Machine Learning Identifies a Signature of Nine Exosomal RNAs That Predicts Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Although alpha fetoprotein (AFP) remains a commonly used serological marker of HCC, the sensitivity and specificity of AFP in detecting HCC is often limited. Exosomal RNA has emerged as a promising diagnostic tool for various cancers, but its use in HCC detection has yet to be fully explored. Here, we employed Machine Learning on 114,602 exosomal RNAs to identify a signature that can predict HCC. The exosomal expression data of 118 HCC patients and 112 healthy individuals were stratified split into Training, Validation and Unseen Test datasets. Feature selection was then performed on the initial training dataset using permutation importance, and the predictive performance of the selected features were tested on the validation dataset using Support Vector Machine (SVM) Classifier. A minimum of nine features were identified to be predictive of HCC and these nine features were then evaluated across six different models in an unseen test set. These features, mainly in the immune, platelet/neutrophil and cytoskeletal pathways, exhibited good predictive performance with ROC-AUC from 0.79-0.88 in the unseen test set. Hence, these nine exosomal RNAs have potential to be clinically useful minimally invasive biomarkers for HCC.

Third-Generation Single-Molecule Sequencing for Preimplantation Genetic Testing of Aneuploidy and Segmental Imbalances.

BACKGROUND: Current strategies for preimplantation genetic testing for aneuploidy or structural rearrangements (PGT-A/SR) rely mainly on next-generation sequencing (NGS) and microarray platforms, which are robust but require expensive instrumentation. We explored the suitability of third-generation single-molecule sequencing as a PGT-A/SR screening platform for both aneuploidy and segmental imbalance. METHODS: Single-cell and multicell replicates from aneuploid or segmentally unbalanced cell lines (n = 208) were SurePlex-amplified, randomized, and subjected to (a) Nanopore-based single-molecule sequencing (Oxford Nanopore Technologies) and (b) NGS using a leading commercial PGT-A solution (Illumina VeriSeq PGS). Archival SurePlex-amplified trophectoderm biopsy samples (n = 96) previously analyzed using the commercial kit were blinded and reanalyzed using Nanopore. RESULTS: Nanopore-based PGT-A identified the specific aberration in 95.45% (84/88) and 97.78% (88/90) of single-/multicells with an aneuploidy or segmental imbalance (10-30.5 Mb), respectively. Comparison against the commercial kit's results revealed concordances of 98.86% (87/88) and 98.89% (89/90) for the aneuploid and segmentally unbalanced (10-30.5 Mb aberration) samples, respectively. Detection sensitivity for smaller segmental imbalances (5-5.8 Mb aberration, n = 30) decreased markedly on both platforms. Nanopore-based PGT-A reanalysis of trophectoderm biopsy samples was 97.92% (94/96) concordant with the commercial kit results. CONCLUSION: Up to 24 SurePlex-amplified single-cell, multicell, or trophectoderm samples could be sequenced in a single MinION flow-cell for subsequent preimplantation genetic testing for aneuploidy or structural rearrangements (PGT-A/SR) analysis, with results obtainable in ≤3 days and at per-sample costs that are competitive with commercial offerings. Nanopore's third-generation single-molecule sequencing represents a viable alternative to current commercial NGS-based PGT-A solutions for aneuploidy and segmental imbalance (≥10 Mb) screening of single-/multicell or trophectoderm biopsy samples.

Robust SNP-based prediction of rheumatoid arthritis through machine-learning-optimized polygenic risk score.

BACKGROUND: The popular statistics-based Genome-wide association studies (GWAS) have provided deep insights into the field of complex disorder genetics. However, its clinical applicability to predict disease/trait outcomes remains unclear as statistical models are not designed to make predictions. This study employs statistics-free machine-learning (ML)-optimized polygenic risk score (PRS) to complement existing GWAS and bring the prediction of disease/trait outcomes closer to clinical application. Rheumatoid Arthritis (RA) was selected as a model disease to demonstrate the robustness of ML in disease prediction as RA is a prevalent chronic inflammatory joint disease with high mortality rates, affecting adults at the economic prime. Early identification of at-risk individuals may facilitate measures to mitigate the effects of the disease. METHODS: This study employs a robust ML feature selection algorithm to identify single nucleotide polymorphisms (SNPs) that can predict RA from a set of training data comprising RA patients and population control samples. Thereafter, selected SNPs were evaluated for their predictive performances across 3 independent, unseen test datasets. The selected SNPs were subsequently used to generate PRS which was also evaluated for its predictive capacity as a sole feature. RESULTS: Through robust ML feature selection, 9 SNPs were found to be the minimum number of features for excellent predictive performance (AUC > 0.9) in 3 independent, unseen test datasets. PRS based on these 9 SNPs was significantly associated with (P < 1 × 10-16) and predictive (AUC > 0.9) of RA in the 3 unseen datasets. A RA ML-PRS calculator of these 9 SNPs was developed ( https://xistance.shinyapps.io/prs-ra/ ) to facilitate individualized clinical applicability. The majority of the predictive SNPs are protective, reside in non-coding regions, and are either predicted to be potentially functional SNPs (pfSNPs) or in high linkage disequilibrium (r2 > 0.8) with un-interrogated pfSNPs. CONCLUSIONS: These findings highlight the promise of this ML strategy to identify useful genetic features that can robustly predict disease and amenable to translation for clinical application.

Single-Tube Screen for Rapid Detection of Repeat Expansions in Seven Common Spinocerebellar Ataxias.

BACKGROUND: The autosomal dominantly inherited and genetically heterogeneous spinocerebellar ataxias (SCAs) exhibit highly similar clinical presentations. Many are caused by repeat expansions, of which at least 8 involve CAG repeats. Repeat expansion detection is the only method to confirm disease status in symptomatic individuals. We present a novel strategy to simultaneously screen for the presence of CAG repeat expansion in the genes responsible for SCAs 1, 2, 3, 6, 7, 12, and dentatorubral-pallidoluysian atrophy using a simplified single-tube assay. METHODS: The method employs differentially labeled locus-specific primers and a common triplet-primed primer. Amplified products from each locus are distinguished by a combination of the product size and the fluorophore tag. The upper size limit of the normal allele range was used as the cutoff for distinguishing normal from potentially affected samples, with repeat expansion detected by presence of electrophoretic peaks extending beyond the cutoff. RESULTS: Blinded evaluation of the assay on 60 genotype-known DNA samples correctly detected repeat expansion in the expected SCA repeat locus for all 31 DNA samples. CONCLUSIONS: In principle, this strategy can be applied to the simultaneous screening of any group of disease genes sharing the same repetitive units and/or their reverse complement.

High-Throughput Methylation-Specific Triplet-Primed PCR and Melting Curve Analysis for Selective and Reliable Identification of Actionable FMR1 Genotypes.

Methylated FMR1 full-mutation expansions cause fragile X syndrome. FMR1 premutation carriers are susceptible to other late-onset conditions, and women with premutation are at risk of transmitting a fully expanded FMR1 allele to offspring. Identification of individuals with actionable FMR1 genotypes (full-mutation males and females, and premutation females at risk for primary ovarian insufficiency and/or having fragile X-affected offspring) can enable timely access to intervention services and genetic counseling. This study presents a rapid, first-tier test based on melting curve analysis of methylation-specific triplet-primed PCR amplicons (msTP-PCR MCA) for concurrent detection of FMR1 CGG-repeat expansions and their methylation status. The msTP-PCR MCA assay was optimized on 20 fragile X reference samples, and its performance was evaluated on 111 peripheral blood-derived DNA samples from patients who have undergone prior molecular testing with PCR and/or Southern blot analysis. The msTP-PCR MCA assay detected all samples with a methylated FMR1 CGG-repeat expansion, and had sensitivity, specificity, positive predictive value, and negative predictive values of 100%, 92.06%, 91.1%, and 100%, respectively. The msTP-PCR MCA assay identified premutation/full-mutation mosaicism down to 1%, detected skewed inactivation in females with FMR1 expansions, and enabled selective identification of all individuals with an actionable FMR1 genotype. The msTP-PCR MCA assay may aid in fragile X screening of at-risk populations and newborns and voluntary carrier screening of women of reproductive age.

Functional coding haplotypes and machine-learning feature elimination identifies predictors of Methotrexate Response in Rheumatoid Arthritis patients.

BACKGROUND: Major challenges in large scale genetic association studies include not only the identification of causative single nucleotide polymorphisms (SNPs), but also accounting for SNP-SNP interactions. This study thus proposes a novel feature engineering approach integrating potentially functional coding haplotypes (pfcHap) with machine-learning (ML) feature selection to identify biologically meaningful, possibly causative genetic factors, that take into consideration potential SNP-SNP interactions within the pfcHap, to best predict for methotrexate (MTX) response in rheumatoid arthritis (RA) patients. METHODS: Exome sequencing from 349 RA patients were analysed, of which they were split into training and unseen test set. Inferred pfcHaps were combined with 30 non-genetic features to undergo ML recursive feature elimination with cross-validation using the training set. Predictive capacity and robustness of the selected features were assessed using six popular machine learning models through a train set cross-validation and evaluated in an unseen test set. FINDINGS: Significantly, 100 features (95 pfcHaps, 5 non-genetic factors) were identified to have good predictive performance (AUC: 0.776-0.828; Sensitivity: 0.656-0.813; Specificity: 0.684-0.868) across all six ML models in an unseen test dataset for the prediction of MTX response in RA patients. INTERPRETATION: Majority of the predictive pfcHap SNPs were predicted to be potentially functional and some of the genes in which the pfcHap resides in were identified to be associated with previously reported MTX/RA pathways. FUNDING: Singapore Ministry of Health's National Medical Research Council (NMRC) [NMRC/CBRG/0095/2015; CG12Aug17; CGAug16M012; NMRC/CG/017/2013]; National Cancer Center Research Fund and block funding Duke-NUS Medical School.; Singapore Ministry of Education Academic Research Fund Tier 2 grant MOE2019-T2-1-138.

Machine learning using genetic and clinical data identifies a signature that robustly predicts methotrexate response in rheumatoid arthritis.

OBJECTIVE: To develop a hypothesis-free model that best predicts response to MTX drug in RA patients utilizing biologically meaningful genetic feature selection of potentially functional single nucleotide polymorphisms (pfSNPs) through robust machine learning (ML) feature selection methods. METHODS: MTX-treated RA patients with known response were divided in a 4:1 ratio into training and test sets. From the patients' exomes, potential features for classifier prediction were identified from pfSNPs and non-genetic factors through ML using recursive feature elimination with cross-validation incorporating the random forest classifier. Feature selection was repeated on random subsets of the training cohort, and consensus features were assembled into the final feature set. This feature set was evaluated for predictive potential using six ML classifiers, first by cross-validation within the training set, and finally by analysing its performance with the unseen test set. RESULTS: The final feature set contains 56 pfSNPs and five non-genetic factors. The majority of these pfSNPs are located in pathways related to RA pathogenesis or MTX action and are predicted to modulate gene expression. When used for training in six ML classifiers, performance was good in both the training set (area under the curve: 0.855-0.916; sensitivity: 0.715-0.892; and specificity: 0.733-0.862) and the unseen test set (area under the curve: 0.751-0.826; sensitivity: 0.581-0.839; and specificity: 0.641-0.923). CONCLUSION: Sensitive and specific predictors of MTX response in RA patients were identified in this study through a novel strategy combining biologically meaningful and machine learning feature selection and training. These predictors may facilitate better treatment decision-making in RA management.

Sentiments Regarding COVID-19 Vaccination among Graduate Students in Singapore.

As the COVID-19 pandemic rages unabated, and with more infectious variants, vaccination may offer a way to transit out of strict restrictions on physical human interactions to curb the virus spread and prevent overwhelming the healthcare system. However, vaccine hesitancy threatens to significantly impact our progress towards achieving this. It is thus important to understand the sentiments regarding vaccination for different segments of the population to facilitate the development of effective strategies to persuade these groups. Here, we surveyed the COVID-19 vaccination sentiments among a highly educated group of graduate students from the National University of Singapore (NUS). Graduate students who are citizens of 54 different countries, mainly from Asia, pursue studies in diverse fields, with 32% expressing vaccine hesitancy. Citizenship, religion, country of undergraduate/postgraduate studies, exposure risk and field of study are significantly associated with vaccine sentiments. Students who are Chinese citizens or studied in Chinese Universities prior to joining NUS are more hesitant, while students of Indian descent or studied in India are less hesitant about vaccination. Side effects, safety issues and vaccine choice are the major concerns of the hesitant group. Hence, this study would facilitate the development of strategies that focus on these determinants to enhance vaccine acceptance.

Clinicopathological-Associated Regulatory Network of Deregulated circRNAs in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common and lethal cancers worldwide. Here, we present a novel strategy to identify key circRNA signatures of clinically relevant co-expressed circRNA-mRNA networks in pertinent cancer-pathways that modulate prognosis of HCC patients, by integrating clinic-pathological features, circRNA and mRNA expression profiles. Through further integration with miRNA expression profiles, clinically relevant competing-endogenous-RNA (ceRNA) networks of circRNA-miRNA-mRNAs were constructed. At least five clinically relevant nodal-circRNAs, co-expressed with numerous genes, were identified from the circRNA-mRNA networks. These nodal circRNAs upregulated proliferation (except circRaly) and transformation in cells. The most upregulated nodal-circRNA, circGPC3, associated with higher-grade tumors and co-expressed with 33 genes, competes with 11 mRNAs for two shared miRNAs. circGPC3 was experimentally demonstrated to upregulate cell-cycle and migration/invasion in both transformed and non-transformed liver cell-lines. circGPC3 was further shown to act as a sponge of miR-378a-3p to regulate APSM (Abnormal spindle-like microcephaly associated) expression and modulate cell transformation. This study identifies 5 key nodal master circRNAs in a clinically relevant circRNA-centric network that are significantly associated with poorer prognosis of HCC patients and promotes tumorigenesis in cell-lines. The identification and characterization of these key circRNAs in clinically relevant circRNA-mRNA and ceRNA networks may facilitate the design of novel strategies targeting these important regulators for better HCC prognosis.

Simultaneous Screening of the FRAXA and FRAXE Loci for Rapid Detection of FMR1 CGG and/or AFF2 CCG Repeat Expansions by Triplet-Primed PCR.

Moderate to hyper-expansion of trinucleotide repeats at the FRAXA and FRAXE fragile sites, with or without concurrent hypermethylation, has been associated with intellectual disability and other conditions. Unlike molecular diagnosis of FMR1 CGG repeat expansions in FRAXA, current detection of AFF2 CCG repeat expansions in FRAXE relies on low-throughput and otherwise inefficient techniques combining Southern blot analysis and PCR. A novel triplet-primed PCR assay was developed for simultaneous screening for trinucleotide repeat expansions at the FRAXA and FRAXE fragile sites, and was validated using archived clinical samples of known FMR1 and AFF2 genotypes. Population samples and FRAXE-affected samples were sequenced for the evaluation of variations in the AFF2 CCG repeat structure. The duplex assay accurately identified expansions at the FMR1 and AFF2 trinucleotide repeat loci. On Sanger sequencing of the AFF2 CCG repeat, the single-nucleotide polymorphism variant rs868914124(C) that effectively adds two CCG repeats at the 5'-end, was enriched in the Malay population and with short repeats (<11 CCGs), and was present in all six expanded AFF2 alleles of this study. All expanded AFF2 alleles contained multiple non-CCG interruptions toward the 5'-end of the repeat. A sensitive, robust, and rapid assay has been developed for the simultaneous detection of expansion mutations at the FMR1 and AFF2 trinucleotide repeat loci, simplifying screening for FRAXA- and FRAXE-associated disorders.

Robust Performance of Potentially Functional SNPs in Machine Learning Models for the Prediction of Atorvastatin-Induced Myalgia.

Statins can cause muscle symptoms resulting in poor adherence to therapy and increased cardiovascular risk. We hypothesize that combinations of potentially functional SNPs (pfSNPs), rather than individual SNPs, better predict myalgia in patients on atorvastatin. This study assesses the value of potentially functional single nucleotide polymorphisms (pfSNPs) and employs six machine learning algorithms to identify the combination of SNPs that best predict myalgia. Methods: Whole genome sequencing of 183 Chinese, Malay and Indian patients from Singapore was conducted to identify genetic variants associated with atorvastatin induced myalgia. To adjust for confounding factors, demographic and clinical characteristics were also examined for their association with myalgia. The top factor, sex, was then used as a covariate in the whole genome association analyses. Variants that were highly associated with myalgia from this and previous studies were extracted, assessed for potential functionality (pfSNPs) and incorporated into six machine learning models. Predictive performance of a combination of different models and inputs were compared using the average cross validation area under ROC curve (AUC). The minimum combination of SNPs to achieve maximum sensitivity and specificity as determined by AUC, that predict atorvastatin-induced myalgia in most, if not all the six machine learning models was determined. Results: Through whole genome association analyses using sex as a covariate, a larger proportion of pfSNPs compared to non-pf SNPs were found to be highly associated with myalgia. Although none of the individual SNPs achieved genome wide significance in univariate analyses, machine learning models identified a combination of 15 SNPs that predict myalgia with good predictive performance (AUC >0.9). SNPs within genes identified in this study significantly outperformed SNPs within genes previously reported to be associated with myalgia. pfSNPs were found to be more robust in predicting myalgia, outperforming non-pf SNPs in the majority of machine learning models tested. Conclusion: Combinations of pfSNPs that were consistently identified by different machine learning models to have high predictive performance have good potential to be clinically useful for predicting atorvastatin-induced myalgia once validated against an independent cohort of patients.

Highly deregulated lncRNA LOC is associated with overall worse prognosis in Hepatocellular Carcinoma patients.

Although numerous long non-coding RNAs (lncRNAs) were reported to be deregulated in Hepatocellular Carcinoma (HCC), experimentally characterized, and/or associated with patient's clinical characteristics, there is, thus far, minimal concerted research strategy to identify deregulated lncRNAs that modulate prognosis of HCC patients. Here, we present a novel strategy where we identify lncRNAs, which are not only de-regulated in HCC patients, but are also associated with pertinent clinical characteristics, potentially contributing to the prognosis of HCC patients. LOC101926913 (LOC) was further characterized because it is the most highly differentially expressed amongst those that are associated with the most number of clinical features (tumor-stage, vascular and tumor invasion and poorer overall survival). Experimental gain- and loss-of-function manipulation of LOC in liver cell-lines highlight LOC as a potential onco-lncRNA promoting cell proliferation, anchorage independent growth and invasion. LOC expression in cells up-regulated genes involved in GTPase-activities and downregulated genes associated with cellular detoxification, oxygen- and drug-transport. Hence, LOC may represent a novel therapeutic target, modulating prognosis of HCC patients through up-regulating GTPase-activities and down-regulating detoxification, oxygen- and drug-transport. This strategy may thus be useful for the identification of clinically relevant lncRNAs as potential biomarkers/targets that modulate prognosis in other cancers as well.

Rapid Molecular Screen of Spinocerebellar Ataxia Types 1, 2, and 3 by Triplet-Primed PCR and Melting Curve Analysis.

The autosomal dominantly inherited spinocerebellar ataxias (SCAs) can be caused by dynamic mutations of short tandem repeats within various genes. Because of the significant clinical overlap among the various SCA types, molecular screening of multiple genetic loci by fluorescent PCR and capillary electrophoresis is necessary to identify the causative repeat expansion. We describe a simple, rapid, and inexpensive strategy to screen for CAG repeat expansion mutations at the ATXN1, ATXN2, and ATXN3 loci using melting curve analysis of triplet-primed PCR products. Plasmid DNAs of known repeat sizes were used to generate threshold melt peak temperatures, which rapidly and effectively distinguish samples carrying an expanded allele from those carrying nonexpanded alleles. Melting curve analysis-positive samples were confirmed by capillary electrophoresis sizing of the triplet-primed PCR products. All three assays achieved 100% sensitivity, with 95% CIs of 67.86% to 100% (SCA1), 74.65% to 100% (SCA2), and 91.58% to 100% (SCA3). The SCA1 assay also achieved 100% specificity (95% CI, 97.52%-100%), whereas the SCA2 and SCA3 assays achieved specificity of 99.46% (95% CI, 96.56%-99.97%) and 99.32% (95% CI, 95.70%-99.96%), respectively. These screening assays provide robust and highly accurate detection of expanded alleles and are amenable to large-scale screening while minimizing the need for capillary electrophoresis sizing for every sample.

Network of clinically-relevant lncRNAs-mRNAs associated with prognosis of hepatocellular carcinoma patients.

Long non-coding RNAs (lncRNAs) are often aberrantly expressed in Hepatocellular Carcinoma (HCC). We hypothesize that lncRNAs modulate HCC prognoses through differential deregulation of key lncRNAs affecting important gene network in key cancer pathways associated with pertinent clinical phenotype. Here, we present a novel approach integrating lncRNA-mRNA expression profiles with clinical characteristics to identify lncRNA signatures in clinically-relevant co-expression lncRNA-mRNA networks residing in pertinent cancer pathways. Notably one network, associated with poorer prognosis, comprises five up-regulated lncRNAs significantly correlated (|Pearson Correlation Coefficient|≥ 0.9) with 91 up-regulated genes in the cell-cycle and Rho-GTPase pathways. All 5 lncRNAs and 85/91 (93.4%) of the correlated genes were significantly associated with higher tumor-grade while 3/5 lncRNAs were also associated with no tumor capsule. Interestingly, 2/5 lncRNAs that are correlated with numerous genes in this oncogenic network were experimentally shown to up-regulate genes involved in cell-cycle and transcriptional regulation. Another network comprising 4 down-regulated lncRNAs and 8 down-regulated metallothionein-family genes are significantly associated with tumor invasion. The identification of these key lncRNAs signatures that deregulate important network of genes in key cancer pathways associated with pertinent clinical phenotype may facilitate the design of novel therapeutic strategies targeting these 'master' regulators for better patient outcome.

IL-13-driven alterations in hepatic cholesterol handling contributes to hypercholesterolemia in a rat model of minimal change disease.

Circulating factors have been implicated in the pathogenesis of minimal change disease (MCD), and may have direct effects on cholesterol metabolism. This study investigated the pathogenesis of hypercholesterolemia in an IL-13 overexpression rat model of MCD prior to the onset of proteinuria, so as to establish the direct contribution of IL-13, especially with regard to hepatic cholesterol handling. In this model of MCD, the temporal relationship between hypercholesterolemia and proteinuria was first identified. Plasma proprotein convertase subtilisin/kexin type 9 (Pcsk9) and liver ATP-binding cassette sub-family G member 5 (Abcg5) were measured using ELISA. Liver Ldlr and liver X receptor alpha (Lxra) were quantified with Western blot. Abcg5-mediated cholesterol efflux in IL-13-stimulated rat primary hepatocytes was measured using taurocholate as cholesterol acceptor. The role of Lxra was validated using a luciferase assay in Lxre-luciferase-transfected IL-13-stimulated hepatocytes. IL-13-transfected rats developed hypercholesterolemia prior to proteinuria, with 35% of rats hypercholesterolemic but only 11% proteinuric by Day 20 (P = 0.04). These pre-proteinuric hypercholesterolemic rats showed elevations in total and LDL-cholesterol, but not hypertriglyceridemia or hepatic steatosis. The hypercholesterolemia was associated with increased hepatic Pcsk9 synthesis and enhanced circulating Pcsk9 levels, which correlated strongly with plasma total cholesterol (r = 0.73, P<0.001). The hypercholesterolemia was also contributed by decreased Abcg5 expression and activity, due to reduced Lxra expression. Lxra expression correlated with plasma total cholesterol levels (r = -0.52, P = 0.01), and overexpression of pLxra in rat hepatocytes abrogated the IL-13-mediated down-regulation of Lxre-driven gene expression. In conclusion, we have shown that IL-13 induced changes in hepatic cholesterol handling in a cytokine-induced rat model of MCD, resulting in hypercholesterolemia which can precede the onset of proteinuria.

Towards precision medicine: interrogating the human genome to identify drug pathways associated with potentially functional, population-differentiated polymorphisms.

Drug response variations amongst different individuals/populations are influenced by several factors including allele frequency differences of single nucleotide polymorphisms (SNPs) that functionally affect drug-response genes. Here, we aim to identify drugs that potentially exhibit population differences in response using SNP data mining and analytics. Ninety-one pairwise-comparisons of >22,000,000 SNPs from the 1000 Genomes Project, across 14 different populations, were performed to identify 'population-differentiated' SNPs (pdSNPs). Potentially-functional pdSNPs (pf-pdSNPs) were then selected, mapped into genes, and integrated with drug-gene databases to identify 'population-differentiated' drugs enriched with genes carrying pf-pdSNPs. 1191 clinically-approved drugs were found to be significantly enriched (Z > 2.58) with genes carrying SNPs that were differentiated in one or more population-pair comparisons. Thirteen drugs were found to be enriched with such differentiated genes across all 91 population-pairs. Notably, 82% of drugs, which were previously reported in the literature to exhibit population differences in response were also found by this method to contain a significant enrichment of population specific differentiated SNPs. Furthermore, drugs with genetic testing labels, or those suspected to cause adverse reactions, contained a significantly larger number (P < 0.01) of population-pairs with enriched pf-pdSNPs compared with those without these labels. This pioneering effort at harnessing big-data pharmacogenomics to identify 'population differentiated' drugs could help to facilitate data-driven decision-making for a more personalized medicine.

Architecture of population-differentiated polymorphisms in the human genome.

Population variation in disease and other phenotype are partly attributed to single nucleotide polymorphisms (SNPs) in the human genome. Due to selection pressure, two individuals from the same ancestral population have more genetic similarity compared to individuals from further geographic regions. Here, we elucidated the genomic population differentiation pattern, by interrogating >22,000,000 SNPs. Majority of population-differentiated (pd) SNPs (~95%), including the potentially functional (pf) (~84%) subset reside in non-genic regions, compared to the proportion of all SNPs (58%) found in non-genic regions. This suggests that differences between populations are more likely due to differences in gene regulation rather than protein function. Actin Cytoskeleton, Axonal Guidance and Protein Kinase A signaling pathways are enriched with genes carrying at least three pdSNPs (enriched pdGenes), while Antigen Presentation, Hepatic Fibrosis and Huntington Disease Signalling pathways are over-represented by enriched pf-pdGenes. An inverse correlation between chromosome size and the proportion of pd-/pf-pdSNPs was observed. Smaller chromosomes have relatively more of such SNPs including genes carrying these SNPs. Genes associated with common diseases and enriched with these pd-/pfpdSNPs are localized to 11 different chromosomes, with immune-related disease pd/pf-pdGenes mainly residing in chromosome 6 while neurological disease pd/pf-pdGenes residing in smaller chromosomes including chromosome 21/22. The associated diseases were reported to show population differences in incidence, severity and/or etiology. In summary, this study highlights the non-sporadic nature of population differentiation footprint in the human genome, which can potentially lead to the identification of genomic regions that play roles in the manifestation of phenotypic differences, including in disease predisposition and drug response.

Comprehensive analysis of transcriptome profiles in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma is the second most deadly cancer with late presentation and limited treatment options, highlighting an urgent need to better understand HCC to facilitate the identification of early-stage biomarkers and uncover therapeutic targets for the development of novel therapies for HCC. METHODS: Deep transcriptome sequencing of tumor and paired non-tumor liver tissues was performed to comprehensively evaluate the profiles of both the host and HBV transcripts in HCC patients. Differential gene expression patterns and the dys-regulated genes associated with clinical outcomes were analyzed. Somatic mutations were identified from the sequencing data and the deleterious mutations were predicted. Lastly, human-HBV chimeric transcripts were identified, and their distribution, potential function and expression association were analyzed. RESULTS: Expression profiling identified the significantly upregulated TP73 as a nodal molecule modulating expression of apoptotic genes. Approximately 2.5% of dysregulated genes significantly correlated with HCC clinical characteristics. Of the 110 identified genes, those involved in post-translational modification, cell division and/or transcriptional regulation were upregulated, while those involved in redox reactions were downregulated in tumors of patients with poor prognosis. Mutation signature analysis identified that somatic mutations in HCC tumors were mainly non-synonymous, frequently affecting genes in the micro-environment and cancer pathways. Recurrent mutations occur mainly in ribosomal genes. The most frequently mutated genes were generally associated with a poorer clinical prognosis. Lastly, transcriptome sequencing suggest that HBV replication in the tumors of HCC patients is rare. HBV-human fusion transcripts are a common observation, with favored HBV and host insertion sites being the HBx C-terminus and gene introns (in tumors) and introns/intergenic-regions (in non-tumors), respectively. HBV-fused genes in tumors were mainly involved in RNA binding while those in non-tumors tissues varied widely. These observations suggest that while HBV may integrate randomly during chronic infection, selective expression of functional chimeric transcripts may occur during tumorigenesis. CONCLUSIONS: Transcriptome sequencing of HCC patients reveals key cancer molecules and clinically relevant pathways deregulated/mutated in HCC patients and suggests that while HBV may integrate randomly during chronic infection, selective expression of functional chimeric transcripts likely occur during the process of tumorigenesis.

Roles and Regulation of Long Noncoding RNAs in Hepatocellular Carcinoma.

Next-generation sequencing has uncovered thousands of long noncoding RNAs (lncRNA). Many are reported to be aberrantly expressed in various cancers, including hepatocellular carcinoma (HCC), and play key roles in tumorigenesis. This review provides an in-depth discussion of the oncogenic mechanisms reported to be associated with deregulated HCC-associated lncRNAs. Transcriptional expression of lncRNAs in HCC is modulated through transcription factors, or epigenetically by aberrant histone acetylation or DNA methylation, and posttranscriptionally by lncRNA transcript stability modulated by miRNAs and RNA-binding proteins. Seventy-four deregulated lncRNAs have been identified in HCC, of which, 52 are upregulated. This review maps the oncogenic roles of these deregulated lncRNAs by integrating diverse datasets including clinicopathologic features, affected cancer phenotypes, associated miRNA and/or protein-interacting partners as well as modulated gene/protein expression. Notably, 63 deregulated lncRNAs are significantly associated with clinicopathologic features of HCC. Twenty-three deregulated lncRNAs associated with both tumor and metastatic clinical features were also tumorigenic and prometastatic in experimental models of HCC, and eight of these mapped to known cancer pathways. Fifty-two upregulated lncRNAs exhibit oncogenic properties and are associated with prominent hallmarks of cancer, whereas 22 downregulated lncRNAs have tumor-suppressive properties. Aberrantly expressed lncRNAs in HCC exert pleiotropic effects on miRNAs, mRNAs, and proteins. They affect multiple cancer phenotypes by altering miRNA and mRNA expression and stability, as well as through effects on protein expression, degradation, structure, or interactions with transcriptional regulators. Hence, these insights reveal novel lncRNAs as potential biomarkers and may enable the design of precision therapy for HCC.

Robust Preimplantation Genetic Testing Strategy for Myotonic Dystrophy Type 1 by Bidirectional Triplet-Primed Polymerase Chain Reaction Combined With Multi-microsatellite Haplotyping Following Whole-Genome Amplification.

Myotonic dystrophy type 1 (DM1) is caused by expansion of the DMPK CTG trinucleotide repeat. Disease transmission to offspring can be avoided through prenatal diagnosis or preimplantation genetic testing for monogenic disorders (PGT-M). We describe a robust strategy for DM1 PGT-M that can be applied to virtually any at-risk couple. This strategy utilizes whole-genome amplification, followed by triplet-primed PCR (TP-PCR) detection of expanded DMPK alleles, in parallel with single-tube haplotype analysis of 12 closely linked and highly polymorphic microsatellite markers. Bidirectional TP-PCR and dodecaplex marker PCR assays were optimized and validated on whole-genome amplified single lymphoblasts isolated from DM1 reference cell lines, and tested on a simulated PGT-M case comprising a parent-offspring trio and three simulated embryos. Bidirectional DMPK TP-PCR reliably detects repeat expansions even in the presence of non-CTG interruptions at either end of the expanded allele. Misdiagnoses, diagnostic ambiguity, and couple-specific assay customization are further minimized by the use of multi-marker haplotyping, preventing the loss of potentially unaffected embryos for transfer.