Genome-Wide DNA Methylation Profiles in Whole-Blood and Buccal Samples-Cross-Sectional, Longitudinal, and across Platforms

The field of DNA methylation research is rapidly evolving, focusing on disease and phenotype changes over time using methylation measurements from diverse tissue sources and multiple array platforms. Consequently, identifying the extent of longitudinal, inter-tissue, and inter-platform variation in DNA methylation is crucial for future advancement. DNA methylation was measured in 375 individuals, with 197 of those having 2 blood sample measurements ~10 years apart. Whole-blood samples were measured on Illumina Infinium 450K and EPIC methylation arrays, and buccal samples from a subset of 58 participants were measured on EPIC array. The data were analyzed with the aims to examine the correlation between methylation levels in longitudinal blood samples in 197 individuals, examine the correlation between methylation levels in the blood and buccal samples in 58 individuals, and examine the correlation between blood methylation profiles assessed on the EPIC and 450K arrays in 83 individuals. We identified 136,833, 7674, and 96,891 CpGs significantly and strongly correlated (>0.50) longitudinally, across blood and buccal samples as well as array platforms, respectively. A total of 3674 of these CpGs were shared across all three sets. Analysis of these shared CpGs identified previously found associations with aging, ancestry, and 7016 mQTLs as well.

Cross-Platform Evaluation of Commercially Targeted and Untargeted Metabolomics Approaches to Optimize the Investigation of Psychiatric Disease

Metabolomics methods often encounter trade-offs between quantification accuracy and coverage, with truly comprehensive coverage only attainable through a multitude of complementary assays. Due to the lack of standardization and the variety of metabolomics assays, it is difficult to integrate datasets across studies or assays. To inform metabolomics platform selection, with a focus on posttraumatic stress disorder (PTSD), we review platform use and sample sizes in psychiatric metabolomics studies and then evaluate five prominent metabolomics platforms for coverage and performance, including intra-/inter-assay precision, accuracy, and linearity. We found performance was variable between metabolite classes, but comparable across targeted and untargeted approaches. Within all platforms, precision and accuracy were highly variable across classes, ranging from 0.9-63.2% (coefficient of variation) and 0.6-99.1% for accuracy to reference plasma. Several classes had high inter-assay variance, potentially impeding dissociation of a biological signal, including glycerophospholipids, organooxygen compounds, and fatty acids. Coverage was platform-specific and ranged from 16-70% of PTSD-associated metabolites. Non-overlapping coverage is challenging; however, benefits of applying multiple metabolomics technologies must be weighed against cost, biospecimen availability, platform-specific normative levels, and challenges in merging datasets. Our findings and open-access cross-platform dataset can inform platform selection and dataset integration based on platform-specific coverage breadth/overlap and metabolite-specific performance.

Transcriptomic and Genomic Testing to Guide Individualized Treatment in Chemoresistant Gastric Cancer Case

Gastric cancer is globally the fifth leading cause of cancer death. We present a case report describing the unique genomic characteristics of an Epstein–Barr virus-negative gastric cancer with esophageal invasion and regional lymph node metastasis. Genomic tests were performed first with the stomach biopsy using platforms FoundationOne, OncoDNA, and Oncopanel at Dana Farber Institute. Following neoadjuvant chemotherapy, residual tumor was resected and the stomach and esophageal residual tumor samples were compared with the initial biopsy by whole exome sequencing and molecular pathway analysis platform Oncobox. Copy number variation profiling perfectly matched the whole exome sequencing results. A moderate agreement was seen between the diagnostic platforms in finding mutations in the initial biopsy. Final data indicate somatic activating mutation Q546K in PIK3CA gene, somatic frameshifts in PIH1D1 and FBXW7 genes, stop-gain in TP53BP1, and a few somatic mutations of unknown significance. RNA sequencing analysis revealed upregulated expressions of MMP7, MMP9, BIRC5, and PD-L1 genes and strongly differential regulation of several molecular pathways linked with the mutations identified. According to test results, the patient received immunotherapy with anti-PD1 therapy and is now free of disease for 2 years. Our data suggest that matched tumor and normal tissue analyses have a considerable advantage over tumor biopsy-only genomic tests in stomach cancer.

Characterization of Bulk Phosphatidylcholine Compositions in Human Plasma Using Side-Chain Resolving Lipidomics

Kit-based assays, such as AbsoluteIDQ^TM p150, are widely used in large cohort studies and provide a standardized method to quantify blood concentrations of phosphatidylcholines (PCs). Many disease-relevant associations of PCs were reported using this method. However, their interpretation is hampered by lack of functionally-relevant information on the detailed fatty acid side-chain compositions as only the total number of carbon atoms and double bonds is identified by the kit. To enable more substantiated interpretations, we characterized these PC sums using the side-chain resolving Lipidyzer^TM platform, analyzing 223 samples in parallel to the AbsoluteIDQ^TM. Combining these datasets, we estimated the quantitative composition of PC sums and subsequently tested their replication in an independent cohort. We identified major constituents of 28 PC sums, revealing also various unexpected compositions. As an example, PC 16:0_22:5 accounted for more than 50% of the PC sum with in total 38 carbon atoms and 5 double bonds (PC aa 38:5). For 13 PC sums, we found relatively high abundances of odd-chain fatty acids. In conclusion, our study provides insights in PC compositions in human plasma, facilitating interpretation of existing epidemiological data sets and potentially enabling imputation of PC compositions for future meta-analyses of lipidomics data.

Assessment of a Highly Multiplexed RNA Sequencing Platform and Comparison to Existing High-Throughput Gene Expression Profiling Techniques

The need to reduce per sample cost of RNA-seq profiling for scalable data generation has led to the emergence of highly multiplexed RNA-seq. These technologies utilize barcoding of cDNA sequences in order to combine multiple samples into a single sequencing lane to be separated during data processing. In this study, we report the performance of one such technique denoted as sparse full length sequencing (SFL), a ribosomal RNA depletion-based RNA sequencing approach that allows for the simultaneous sequencing of 96 samples and higher. We offer comparisons to well established single-sample techniques, including: full coverage Poly-A capture RNA-seq, microarrays, as well as another low-cost highly multiplexed technique known as 3' digital gene expression (3'DGE). Data was generated for a set of exposure experiments on immortalized human lung epithelial (AALE) cells in a two-by-two study design, in which samples received both genetic and chemical perturbations of known oncogenes/tumor suppressors and lung carcinogens. SFL demonstrated improved performance over 3'DGE in terms of coverage, power to detect differential gene expression, and biological recapitulation of patterns of differential gene expression from in vivo lung cancer mutation signatures.

Key differences between 13 KRAS mutation detection technologies and their relevance for clinical practice

Introduction

This study assessed KRAS mutation detection and functional characteristics across 13 distinct technologies and assays available in clinical practice, in a blinded manner.

Methods

Five distinct KRAS-mutant cell lines were used to study five clinically relevant KRAS mutations: p.G12C, p.G12D, p.G12V, p.G13D and p.Q61H. 50 cell line admixtures with low (50 and 100) mutant KRAS allele copies at 20%, 10%, 5%, 1% and 0.5% frequency were processed using quantitative PCR (qPCR) (n=3), matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF) (n=2), next-generation sequencing (NGS) (n=6), digital PCR (n=1) and Sanger capillary sequencing (n=1) assays. Important performance differences were revealed, particularly assay sensitivity and turnaround time.

Results

Overall 406/728 data points across all 13 technologies were identified correctly. Successful genotyping of admixtures ranged from 0% (Sanger sequencing) to 100% (NGS). 5/6 NGS platforms reported similar allelic frequency for each sample. One NGS assay detected mutations down to a frequency of 0.5% and correctly identified all 56 samples (Oncomine Focus Assay, Thermo Fisher Scientific). One qPCR (Idylla, Biocartis) and MALDI-TOF (UltraSEEK, Agena Bioscience) assay identified 96% (all 100 copies and 23/25 at 50 copies input) and 92% (23/25 at 100 copies and 23/25 at 50 copies input) of samples, respectively. The digital PCR assay (KRAS PrimePCR ddPCR, Bio-Rad Laboratories) identified 60% (100 copies) and 52% (50 copies) of samples correctly. Turnaround time from sample to results ranged from ~2 hours (Idylla CE-IVD) to 2 days (TruSight Tumor 15 and Sentosa CE-IVD), to 2 weeks for certain NGS assays; the level of required expertise ranged from minimal (Idylla CE-IVD) to high for some technologies.

Discussion

This comprehensive parallel assessment used high molecular weight cell line DNA as a model system to address key questions for a laboratory when implementing routine KRAS testing. As most of the technologies are available for additional molecular biomarkers, this study may be informative for other applications.

Defining a personal, allele-specific, and single-molecule long-read transcriptome

Personal transcriptomes in which all of an individual's genetic variants (e.g., single nucleotide variants) and transcript isoforms (transcription start sites, splice sites, and polyA sites) are defined and quantified for full-length transcripts are expected to be important for understanding individual biology and disease, but have not been described previously. To obtain such transcriptomes, we sequenced the lymphoblastoid transcriptomes of three family members (GM12878 and the parents GM12891 and GM12892) by using a Pacific Biosciences long-read approach complemented with Illumina 101-bp sequencing and made the following observations. First, we found that reads representing all splice sites of a transcript are evident for most sufficiently expressed genes ≤3 kb and often for genes longer than that. Second, we added and quantified previously unidentified splicing isoforms to an existing annotation, thus creating the first personalized annotation to our knowledge. Third, we determined SNVs in a de novo manner and connected them to RNA haplotypes, including HLA haplotypes, thereby assigning single full-length RNA molecules to their transcribed allele, and demonstrated Mendelian inheritance of RNA molecules. Fourth, we show how RNA molecules can be linked to personal variants on a one-by-one basis, which allows us to assess differential allelic expression (DAE) and differential allelic isoforms (DAI) from the phased full-length isoform reads. The DAI method is largely independent of the distance between exon and SNV--in contrast to fragmentation-based methods. Overall, in addition to improving eukaryotic transcriptome annotation, these results describe, to our knowledge, the first large-scale and full-length personal transcriptome.