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 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.

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.

AKT2 deficiency alleviates doxorubicin-induced cardiac injury via alleviating oxidative stress in cardiomyocytes.

Doxorubicin (DOX), a widely used chemotherapy agent in cancer treatment, encounters limitations in clinical efficacy due to associated cardiotoxicity. This study aims to explore the role of AKT serine/threonine kinase 2 (AKT2) in mitigating DOX-induced oxidative stress within the heart through both intracellular and extracellular signaling pathways. Utilizing Akt2 knockout (KO) and Nrf2 KO murine models, alongside neonatal rat cardiomyocytes (NRCMs), we systematically investigate the impact of AKT2 deficiency on DOX-induced cardiac injury. Our findings reveal that DOX administration induces significant oxidative stress, a primary contributor to cardiac injury. Importantly, Akt2 deficiency exhibits a protective effect by alleviating DOX-induced oxidative stress. Mechanistically, Akt2 deficiency facilitates nuclear translocation of NRF2, thereby suppressing intracellular oxidative stress by promoting the expression of antioxidant genes. Furthermore, We also observed that AKT2 inhibition facilitates superoxide dismutase 2 (SOD2) expression both inside macrophages and SOD2 secretion to the extracellular matrix, which is involved in lowering oxidative stress in cardiomyocytes upon DOX stimulation. The present study underscores the important role of AKT2 in mitigating DOX-induced oxidative stress through both intracellular and extracellular signaling pathways. Additionally, our findings propose promising therapeutic strategies for addressing DOX-induced cardiomyopathy in clinic.

Cardiac injury activates STING signaling via upregulating SIRT6 in macrophages after myocardial infarction.

AIMS: This work sought to investigate the mechanism underlying the STING signaling pathway during myocardial infarction (MI), and explore the involvement and the role of SIRT6 in the process. MAIN METHODS: Mice underwent the surgery of permanent left anterior descending (LAD) artery constriction. Primary cardiomyocytes (CMs) and fibroblasts were subjected to hypoxia to mimic MI in vitro. STING expression was assessed in the infarct heart, and the effect of STING inhibition on cardiac fibrosis was explored. This study also evaluated the regulatory effect of STING by SIRT6 in macrophages. KEY FINDINGS: STING protein was increased in the infarct heart tissue, highlighting its involvement in the post-MI inflammatory response. Hypoxia-induced death of CMs and fibroblasts contributed to the upregulation of STING in macrophages, establishing the involvement of STING in the intercellular signaling during MI. Inhibition of STING resulted in a significant reduction of cardiac fibrosis at day 14 after MI. Additionally, this study identified SIRT6 as a key regulator of STING via influencing its acetylation and ubiquitination in macrophages, providing novel insights into the posttranscriptional modification and expression of STING at the acute phase after myocardial infarction. SIGNIFICANCE: This work shows the key role of SIRT6/STING signaling in the pathogenesis of cardiac injury after MI, suggesting that targeting this regulatory pathway could be a promising strategy to attenuate cardiac fibrosis after MI.

Integrated Bulk and Single-cell RNA Sequencing Data Constructs and Validates a Prognostic Model for Non-small Cell Lung Cancer.

Background: Most of the current research on prognostic model construction for non-small cell lung cancer (NSCLC) only involves in bulk RNA-seq data without integration of single-cell RNA-seq (scRNA-seq) data. Besides, most of the prognostic models are constructed by predictive genes, ignoring other predictive variables such as clinical features. Methods: We obtained scRNA-seq data from GEO database and bulk RNA-seq data from TCGA database. We construct a prognostic model through the Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression. Furthermore, we performed ESTIMATE, CIBERSORT, immune checkpoint-related analyses and compared drug sensitivity using pRRophetic method judged by IC50 between different risk groups. Results: 14 tumor-related genes were extracted for model construction. The AUC for 1-, 3-, and 5 years overall survival prediction in TCGA and three validation cohorts were almost higher than 0.65, some of which were even higher than 0.7, even 0.8. Besides, calibration curves suggested no departure between model prediction and perfect fit. Additionally, immune-related and drug sensitivity results revealed potential targets and strategies for treatment, which can provide clinical guidance. Conclusion: We integrated traditional bulk RNA-seq and scRNA-seq data, along with predictive clinical features to develop a prognostic model for patients with NSCLC. According to the constructed model, patients in different groups can follow precise and individual therapeutic schedules based on immune characteristics as well as drug sensitivity.

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.

The role of Keap1-Nrf2 signaling pathway during the progress and therapy of diabetic retinopathy.

Diabetic retinopathy is a complex and progressive ocular complication of diabetes mellitus and is a leading cause of blindness in people of working age worldwide. The pathophysiology of diabetic retinopathy involves multifactorial processes, including oxidative stress, inflammation and vascular abnormalities. Understanding the underlying molecular mechanisms involved in its pathogenesis is essential for the development of effective therapeutic interventions. One of the pathways receiving increasing attention is the Keap1-Nrf2 signaling pathway, which regulates the cellular response to oxidative stress by activating Nrf2. In this review, we analyze the current evidence linking Keap1-Nrf2 signaling pathway dysregulation to diabetic retinopathy. In addition, we explore the potential therapeutic implications and the challenges of targeting this pathway for disease management. A comprehensive understanding of the molecular mechanisms of diabetic retinopathy and the therapeutic potential of the Keap1-Nrf2 pathway may pave the way for innovative and effective interventions to combat this vision-threatening disease.

Enhancing phase I dose-finding trials design through dynamic borrowing information and handling late-onset toxicity.

Introduction: In recent years, there has been a growing trend among regulatory agencies to consider the use of historical controls in clinical trials as a means of improving the efficiency of trial design. In this paper, to enhance the statistical operating characteristic of Phase I dose-finding trials, we propose a novel model-assisted design method named "MEM-Keyboard". Methods: The proposed design is based on the multisource exchangeability models (MEMs) that allows for dynamic borrowing of information from multiple supplemental data sources, including historical trial data, to inform the dose-escalation process. Furthermore, with the frequent occurrence of delayed toxicity in novel anti-cancer drugs, we extended our proposed method to handle late-onset toxicity by incorporating historical data. This extended method is referred to as "MEM-TITE-Keyboard" and aims to improve the efficiency of early clinical trials. Results: Simulation studies have indicated that the proposed methods can improve the probability of correctly selecting the maximum tolerated dose (MTD) with an acceptable level of risk, compared to designs that do not account for information borrowing and late-onset toxicity. Discussion: The MEM-Keyboard and MEM-TITE-Keyboard, easy to implement in practice, provide a useful tool for identifying MTD and accelerating drug development.

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.

Silencing of genes by promoter hypermethylation shapes tumor microenvironment and resistance to immunotherapy in clear-cell renal cell carcinomas.

The efficacy of immune checkpoint inhibitors varies in clear-cell renal cell carcinoma (ccRCC), with notable primary resistance among patients. Here, we integrate epigenetic (DNA methylation) and transcriptome data to identify a ccRCC subtype characterized by cancer-specific promoter hypermethylation and epigenetic silencing of Polycomb targets. We develop and validate an index of methylation-based epigenetic silencing (iMES) that predicts primary resistance to immune checkpoint inhibition (ICI) in the BIONIKK trial. High iMES is associated with VEGF pathway silencing, endothelial cell depletion, immune activation/suppression, EZH2 activation, BAP1/SETD2 deficiency, and resistance to ICI. Combination therapy with hypomethylating agents or tyrosine kinase inhibitors may benefit patients with high iMES. Intriguingly, tumors with low iMES exhibit increased endothelial cells and improved ICI response, suggesting the importance of angiogenesis in ICI treatment. We also develop a transcriptome-based analogous system for extended applicability of iMES. Our study underscores the interplay between epigenetic alterations and tumor microenvironment in determining immunotherapy response.

Delineating the interplay between oncogenic pathways and immunity in anaplastic Wilms tumors.

Wilms tumors are highly curable in up to 90% of cases with a combination of surgery and radio-chemotherapy, but treatment-resistant types such as diffuse anaplastic Wilms tumors pose significant therapeutic challenges. Our multi-omics profiling unveils a distinct desert-like diffuse anaplastic Wilms tumor subtype marked by immune/stromal cell depletion, TP53 alterations, and cGAS-STING pathway downregulation, accounting for one-third of all diffuse anaplastic cases. This subtype, also characterized by reduced CD8 and CD3 infiltration and active oncogenic pathways involving histone deacetylase and DNA repair, correlates with poor clinical outcomes. These oncogenic pathways are found to be conserved in anaplastic Wilms tumor cell models. We identify histone deacetylase and/or WEE1 inhibitors as potential therapeutic vulnerabilities in these tumors, which might also restore tumor immunogenicity and potentially enhance the effects of immunotherapy. These insights offer a foundation for predicting outcomes and personalizing treatment strategies for aggressive pediatric Wilms tumors, tailored to individual immunological landscapes.

Development and validation of radiomics nomograms for preoperative prediction of characteristics in non-small cell lung cancer and circulating tumor cells.

To develop and validate 3 radiomics nomograms for preoperative prediction of pathological and progression diagnosis in non-small cell lung cancer (NSCLC) as well as circulating tumor cells (CTCs). A total of 224 and 134 patients diagnosed with NSCLC were respectively gathered in 2018 and 2019 in this study. There were totally 1197 radiomics features that were extracted and quantified from the images produced by computed tomography. Then we selected the radiomics features with predictive value by least absolute shrinkage and selection operator and combined them into radiomics signature. Logistic regression models were built using radiomics signature as the only predictor, which were then converted to nomograms for individualized predictions. Finally, the performance of the nomograms was assessed on both cohorts. Additionally, immunohistochemical correlation analysis was also performed. As for discrimination, the area under the curve of pathological diagnosis nomogram and progression diagnosis nomogram in NSCLC were both higher than 90% in the training cohort and higher than 80% in the validation cohort. The performance of the CTC-diagnosis nomogram was somehow unexpected where the area under the curve were range from 60% to 70% in both cohorts. As for calibration, nonsignificant statistics (P > .05) yielded by Hosmer-Lemeshow tests suggested no departure between model prediction and perfect fit. Additionally, decision curve analyses demonstrated the clinically usefulness of the nomograms. We developed radiomics-based nomograms for pathological, progression and CTC diagnosis prediction in NSCLC respectively. Nomograms for pathological and progression diagnosis were demonstrated well-performed to facilitate the individualized preoperative prediction, while the nomogram for CTC-diagnosis prediction needed improvement.

The role of sirtuins in the regulatin of oxidative stress during the progress and therapy of type 2 diabetes mellitus.

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder characterized by insulin resistance and impaired glucose homeostasis. Oxidative stress, arising from an imbalance between reactive oxygen species (ROS) production and antioxidant defense systems, plays a significant role in the development and progression of T2DM. The sirtuin family, particularly Sirt1, Sirt3, and Sirt6, have emerged as key regulators of oxidative stress in various cellular processes. This review aims to explore the role of the sirtuin family in oxidative stress during the progression of T2DM and their potential as therapeutic targets. We discussed the mechanisms through which sirtuins modulate oxidative stress, their impact on insulin sensitivity, and beta-cell function involved in T2DM. Furthermore, we highlight drugs targeting sirtuin activation and related complications in T2DM. This review summarizes the role as well as mechanism of sirtuins in the regulation of oxidative stress in T2DM and available drugs targeting sirtuins in clinic, which may provide novel insights into the mechanism and therapy of T2DM.

A Unique Gene Signature Predicting Recurrence Free Survival in Stage IA Lung Adenocarcinoma.

Objective: Resected stage IA lung adenocarcinoma (LUAD) has a reported 5-year recurrence free survival (RFS) of 63-81%. A unique gene signature stratifying patients with early stage LUAD as high or low-risk of recurrence would be valuable. Methods: GEO datasets combining European and North American LUAD patients (n=684) were filtered for stage IA (n=105) to develop a robust signature for recurrence (RFSscore). Univariate Cox proportional hazard regression model was used to assess associations of gene expression with RFS and OS. Leveraging a bootstrap approach of these identified upregulated genes allowed construction of a model which was evaluated by Area Under the Received Operating Characteristics. The optimal signature has RFSscore calculated via a linear combination of expression of selected genes weighted by the corresponding Cox regression derived coefficients. Log-rank analysis calculated RFS and OS. Results were validated using the LUAD TCGA transcriptomic NGS based dataset. Results: Rigorous bioinformatic analysis identified a signature of 4 genes: KNSTRN, PAFAH1B3, MIF, CHEK1. Kaplan-Meier analysis of stage IA LUAD with this signature resulted in 5-year RFS for low-risk of 90% compared to 53% for high-risk (HR 6.55, 95%CI 2.65-16.18, p-value <0.001), confirming the robustness of the gene signature with its clinical significance. Validation of the signature using TCGA dataset resulted in an AUC of 0.797 and 5-year RFS for low and high-risk stage IA patients being 91% and 67%, respectively (HR 3.44, 95%CI 1.16-10.23, p-value=0.044). Conclusions: This 4 gene signature stratifies European and North American patients with pathologically confirmed stage IA LUAD into low and high-risk groups for OS and more importantly RFS.

Local continual reassessment methods for dose finding and optimization in drug-combination trials.

Due to the limited sample size and large dose exploration space, obtaining a desirable dose combination is a challenging task in the early development of combination treatments for cancer patients. Most existing designs for optimizing the dose combination are model-based, requiring significant efforts to elicit parameters or prior distributions. Model-based designs also rely on intensive model calibration and may yield unstable performance in the case of model misspecification or sparse data. We propose to employ local, underparameterized models for dose exploration to reduce the hurdle of model calibration and enhance the design robustness. Building upon the framework of the partial ordering continual reassessment method, we develop local data-based continual reassessment method designs for identifying the maximum tolerated dose combination, using toxicity only, and the optimal biological dose combination, using both toxicity and efficacy, respectively. The local data-based continual reassessment method designs only model the local data from neighboring dose combinations. Therefore, they are flexible in estimating the local space and circumventing unstable characterization of the entire dose-exploration surface. Our simulation studies show that our approach has competitive performance compared to widely used methods for finding maximum tolerated dose combination, and it has advantages over existing model-based methods for optimizing optimal biological dose combination.

Integration of scRNA-seq and bulk RNA-seq constructs a stemness-related signature for predicting prognosis and immunotherapy responses in hepatocellular carcinoma.

PURPOSE: Cancer stem cells are associated with unfavorable prognosis in hepatocellular carcinoma (HCC). However, existing stemness-related biomarkers and prognostic models are limited. METHODS: The stemness-related signatures were derived from taking the union of the results obtained by performing WGCNA and CytoTRACE analysis at the bulk RNA-seq and scRNA-seq levels, respectively. Univariate Cox regression and the LASSO were applied for filtering prognosis-related signatures and selecting variables. Finally, ten gene signatures were identified to construct the prognostic model. We evaluated the differences in survival, genomic alternation, biological processes, and degree of immune cell infiltration in the high- and low-risk groups. pRRophetic and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms were utilized to predict chemosensitivity and immunotherapy response. Human Protein Atlas (HPA) database was used to evaluate the protein expressions. RESULTS: A stemness-related prognostic model was constructed with ten genes including YBX1, CYB5R3, CDC20, RAMP3, LDHA, MTHFS, PTRH2, SRPRB, GNA14, and CLEC3B. Kaplan-Meier and ROC curve analyses showed that the high-risk group had a worse prognosis and the AUC of the model in four datasets was greater than 0.64. Multivariate Cox regression analyses verified that the model was an independent prognostic indicator in predicting overall survival, and a nomogram was then built for clinical utility in predicting the prognosis of HCC. Additionally, chemotherapy drug sensitivity and immunotherapy response analyses revealed that the high-risk group exhibited a higher likelihood of benefiting from these treatments. CONCLUSION: The novel stemness-related prognostic model is a promising biomarker for estimating overall survival in HCC.

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.

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.