Multitarget Stool RNA Test for Colorectal Cancer Screening.

Importance: Noninvasive tests for colorectal cancer screening must include sensitive detection of colorectal cancer and precancerous lesions. These tests must be validated for the intended-use population, which includes average-risk individuals 45 years or older. Objective: To evaluate the sensitivity and specificity of a noninvasive, multitarget stool RNA (mt-sRNA) test (ColoSense) test compared with results from a colonoscopy. Design, Setting, and Participants: This phase 3 clinical trial (CRC-PREVENT) was a blinded, prospective, cross-sectional study to support a premarket approval application for a class III medical device. A total of 8920 participants were identified online using social media platforms and enrolled from June 2021 to June 2022 using a decentralized nurse call center. All participants completed the mt-sRNA test, which incorporated a commercially available fecal immunochemical test (FIT), concentration of 8 RNA transcripts, and participant-reported smoking status. Stool samples were collected prior to participants completing a colonoscopy at their local endoscopy center. The mt-sRNA test results (positive or negative) were compared with index lesions observed on colonoscopy. Over the course of 12 months, individuals 45 years and older were enrolled in the clinical trial using the decentralized recruitment strategy. Participants were enrolled from 49 US states and obtained colonoscopies at more than 3800 different endoscopy centers. Main Outcomes and Measures: The primary outcomes included the sensitivity of the mt-sRNA test for detecting colorectal cancer and advanced adenomas and the specificity for no lesions on colonoscopy. Results: The mean (range) age of participants was 55 (45-90) years, with 4% self-identified as Asian, 11% as Black, and 7% as Hispanic. Of the 8920 eligible participants, 36 (0.40%) had colorectal cancer and 606 (6.8%) had advanced adenomas. The mt-sRNA test sensitivity for detecting colorectal cancer was 94%, sensitivity for detecting advanced adenomas was 46%, and specificity for no lesions on colonoscopy was 88%. The mt-sRNA test showed significant improvement in sensitivity for colorectal cancer (94% vs 78%; McNemar P = .01) and advanced adenomas (46% vs 29%; McNemar P < .001) compared with results of the FIT. Conclusions and Relevance: In individuals 45 years and older, the mt-sRNA test showed high sensitivity for colorectal neoplasia (colorectal cancer and advanced adenoma) with significant improvement in sensitivity relative to the FIT. Specificity for no lesions on colonoscopy was comparable to existing molecular diagnostic tests. Trial Registration: ClinicalTrials.gov Identifier: NCT04739722.

Identifying Root Causes of Important Service Failures across Medical Examination Processes with Integration of 4M1E, ITLV, GRA, DEMATEL and FMEA.

Medical examination plays an essential role in most medical treatment processes, and thus, the quality of service relevant to medical examination has great impact on patient satisfaction. The targeted hospital has long been faced with the problem that patient satisfaction of its medical examination department is below average. An assessment model, integrating 4M1E, ITLV, GRA, DEMATEL and FMEA, was developed in this study to identify the root causes of important service failures across medical examination processes, where (1) a cause-and-effect diagram was enhanced with 4M1E, identifying the list of failure modes relevant to service quality over the medical examination process with the 4M1E analysis framework, (2) FMEA experts were enabled to report their assessment results in their preferred ways by using the ITLV scheme, (3) causes of failure to failure modes with was figured out with DEMATEL, and (4) the evaluation results were improved by integrating GRA. Experimental results obtained by the proposed approach are compared with several benchmarks, and it was observed that (1) the results obtained by the proposed model are more suitable when FMEA experts prefer using different assessment languages versus other approaches; (2) the proposed model can figure out the key root causes according to their impact on overall failure modes.

Predicting which genes will respond to transcription factor perturbations.

The ability to predict which genes will respond to the perturbation of a transcription factor serves as a benchmark for our systems-level understanding of transcriptional regulatory networks. In previous work, machine learning models have been trained to predict static gene expression levels in a biological sample by using data from the same or similar samples, including data on their transcription factor binding locations, histone marks, or DNA sequence. We report on a different challenge-training machine learning models to predict which genes will respond to the perturbation of a transcription factor without using any data from the perturbed cells. We find that existing transcription factor location data (ChIP-seq) from human cells have very little detectable utility for predicting which genes will respond to perturbation of a transcription factor. Features of genes, including their preperturbation expression level and expression variation, are very useful for predicting responses to perturbation of any transcription factor. This shows that some genes are poised to respond to transcription factor perturbations and others are resistant, shedding light on why it has been so difficult to predict responses from binding locations. Certain histone marks, including H3K4me1 and H3K4me3, have some predictive power when located downstream of the transcription start site. However, the predictive power of histone marks is much less than that of gene expression level and expression variation. Sequence-based or epigenetic properties of genes strongly influence their tendency to respond to direct transcription factor perturbations, partially explaining the oft-noted difficulty of predicting responsiveness from transcription factor binding location data. These molecular features are largely reflected in and summarized by the gene's expression level and expression variation. Code is available at https://github.com/BrentLab/TFPertRespExplainer.

Multitarget Stool RNA Test for Noninvasive Detection of Colorectal Neoplasias in a Multicenter, Prospective, and Retrospective Cohort.

INTRODUCTION: Effective colorectal cancer (CRC) prevention and screening requires sensitive detection of all advanced neoplasias (CRC and advanced adenomas [AA]). However, existing noninvasive screening approaches cannot accurately detect adenomas with high sensitivity. METHODS: Here, we describe a multifactor assay (RNA-FIT test) that combines 8 stool-derived eukaryotic RNA biomarkers, patient demographic information (smoking status), and a fecal immunochemical test (FIT) to sensitively detect advanced colorectal neoplasias and other non-advanced adenomas in a 1,305-patient, average-risk, prospective cohort. This cohort was supplemented with a 22-patient retrospective cohort consisting of stool samples obtained from patients diagnosed with AA or CRC before treatment or resection. Participants within these cohorts were evaluated with the RNA-FIT assay and an optical colonoscopy. RNA-FIT test results were compared with colonoscopy findings. RESULTS: Model performance was assessed through 5-fold internal cross-validation of the training set (n = 939) and by using the model on a hold out testing set (n = 388). When used on the hold out testing set, the RNA-FIT test attained a 95% sensitivity for CRC (n = 22), 62% sensitivity for AA (n = 52), 25% sensitivity for other non-AA (n = 139), 80% specificity for hyperplastic polyps (n = 74), and 85% specificity for no findings on a colonoscopy (n = 101). DISCUSSION: The RNA-FIT assay demonstrated clinically relevant detection of all grades of colorectal neoplasia, including carcinomas, AAs, and ONAs. This assay could represent a noninvasive option to screen for both CRC and precancerous adenomas.

Dual threshold optimization and network inference reveal convergent evidence from TF binding locations and TF perturbation responses.

A high-confidence map of the direct, functional targets of each transcription factor (TF) requires convergent evidence from independent sources. Two significant sources of evidence are TF binding locations and the transcriptional responses to direct TF perturbations. Systematic data sets of both types exist for yeast and human, but they rarely converge on a common set of direct, functional targets for a TF. Even the few genes that are both bound and responsive may not be direct functional targets. Our analysis shows that when there are many nonfunctional binding sites and many indirect targets, nonfunctional sites are expected to occur in the cis-regulatory DNA of indirect targets by chance. To address this problem, we introduce dual threshold optimization (DTO), a new method for setting significance thresholds on binding and perturbation-response data, and show that it improves convergence. It also enables comparison of binding data to perturbation-response data that have been processed by network inference algorithms, which further improves convergence. The combination of dual threshold optimization and network inference greatly expands the high-confidence TF network map in both yeast and human. Next, we analyze a comprehensive new data set measuring the transcriptional response shortly after inducing overexpression of a yeast TF. We also present a new yeast binding location data set obtained by transposon calling cards and compare it to recent ChIP-exo data. These new data sets improve convergence and expand the high-confidence network synergistically.

Cdk8 and Ssn801 Regulate Oxidative Stress Resistance and Virulence in Cryptococcus neoformans.

Cryptococcus neoformans kills 200,000 people worldwide each year. After inhalation, this environmental yeast proliferates either extracellularly or within host macrophages. Under conditions of immunocompromise, cryptococci disseminate from the lungs to the brain, causing a deadly meningoencephalitis that is difficult and expensive to treat. Cryptococcal adaptation to the harsh lung environment is a critical first step in its pathogenesis, and consequently a compelling topic of study. This adaptation is mediated by a complex transcriptional program that integrates cellular responses to environmental stimuli. Although several key regulators in this process have been examined, one that remains understudied in C. neoformans is the Mediator complex. In other organisms, this complex promotes transcription of specific genes by increasing assembly of the RNA polymerase II preinitiation complex. We focused on the Kinase Module of Mediator, which consists of cyclin C (Ssn801), cyclin-dependent kinase 8 (Cdk8), Med12, and Med13. This module provides important inhibitory control of Mediator complex assembly and activity. Using transcriptomics, we discovered that Cdk8 and Ssn801 together regulate cryptococcal functions such as the ability to grow on acetate and the response to oxidative stress, both of which were experimentally validated. Deletion of CDK8 yielded altered mitochondrial morphology and the dysregulation of genes involved in oxidation-reduction processes. This strain exhibited increased susceptibility to oxidative stress, resulting in an inability of mutant cells to proliferate within phagocytes, decreased lung burdens, and attenuated virulence in vivo These findings increase our understanding of cryptococcal adaptation to the host environment and its regulation of oxidative stress resistance and virulence.IMPORTANCECryptococcus neoformans is a fungal pathogen that primarily affects severely immunocompromised patients, resulting in 200,000 deaths every year. This yeast occurs in the environment and can establish disease upon inhalation into the lungs of a mammalian host. In this harsh environment it must survive engulfment by host phagocytes, including the oxidative stresses it experiences inside them. To adapt to these challenging conditions, C. neoformans deploys a variety of regulatory proteins to alter gene expression levels and enhance its ability to survive. We have elucidated the role of a protein complex that regulates the cryptococcal response to oxidative stress, survival within phagocytes, and ability to cause disease. These findings are important because they advance our understanding of cryptococcal disease, which we hope will help in the efforts to control this devastating infection.

Neutrophil adhesion in brain capillaries reduces cortical blood flow and impairs memory function in Alzheimer's disease mouse models.

Cerebral blood flow (CBF) reductions in Alzheimer's disease patients and related mouse models have been recognized for decades, but the underlying mechanisms and resulting consequences for Alzheimer's disease pathogenesis remain poorly understood. In APP/PS1 and 5xFAD mice we found that an increased number of cortical capillaries had stalled blood flow as compared to in wild-type animals, largely due to neutrophils that had adhered in capillary segments and blocked blood flow. Administration of antibodies against the neutrophil marker Ly6G reduced the number of stalled capillaries, leading to both an immediate increase in CBF and rapidly improved performance in spatial and working memory tasks. This study identified a previously uncharacterized cellular mechanism that explains the majority of the CBF reduction seen in two mouse models of Alzheimer's disease and demonstrated that improving CBF rapidly enhanced short-term memory function. Restoring cerebral perfusion by preventing neutrophil adhesion may provide a strategy for improving cognition in Alzheimer's disease patients.

NetProphet 2.0: mapping transcription factor networks by exploiting scalable data resources.

MOTIVATION: Cells process information, in part, through transcription factor (TF) networks, which control the rates at which individual genes produce their products. A TF network map is a graph that indicates which TFs bind and directly regulate each gene. Previous work has described network mapping algorithms that rely exclusively on gene expression data and 'integrative' algorithms that exploit a wide range of data sources including chromatin immunoprecipitation sequencing (ChIP-seq) of many TFs, genome-wide chromatin marks, and binding specificities for many TFs determined in vitro. However, such resources are available only for a few major model systems and cannot be easily replicated for new organisms or cell types. RESULTS: We present NetProphet 2.0, a 'data light' algorithm for TF network mapping, and show that it is more accurate at identifying direct targets of TFs than other, similarly data light algorithms. In particular, it improves on the accuracy of NetProphet 1.0, which used only gene expression data, by exploiting three principles. First, combining multiple approaches to network mapping from expression data can improve accuracy relative to the constituent approaches. Second, TFs with similar DNA binding domains bind similar sets of target genes. Third, even a noisy, preliminary network map can be used to infer DNA binding specificities from promoter sequences and these inferred specificities can be used to further improve the accuracy of the network map. AVAILABILITY AND IMPLEMENTATION: Source code and comprehensive documentation are freely available at https://github.com/yiming-kang/NetProphet_2.0. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.