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.

Optimizing dose-schedule regimens with bayesian adaptive designs: opportunities and challenges.

Due to the small sample sizes in early-phase clinical trials, the toxicity and efficacy profiles of the dose-schedule regimens determined for subsequent trials may not be well established. The recent development of novel anti-tumor treatments and combination therapies further complicates the problem. Therefore, there is an increasing recognition of the essential place of optimizing dose-schedule regimens, and new strategies are now urgently needed. Bayesian adaptive designs provide a potentially effective way to evaluate several doses and schedules simultaneously in a single clinical trial with higher efficiency, but real-world implementation examples of such adaptive designs are still few. In this paper, we cover the critical factors associated with dose-schedule optimization and review the related innovative Bayesian adaptive designs. The assumptions, characteristics, limitations, and application scenarios of those designs are introduced. The review also summarizes some unresolved issues and future research opportunities for dose-schedule optimization.

Seamless phase II/III design: a useful strategy to reduce the sample size for dose optimization.

BACKGROUND: The traditional more-is-better dose selection paradigm, originally developed for cytotoxic chemotherapeutics, can be problematic when applied to the development of novel molecularly targeted agents. Recognizing this issue, the US Food and Drug Administration initiated Project Optimus to reform the dose optimization and selection paradigm in oncology drug development, emphasizing the need for greater attention to benefit-risk considerations. METHODS: We identify different types of phase II/III dose-optimization designs, classified according to trial objectives and endpoint types. Through computer simulations, we examine their operating characteristics and discuss the relevant statistical and design considerations for effective dose optimization. RESULTS: Phase II/III dose-optimization designs are capable of controlling family-wise type I error rates and achieving appropriate statistical power with substantially smaller sample sizes than the conventional approach while also reducing the number of patients who experience toxicity. Depending on the design and scenario, the sample size savings range from 16.6% to 27.3%, with a mean savings of 22.1%. CONCLUSIONS: Phase II/III dose-optimization designs offer an efficient way to reduce sample sizes for dose optimization and accelerate the development of targeted agents. However, because of interim dose selection, the phase II/III dose-optimization design presents logistical and operational challenges and requires careful planning and implementation to ensure trial integrity.

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.

Shotgun-2: A Bayesian phase I/II basket trial design to identify indication-specific optimal biological doses.

For novel molecularly targeted agents and immunotherapies, the objective of dose-finding is often to identify the optimal biological dose, rather than the maximum tolerated dose. However, optimal biological doses may not be the same for different indications, challenging the traditional dose-finding framework. Therefore, we proposed a Bayesian phase I/II basket trial design, named "shotgun-2," to identify indication-specific optimal biological doses. A dose-escalation part is conducted in stage I to identify the maximum tolerated dose and admissible dose sets. In stage II, dose optimization is performed incorporating both toxicity and efficacy for each indication. Simulation studies under both fixed and random scenarios show that, compared with the traditional "phase I + cohort expansion" design, the shotgun-2 design is robust and can improve the probability of correctly selecting the optimal biological doses. Furthermore, this study provides a useful tool for identifying indication-specific optimal biological doses and accelerating drug development.

Metabolism-associated molecular classification of gastric adenocarcinoma.

Most gastric cancers (GC) are adenocarcinomas, whereas GC is a highly heterogeneous disease due to its molecular heterogeneity. However, traditional morphology-based classification systems, including the WHO classification and Lauren's classification, have limited utility in guiding clinical treatment. We performed nonnegative matrix factorization (NMF) clustering based on 2752 metabolism-associated genes. We characterized each of the subclasses from multiple angles, including subclass-associated metabolism signatures, immune cell infiltration, clinic10al characteristics, drug sensitivity, and pathway enrichment. As a result, four subtypes were identified: immune suppressed, metabolic, mesenchymal/immune exhausted and hypermutated. The subtypes exhibited significant prognostic differences, which suggests that the metabolism-related classification has clinical significance. Metabolic and hypermutated subtypes have better overall survival, and the hypermutated subtype is likely to be sensitive to anti-PD-1 immunotherapy. In addition, our work showed a strong connection with previously established classifications, especially Lei's subtype, to which we provided an interpretation based on the immune cell infiltration perspective, deepening the understanding of GC heterogeneity. Finally, a 120-gene classifier was generated to determine the GC classification, and a 10-gene prognostic model was developed for survival time prediction.

FoxO3a cooperates with RUNX1 to promote chondrogenesis and terminal hypertrophic of the chondrogenic progenitor cells.

FoxO transcription factors (FoxOs) have recently been shown to protect against chondrocyte dysfunction and modulate cartilage homeostasis in osteoarthritis. The mechanism underlying of FoxOs regulate chondrocyte differentiation remains unknown. Runt related transcription factor 1 (RUNX1) mediated both chondrocyte and osteoblast differentiation. Our data showed that FoxO3a and RUNX1 are co-expressed in ATDC5 cells and undifferentiated mesenchyme cells and have similar high levels in chondrocytes undergoing transition from proliferation to hypertrophy. Overexpression of FoxO3a in ATDC5 cells or mouse mesenchymal cells resulted in a potent induction of the chondrocyte differentiation markers. Knockdown FoxO3a or RUNX1 potently inhibits the expressions of chondrocyte differentiation markers, including Sox9, Aggrecan, Col2, and hypertrophic chondrocyte markers including RUNX2, ColX, MMP13 and ADAMTs-5 in ATDC5 cells. Co-immunoprecipitation showed that FoxO3a binds the transcriptional regulator RUNX1. Immunohistochemistry showed that FoxO3a and RUNX1 are highly co-expressed in the proliferative chondrocytes of the growth plates in the hind limbs of newborn mice. Collectively, we revealed that FoxO3a cooperated with RUNX1 promoted chondrocyte differentiation through enhancing both early chondrogenesis and terminal hypertrophic of the chondrogenic progenitor cells, indicating FoxO3a interacting with RUNX1 may be a therapeutic target for the treatment of osteoarthritis and other bone diseases.

Geography-dependent symbiont communities in two oligophagous aphid species.

Aphids and their diverse symbionts have become a good model to study bacteria-arthropod symbiosis. The feeding habits of aphids are usually influenced by a variety of symbionts. Most studies on symbiont diversity have focused on polyphagous aphids, while symbiont community patterns for oligophagous aphids remain unclear. Here, we surveyed the bacterial communities in natural populations of two oligophagous aphids, Melanaphis sacchari and Neophyllaphis podocarpi, in natural populations. Seven common symbionts were detected, among which Buchnera aphidicola and Wolbachia were the most prevalent. In addition, an uncommon Sodalis-like symbiont was also detected in these two aphids, and Gilliamella was found in some samples of M. sacchari. We further assessed the significant variation in symbiont communities within the two aphid species, geographical regions and host specialization using statistical and ordination analyses. Geography was an important factor in shaping the symbiont community structure in these oligophagous aphids. Furthermore, the strong geographical influence may be related to specific environmental factors, especially temperature, among different regions. These findings extend our knowledge of the significance of geography and its associated environmental conditions in the symbiont community structure associated with oligophagous aphids.

Optimal Bayesian hierarchical model to accelerate the development of tissue-agnostic drugs and basket trials.

Tissue-agnostic trials and basket trials enroll patients based on their genetic biomarkers, not tumor type, in an attempt to determine if a new drug can successfully treat disease conditions based on biomarkers. The Bayesian hierarchical model (BHM) provides an attractive approach to design phase II tissue-agnostic trials by allowing information borrowing across multiple disease types. In this article, we elucidate two intrinsic and inevitable issues that may limit the use of BHM to tissue-agnostic trials: sensitivity to the prior specification of the shrinkage parameter and the competing "interest" among disease types in increasing power and controlling type I error. To address these issues, we propose the optimal BHM (OBHM) and clustered OBHM (COBHM) approaches. In these approach, we first specify a flexible utility function to quantify the tradeoff between type I error and power across disease types based on the study objectives, and then we select the prior of the shrinkage parameter to optimize the utility function of clinical and regulatory interest. COBHM further utilizes a simple Bayesian rule to cluster tumor types into sensitive and insensitive subgroups to achieve more accurate information borrowing. Simulation study shows that the OBHM and especially COBHM have desirable operating characteristics, outperforming some existing methods. COBHM effectively balances power and type I error, addresses the sensitivity of the prior selection, and reduces the "unwarranted" subjectivity in the prior selection. It provides a systematic, rigorous way to apply BHM and solve the common problem of blindingly using a non-informative inverse-gamma prior (with a large variance) or priors arbitrarily chosen that may lead to problematic statistical properties.

Shotgun: A Bayesian seamless phase I-II design to accelerate the development of targeted therapies and immunotherapy.

Drug development of novel antitumor agents is conventionally divided by phase and cancer indication. With the advent of new molecularly targeted therapies and immunotherapies, this approach has become inefficient and dysfunctional. We propose a Bayesian seamless phase I-II "shotgun" design to evaluate the safety and antitumor efficacy of a new drug in multiple cancer indications simultaneously. "Shotgun" is used to describe the design feature that the trial begins with an all-comer dose finding phase to identify the maximum tolerated dose (MTD) or recommended phase II dose (RP2D), and then is seamlessly split to multiple indication-specific cohort expansions. Patients treated during dose finding are rolled over to the cohort expansion for more efficient evaluation of efficacy, while patients enrolled in cohort expansion contribute to the continuous learning of the safety and tolerability of the new drug. During cohort expansion, interim analyses are performed to discontinue ineffective or unsafe expansion cohorts early. To improve the efficiency of such interim analyses, we propose a clustered Bayesian hierarchical model (CBHM) to adaptively borrow information across indications. A simulation study shows that compared to conventional approaches and the standard Bayesian hierarchical model, the shotgun design has substantially higher probabilities to discover indications that are responsive to the treatment in question, and is associated with a reasonable false discovery rate. The shotgun provides a phase I-II trial design for accelerating drug development and to build a more robust foundation for subsequent phase III trials. The proposed CBHM methodology also provides an efficient design for basket trials.

Tumour microenvironment-based molecular profiling reveals ideal candidates for high-grade serous ovarian cancer immunotherapy.

OBJECTIVE: Due to limited immunological profiles of high-grade serous ovarian cancer (HGSOC), we aimed to characterize its molecular features to determine whether a specific subset that can respond to immunotherapy exists. MATERIALS AND METHODS: A training cohort of 418 HGSOC samples from TCGA was analysed by consensus non-negative matrix factorization. We correlated the expression patterns with the presence of immune cell infiltrates, immune regulatory molecules and other genomic or epigenetic features. Two independent cohorts containing 482 HGSOCs and in vitro experiments were used for validation. RESULTS: We identified immune and non-immune groups where the former was enriched in signatures that reflect immune cells, infiltration and PD-1 signalling (all, P < 0.001), and presented with a lower chromosomal aberrations but increased neoantigens, tumour mutation burden, and microsatellite instability (all, P < 0.05); this group was further refined into two microenvironment-based subtypes characterized by either immunoactivation or carcinoma-associated fibroblasts (CAFs) and distinct prognosis. CAFs-immune subtype was enriched for factors that mediate immunosuppression and promote tumour progression, including highly expressed stromal signature, TGF-ß signalling, epithelial-mesenchymal transition and tumour-associated M2-polarized macrophages (all, P < 0.001). Robustness of these immune-specific subtypes was verified in validation cohorts, and in vitro experiments indicated that activated-immune subtype may benefit from anti-PD1 antibody therapy (P < 0.05). CONCLUSION: Our findings revealed two immune subtypes with different responses to immunotherapy and indicated that some HGSOCs may be susceptible to immunotherapies or combination 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.

A group sequential design and sample size estimation for an immunotherapy trial with a delayed treatment effect.

A delayed treatment effect is often observed in the confirmatory trials for immunotherapies and is reflected by a delayed separation of the survival curves of the immunotherapy groups versus the control groups. This phenomenon makes the design based on the log-rank test not applicable because this design would violate the proportional hazard assumption and cause loss of power. Thus, we propose a group sequential design allowing early termination on the basis of efficacy based on a more powerful piecewise weighted log-rank test for an immunotherapy trial with a delayed treatment effect. We present an approach on the group sequential monitoring, in which the information time is defined based on the number of events occurring after the delay time. Furthermore, we developed a one-dimensional search algorithm to determine the required maximum sample size for the proposed design, which uses an analytical estimation obtained by the inflation factor as an initial value and an empirical power function calculated by a simulation-based procedure as an objective function. In the simulation, we tested the unstable accuracy of the analytical estimation, the consistent accuracy of the maximum sample size determined by the search algorithm and the advantages of the proposed design on saving sample size.

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.

Diversity of bacterial symbionts associated with Myzus persicae (Sulzer) (Hemiptera: Aphididae: Aphidinae) revealed by 16S rRNA Illumina sequencing.

Aphids are known to be associated with a variety of symbiotic bacteria. To improve our knowledge of the bacterial diversity of polyphagous aphids, in the present study, we investigated the microbiota of the cosmopolitan agricultural pest Myzus persicae (Sulzer). Ninety-two aphid samples collected from different host plants in various regions of China were examined using high-throughput amplicon sequencing. We comprehensively characterized the symbiont diversity of M. persicae and assessed the variations in aphid-associated symbiont communities. We detected a higher diversity of symbionts than has been previously observed. M. persicae hosted the primary endosymbiont Buchnera aphidicola and seven secondary symbionts, among which Wolbachia was the most prevalent and Rickettsia, Arsenophonus, and Spiroplasma were reported for the first time. Ordination analyses and statistical tests revealed that the symbiont flora associated with M. persicae did not change with respect to host plant or geography, which may be due to frequent migrations between different aphid populations. These findings will advance our knowledge of the microbiota of polyphagous insects and will enrich our understanding of assembly of host-microbiome systems.

Comparing Bayesian early stopping boundaries for phase II clinical trials.

When designing phase II clinical trials, it is important to construct interim monitoring rules that achieve a balance between reliable early stopping for futility or safety and maintaining a high true positive probability (TPP), which is the probability of not stopping if the new treatment is truly safe and effective. We define and compare several methods for specifying early stopping boundaries as functions of interim sample size, rather than as fixed cut-offs, using Bayesian posterior probabilities as decision criteria. We consider boundaries with constant, linear, or exponential shapes. For design optimization criteria, we use the TPP and mean number of patients enrolled in the trial. Simulations to evaluate and compare the designs' operating characteristics under a range of scenarios show that, while there is no uniformly optimal boundary, an appropriately calibrated exponential shape maintains high TPP while limiting the number of patients assigned to a treatment with an inferior response rate or an excessive toxicity rate.