Neutralizing activity of BBIBP-CorV vaccine-elicited sera against Beta, Delta and other SARS-CoV-2 variants of concern.

The global pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in the generation of variants that may diminish host immune responses to vaccine formulations. Here we show a registered observational clinical trial (NCT04795414), we assess the safety and immunogenicity of the inactivated SARS-CoV-2 vaccine BBIBP-CorV in a cohort of 1006 vaccine recipients. No serious adverse events are observed during the term of the study. Detectable virus-specific antibody is measured and determined to be neutralizing in 698/760 (91.84%) vaccine recipients on day 28 post second vaccine dose and in 220/581 (37.87%) vaccine recipients on day 180 post second vaccine dose, whereas vaccine-elicited sera show varying degrees of reduction in neutralization against a range of key SARS-CoV-2 variants, including variant Alpha, Beta, Gamma, Iota, and Delta. Our work show diminished neutralization potency against multiple variants in vaccine-elicited sera, which indicates the potential need for additional boost vaccinations.

Plasma Short-Chain Fatty Acids Differences in Multiple System Atrophy from Parkinson's Disease.

BACKGROUND: Multiple system atrophy (MSA) and Parkinson's disease (PD) have overlapping symptoms, making diagnosis challenging. Short-chain fatty acids (SCFAs) are produced exclusively by gut microbiota and were reduced in feces of MSA patients. However, plasma SCFA concentrations in MSA patients have not been investigated. OBJECTIVE: We aimed to investigate the plasma SCFAs in MSA patients and to identify the potential differential diagnostic ability. METHODS: Plasma SCFA were measured in 25 MSA patients, 46 healthy controls, and 46 PD patients using gas chromatography-mass spectrometry. Demographic and clinical characteristics of the participants were evaluated. RESULTS: Acetic acid concentration was lower in MSA patients than in healthy controls. Acetic acid and propionic acid concentrations were lower in MSA and MSA with predominant parkinsonism (MSA-P) patients than in PD patients. A receiver operating characteristic curve (ROC) analysis revealed reduced acetic acid concentration discriminated MSA patients from healthy controls with 76% specificity but only 57% sensitivity and an area under the curve (AUC) of 0.68 (95% confidence interval (CI): 0.55-0.81). Combined acetic acid and propionic acid concentrations discriminated MSA patients from PD patients with an AUC of 0.82 (95% CI: 0.71-0.93), 84% specificity and 76% sensitivity. Especially, with combined acetic acid and propionic acid concentrations, MSA-P patients were separated from PD patients with an AUC of 0.89 (95% CI: 0.80-0.97), 91% specificity and 80% sensitivity. CONCLUSION: Plasma SCFAs were decreased in MSA patients. The combined acetic acid and propionic acid concentrations may be a potential biomarker for differentiating MSA patients from PD patients.

R-TPI: rolling toxicity probability interval design to shorten the duration and maintain safety of phase I trials.

To shorten trial duration and improve safety of Phase I trials, we propose R-TPI, a rolling enrollment design that combines the features in model-based designs such as mTPI-2 and rule-based designs such as rolling six. R-TPI employs a novel rolling enrollment scheme, which allows concurrent patient enrollment that is faster than cohort-based enrollment. Bench-marking against rolling six, we find that the R-TPI design is as fast in completing clinical trials but with fewer toxicity events and higher chance of finding the maximum tolerated dose (MTD) in the single scenario laid out in the 2008 rolling six publication. We also find that in a broad setting involving multiple scenarios, R-TPI is generally faster, safer, and more reliable than standard designs. R-TPI is a general design that can be applied to adult and pediatric Phase I trials. It reduces the length of trial duration, leads to safer trials with fewer toxicity events, and maintains relatively a high chance of identifying the MTD.

Bayesian Approaches to Subgroup Analysis and Related Adaptive Clinical Trial Designs.

We review Bayesian and Bayesian decision theoretic approaches to subgroup analysis and applications to subgroup-based adaptive clinical trial designs. Subgroup analysis refers to inference about subpopulations with significantly distinct treatment effects. The discussion mainly focuses on inference for a benefiting subpopulation, that is, a characterization of a group of patients who benefit from the treatment under consideration more than the overall population. We introduce alternative approaches and demonstrate them with a small simulation study. Then, we turn to clinical trial designs. When the selection of the interesting subpopulation is carried out as the trial proceeds, the design becomes an adaptive clinical trial design, using subgroup analysis to inform the randomization and assignment of treatments to patients. We briefly review some related designs. There are a variety of approaches to Bayesian subgroup analysis. Practitioners should consider the type of subpopulations in which they are interested and choose their methods accordingly. We demonstrate how subgroup analysis can be carried out by different Bayesian methods and discuss how they identify slightly different subpopulations.

A Bayesian interval dose-finding design addressingOckham's razor: mTPI-2.

There has been an increasing interest in using interval-based Bayesian designs for dose finding, one of which is the modified toxicity probability interval (mTPI) method. We show that the decision rules in mTPI correspond to an optimal rule under a formal Bayesian decision theoretic framework. However, the probability models in mTPI are overly sharpened by the Ockham's razor, which, while in general helps with parsimonious statistical inference, leads to undesirable decisions from safety perspective. We propose a new framework that blunts the Ockham's razor, and demonstrate the superior performance of the new method, called mTPI-2. An online web tool is provided for users who can generate the design, conduct clinical trials, and examine operating characteristics of the designs.

Toxicity and Efficacy Probability Interval Design for Phase I Adoptive Cell Therapy Dose-Finding Clinical Trials.

Recent trials of adoptive cell therapy (ACT), such as the chimeric antigen receptor (CAR) T-cell therapy, have demonstrated promising therapeutic effects for cancer patients. A main issue in the product development is to determine the appropriate dose of ACT. Traditional phase I trial designs for cytotoxic agents explicitly assume that toxicity increases monotonically with dose levels and implicitly assume the same for efficacy to justify dose escalation. ACT usually induces rapid responses, and the monotonic dose-response assumption is unlikely to hold due to its immunobiologic activities. We propose a toxicity and efficacy probability interval (TEPI) design for dose finding in ACT trials. This approach incorporates efficacy outcomes to inform dosing decisions to optimize efficacy and safety simultaneously. Rather than finding the maximum tolerated dose (MTD), the TEPI design is aimed at finding the dose with the most desirable outcome for safety and efficacy. The key features of TEPI are its simplicity, flexibility, and transparency, because all decision rules can be prespecified prior to trial initiation. We conduct simulation studies to investigate the operating characteristics of the TEPI design and compare it to existing methods. In summary, the TEPI design is a novel method for ACT dose finding, which possesses superior performance and is easy to use, simple, and transparent. Clin Cancer Res; 23(1); 13-20. ©2016 AACR.

A subgroup cluster-based Bayesian adaptive design for precision medicine.

In precision medicine, a patient is treated with targeted therapies that are predicted to be effective based on the patient's baseline characteristics such as biomarker profiles. Oftentimes, patient subgroups are unknown and must be learned through inference using observed data. We present SCUBA, a Subgroup ClUster-based Bayesian Adaptive design aiming to fulfill two simultaneous goals in a clinical trial, 1) to treatments enrich the allocation of each subgroup of patients to their precision and desirable treatments and 2) to report multiple subgroup-treatment pairs (STPs). Using random partitions and semiparametric Bayesian models, SCUBA provides coherent and probabilistic assessment of potential patient subgroups and their associated targeted therapies. Each STP can then be used for future confirmatory studies for regulatory approval. Through extensive simulation studies, we present an application of SCUBA to an innovative clinical trial in gastroesphogeal cancer.

Deciphering Genomic Underpinnings of Quantitative MRI-based Radiomic Phenotypes of Invasive Breast Carcinoma.

Magnetic Resonance Imaging (MRI) has been routinely used for the diagnosis and treatment of breast cancer. However, the relationship between the MRI tumor phenotypes and the underlying genetic mechanisms remains under-explored. We integrated multi-omics molecular data from The Cancer Genome Atlas (TCGA) with MRI data from The Cancer Imaging Archive (TCIA) for 91 breast invasive carcinomas. Quantitative MRI phenotypes of tumors (such as tumor size, shape, margin, and blood flow kinetics) were associated with their corresponding molecular profiles (including DNA mutation, miRNA expression, protein expression, pathway gene expression and copy number variation). We found that transcriptional activities of various genetic pathways were positively associated with tumor size, blurred tumor margin, and irregular tumor shape and that miRNA expressions were associated with the tumor size and enhancement texture, but not with other types of radiomic phenotypes. We provide all the association findings as a resource for the research community (available at http://compgenome.org/Radiogenomics/). These findings pave potential paths for the discovery of genetic mechanisms regulating specific tumor phenotypes and for improving MRI techniques as potential non-invasive approaches to probe the cancer molecular status.

TEAMS: Toxicity- and Efficacy-based Dose Insertion Design with Adaptive Model Selection for Phase I/II Dose-Escalation Trials in Oncology.

In many oncology clinical trials it is necessary to insert new candidate doses when the prespecified doses are poorly elicited. Formal statistical designs with dose insertion are lacking. We propose a dose insertion design for phase I/II clinical trials in oncology based on both efficacy and toxicity outcomes. We also implement Bayesian model selection during the course of the trial so that better models can be adaptively chosen to achieve more accurate inference. The new design, TEAMS, achieves great operating characteristics in extensive simulation studies due to its ability to adaptively insert new doses as well as perform model selection. As a result, appropriate doses are inserted when necessary and desirable doses are selected with higher probabilities at the end of the trial.

Zodiac: A Comprehensive Depiction of Genetic Interactions in Cancer by Integrating TCGA Data.

BACKGROUND: Genetic interactions play a critical role in cancer development. Existing knowledge about cancer genetic interactions is incomplete, especially lacking evidences derived from large-scale cancer genomics data. The Cancer Genome Atlas (TCGA) produces multimodal measurements across genomics and features of thousands of tumors, which provide an unprecedented opportunity to investigate the interplays of genes in cancer. METHODS: We introduce Zodiac, a computational tool and resource to integrate existing knowledge about cancer genetic interactions with new information contained in TCGA data. It is an evolution of existing knowledge by treating it as a prior graph, integrating it with a likelihood model derived by Bayesian graphical model based on TCGA data, and producing a posterior graph as updated and data-enhanced knowledge. In short, Zodiac realizes "Prior interaction map + TCGA data → Posterior interaction map." RESULTS: Zodiac provides molecular interactions for about 200 million pairs of genes. All the results are generated from a big-data analysis and organized into a comprehensive database allowing customized search. In addition, Zodiac provides data processing and analysis tools that allow users to customize the prior networks and update the genetic pathways of their interest. Zodiac is publicly available at www.compgenome.org/ZODIAC. CONCLUSIONS: Zodiac recapitulates and extends existing knowledge of molecular interactions in cancer. It can be used to explore novel gene-gene interactions, transcriptional regulation, and other types of molecular interplays in cancer.

Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data.

Genomic and radiomic imaging profiles of invasive breast carcinomas from The Cancer Genome Atlas and The Cancer Imaging Archive were integrated and a comprehensive analysis was conducted to predict clinical outcomes using the radiogenomic features. Variable selection via LASSO and logistic regression were used to select the most-predictive radiogenomic features for the clinical phenotypes, including pathological stage, lymph node metastasis, and status of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Cross-validation with receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) was employed as the prediction metric. Higher AUCs were obtained in the prediction of pathological stage, ER, and PR status than for lymph node metastasis and HER2 status. Overall, the prediction performances by genomics alone, radiomics alone, and combined radiogenomics features showed statistically significant correlations with clinical outcomes; however, improvement on the prediction performance by combining genomics and radiomics data was not found to be statistically significant, most likely due to the small sample size of 91 cancer cases with 38 radiomic features and 144 genomic features.