Genomic Findings Research Articles

Predicting prediction: A systematic workflow to analyze factors affecting the classification performance in genomic biomarker discovery.

High throughput technologies in genomics enable the analysis of small alterations in gene expression levels. Patterns of such deviations are an important starting point for the discovery and verification of new biomarker candidates. Identifying such patterns is a challenging task that requires sophisticated machine learning approaches. Currently, there are a variety of classification models, and a common approach is to compare the performance and select the best one for a given classification problem. Since the association between the features of a data set and the performance of a particular classification method is still not fully understood, the main contribution of this work is to provide a new methodology for predicting the prediction results of different classifiers in the field of biomarker discovery. We propose here a three-steps computational workflow that includes an analysis of the data set characteristics, the calculation of the classification accuracy and, finally, the prediction of the resulting classification error. The experiments were carried out on synthetic and microarray datasets. Using this method, we showed that the predictability strongly depends on the discriminatory ability of the features, e.g., sets of genes, in two or multi-class datasets. If a dataset has a certain discriminatory ability, this method enables prediction of the classification performance before applying a learning model. Thus, our results contribute to a better understanding of the relationship between dataset characteristics and the corresponding performance of a machine learning method, and suggest the optimal classification method for a given dataset based on its discriminatory ability.

GenomicKB: a knowledge graph for the human genome.

Genomic Knowledgebase (GenomicKB) is a graph database for researchers to explore and investigate human genome, epigenome, transcriptome, and 4D nucleome with simple and efficient queries. The database uses a knowledge graph to consolidate genomic datasets and annotations from over 30 consortia and portals, including 347 million genomic entities, 1.36 billion relations, and 3.9 billion entity and relation properties. GenomicKB is equipped with a web-based query system (https://gkb.dcmb.med.umich.edu/) which allows users to query the knowledge graph with customized graph patterns and specific constraints on entities and relations. Compared with traditional tabular-structured data stored in separate data portals, GenomicKB emphasizes the relations among genomic entities, intuitively connects isolated data matrices, and supports efficient queries for scientific discoveries. GenomicKB transforms complicated analysis among multiple genomic entities and relations into coding-free queries, and facilitates data-driven genomic discoveries in the future.

HiFi metagenomic sequencing enables assembly of accurate and complete genomes from human gut microbiota

Advances in metagenomic assembly have led to the discovery of genomes belonging to uncultured microorganisms. Metagenome-assembled genomes (MAGs) often suffer from fragmentation and chimerism. Recently, 20 complete MAGs (cMAGs) have been assembled from Oxford Nanopore long-read sequencing of 13 human fecal samples, but with low nucleotide accuracy. Here, we report 102 cMAGs obtained by Pacific Biosciences (PacBio) high-accuracy long-read (HiFi) metagenomic sequencing of five human fecal samples, whose initial circular contigs were selected for complete prokaryotic genomes using our bioinformatics workflow. Nucleotide accuracy of the final cMAGs was as high as that of Illumina sequencing. The cMAGs could exceed 6 Mbp and included complete genomes of diverse taxa, including entirely uncultured RF39 and TANB77 orders. Moreover, cMAGs revealed that regions hard to assemble by short-read sequencing comprised mostly genomic islands and rRNAs. HiFi metagenomic sequencing will facilitate cataloging accurate and complete genomes from complex microbial communities, including uncultured species.

273 (PB053) - Comparison of biomarker selection methods in high-dimensional genomic data

Background: High-dimensional genomic data are indispensable in drug research and development. Genomic data-based biomarker discovery provides comprehensive insights to drug mechanism of action and efficacy prediction. However, it faces the difficulty of selecting suitable signature genes from several thousand or more genes with highly fluctuating and often tightly correlated expression patterns. This study aims to compare the performance of three recent signature gene selection algorithms in several large in vitro cell assays. Methods: Drug response datasets of irinotecan, cetuximab, pelitinib, gefitinib, erlotinib and KRAS (G12C) inhibitor-12 were obtained from the Genomics of Drug Sensitivity in Cancer (GDSC). Cell line gene expression data were gathered from our internal dataset of 20,432 genes, with 1,068 cell lines, across 23 tissues. The three algorithms evaluated were Stable Iterative Variable Selection (SIVS), Precision Lasso (PL) and Whitening Lasso (WL). Efficacy variance between different cancer types (ANOVA p-value) and prediction accuracy of drug efficacy were calculated to evaluate the results. Results: The accuracy of PL predictions was more positively correlated with the number of genes than SIVS and WL. The number of signature genes was limited from 5 to 50 and the most precise results were obtained for all drugs with 30 to 50 genes composite biomarkers. The PL, WL and SIVS methods separately selected 30, 17 and 6 genes as composite biomarkers for irinotecan with an accuracy of 85.7%, 78.2% and 87.3% respectively, in the training set. In validation set, the prediction accuracy of the SIVS panel was the highest at 83.1%. For erlotinib, SIVS, WL and PL picked out 22, 23 and 32 genes as the best composite biomarker with an accuracy of 73%, 83% and 76.8%, respectively. Overall, composite biomarkers selected by SIVS was the superior choice in four of six drugs. In most cases, these three algorithms took less time than 100 iterations of Boruta under the same conditions. Conclusions: Compared with PL and WL, the SIVS method appears to obtain a higher prediction accuracy with a relatively smaller number of genes in this research. PL tended to select many more signature genes than SIVS and WL to yield comparable performance. Overlapping signature genes between the three algorithms was generally low, including for genes belonging to same molecular pathways. The cost and practicality of in vivo experiments would have made it difficult to select a large number of cancer types, drug response, and gene expression data. Therefore, selecting the best predictive composite biomarker in vitro screening for in vivo validation can significantly reduce costs and accelerate the early drug development process. No conflict of interest.

On the Verge of Precision Medicine in Diabetes.

The epidemic of type 2 diabetes (T2D) is a significant global public health challenge and a major cause of morbidity and mortality. Despite the recent proliferation of pharmacological agents for the treatment of T2D, current therapies simply treat the symptom, i.e. hyperglycemia, and do not directly address the underlying disease process or modify the disease course. This article summarizes how genomic discovery has contributed to unraveling the heterogeneity in T2D, reviews relevant discoveries in the pharmacogenetics of five commonly prescribed glucose-lowering agents, presents evidence supporting how pharmacogenetics can be leveraged to advance precision medicine, and calls attention to important research gaps to its implementation to guide treatment choices.

Volatilomics: An Emerging and Promising Avenue for the Detection of Potential Prostate Cancer Biomarkers.

Simple SummaryThe lack of highly specific and sensitive biomarkers for the early detection of prostate cancer (PCa) is a major barrier to its management. Volatilomics emerged as a non-invasive, simple, inexpensive, and easy-to-use approach for cancer screening, characterization of disease progression, and follow-up of the treatment’s success. We provide a brief overview of the potential of volatile organic metabolites (VOMs) for the establishment of PCa biomarkers from non-invasive matrices. Endogenous VOMs have been investigated as potential biomarkers since changes in these VOMs can be characteristic of specific disease processes. Recent studies have shown that the conjugation of the prostate-specific antigen (PSA) screening with other methodologies, such as risk calculators, biomarkers, and imaging tests, can attenuate overdiagnosis and under-detection issues. This means that the combination of volatilomics with other methodologies could be extremely valuable for the differentiation of clinical phenotypes in a group of patients, providing more personalized treatments.Despite the spectacular advances in molecular medicine, including genomics, proteomics, transcriptomics, lipidomics, and personalized medicine, supported by the discovery of the human genome, prostate cancer (PCa) remains the most frequent malignant tumor and a leading cause of oncological death in men. New methods for prognostic, diagnostic, and therapy evaluation are mainly based on the combination of imaging techniques with other methodologies, such as gene or protein profiling, aimed at improving PCa management and surveillance. However, the lack of highly specific and sensitive biomarkers for its early detection is a major hurdle to this goal. Apart from classical biomarkers, the study of endogenous volatile organic metabolites (VOMs) biosynthesized by different metabolic pathways and found in several biofluids is emerging as an innovative, efficient, accessible, and non-invasive approach to establish the volatilomic biosignature of PCa patients, unravelling potential biomarkers. This review provides a brief overview of the challenges of PCa screening methods and emergent biomarkers. We also focus on the potential of volatilomics for the establishment of PCa biomarkers from non-invasive matrices.

Improving GWAS discovery and genomic prediction accuracy in biobank data

Genetically informed, deep-phenotyped biobanks are an important research resource and it is imperative that the most powerful, versatile, and efficient analysis approaches are used. Here, we apply our recently developed Bayesian grouped mixture of regressions model (GMRM) in the UK and Estonian Biobanks and obtain the highest genomic prediction accuracy reported to date across 21 heritable traits. When compared to other approaches, GMRM accuracy was greater than annotation prediction models run in the LDAK or LDPred-funct software by 15% (SE 7%) and 14% (SE 2%), respectively, and was 18% (SE 3%) greater than a baseline BayesR model without single-nucleotide polymorphism (SNP) markers grouped into minor allele frequency-linkage disequilibrium (MAF-LD) annotation categories. For height, the prediction accuracy R2 was 47% in a UK Biobank holdout sample, which was 76% of the estimated [Formula: see text]. We then extend our GMRM prediction model to provide mixed-linear model association (MLMA) SNP marker estimates for genome-wide association (GWAS) discovery, which increased the independent loci detected to 16,162 in unrelated UK Biobank individuals, compared to 10,550 from BoltLMM and 10,095 from Regenie, a 62 and 65% increase, respectively. The average [Formula: see text] value of the leading markers increased by 15.24 (SE 0.41) for every 1% increase in prediction accuracy gained over a baseline BayesR model across the traits. Thus, we show that modeling genetic associations accounting for MAF and LD differences among SNP markers, and incorporating prior knowledge of genomic function, is important for both genomic prediction and discovery in large-scale individual-level studies.

A retrospective cohort analysis of the Yale pediatric genomics discovery program.

The Pediatric Genomics Discovery Program (PGDP) at Yale uses next-generation sequencing (NGS) and translational research to evaluate complex patients with a wide range of phenotypes suspected to have rare genetic diseases. We conducted a retrospective cohort analysis of 356 PGDP probands evaluated between June 2015 and July 2020, querying our database for participant demographics, clinical characteristics, NGS results, and diagnostic and research findings. The three most common phenotypes among the entire studied cohort (n=356) were immune system abnormalities (n=105, 29%), syndromic or multisystem disease (n=103, 29%), and cardiovascular system abnormalities (n=62, 17%). Of 216 patients with final classifications, 77 (36%) received new diagnoses and 139 (64%) were undiagnosed; the remaining 140 patients were still actively being investigated. Monogenetic diagnoses were found in 67 (89%); the largest group had variants in known disease genes but with new contributions such as novel variants (n=31, 40%) or expanded phenotypes (n=14, 18%). Finally, five PGDP diagnoses (8%) were suggestive of novel gene-to-phenotype relationships. A broad range of patients can benefit from single subject studies combining NGS and functional molecular analyses. All pediatric providers should consider further genetics evaluations for patients lacking precise molecular diagnoses.

EBV-associated NK and T-cell lymphoid neoplasms.

Epstein-Barr virus (EBV)-associated neoplasms derived from natural killer (NK) or T cells comprise a group of clinically and biologically heterogenous disorders affecting children and adults, which are overall rare but more prevalent in Asia and South America. This review focuses on neoplasms presenting in the adulthood, addressing recent genomic discoveries as well as therapeutic developments in these highly aggressive disorders. Distinct molecular subtypes of extranodal NK/T-cell lymphomas (ENKTCLs) have been described, with differences in cell of origin, EBV pattern, genomic alterations, clinical characteristics, response to asparaginase-based therapies and to more recent approaches targeting molecular aberrations of the lymphoma. For the last two decades, progress in the clinical management of ENKTCL was based on L-asapraginase containing combinations and the incoroperation of radiotherapy. A subset of cases with PDL1-2 structural alterations may be more responsive to treatment with immune checkpoint inhibitors. Primary nodal EBV+ lymphomas derived from T or NK cells have distinctive features separating them from both peripheral T-cell lymphoma not otherwise specified and ENKTCL. Treatment algorithms correspond to those for advanced ENKTCL. With better understanding of lymphomagenesis, genomic landscape and immunologic aspects of the diseases, future treatment options will include targeted therapies including immune checkpoint inhibitors and novel antibodies.

Investigations of nitazoxanide molecular targets and pathways for the treatment of hepatocellular carcinoma using network pharmacology and molecular docking.

Nitazoxanide has been investigated for colorectal cancer and breast cancer. However, its molecular targets and pathways have not yet been explored for hepatocellular carcinoma (HCC) treatment. Utilizing a network pharmacology approach, nitazoxanide’s potential targets and molecular pathways for HCC treatment were investigated. HCC targets were extracted from the GeneCards database. Potential targets of nitazoxanide were predicted using Swiss Target Prediction and Super Pred. Intersecting targets were analyzed with VENNY online tool. Using Cytoscape, a protein-protein interaction (PPI), cluster, and core targets-pathways networks were constructed. Using the Database for Annotation, Visualization and Integrated Discovery (DAVID), gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted. The nitazoxanide was molecularly docked with anti-HCC core targets by employing Auto Dock Vina. A total of 168 potential targets of nitazoxanide, 13,415 HCC-related targets, and 153 intersecting targets were identified. The top eight anti-HCC core targets were identified: SRC, EGFR, CASP3, MMP9, mTOR, HIF1A, ERBB2, and PPARG. GO enrichment analysis showed that nitazoxanide might have anti-HCC effects by affecting gene targets involved in multiple biological processes (BP) (protein phosphorylation, transmembrane receptor protein tyrosine kinase (RTKs) signaling pathway, positive regulation of MAP kinase activity, etc.). KEGG pathways and core targets-pathways network analysis indicated that pathways in cancer and proteoglycans in cancer are two key pathways that significantly contribute to the anti-HCC effects of nitazoxanide. Results of molecular docking demonstrated the potential for active interaction between the top eight anti-HCC core targets and nitazoxanide. Our research offers a theoretical basis for the notion that nitazoxanide may have distinct therapeutic effects in HCC, and the identified pharmacological targets and pathways might function as biomarkers for HCC therapy.

Simple approach for the histomolecular diagnosis of central nervous system gliomas based on 2021 World Health Organization Classification

The classification of central nervous system (CNS) glioma went through a sequence of developments, between 2006 and 2021, started with only histological approach then has been aided with a major emphasis on molecular signatures in the 4th and 5th editions of the World Health Organization (WHO). The recent reformation in the 5th edition of the WHO classification has focused more on the molecularly defined entities with better characterized natural histories as well as new tumor types and subtypes in the adult and pediatric populations. These new subclassified entities have been incorporated in the 5th edition after the continuous exploration of new genomic, epigenomic and transcriptomic discovery. Indeed, the current guidelines of 2021 WHO classification of CNS tumors and European Association of Neuro-Oncology (EANO) exploited the molecular signatures in the diagnostic approach of CNS gliomas. Our current review presents a practical diagnostic approach for diffuse CNS gliomas and circumscribed astrocytomas using histomolecular criteria adopted by the recent WHO classification. We also describe the treatment strategies for these tumors based on EANO guidelines.

A blockchain-based application for genomic access and variant discovery using smart contracts and homomorphic encryption

Genomic data repositories are rapidly growing due to the decline in the cost of DNA sequencing. This has increased the demand from stakeholders such as researchers to analyze these datasets to advance areas in biomedical research. Genomic datasets are mostly maintained by third-party direct-to-consumer (DTC) genomic companies who operate a business model of collecting DNA data from their customers and selling them to pharmaceutical companies. This puts the privacy of their customers at risk since each individual’s human genome is unique. In addition, customers lose ownership of and access to their genomic data to DTCs and DTCs do not share profits from data sales with them. In this paper, we propose a system based on blockchain technology and homomorphic computation to address the aforementioned problems. We use blockchain transactions and smart contracts to allow genomic data owners (DOs) to have control of their data and sell access to it, and homomorphic computation with secure two-party protocol to enable genomic data users (DUs) to run queries to securely discover DOs of interest. We further optimize the query response time by proposing an approach based on genomic data partitions, binary search trees and bloom filters to reduce the search space. We also propose a blockchain penalty mechanism to encourage parties to behave honestly to avoid malicious behaviors such as uploading fake or non-human genomic data. We conduct our experiments on real genomic datasets and demonstrate that our proposed scheme allows DOs to control access to their data and is feasible and efficient in terms of computation cost, query response time and scalability.

Genome and Transcriptome Sequence Resources and Effector Repertoire of Pythium myriotylum Drechsler.

Genome and Transcriptome Sequence Resources and Effector Repertoire of Pythium myriotylum Drechsler.

Expanding the phylogenetic distribution of cytochrome b-containing methanogenic archaea sheds light on the evolution of methanogenesis

Methane produced by methanogenic archaea has an important influence on Earth’s changing climate. Methanogenic archaea are phylogenetically diverse and widespread in anoxic environments. These microorganisms can be divided into two subgroups based on whether or not they use b-type cytochromes for energy conservation. Methanogens with b-type cytochromes have a wider substrate range and higher growth yields than those without them. To date, methanogens with b-type cytochromes were found exclusively in the phylum “Ca. Halobacteriota” (formerly part of the phylum Euryarchaeota). Here, we present the discovery of metagenome-assembled genomes harboring methyl-coenzyme M reductase genes reconstructed from mesophilic anoxic sediments, together with the previously reported thermophilic “Ca. Methylarchaeum tengchongensis”, representing a novel archaeal order, designated the “Ca. Methylarchaeales”, of the phylum Thermoproteota (formerly the TACK superphylum). These microorganisms contain genes required for methyl-reducing methanogenesis and the Wood-Ljundahl pathway. Importantly, the genus “Ca. Methanotowutia” of the “Ca. Methylarchaeales” encode a cytochrome b-containing heterodisulfide reductase (HdrDE) and methanophenazine-reducing hydrogenase complex that have similar gene arrangements to those found in methanogenic Methanosarcinales. Our results indicate that members of the “Ca. Methylarchaeales” are methanogens with cytochromes and can conserve energy via membrane-bound electron transport chains. Phylogenetic and amalgamated likelihood estimation analyses indicate that methanogens with cytochrome b-containing electron transfer complexes likely evolved before diversification of Thermoproteota or “Ca. Halobacteriota” in the early Archean Eon. Surveys of public sequence databases suggest that members of the lineage are globally distributed in anoxic sediments and may be important players in the methane cycle.

COMPILE: a GWAS computational pipeline for gene discovery in complex genomes

BackgroundGenome-Wide Association Studies (GWAS) are used to identify genes and alleles that contribute to quantitative traits in large and genetically diverse populations. However, traits with complex genetic architectures create an enormous computational load for discovery of candidate genes with acceptable statistical certainty. We developed a streamlined computational pipeline for GWAS (COMPILE) to accelerate identification and annotation of candidate maize genes associated with a quantitative trait, and then matches maize genes to their closest rice and Arabidopsis homologs by sequence similarity.ResultsCOMPILE executed GWAS using a Mixed Linear Model that incorporated, without compression, recent advancements in population structure control, then linked significant Quantitative Trait Loci (QTL) to candidate genes and RNA regulatory elements contained in any genome. COMPILE was validated using published data to identify QTL associated with the traits of α-tocopherol biosynthesis and flowering time, and identified published candidate genes as well as additional genes and non-coding RNAs. We then applied COMPILE to 274 genotypes of the maize Goodman Association Panel to identify candidate loci contributing to resistance of maize stems to penetration by larvae of the European Corn Borer (Ostrinia nubilalis). Candidate genes included those that encode a gene of unknown function, WRKY and MYB-like transcriptional factors, receptor-kinase signaling, riboflavin synthesis, nucleotide-sugar interconversion, and prolyl hydroxylation. Expression of the gene of unknown function has been associated with pathogen stress in maize and in rice homologs closest in sequence identity.ConclusionsThe relative speed of data analysis using COMPILE allowed comparison of population size and compression. Limitations in population size and diversity are major constraints for a trait and are not overcome by increasing marker density. COMPILE is customizable and is readily adaptable for application to species with robust genomic and proteome databases.

DTI-BERT: Identifying Drug-Target Interactions in Cellular Networking Based on BERT and Deep Learning Method.

Drug–target interactions (DTIs) are regarded as an essential part of genomic drug discovery, and computational prediction of DTIs can accelerate to find the lead drug for the target, which can make up for the lack of time-consuming and expensive wet-lab techniques. Currently, many computational methods predict DTIs based on sequential composition or physicochemical properties of drug and target, but further efforts are needed to improve them. In this article, we proposed a new sequence-based method for accurately identifying DTIs. For target protein, we explore using pre-trained Bidirectional Encoder Representations from Transformers (BERT) to extract sequence features, which can provide unique and valuable pattern information. For drug molecules, Discrete Wavelet Transform (DWT) is employed to generate information from drug molecular fingerprints. Then we concatenate the feature vectors of the DTIs, and input them into a feature extraction module consisting of a batch-norm layer, rectified linear activation layer and linear layer, called BRL block and a Convolutional Neural Networks module to extract DTIs features further. Subsequently, a BRL block is used as the prediction engine. After optimizing the model based on contrastive loss and cross-entropy loss, it gave prediction accuracies of the target families of G Protein-coupled receptors, ion channels, enzymes, and nuclear receptors up to 90.1, 94.7, 94.9, and 89%, which indicated that the proposed method can outperform the existing predictors. To make it as convenient as possible for researchers, the web server for the new predictor is freely accessible at: https://bioinfo.jcu.edu.cn/dtibert or http://121.36.221.79/dtibert/. The proposed method may also be a potential option for other DITs.

OTHR-45. Kids First Variant WorkBench: application to germline genomic discoveries in the Children's Brain Tumor Network

The Gabriella Miller Kids First Pediatric Research Program (Kids First) aims at facilitating researchers to uncover new insights into the biology of childhood cancer (CC) and structural birth defects (SBD). Kids First has two initiatives, i) whole genome sequencing of biospecimens from families with CC/SBD, and ii) establishing Kids First Data Resources. Kids First Data Resource Center developed the Kids First Data Resource Portal (KFDRP), a centralized platform to search, view, analyze, and identify currently accessible data from both Kids First and collaborative cohorts, incorporating omics and phenotypic information of 30 studies and 26,300 participants. A recently released KFDRP component is Variant WorkBench (VWB), enabling users to query, mangle, analyze and visualize genomic variants from participating cohorts, with the Children’s Brain Tumor Network (CBTN) being one of the cohorts. VWB supports programming languages such as Python, Spark, SQL and R for in-depth analysis in Apache Zeppelin notebooks. In addition to variant calls and phenotypic information, VWB hosts rich external variant annotations in the public domain, such as Cancer Hotspots, COSMIC and ClinVar. Users can also load additional databases (e.g. Human Gene Mutation Database/HGMD) within a notebook, import custom datasets as temporary query tables, export analysis outputs to local drives, visualize analysis results in multiple chart styles, display local figures, and save notebooks for sharing, further use and Cavatica projects. In an effort to screen tier 1 genes (n=578) from the most recent Cancer Gene Census provided by COSMIC in CBTN, we identified ~127,500 germline variants that are both rare and damaging, or that are already cataloged in the most recent version of ClinVar/HGMD. The whole process took less than one hour which is much faster than conventional methods. VWB enables efficient genomic variant analysis and discoveries in pediatric neuro-oncology research with advanced big data technology.

Development and validation of Duoseq as a novel diagnostic and companion assay for lymphoma and other cancers.

3074 Background: While NGS applications of DNAseq and RNAseq have proven to be powerful tools for genomic discovery, NGS remains underutilized in the clinic. We hypothesized that the clinical translation of NGS would be greatly improved by addressing two critical issues: first, having a single assay for both DNA and RNA sequencing, and second, including all the bioinformatics to enable rapid analysis and reporting of clinically relevant findings within 2 days rather than 2-3 weeks which remains the widespread standard. We developed Duoseq to address these issues. Methods: In most cancer biopsies, RNA constitutes nearly 80% of the total nucleic acid and DNA only 20%. Duoseq incorporates interfering factors that make the ligation of sequencing barcodes to RNA much less efficient than that to DNA to, in effect, invert the proportion of DNA and RNA from the sample. This enables the efficient generation of high quality DNA and RNA libraries. The assay targets over 450 recurrently altered genes in hematologic cancers. We developed secure bioinformatics software that connects to an Illumina sequencer to render four major classes of clinical measurements: mutations, gene expression (cell of origin in DLBCL), Epstein Barr virus (EBV) status and and common translocations (BCL2, BCL6, MYC, IRF4, CCND1). Results: Technicians at three different CLIA labs were trained to perform Duoseq at the respective sites using a local Illumina sequencer. The assay was performed on 111 FFPE lymphoma cases including diffuse large B cell lymphoma, and other B and T cell lymphomas that were characterized using clinical standard assays. The standards included Sanger sequencing or Foundation One testing (mutations), in situ hybridization (EBV), immunohistochemistry or Nanostring (expression) and fluorescent in situ hybridization (translocation). The included Duoseq software provided the bioinformatics results as well as annotations. With a minimum input of 50ng of nucleic acid/sample, Duoseq generated a mean depth of coverage of 438X. We found that, compared to clinical standards, Duoseq performed accurately for mutations (95.9%), EBV status (96.5%), translocations (94.1%) and expression (100%). In addition, Duoseq provided information on clonality, specific translocation partners and identified novel fusions that were not available from standard assays. Conclusions: Duoseq accurately recapitulates the clinical workup of lymphomas replacing the need for single analyte tests such as FISH, while providing a wealth of additional data. The simplicity of the assay and the included bioinformatics software enables its performance at any clinical lab without the need for specialized personnel or computing infrastructure. We anticipate the application of Duoseq as a diagnostic assay, as well as in clinical trial selection and companion diagnostic applications owing to its high accuracy and short turnaround time.

Increasing genomic discovery in newly diagnosed multiple myeloma: defining disease biology and its correlation to risk.

Our understanding of MM genomics has expanded rapidly in the past 5-10 years as a consequence of cytogenetic analyses obtained in routine clinical practice as well as the ability to perform whole-exome/genome sequencing and gene expression profiling on large patient data sets. This knowledge has offered new insights into disease biology and is increasingly defining high-risk genomic patterns. In this manuscript, we present a thorough review of our current knowledge of MM genomics. The epidemiology and biology of chromosomal abnormalities including both copy number abnormalities and chromosomal translocation are described in full with a focus on those most clinically impactful such as 1q amplification and del(17p) as well as certain chromosome 14 translocations. A review of our ever-expanding knowledge of genetic mutations derived from recent whole-genome/exome data sets is then reviewed including those that drive disease pathogenesis from precursor states as well as those that may impact clinical outcomes. We then transition and attempt to elucidate how both chromosomal abnormalities and gene mutations are evolving our understanding of disease risk. We conclude by offering our perspectives moving forward as to how we might apply whole-genome/exome-level data in addition to routine cytogenetic analyses to improve patient outcomes as well as further knowledge gaps that must be addressed.

Downregulated ADAMTS1 Incorporating A2M Contributes to Tumorigenesis and Alters Tumor Immune Microenvironment in Lung Adenocarcinoma.

Simple SummaryLung cancer is the most dreadful cancer type and has the worst cancer-related clinical outcomes. This study used specimens from the in-house lung cancer cohort and public cohort to verify the roles of downregulated ADAMTS1, a protease remodeling extracellular matrix, to facilitate cancer promotion and progress. Based on the clinical specimens, cell and animal study with the aid of the public databases, we concluded that downregulated expression of ADAMTS1 might promote tumor progression and metastasis and modify the tumor microenvironment in lung cancer. Further investigation would be required for its application in treating lung cancer.Lung adenocarcinoma (LUAD) still holds the most dreadful clinical outcomes worldwide. Despite advanced treatment strategies, there are still some unmet needs. Next-generation sequencing of large-scale cancer genomics discovery projects combined with bioinformatics provides the opportunity to take a step forward in meeting clinical conditions. Based on in-house and The Cancer Genome Atlas (TCGA) cohorts, the results showed decreased levels of ADAMTS1 conferred poor survival compared with normal parts. Gene set enrichment analyses (GSEA) indicated the negative correlation between ADAMTS1 and the potential roles of epithelial–mesenchymal transition (EMT), metastasis, and poor prognosis in LUAD patients. With the knockdown of ADAMTS1, A549 lung cancer cells exhibited more aggressive behaviors such as EMT and increased migration, resulting in cancer metastasis in a mouse model. The pathway interaction network disclosed the linkage of downregulated α2-macroglobulin (A2M), which regulates EMT and metastasis. Furthermore, immune components analysis indicated a positive relationship between ADAMTS1 and the infiltrating levels of multiple immune cells, especially anticancer CD4+ T cells in LUAD. Notably, ADAMTS1 expression was also inversely correlated with the accumulation of immunosuppressive myeloid-derived suppressor cells and regulatory T cells, implying the downregulated ADAMTS1 mediated immune adjustment to fit the tumor survival disadvantages in LUAD patients. In conclusion, our study indicates that ADAMTS1 interacts with A2M in regulating EMT and metastasis in LUAD. Additionally, ADAMTS1 contributes to poor prognosis and immune infiltration in LUAD patients