Meta-Prediction of Coronary Artery Disease Risk.

Coronary artery disease (CAD) remains the leading cause of mortality and morbidity worldwide. Recent advances in large-scale genome-wide association studies have highlighted the potential of genetic risk, captured as polygenic risk scores (PRS), in clinical prevention. However, the current clinical utility of PRS models is limited to identifying high-risk populations based on the top percentiles of genetic susceptibility. While some studies have attempted integrative prediction using genetic and non-genetic factors, many of these studies have been cross-sectional and focused solely on risk stratification. Our primary objective in this study was to integrate unmodifiable (age / genetics) and modifiable (clinical / biometric) risk factors into a prospective prediction framework which also produces actionable and personalized risk estimates for the purpose of CAD prevention in a heterogenous adult population. Thus, we present an integrative, omnigenic, meta-prediction framework that effectively captures CAD risk subgroups, primarily distinguished by degree and nature of genetic risk, with distinct risk reduction profiles predicted from standard clinical interventions. Initial model development considered ~ 2,000 predictive features, including demographic data, lifestyle factors, physical measurements, laboratory tests, medication usage, diagnoses, and genetics. To power our meta-prediction approach, we stratified the UK Biobank into two primary cohorts: 1) a prevalent CAD cohort used to train baseline and prospective predictive models for contributing risk factors and diagnoses, and 2) an incident CAD cohort used to train the final CAD incident risk prediction model. The resultant 10-year incident CAD risk model is composed of 35 derived meta-features from models trained on the prevalent risk cohort, most of which are predicted baseline diagnoses with multiple embedded PRSs. This model achieved an AUC of 0.81 and macro-averaged F1-score of 0.65, outperforming standard clinical scores and prior integrative models. We further demonstrate that individualized risk reduction profiles can be derived from this model, with genetic risk mediating the degree of risk reduction achieved by standard clinical interventions.

Genetically-informed prediction of short-term Parkinson's disease progression.

Parkinson's disease (PD) treatments modify disease symptoms but have not been shown to slow progression, characterized by gradual and varied motor and non-motor changes overtime. Variation in PD progression hampers clinical research, resulting in long and expensive clinical trials prone to failure. Development of models for short-term PD progression prediction could be useful for shortening the time required to detect disease-modifying drug effects in clinical studies. PD progressors were defined by an increase in MDS-UPDRS scores at 12-, 24-, and 36-months post-baseline. Using only baseline features, PD progression was separately predicted across all timepoints and MDS-UPDRS subparts in independent, optimized, XGBoost models. These predictions plus baseline features were combined into a meta-predictor for 12-month MDS UPDRS Total progression. Data from the Parkinson's Progression Markers Initiative (PPMI) were used for training with independent testing on the Parkinson's Disease Biomarkers Program (PDBP) cohort. 12-month PD total progression was predicted with an F-measure 0.77, ROC AUC of 0.77, and PR AUC of 0.76 when tested on a hold-out PPMI set. When tested on PDBP we achieve a F-measure 0.75, ROC AUC of 0.74, and PR AUC of 0.73. Exclusion of genetic predictors led to the greatest loss in predictive accuracy; ROC AUC of 0.66, PR AUC of 0.66-0.68 for both PPMI and PDBP testing. Short-term PD progression can be predicted with a combination of survey-based, neuroimaging, physician examination, and genetic predictors. Dissection of the interplay between genetic risk, motor symptoms, non-motor symptoms, and longer-term expected rates of progression enable generalizable predictions.

Rapid, Reference-Free human genotype imputation with denoising autoencoders.

Genotype imputation is a foundational tool for population genetics. Standard statistical imputation approaches rely on the co-location of large whole-genome sequencing-based reference panels, powerful computing environments, and potentially sensitive genetic study data. This results in computational resource and privacy-risk barriers to access to cutting-edge imputation techniques. Moreover, the accuracy of current statistical approaches is known to degrade in regions of low and complex linkage disequilibrium. Artificial neural network-based imputation approaches may overcome these limitations by encoding complex genotype relationships in easily portable inference models. Here, we demonstrate an autoencoder-based approach for genotype imputation, using a large, commonly used reference panel, and spanning the entirety of human chromosome 22. Our autoencoder-based genotype imputation strategy achieved superior imputation accuracy across the allele-frequency spectrum and across genomes of diverse ancestry, while delivering at least fourfold faster inference run time relative to standard imputation tools.

Impact of polygenic risk communication: an observational mobile application-based coronary artery disease study.

We developed a smartphone application, MyGeneRank, to conduct a prospective observational cohort study (NCT03277365) involving the automated generation, communication, and electronic capture of response to a polygenic risk score (PRS) for coronary artery disease (CAD). Adults with a smartphone and an existing 23andMe genetic profiling self-referred to the study. We evaluated self-reported actions taken in response to personal CAD PRS information, with special interest in the initiation of lipid-lowering therapy. 19% (721/3,800) of participants provided complete responses for baseline and follow-up use of lipid-lowering therapy. 20% (n = 19/95) of high CAD PRS vs 7.9% (n = 8/101) of low CAD PRS participants initiated lipid-lowering therapy at follow-up (p-value = 0.002). Both the initiation of statin and non-statin lipid-lowering therapy was associated with degree of CAD PRS: 15.2% (n = 14/92) vs 6.0% (n = 6/100) for statins (p-value = 0.018) and 6.8% (n = 8/118) vs 1.6% (n = 2/123) for non-statins (p-value = 0.022) in high vs low CAD PRS, respectively. High CAD PRS was also associated with earlier initiation of lipid lowering therapy (average age of 52 vs 65 years in high vs low CAD PRS respectively, p-value = 0.007). Overall, degree of CAD PRS was associated with use of any lipid-lowering therapy at follow-up: 42.4% (n = 56/132) vs 28.5% (n = 37/130) (p-value = 0.009). We find that digital communication of personal CAD PRS information is associated with increased and earlier lipid-lowering initiation in individuals of high CAD PRS. Loss to follow-up is the primary limitation of this study. Alternative communication routes, and long-term studies with EHR-based outcomes are needed to understand the generalizability and durability of this finding.

How Does C-V2X Help Autonomous Driving to Avoid Accidents?

Accidents are continuously reported for autonomous driving vehicles including those with advanced sensors installed. Some of accidents are usually caused by bad weather, poor lighting conditions and non-line-of-sight obstacles. Cellular Vehicle-to-Everything (C-V2X) radio technology can significantly improve those weak spots for autonomous driving. This paper describes one of the C-V2X system solutions: Vulnerable Road User Collision Warning (VRUCW) for autonomous driving. The paper provides the system architecture, design logic, network topology, message flow, artificial intelligence (AI) and network security feature. As a reference it also includes a commercial project with its test results.

Monogenic and Polygenic Models of Coronary Artery Disease.

PURPOSE OF THE REVIEW: Coronary artery disease (CAD) is a common disease globally attributable to the interplay of complex genetic and lifestyle factors. Here, we review how genomic sequencing advances have broadened the fundamental understanding of the monogenic and polygenic contributions to CAD and how these insights can be utilized, in part by creating polygenic risk estimates, for improved disease risk stratification at the individual patient level. RECENT FINDINGS: Initial studies linking premature CAD with rare familial cases of elevated blood lipids highlighted high-risk monogenic contributions, predominantly presenting as familial hypercholesterolemia (FH). More commonly CAD genetic risk is a function of multiple, higher frequency variants each imparting lower magnitude of risk, which can be combined to form polygenic risk scores (PRS) conveying significant risk to individuals at the extremes. However, gaps remain in clinical validation of PRSs, most notably in non-European populations. With improved and more broadly utilized genomic sequencing technologies, the genetic underpinnings of coronary artery disease are being unraveled. As a result, polygenic risk estimation is poised to become a widely used and powerful tool in the clinical setting. While the use of PRSs to augment current clinical risk stratification for optimization of cardiovascular disease risk by lifestyle change or therapeutic targeting is promising, we await adequately powered, prospective studies, demonstrating the clinical utility of polygenic risk estimation in practice.

Genotype imputation and variability in polygenic risk score estimation.

BACKGROUND: Polygenic risk scores (PRSs) are a summarization of an individual's genetic risk for a disease or trait. These scores are being generated in research and commercial settings to study how they may be used to guide healthcare decisions. PRSs should be updated as genetic knowledgebases improve; however, no guidelines exist for their generation or updating. METHODS: Here, we characterize the variability introduced in PRS calculation by a common computational process used in their generation-genotype imputation. We evaluated PRS variability when performing genotype imputation using 3 different pre-phasing tools (Beagle, Eagle, SHAPEIT) and 2 different imputation tools (Beagle, Minimac4), relative to a WGS-based gold standard. Fourteen different PRSs spanning different disease architectures and PRS generation approaches were evaluated. RESULTS: We find that genotype imputation can introduce variability in calculated PRSs at the individual level without any change to the underlying genetic model. The degree of variability introduced by genotype imputation differs across algorithms, where pre-phasing algorithms with stochastic elements introduce the greatest degree of score variability. In most cases, PRS variability due to imputation is minor (< 5 percentile rank change) and does not influence the interpretation of the score. PRS percentile fluctuations are also reduced in the more informative tails of the PRS distribution. However, in rare instances, PRS instability at the individual level can result in singular PRS calculations that differ substantially from a whole genome sequence-based gold standard score. CONCLUSIONS: Our study highlights some challenges in applying population genetics tools to individual-level genetic analysis including return of results. Rare individual-level variability events are masked by a high degree of overall score reproducibility at the population level. In order to avoid PRS result fluctuations during updates, we suggest that deterministic imputation processes or the average of multiple iterations of stochastic imputation processes be used to generate and deliver PRS results.

CHAC2 is essential for self-renewal and glutathione maintenance in human embryonic stem cells.

Glutathione (GSH), the major non-enzymatic antioxidant, plays a critical role in cellular reactive oxygen species (ROS) neutralization. Moreover, GSH is required for the self-renewal maintenance of human embryonic stem cells (hESCs), and is highly accumulated in undifferentiated cells. Among 8 GSH biosynthesis-related enzymes, we found CHAC2 is highly enriched in undifferentiated hESCs. CHAC2 downregulation in hESCs efficiently decreased the levels of GSH and blocked self-renewal. The self-renewal of sh-CHAC2 cells can be rescued by GSH supplement. CHAC2 downregulation promoted mesoderm differentiation and hampered both teratoma formation and the expression of Nrf2 and glutamate-cysteine ligase (GCL). Notably, CHAC1 knockdown restored the self-renewability of CHAC2-downregulated cells. Although both CHAC1 and CHAC2 purified protein alone showed the catalytic activities to GSH, our data extraordinarily revealed that CHAC2 prevented CHAC1-mediated GSH degradation, which suggests that CHAC2 competes with CHAC1 to maintain GSH homeostasis. This is the first report to demonstrate that CHAC2 is critical for GSH maintenance and the novel roles of the CHAC family in hESC renewal.

Imprinted NanoVelcro Microchips for Isolation and Characterization of Circulating Fetal Trophoblasts: Toward Noninvasive Prenatal Diagnostics.

Circulating fetal nucleated cells (CFNCs) in maternal blood offer an ideal source of fetal genomic DNA for noninvasive prenatal diagnostics (NIPD). We developed a class of nanoVelcro microchips to effectively enrich a subcategory of CFNCs, i.e., circulating trophoblasts (cTBs) from maternal blood, which can then be isolated with single-cell resolution by a laser capture microdissection (LCM) technique for downstream genetic testing. We first established a nanoimprinting fabrication process to prepare the LCM-compatible nanoVelcro substrates. Using an optimized cTB-capture condition and an immunocytochemistry protocol, we were able to identify and isolate single cTBs (Hoechst+/CK7+/HLA-G+/CD45-, 20 µm > sizes > 12 µm) on the imprinted nanoVelcro microchips. Three cTBs were polled to ensure reproducible whole genome amplification on the cTB-derived DNA, paving the way for cTB-based array comparative genomic hybridization (aCGH) and short tandem repeats analysis. Using maternal blood samples collected from expectant mothers carrying a single fetus, the cTB-derived aCGH data were able to detect fetal genders and chromosomal aberrations, which had been confirmed by standard clinical practice. Our results support the use of nanoVelcro microchips for cTB-based noninvasive prenatal genetic testing, which holds potential for further development toward future NIPD solution.

Efficient Generation of Chemically Induced Mesenchymal Stem Cells from Human Dermal Fibroblasts.

Human mesenchymal stromal/stem cells (MSCs) are multipotent and currently undergoing hundreds of clinical trials for disease treatments. To date, no studies have generated induced MSCs from skin fibroblasts with chemicals or growth factors. Here, we established the first chemical method to convert primary human dermal fibroblasts into multipotent, induced MSC-like cells (iMSCs). The conversion method uses a defined cocktail of small molecules and growth factors, and it can achieve efficient conversion with an average rate of 38% in 6 days. The iMSCs have much higher clonogenicity than fibroblasts, and they can be maintained and expanded in regular MSC medium for at least 8 passages and further differentiated into osteoblasts, adipocytes, and chondrocytes. Moreover, the iMSCs can suppress LPS-mediated acute lung injury as effectively as bone marrow-derived mesenchymal stem cells. This finding may greatly benefit stem cell biology, cell therapy, and regenerative medicine.

Subclassification of prostate cancer circulating tumor cells by nuclear size reveals very small nuclear circulating tumor cells in patients with visceral metastases.

BACKGROUND: Although enumeration of circulating tumor cells (CTCs) has shown some clinical value, the pool of CTCs contains a mixture of cells that contains additional information that can be extracted. The authors subclassified CTCs by shape features focusing on nuclear size and related this with clinical information. METHODS: A total of 148 blood samples were obtained from 57 patients with prostate cancer across the spectrum of metastatic states: no metastasis, nonvisceral metastasis, and visceral metastasis. CTCs captured and enumerated on NanoVelcro Chips (CytoLumina, Los Angeles, Calif) were subjected to pathologic review including nuclear size. The distribution of nuclear size was analyzed using a Gaussian mixture model. Correlations were made between CTC subpopulations and metastatic status. RESULTS: Statistical modeling of nuclear size distribution revealed 3 distinct subpopulations: large nuclear CTCs, small nuclear CTCs, and very small nuclear CTCs (vsnCTCs). Small nuclear CTCs and vsnCTC identified those patients with metastatic disease. However, vsnCTC counts alone were found to be elevated in patients with visceral metastases when compared with those without (0.36 ± 0.69 vs 1.95 ± 3.77 cells/mL blood; P<.001). Serial enumeration studies suggested the emergence of vsnCTCs occurred before the detection of visceral metastases. CONCLUSIONS: There are morphologic subsets of CTCs that can be identified by fundamental pathologic approaches, such as nuclear size measurement. The results of this observational study strongly suggest that CTCs contain relevant information regarding disease status. In particular, the detection of vsnCTCs was found to be correlated with the presence of visceral metastases and should be formally explored as a putative blood-borne biomarker to identify patients at risk of developing this clinical evolution of prostate cancer.

Programming thermoresponsiveness of NanoVelcro substrates enables effective purification of circulating tumor cells in lung cancer patients.

Unlike tumor biopsies that can be constrained by problems such as sampling bias, circulating tumor cells (CTCs) are regarded as the "liquid biopsy" of the tumor, providing convenient access to all disease sites, including primary tumor and fatal metastases. Although enumerating CTCs is of prognostic significance in solid tumors, it is conceivable that performing molecular and functional analyses on CTCs will reveal much significant insight into tumor biology to guide proper therapeutic intervention. We developed the Thermoresponsive NanoVelcro CTC purification system that can be digitally programmed to achieve an optimal performance for purifying CTCs from non-small cell lung cancer (NSCLC) patients. The performance of this unique CTC purification system was optimized by systematically modulating surface chemistry, flow rates, and heating/cooling cycles. By applying a physiologically endurable stimulation (i.e., temperature between 4 and 37 °C), the mild operational parameters allow minimum disruption to CTCs' viability and molecular integrity. Subsequently, we were able to successfully demonstrate culture expansion and mutational analysis of the CTCs purified by this CTC purification system. Most excitingly, we adopted the combined use of the Thermoresponsive NanoVelcro system with downstream mutational analysis to monitor the disease evolution of an index NSCLC patient, highlighting its translational value in managing NSCLC.