High-throughput Genomic Assays Research Articles

GetPrimers: A generalized PCR-based genetic targeting primer designer enabling easy and standardized targeted gene modification across multiple systems.

Genetic targeting (e.g., gene knockout and tagging) based on polymerase chain reaction (PCR) is a simple yet powerful approach for studying gene functions. Although originally developed in classic budding and fission yeast models, the same principle applies to other eukaryotic systems with efficient homologous recombination. One-stepPCR-based genetic targeting is conventionally used but the sizes of the homologous arms that it generates for recombination-mediated genetic targeting are usually limited. Alternatively, gene targeting can also be performed via fusion PCR, which can create homologous arms that are orders of magnitude larger, therefore substantially increasing the efficiency of recombination-mediated genetic targeting. Here, we present GetPrimers (https://www.evomicslab.org/app/getprimers/), a generalized computational framework and web toolto assist automatic targeting and verification primer design for both one-stepPCR-based and fusionPCR-based genetic targeting experiments. Moreover, GetPrimers by design runs for any given genetic background of any species with full genome scalability. Therefore, GetPrimers is capable of empowering high-throughput functional genomic assays at multipopulation and multispecies levels. Comprehensive experimental validations have been performed for targeting and verification primers designed by GetPrimers across multiple organism systems and experimental setups. We anticipate GetPrimers to become a highly useful and popular tool to facilitate easy and standardized gene modification across multiple systems.

Understanding and modeling human traits and diseases: Insights from the comparative genomics resources of Zoonomia

Understanding and modeling human traits and diseases: Insights from the comparative genomics resources of Zoonomia

Nanopore Sequencing to Identify Transposable Element Insertions and Their Epigenetic Modifications.

Over the past 20years, high-throughput genomic assays have fundamentally changed how transposable elements (TEs) are studied. While short-read DNA sequencing has been at the heart of these efforts, novel technologies that generate longer reads are driving a shift in the field. Long-read sequencing now permits locus-specific approaches to locate individual TE insertions and understand their epigenetic and transcriptional regulation, while still profiling TE activity genome-wide. Here we provide detailed guidelines to implement Oxford Nanopore Technologies (ONT) sequencing to identify polymorphic TE insertions and profile TE epigenetic landscapes. Using human long interspersed element-1 (LINE-1, L1) as an example, we explain the procedures involved, including final visualization, and potential bottlenecks and pitfalls. ONT sequencing will be, in our view, a workhorse technology for the foreseeable future in the TE field.

Ordinalbayes: Fitting Ordinal Bayesian Regression Models to High-Dimensional Data Using R

The stage of cancer is a discrete ordinal response that indicates the aggressiveness of disease and is often used by physicians to determine the type and intensity of treatment to be administered. For example, the FIGO stage in cervical cancer is based on the size and depth of the tumor as well as the level of spread. It may be of clinical relevance to identify molecular features from high-throughput genomic assays that are associated with the stage of cervical cancer to elucidate pathways related to tumor aggressiveness, identify improved molecular features that may be useful for staging, and identify therapeutic targets. High-throughput RNA-Seq data and corresponding clinical data (including stage) for cervical cancer patients have been made available through The Cancer Genome Atlas Project (TCGA). We recently described penalized Bayesian ordinal response models that can be used for variable selection for over-parameterized datasets, such as the TCGA-CESC dataset. Herein, we describe our ordinalbayes R package, available from the Comprehensive R Archive Network (CRAN), which enhances the runjags R package by enabling users to easily fit cumulative logit models when the outcome is ordinal and the number of predictors exceeds the sample size, P > N, such as for TCGA and other high-throughput genomic data. We demonstrate the use of this package by applying it to the TCGA cervical cancer dataset. Our ordinalbayes package can be used to fit models to high-dimensional datasets, and it effectively performs variable selection.

Multi-resolution characterization of molecular taxonomies in bulk and single-cell transcriptomics data.

As high-throughput genomics assays become more efficient and cost effective, their utilization has become standard in large-scale biomedical projects. These studies are often explorative, in that relationships between samples are not explicitly defined a priori, but rather emerge from data-driven discovery and annotation of molecular subtypes, thereby informing hypotheses and independent evaluation. Here, we present K2Taxonomer, a novel unsupervised recursive partitioning algorithm and associated R package that utilize ensemble learning to identify robust subgroups in a ‘taxonomy-like’ structure. K2Taxonomer was devised to accommodate different data paradigms, and is suitable for the analysis of both bulk and single-cell transcriptomics, and other ‘-omics’, data. For each of these data types, we demonstrate the power of K2Taxonomer to discover known relationships in both simulated and human tissue data. We conclude with a practical application on breast cancer tumor infiltrating lymphocyte (TIL) single-cell profiles, in which we identified co-expression of translational machinery genes as a dominant transcriptional program shared by T cells subtypes, associated with better prognosis in breast cancer tissue bulk expression data.

Methods of massive parallel reporter assays for investigation of enhancers

The correct deployment of genetic programs for development and differentiation relies on finely coordinated regulation of specific gene sets. Genomic regulatory elements play an exceptional role in this process. There are few types of gene regulatory elements, including promoters, enhancers, insulators and silencers. Alterations of gene regulatory elements may cause various pathologies, including cancer, congenital disorders and autoimmune diseases. The development of high-throughput genomic assays has made it possible to significantly accelerate the accumulation of information about the characteristic epigenetic properties of regulatory elements. In combination with high-throughput studies focused on the genome-wide distribution of epigenetic marks, regulatory proteins and the spatial structure of chromatin, this significantly expands the understanding of the principles of epigenetic regulation of genes and allows potential regulatory elements to be searched for in silico. However, common experimental approaches used to study the local characteristics of chromatin have a number of technical limitations that may reduce the reliability of computational identification of genomic regulatory sequences. Taking into account the variability of the functions of epigenetic determinants and complex multicomponent regulation of genomic elements activity, their functional verification is often required. A plethora of methods have been developed to study the functional role of regulatory elements on the genome scale. Common experimental approaches for in silico identification of regulatory elements and their inherent technical limitations will be described. The present review is focused on original high-throughput methods of enhancer activity reporter analysis that are currently used to validate predicted regulatory elements and to perform de novo searches. The methods described allow assessing the functional role of the nucleotide sequence of a regulatory element, to determine its exact boundaries and to assess the influence of the local state of chromatin on the activity of enhancers and gene expression. These approaches have contributed substantially to the understanding of the fundamental principles of gene regulation.

Abstract B038: A unified genome-wide analysis of dysfunctional T-cell states in cancer and chronic viral infection

Abstract Tumor-specific T-cells that have differentiated into a terminal dysfunctional state exist in the tumor microenvironment. A systematic understanding of the requirements of immunotherapeutic rescue of these cells is critically needed to improve clinical results in patients. Mouse models of chronic infection and cancer have been studied to elucidate biologic mechanisms of persistent antigen stimulation resulting in T-cell dysfunction, or “exhaustion.” Recently, chromatin accessibility imprinting has been associated with T-cells falling back into the dysfunctional state after temporary rescue by checkpoint blockade, suggesting epigenetic mechanisms in control of T-cell dysfunction. However, comprehensive characterization of T-cell dysfunction across models based on their epigenetic and transcriptional profiles is lacking.We collected 106 chromatin accessibility (ATAC-seq) samples and 87 gene expression (RNA-seq) samples from seven recent publications. We analyzed these data by first applying batch effect correction using generalized linear modeling. This enabled mapping profiles of chromatin accessibility peaks in gene promoters and enhancers from different studies into the same space. We observed that epigenetic profiles of dysfunctional tumor-infiltrating T-cells and dysfunctional T-cells in chronic viral infection were, surprisingly, extremely similar. Furthermore, a recently characterized discrete distinction between epigenetic profiles of early (day 7-8) and late (day 28-35) dysfunction in the tumor was recapitulated in the model of chronic infection. Overall we observed across mouse models that T-cells committed to becoming dysfunctional early after an immune challenge, rather than first mounting and then loosing an effector response. These observations were also largely recapitulated in gene expression analysis. Differentially expressed genes with massive differential accessibility of their promoter and enhancer peaks during development of dysfunction, observed consistently across models, including transcription factors (TF) well studied in immunity such as Tcf7, Lef1, Satb1, Ikzf2, Tox, are good candidates for further targeted analysis.We then turned to TF binding analysis. We associated absolute levels of chromatin accessibility in peaks of each sample with TF binding (predicted by motif analysis) using regularized negative binomial regression with cross-validation. We estimated the effect of each TF in each sample, which allowed us to map chromatin accessibility profiles into the TF activity space of much lower dimensionality. This mapping largely preserved the hierarchy of relative similarities between samples. We identified key TFs whose binding was associated with open or closed chromatin in functional and dysfunctional cell states. For example, not surprisingly, binding of well known effector factors Eomes and Batf was associated with closed chromatin in naive cells and open chromatin in effector cells. Strikingly, the strongest association with closing chromatin in dysfunction, consistently across mouse models, was observed for Tcf7/Lef1 binding, further suggesting the role of these TFs in establishing the terminal CD8 T-cell dysfunctional state. Notably, we found two large groups of TFs anti-correlated to each other whose predicted binding sites had higher accessibility in either functional or dysfunctional cells. This suggested that coordinated activity of a broad range of TFs (not necessarily binding at the same sites) might be responsible for establishing and maintaining T-cell functional states, and focusing on one or a handful of TFs is not sufficient to explain them. This analysis provides a better systematic understanding of cell-intrinsic mechanisms driving different functional states of CD8 T-cells, and the developed computational methods are broadly applicable in other experimental setups where diverse cell states are profiled by high-throughput genomic assays. Citation Format: Yuri Pritykin, Christina Leslie. A unified genome-wide analysis of dysfunctional T-cell states in cancer and chronic viral infection [abstract]. In: Proceedings of the Fourth CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; Sept 30-Oct 3, 2018; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2019;7(2 Suppl):Abstract nr B038.

Facilitating the Calculation of the Efficient Score Using Symbolic Computing

ABSTRACTThe score statistic continues to be a fundamental tool for statistical inference. In the analysis of data from high-throughput genomic assays, inference on the basis of the score usually enjoys greater stability, considerably higher computational efficiency, and lends itself more readily to the use of resampling methods than the asymptotically equivalent Wald or likelihood ratio tests. The score function often depends on a set of unknown nuisance parameters which have to be replaced by estimators, but can be improved by calculating the efficient score, which accounts for the variability induced by estimating these parameters. Manual derivation of the efficient score is tedious and error-prone, so we illustrate using computer algebra to facilitate this derivation. We demonstrate this process within the context of a standard example from genetic association analyses, though the techniques shown here could be applied to any derivation, and have a place in the toolbox of any modern statistician. We further show how the resulting symbolic expressions can be readily ported to compiled languages, to develop fast numerical algorithms for high-throughput genomic analysis. We conclude by considering extensions of this approach. The code featured in this report is available online as part of the supplementary material.

EAMA: Empirically adjusted meta-analysis for large-scale simultaneous hypothesis testing in genomic experiments.

Recent developments in high throughput genomic assays have opened up the possibility of testing hundreds and thousands of genes simultaneously. However, adhering to the regular statistical assumptions regarding the null distributions of test statistics in such large-scale multiple testing frameworks has the potential of leading to incorrect significance testing results and biased inference. This problem gets worse when one combines results from different independent genomic experiments with a possibility of ending up with gross false discoveries of significant genes. In this article, we develop a meta-analysis method of combining p-values from different independent experiments involving large-scale multiple testing frameworks, through empirical adjustments of the individual test statistics and p-values. Even though, it is based on various existing ideas, this specific combination is novel and potentially useful. Through simulation studies and real genomic datasets we show that our method outperforms the standard meta-analysis approach of significance testing in terms of accurately identifying the truly significant set of genes.

The Utilization of Formalin Fixed-Paraffin-Embedded Specimens in High Throughput Genomic Studies.

High throughput genomic assays empower us to study the entire human genome in short time with reasonable cost. Formalin fixed-paraffin-embedded (FFPE) tissue processing remains the most economical approach for longitudinal tissue specimen storage. Therefore, the ability to apply high throughput genomic applications to FFPE specimens can expand clinical assays and discovery. Many studies have measured the accuracy and repeatability of data generated from FFPE specimens using high throughput genomic assays. Together, these studies demonstrate feasibility and provide crucial guidance for future studies using FFPE specimens. Here, we summarize the findings of these studies and discuss the limitations of high throughput data generated from FFPE specimens across several platforms that include microarray, high throughput sequencing, and NanoString.

Temporal prediction of future state occupation in a multistate model from high-dimensional baseline covariates via pseudo-value regression

ABSTRACTIn many complex diseases such as cancer, a patient undergoes various disease stages before reaching a terminal state (say disease free or death). This fits a multistate model framework where a prognosis may be equivalent to predicting the state occupation at a future time t. With the advent of high-throughput genomic and proteomic assays, a clinician may intent to use such high-dimensional covariates in making better prediction of state occupation. In this article, we offer a practical solution to this problem by combining a useful technique, called pseudo-value (PV) regression, with a latent factor or a penalized regression method such as the partial least squares (PLS) or the least absolute shrinkage and selection operator (LASSO), or their variants. We explore the predictive performances of these combinations in various high-dimensional settings via extensive simulation studies. Overall, this strategy works fairly well provided the models are tuned properly. Overall, the PLS turns out to be slightly better than LASSO in most settings investigated by us, for the purpose of temporal prediction of future state occupation. We illustrate the utility of these PV-based high-dimensional regression methods using a lung cancer data set where we use the patients’ baseline gene expression values.

VariancePartition: interpreting drivers of variation in complex gene expression studies.

BackgroundAs large-scale studies of gene expression with multiple sources of biological and technical variation become widely adopted, characterizing these drivers of variation becomes essential to understanding disease biology and regulatory genetics.ResultsWe describe a statistical and visualization framework, variancePartition, to prioritize drivers of variation based on a genome-wide summary, and identify genes that deviate from the genome-wide trend. Using a linear mixed model, variancePartition quantifies variation in each expression trait attributable to differences in disease status, sex, cell or tissue type, ancestry, genetic background, experimental stimulus, or technical variables. Analysis of four large-scale transcriptome profiling datasets illustrates that variancePartition recovers striking patterns of biological and technical variation that are reproducible across multiple datasets.ConclusionsOur open source software, variancePartition, enables rapid interpretation of complex gene expression studies as well as other high-throughput genomics assays. variancePartition is available from Bioconductor: http://bioconductor.org/packages/variancePartition.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1323-z) contains supplementary material, which is available to authorized users.

A time-and-motion approach to micro-costing of high-throughput genomic assays.

Genomic technologies are increasingly used to guide clinical decision-making in cancer control. Economic evidence about the cost-effectiveness of genomic technologies is limited, in part because of a lack of published comprehensive cost estimates. In the present micro-costing study, we used a time-and-motion approach to derive cost estimates for 3 genomic assays and processes-digital gene expression profiling (gep), fluorescence in situ hybridization (fish), and targeted capture sequencing, including bioinformatics analysis-in the context of lymphoma patient management. The setting for the study was the Department of Lymphoid Cancer Research laboratory at the BC Cancer Agency in Vancouver, British Columbia. Mean per-case hands-on time and resource measurements were determined from a series of direct observations of each assay. Per-case cost estimates were calculated using a bottom-up costing approach, with labour, capital and equipment, supplies and reagents, and overhead costs included. The most labour-intensive assay was found to be fish at 258.2 minutes per case, followed by targeted capture sequencing (124.1 minutes per case) and digital gep (14.9 minutes per case). Based on a historical case throughput of 180 cases annually, the mean per-case cost (2014 Canadian dollars) was estimated to be $1,029.16 for targeted capture sequencing and bioinformatics analysis, $596.60 for fish, and $898.35 for digital gep with an 807-gene code set. With the growing emphasis on personalized approaches to cancer management, the need for economic evaluations of high-throughput genomic assays is increasing. Through economic modelling and budget-impact analyses, the cost estimates presented here can be used to inform priority-setting decisions about the implementation of such assays in clinical practice.

A comparative analytical assay of gene regulatory networks inferred using microarray and RNA-seq datasets.

A Gene Regulatory Network (GRN) is a collection of interactions between molecular regulators and their targets in cells governing gene expression level. Omics data explosion generated from high-throughput genomic assays such as microarray and RNA-Seq technologies and the emergence of a number of pre-processing methods demands suitable guidelines to determine the impact of transcript data platforms and normalization procedures on describing associations in GRNs. In this study exploiting publically available microarray and RNA-Seq datasets and a gold standard of transcriptional interactions in Arabidopsis, we performed a comparison between six GRNs derived by RNA-Seq and microarray data and different normalization procedures. As a result we observed that compared algorithms were highly data-specific and Networks reconstructed by RNA-Seq data revealed a considerable accuracy against corresponding networks captured by microarrays. Topological analysis showed that GRNs inferred from two platforms were similar in several of topological features although we observed more connectivity in RNA-Seq derived genes network. Taken together transcriptional regulatory networks obtained by Robust Multiarray Averaging (RMA) and Variance-Stabilizing Transformed (VST) normalized data demonstrated predicting higher rate of true edges over the rest of methods used in this comparison.

Immune signaling-based Cascade Propagation approach re-stratifies HNSCC patients

The availability of high-throughput genomic assays and rich electronic medical records allows us to identify cancer subtypes with greater accuracy and resolution. The integration of multiplatform, heterogenous, and high dimensional data remains an enormous challenge in using big data in bioinformatics research. Previous methods have been developed for patient stratification, however, these approaches did not incorporate prior knowledge and offer limited biology insight. New computational methods are needed to better utilize multiple types of information to identify clinically meaningful subtypes. Recent studies have shown that many immune functional genes are associated with cancer progression, recurrence and prognosis in head and neck squamous cell carcinoma (HNSCC). Therefore, we developed a novel immune signaling based Cascade Propagation (CasP) subtyping approach to stratify HNSCC patients. Unlike previous stratification methods that use only patient genomic data, our approach makes use of prior biological information such as immune signaling and protein-protein interactions, as well as patient survival information. CasP is a multi-step stratification procedure, composed of a dynamic network tree cutting step followed by a mutational stratification step. Using this approach, HNSCC patients were first stratified into clinically relative subgroups with different survival outcomes and distinct immunogenic features. We found that the good outcome of a subgroup of HNSCC patients was due to an enhanced immune response. The gene sets were characterized by a significant activation of T cell receptor signaling pathways, in addition to other important cancer related pathways such as PI3K and JAK/STAT signaling pathways. Further stratification of patients based on somatic mutation profiles detected three survival-distinct subnetworks. Our newly developed CasP subtyping approach allowed us to integrate multiple data types and identify clinically relevant subtypes of HNSCC patients.

ChIP and Chips: Introducing the WormPharm for correlative studies employing pharmacology and genome-wide analyses in C. elegans.

We present the WormPharm, an automated microfluidic platform that utilizes an axenic medium to culture C. elegans. The WormPharm is capable of sustaining C. elegans for extended periods, while recording worm development and growth with high temporal resolution ranging from seconds to minutes over several days to months. We demonstrate the utility of the device to monitor C. elegans growth in the presence of varying doses of nicotine and alcohol. Furthermore, we show that C. elegans cultured in the WormPharm are amendable for high-throughput genomic assays, i.e. chromatin-immunoprecipitation followed by next generation sequencing, and confirm that nematodes grown in monoxenic and axenic cultures exhibit genetic modifications that correlate with observed phenotypes. The WormPharm is a powerful tool for analyzing the effects of chemical, nutritional and environmental variations on organism level responses in conjunction with genome-wide changes in C. elegans.

PD-L1 and PD-L2 Genetic Alterations Define Classical Hodgkin Lymphoma and Predict Outcome.

Classical Hodgkin lymphomas (cHLs) include small numbers of malignant Reed-Sternberg cells within an extensive but ineffective inflammatory/immune cell infiltrate. In cHL, chromosome 9p24.1/PD-L1/PD-L2 alterations increase the abundance of the PD-1 ligands, PD-L1 and PD-L2, and their further induction through Janus kinase 2-signal transducers and activators of transcription signaling. The unique composition of cHL limits its analysis with high-throughput genomic assays. Therefore, the precise incidence, nature, and prognostic significance of PD-L1/PD-L2 alterations in cHL remain undefined. We used a fluorescent in situ hybridization assay to evaluate CD274/PD-L1 and PDCD1LG2/PD-L2 alterations in 108 biopsy specimens from patients with newly diagnosed cHL who were treated with the Stanford V regimen and had long-term follow-up. In each case, the frequency and magnitude of 9p24.1 alterations-polysomy, copy gain, and amplification-were determined, and the expression of PD-L1 and PD-L2 was evaluated by immunohistochemistry. We also assessed the association of 9p24.1 alterations with clinical parameters, which included stage (early stage I/II favorable risk, early stage unfavorable risk, advanced stage [AS] III/IV) and progression-free survival (PFS). Ninety-seven percent of all evaluated cHLs had concordant alterations of the PD-L1 and PD-L2 loci (polysomy, 5% [five of 108]; copy gain, 56% [61 of 108]; amplification, 36% [39 of 108]). There was an association between PD-L1 protein expression and relative genetic alterations in this series. PFS was significantly shorter for patients with 9p24.1 amplification, and the incidence of 9p24.1 amplification was increased in patients with AS cHL. PD-L1/PD-L2 alterations are a defining feature of cHL. Amplification of 9p24.1 is more common in patients with AS disease and associated with shorter PFS in this series. Further analyses of 9p24.1 alterations in patients treated with standard cHL induction regimens or checkpoint blockade are warranted.

PD-L1 and PD-L2 Genetic Alterations Define Classical Hodgkin Lymphoma and Predict Outcome

▪Introduction. Classical Hodgkin Lymphomas (cHL) include small numbers of malignant Reed-Sternberg (RS) cells within an extensive but ineffective inflammatory/immune cell infiltrate. In cHL, chromosome 9p24.1 alterations increase the abundance of the PD-1 ligands, PD-L1 and PD-L2, and their further induction via JAK2-STAT signaling. PD-1 ligands engage the PD-1 receptor on T-cells and induce PD-1 signaling and T-cell exhaustion. Tumor cells expressing PD-1 ligands on their surface utilize the PD-1 pathway to evade an effective immune response. In recent pilot studies, PD-1 blockade was associated with high response rates and durable remissions in relapsed/refractory cHL.The unique composition of cHL limits its analysis with high throughput genomic assays. Therefore, the precise incidence, nature and prognostic significance of PD-L1 and PD-L2 alterations in cHL remain undefined. Herein, we utilize a recently developed fluorescence in situ hybridization (FISH) assay to characterize 9p24.1/PD-L1/PD-L2 alterations in a cohort of 108 newly diagnosed cHL patients (pts) who were uniformly treated with StanfordV (a combined modality therapy regimen) and have longterm followup.Methods. Pts were characterized as Ann Arbor early stage I/II favorable risk (ES-F), early stage unfavorable risk (bulk ≥ 10cm or ≥ .33 mediastinal dimension and/or B symptoms) (ES-U) or advanced stage III/IV (AS). ES-F pts received 8 weeks of Stanford V and 30 Gy involved field radiation (IFR); ES-U and AS pts received 12 weeks of Stanford V and 36 Gy IFR to initial sites > 5 cm.FISH was performed on formalin-fixed paraffin-embedded diagnostic biopsy specimens using bacterial artificial chromosome probes which covered CD274/PD-L1 (labeled with spectrum orange) and PDCD1LG2/PD-L2 (labeled with spectrum green) and a control centromeric probe (spectrum aqua-labeled CEP9, from 9p11-q11). Malignant RS cells were identified by their nuclear morphologic features and 50 RS cells/case were analyzed. Nuclei with a target:control probe ratio of at least 3:1 were defined as amplified (amp), those with a probe ratio of more than 1:1 but less than 3:1 were classified as relative copy gain, and those with a probe ratio of 1:1 but more than 2 copies of each probe were defined as polysomic for chromosome 9p. In each case, the percent and magnitude of disomy, polysomy, copy gain and amp were noted. In accordance with clinically approved diagnostic criteria, cases were classified by the highest observed level of 9p24.1 alteration. Specifically, cases with polysomy lacked copy gain or amp and cases with copy gain lacked amp.Immunohistochemical staining for PD-L1/PAX5 was performed as previously described and PD-L1 expression in PAX5 dim+ malignant RS cells and PAX5- infiltrating normal cells was assessed separately.Results. Almost all newly diagnosed cHL pts in this series had concordant alterations of the PD-L1 and PD-L2 loci; disomy was found in only 1% (1/108), polysomy in 5% (5/108), copy gain in 56% (61/108) and amp in 36% (39/108) of study pts. There was a correlation between intensity of PD-L1 protein expression and relative genetic alterations in this series. Two additional pts had translocations of PD-L1 or PD-L2 (2%, 2/108).We next assessed the association between specific types of PD-L1/PD-L2 alterations, clinical risk factors and outcome. Overall, the progression-free survival (PFS) was significantly lower for AS pts compared to ES-F/U pts (p=0.017). A model of PFS for the cHL pts by genetic alteration indicated that PFS was also significantly lower for pts with amp (p=0.02). Consistent with these findings, the incidence of 9p24.1 amp increased by clinical risk group: ES-F, 24%; ES-U, 34%; and AS, 50% (p=0.024, Kruskal-Wallis test). Therefore, we fit a full model of clinical and genetic factors including B-symptoms, bulk, stage and amp. Despite the association of amp with increased clinical risk groups, the genetic alteration further delineated PFS in the multivariate model (p=0.075).Conclusions.PD-L1/PD-L2 alterations are a defining feature of cHL with rare polysomy and more frequent copy gain and amp. There is an increased incidence of amp in pts with AS disease and a highly significant association of PD-L1/PD-L2 amp with PFS. These findings underscore the importance of genetically defined PD-1 mediated immune evasion in cHL and provide a rationale for the efficacy of PD-1 blockade in this disease. DisclosuresRodig:Perkin Elmer: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding. Shipp:BMS: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bayer: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding; Gilead: Consultancy; Merck: Membership on an entity's Board of Directors or advisory committees.

Decoding neuroproteomics: integrating the genome, translatome and functional anatomy.

The immense intercellular and intracellular heterogeneity of the CNS presents major challenges for high-throughput omic analyses. Transcriptional, translational and post-translational regulatory events are localized to specific neuronal cell types or subcellular compartments, resulting in discrete patterns of protein expression and activity. A spatial and quantitative knowledge of the neuroproteome is therefore critical to understanding both normal and pathological aspects of the functional genomics and anatomy of the CNS. Improvements in mass spectrometry allow the profiling of proteins at a sufficient depth to complement results from high-throughput genomic and transcriptomic assays. However, there are challenges in integrating proteomic data with other data modalities and even greater challenges in obtaining comprehensive neuroproteomic data with cell-type specificity. Here we discuss how proteomics should be exploited to enhance high-throughput functional genomic analysis by tighter integration of data analyses. We also discuss experimental strategies to achieve finer cellular and subcellular resolution in transcriptomic and proteomic studies of neural tissues.

Use of genotyping by sequencing data to develop a high-throughput and multifunctional SNP panel for conservation applications in Pacific lamprey.

Next-generation sequencing data can be mined for highly informative single nucleotide polymorphisms (SNPs) to develop high-throughput genomic assays for nonmodel organisms. However, choosing a set of SNPs to address a variety of objectives can be difficult because SNPs are often not equally informative. We developed an optimal combination of 96 high-throughput SNP assays from a total of 4439 SNPs identified in a previous study of Pacific lamprey (Entosphenus tridentatus) and used them to address four disparate objectives: parentage analysis, species identification and characterization of neutral and adaptive variation. Nine of these SNPs are FST outliers, and five of these outliers are localized within genes and significantly associated with geography, run-timing and dwarf life history. Two of the 96 SNPs were diagnostic for two other lamprey species that were morphologically indistinguishable at early larval stages and were sympatric in the Pacific Northwest. The majority (85) of SNPs in the panel were highly informative for parentage analysis, that is, putatively neutral with high minor allele frequency across the species' range. Results from three case studies are presented to demonstrate the broad utility of this panel of SNP markers in this species. As Pacific lamprey populations are undergoing rapid decline, these SNPs provide an important resource to address critical uncertainties associated with the conservation and recovery of this imperiled species.