomics data. A total of 105 samples with thymoma was analyzed from TCGA and these samples were divided into subgroups with MG (MGT) or without MG (MGF) according to clinical information. We then characterized the differential gene expression, pathway activity, somatic mutation frequency, and likelihood of responding to chemotherapies and immunotherapies of the two identified subgroups. MGT subgroup was characterized by elevated inflammatory responses and metabolically related pathways, whereas the MGF subgroup was predicted to be more sensitive to chemotherapy and presented with mesenchymal characteristics. More copy number amplifications and deletions were observed in MGT, whereas GTF2I mutations occur at significantly higher frequencies in MGF. Two molecular subtypes were further identified within MGF samples by unsupervised clustering where one subtype was enriched in TGF-ß and WNT pathways with higher sensitivity to relevant targeted drugs but hardly respond to immunotherapy. For another subtype, a higher recurrence rate of thymoma and more likelihood of responding to immunotherapy were observed. Our findings presented a comprehensive molecular characterization of thymoma patients given the status of MG, and provided potential strategies to help individualized management and treatment.

Neo-adjuvant radiation therapy provides a survival advantage in T3-T4 nodal positive gastric and gastroesophageal junction adenocarcinoma: a SEER database analysis.

BACKGROUND: Due to negative results in clinical trials of postoperative chemoradiation for gastric cancer, at present, there is a tendency to move chemoradiation therapy forward in gastric and gastroesophageal junction (GEJ) adenocarcinoma. Several randomized controlled trials (RCTs) are currently recruiting subjects to investigate the effect of neo-adjuvant radiotherapy (NRT) in gastric and GEJ cancer. Large retrospective studies may be beneficial in clarifying the potential benefit of NRT, providing implications for RCTs. METHODS: We retrieved the clinicopathological and treatment data of gastric and GEJ adenocarcinoma patients who underwent surgical resection and chemotherapy between 2004 and 2015 from Surveillance, Epidemiology, and End Results (SEER) database. We compared survival between NRT and non-NRT patients among four clinical subgroups (T1-2N-, T1-2N+, T3-4N-, and T3-4N+). RESULTS: Overall, 5272 patients were identified, among which 1984 patients received NRT. After adjusting confounding variables, significantly improved survival between patients with and without NRT was only observed in T3-4N+ subgroup [hazard ratio (HR) 0.79, 95% confidence interval (CI): 0.66-0.95; P = 0.01]. Besides, Kaplan-Meier plots showed significant cause-specific survival advantage of NRT in intestinal type (P <  0.001), but not in diffuse type (P = 0.11) for T3-4N+ patients. In the multivariate competing risk model, NRT still showed survival advantage only in T3-4 N+ patients (subdistribution HR: 0.77; 95% CI: 0.64-0.93; P = 0.006), but not in other subgroups. CONCLUSIONS: NRT might benefit resectable gastric and GEJ cancer patients of T3-4 stages with positive lymph nodes, particularly for intestinal-type. Nevertheless, these results should be interpreted with caution, and more data from ongoing RCTs are warranted.