Survival of metastatic melanoma patients after dendritic cell vaccination correlates with expression of leukocyte phosphatidylethanolamine-binding protein 1 / Raf Kinase inhibitory protein

Item does not contain fulltextImmunotherapy for metastatic melanoma offers great promise but, to date, only a subset of patients have responded. There is an urgent need to identify ways of allocating patients to the most beneficial therapy, to increase survival and decrease therapy-associated morbidity and costs. Blood-based biomarkers are of particular interest because of their straightforward implementation in routine clinical care. We sought to identify markers for dendritic cell (DC) vaccine-based immunotherapy against metastatic melanoma through gene expression analysis of peripheral blood mononuclear cells. A large-scale microarray analysis of 74 samples from two treatment centers, taken directly after the first round of DC vaccination, was performed. We found that phosphatidylethanolamine binding protein 1 (PEBP1)/Raf Kinase inhibitory protein (RKIP) expression can be used to identify a significant proportion of patients who performed poorly after DC vaccination. This result was validated by q-PCR analysis on blood samples from a second cohort of 95 patients treated with DC vaccination in four different centers. We conclude that low PEBP1 expression correlates with poor overall survival after DC vaccination. Intriguingly, this was only the case for expression of PEBP1 after, but not prior to, DC vaccination. Moreover, the change in PEBP1 expression upon vaccination correlated well with survival. Further analyses revealed that PEBP1 expression positively correlated with genes involved in T cell responses but inversely correlated with genes associated with myeloid cells and aberrant inflammation including STAT3, NOTCH1, and MAPK1. Concordantly, PEBP1 inversely correlated with the myeloid/lymphoid-ratio and was suppressed in patients suffering from chronic inflammatory disease

Genotyping of single nucleotide polymorphisms related to attention-deficit hyperactivity disorder

Pharmacological treatment of several diseases, such as attention-deficit hyperactivity disorder (ADHD), presents marked variability in efficiency and its adverse effects. The genotyping of specific single nucleotide polymorphisms (SNPs) can support the prediction of responses to drugs and the genetic risk of presenting comorbidities associated with ADHD. This study presents two rapid and affordable microarray-based strategies to discriminate three clinically important SNPs in genes ADRA2A, SL6CA2, and OPRM1 (rs1800544, rs5569, and rs1799971, respectively). These approaches are allele-specific oligonucleotide hybridization (ASO) and a combination of allele-specific amplification (ASA) and solid-phase hybridization. Buccal swab and blood samples taken from ADHD patients and controls were analyzed by ASO, ASA, and a gold-reference method. The results indicated that ASA is superior in genotyping capability and analytical performance.This research has been funded through projects FEDER MINECO INNPACTO IPT-2011-1132-010000, CTQ/2013/45875R, and PrometeoII/2014/040 (GVA).Tortajada-Genaro, LA.; Mena-Mollá, S.; Niñoles Rodenes, R.; Puigmule, M.; Viladevall, L.; Maquieira Catala, Á. (2016). Genotyping of single nucleotide polymorphisms related to attention-deficit hyperactivity disorder. Analytical and Bioanalytical Chemistry. 408(9):2339-2345. https://doi.org/10.1007/s00216-016-9332-3S233923454089Cortese S. The neurobiology and genetics of Attention-Deficit/Hyperactivity Disorder (ADHD): what every clinician should know. Eur J Paediatr Neurol. 2012;16:422–33.Contini V, Rovaris DL, Victor MM, Grevet EH, Rohde LA, Bau CH. Pharmacogenetics of response to methylphenidate in adult patients with attention-deficit/hyperactivity disorder (ADHD): a systematic review. Eur Neuropsychopharmacol. 2013;23:555–60.Gardiner SJ, Begg EJ. Pharmacogenetics, drug-metabolizing enzymes, and clinical practice. Pharmacol Rev. 2006;58(3):521–90.Abul-Husn NS, Obeng AO, Sanderson SC, Gottesman O, Scott SA. Implementation and utilization of genetic testing in personalized medicine. Pharmacogenomics Pers Med. 2014;7:227.Altman RB, Flockhart D, Goldstein DB, editors. Principles of pharmacogenetics and pharmacogenomics. Cambridge: Cambridge University Press; 2012.Hawi Z, Cummins TDR, Tong J, Johnson B, Lau R, Samarrai W, et al. The molecular genetic architecture of attention deficit hyperactivity disorder. Mol Psychiatry. 2015;20:289–97.Limaye N. Pharmacogenomics, Theranostics and Personalized Medicine-the complexities of clinical trials: challenges in the developing world. Appl Transl Genomics. 2013;2:17–21.Manolio TA, Chisholm RL, Ozenberger B, Roden DM, Williams MS, Wilson R, et al. Implementing genomic medicine in the clinic: the future is here. Genet Med. 2013;15:258–67.Kim S, Misra A. PharmGKB: the Pharmacogenomics Knowledge Base. Annu Rev Biomed Eng. 2007;9:289–320.Lucarelli F, Tombelli S, Minunni M, Marrazza G, Mascini M. Electrochemical and piezoelectric DNA biosensors for hybridisation detection. Anal Chim Acta. 2008;609:139–59.Knez K, Spasic D, Janssen KP, Lammertyn J. Emerging technologies for hybridization based single nucleotide polymorphism detection. Analyst. 2014;139:353–70.Choi JY, Kim YT, Byun JY, Ahn J, Chung S, Gweon DG, et al. Integrated allele-specific polymerase chain reaction–capillary electrophoresis microdevice for single nucleotide polymorphism genotyping. Lab Chip. 2012;12:5146–54.Ragoussis J. Genotyping Technologies for Genetic Research. Annu Rev Genomics Hum Genet. 2009;10:117–33.Sethi D, Gandhi RP, Kuma P, Gupta KC. Chemical strategies for immobilization of oligonucleotides. Biotechnol J. 2009;4:1513–29.Bañuls MJ, Morais SB, Tortajada-Genaro LA, Maquieira A. Microarray Developed on Plastic Substrates. Microarray Technology: Methods and Applications, 2016; 37-51.Tortajada-Genaro LA, Rodrigo A, Hevia E, Mena S, Niñoles R, Maquieira A. Microarray on digital versatile disc for identification and genotyping of Salmonella and Campylobacter in meat products. Anal Bioanal Chem. 2015;407:7285–94.Kieling C, Genro JP, Hutz MH, Rohde LA. A current update on ADHD pharmacogenomics. Pharmacogenomics. 2010;11:407–19.Kim BN, Kim JW, Cummins TD, Bellgrove MA, Hawi Z, Hong SB, et al. Norepinephrine genes predict response time variability and methylphenidate-induced changes in neuropsychological function in attention deficit hyperactivity disorder. J Clin Psychopharmacol. 2013;33:356–62.Carpentier PJ, Arias Vasquez A, Hoogman M, Onnink M, Kan CC, Kooij JJS, et al. Shared and unique genetic contributions to attention deficit/hyperactivity disorder and substance use disorders: A pilot study of six candidate genes. Eur Neuropsychopharmacol. 2013;23:448–57.Zhang Y, Haraksingh R, Grubert F, Abyzov A, Gerstein M, Weissman S, et al. Child development and structural variation in the human genome. Child Dev. 2013;84:34–48.Asari M, Watanabe S, Matsubara K, Shiono H, Shimizu K. Single nucleotide polymorphism genotyping by mini-primer allele-specific amplification with universal reporter primers for identification of degraded DNA. Anal Biochem. 2009;386:85–90.Choi JY, Kim YT, Ahn J, Kim KS, Gweon DG, Seo TS. Integrated allele-specific polymerase chain reaction–capillary electrophoresis microdevice for single nucleotide polymorphism genotyping. Biosens Bioelectron. 2012;35:327–34.Konstantou JK, Ioannou PC, Christopoulos TK. Dual-allele dipstick assay for genotyping single nucleotide polymorphisms by primer extension reaction. Eur J Hum Genet. 2009;17:105–11.Sebastian T, Cooney CG, Parker J, Qu P, Perov A, Golova JB, et al. Integrated amplification microarray system in a lateral flow cell for warfarin genotyping from saliva. Clin Chim Acta. 2014;429:198–205

A genome-wide scan for common alleles affecting risk for autism

Although autism spectrum disorders (ASDs) have a substantial genetic basis, most of the known genetic risk has been traced to rare variants, principally copy number variants (CNVs). To identify common risk variation, the Autism Genome Project (AGP) Consortium genotyped 1558 rigorously defined ASD families for 1 million single-nucleotide polymorphisms (SNPs) and analyzed these SNP genotypes for association with ASD. In one of four primary association analyses, the association signal for marker rs4141463, located within MACROD2, crossed the genome-wide association significance threshold of P < 5 × 10−8. When a smaller replication sample was analyzed, the risk allele at rs4141463 was again over-transmitted; yet, consistent with the winner's curse, its effect size in the replication sample was much smaller; and, for the combined samples, the association signal barely fell below the P < 5 × 10−8 threshold. Exploratory analyses of phenotypic subtypes yielded no significant associations after correction for multiple testing. They did, however, yield strong signals within several genes, KIAA0564, PLD5, POU6F2, ST8SIA2 and TAF1C

Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets

Current genome-wide association studies (GWAS) use commercial genotyping microarrays that can assay over a million single nucleotide polymorphisms (SNPs). The number of SNPs is further boosted by advanced statistical genotype-imputation algorithms and large SNP databases for reference human populations. The testing of a huge number of SNPs needs to be taken into account in the interpretation of statistical significance in such genome-wide studies, but this is complicated by the non-independence of SNPs because of linkage disequilibrium (LD). Several previous groups have proposed the use of the effective number of independent markers (Me) for the adjustment of multiple testing, but current methods of calculation for Me are limited in accuracy or computational speed. Here, we report a more robust and fast method to calculate Me. Applying this efficient method [implemented in a free software tool named Genetic type 1 error calculator (GEC)], we systematically examined the Me, and the corresponding p-value thresholds required to control the genome-wide type 1 error rate at 0.05, for 13 Illumina or Affymetrix genotyping arrays, as well as for HapMap Project and 1000 Genomes Project datasets which are widely used in genotype imputation as reference panels. Our results suggested the use of a p-value threshold of ~10−7 as the criterion for genome-wide significance for early commercial genotyping arrays, but slightly more stringent p-value thresholds ~5 × 10−8 for current or merged commercial genotyping arrays, ~10−8 for all common SNPs in the 1000 Genomes Project dataset and ~5 × 10−8 for the common SNPs only within genes

Survival of metastatic melanoma patients after dendritic cell vaccination correlates with expression of leukocyte phosphatidylethanolamine-binding protein 1/Raf kinase inhibitory protein

Immunotherapy for metastatic melanoma offers great promise but, to date, only a subset of patients have responded. There is an urgent need to identify ways of allocating patients to the most beneficial therapy, to increase survival and decrease therapy-associated morbidity and costs. Blood-based biomarkers are of particular interest because of their straightforward implementation in routine clinical care. We sought to identify markers for dendritic cell (DC) vaccine-based immunotherapy against metastatic melanoma through gene expression analysis of peripheral blood mononuclear cells. A large-scale microarray analysis of 74 samples from two treatment centers, taken directly after the first round of DC vaccination, was performed. We found that phosphatidylethanolamine binding protein 1 (_PEBP1_)/ Raf Kinase inhibitory protein (RKIP) expression can be used to identify a significant proportion of patients who performed poorly after DC vaccination. This result was validated by q-PCR analysis on blood samples from a second cohort of 95 patients treated with DC vaccination in four different centers. We conclude that low _PEBP1_ expression correlates with poor overall survival after DC vaccination. Intriguingly, this was only the case for expression of _PEBP1_ after, but not prior to, DC vaccination. Moreover, the change in _PEBP1_ expression upon vaccination correlated well with survival. Further analyses revealed that _PEBP1_ expression positively correlated with genes involved in T cell responses but inversely correlated with genes associated with myeloid cells and aberrant inflammation including _STAT3, NOTCH1,_ and _MAPK1_. Concordantly, _PEBP1_ inversely correlated with the myeloid/ lymphoid-ratio and was suppressed in patients suffering from chronic inflammatory disease

The small-nucleolar RNAs commonly used for microRNA normalisation correlate with tumour pathology and prognosis

Background:To investigate small-nucleolar RNAs (snoRNAs) as reference genes when measuring miRNA expression in tumour samples, given emerging evidence for their role in cancer.Methods:Four snoRNAs, commonly used for normalisation, RNU44, RNU48, RNU43 and RNU6B, and miRNA known to be associated with pathological factors, were measured by real-time polymerase chain reaction in two patient series: 219 breast cancer and 46 head and neck squamous cell carcinoma (HNSCC). SnoRNA and miRNA were then correlated with clinicopathological features and prognosis.Results:Small-nucleolar RNA expression was as variable as miRNA expression (miR-21, miR-210, miR-10b). Normalising miRNA PCR expression data to these recommended snoRNAs introduced bias in associations between miRNA and pathology or outcome. Low snoRNA expression correlated with markers of aggressive pathology. Low levels of RNU44 were associated with a poor prognosis. RNU44 is an intronic gene in a cluster of highly conserved snoRNAs in the growth arrest specific 5 (GAS5) transcript, which is normally upregulated to arrest cell growth under stress. Low-tumour GAS5 expression was associated with a poor prognosis. RNU48 and RNU43 were also identified as intronic snoRNAs within genes that are dysregulated in cancer.Conclusion:Small-nucleolar RNAs are important in cancer prognosis, and their use as reference genes can introduce bias when determining miRNA expression. © 2011 Cancer Research UK All rights reserved

PDK1 and HR46 Gene Homologs Tie Social Behavior to Ovary Signals

The genetic basis of division of labor in social insects is a central question in evolutionary and behavioral biology. The honey bee is a model for studying evolutionary behavioral genetics because of its well characterized age-correlated division of labor. After an initial period of within-nest tasks, 2–3 week-old worker bees begin foraging outside the nest. Individuals often specialize by biasing their foraging efforts toward collecting pollen or nectar. Efforts to explain the origins of foraging specialization suggest that division of labor between nectar and pollen foraging specialists is influenced by genes with effects on reproductive physiology. Quantitative trait loci (QTL) mapping of foraging behavior also reveals candidate genes for reproductive traits. Here, we address the linkage of reproductive anatomy to behavior, using backcross QTL analysis, behavioral and anatomical phenotyping, candidate gene expression studies, and backcross confirmation of gene-to-anatomical trait associations. Our data show for the first time that the activity of two positional candidate genes for behavior, PDK1 and HR46, have direct genetic relationships to ovary size, a central reproductive trait that correlates with the nectar and pollen foraging bias of workers. These findings implicate two genes that were not known previously to influence complex social behavior. Also, they outline how selection may have acted on gene networks that affect reproductive resource allocation and behavior to facilitate the evolution of social foraging in honey bees

Structural and non-coding variants increase the diagnostic yield of clinical whole genome sequencing for rare diseases

BACKGROUND: Whole genome sequencing is increasingly being used for the diagnosis of patients with rare diseases. However, the diagnostic yields of many studies, particularly those conducted in a healthcare setting, are often disappointingly low, at 25–30%. This is in part because although entire genomes are sequenced, analysis is often confined to in silico gene panels or coding regions of the genome. METHODS: We undertook WGS on a cohort of 122 unrelated rare disease patients and their relatives (300 genomes) who had been pre-screened by gene panels or arrays. Patients were recruited from a broad spectrum of clinical specialties. We applied a bioinformatics pipeline that would allow comprehensive analysis of all variant types. We combined established bioinformatics tools for phenotypic and genomic analysis with our novel algorithms (SVRare, ALTSPLICE and GREEN-DB) to detect and annotate structural, splice site and non-coding variants. RESULTS: Our diagnostic yield was 43/122 cases (35%), although 47/122 cases (39%) were considered solved when considering novel candidate genes with supporting functional data into account. Structural, splice site and deep intronic variants contributed to 20/47 (43%) of our solved cases. Five genes that are novel, or were novel at the time of discovery, were identified, whilst a further three genes are putative novel disease genes with evidence of causality. We identified variants of uncertain significance in a further fourteen candidate genes. The phenotypic spectrum associated with RMND1 was expanded to include polymicrogyria. Two patients with secondary findings in FBN1 and KCNQ1 were confirmed to have previously unidentified Marfan and long QT syndromes, respectively, and were referred for further clinical interventions. Clinical diagnoses were changed in six patients and treatment adjustments made for eight individuals, which for five patients was considered life-saving. CONCLUSIONS: Genome sequencing is increasingly being considered as a first-line genetic test in routine clinical settings and can make a substantial contribution to rapidly identifying a causal aetiology for many patients, shortening their diagnostic odyssey. We have demonstrated that structural, splice site and intronic variants make a significant contribution to diagnostic yield and that comprehensive analysis of the entire genome is essential to maximise the value of clinical genome sequencing

Structural and non-coding variants increase the diagnostic yield of clinical whole genome sequencing for rare diseases

BACKGROUND: Whole genome sequencing is increasingly being used for the diagnosis of patients with rare diseases. However, the diagnostic yields of many studies, particularly those conducted in a healthcare setting, are often disappointingly low, at 25-30%. This is in part because although entire genomes are sequenced, analysis is often confined to in silico gene panels or coding regions of the genome.METHODS: We undertook WGS on a cohort of 122 unrelated rare disease patients and their relatives (300 genomes) who had been pre-screened by gene panels or arrays. Patients were recruited from a broad spectrum of clinical specialties. We applied a bioinformatics pipeline that would allow comprehensive analysis of all variant types. We combined established bioinformatics tools for phenotypic and genomic analysis with our novel algorithms (SVRare, ALTSPLICE and GREEN-DB) to detect and annotate structural, splice site and non-coding variants.RESULTS: Our diagnostic yield was 43/122 cases (35%), although 47/122 cases (39%) were considered solved when considering novel candidate genes with supporting functional data into account. Structural, splice site and deep intronic variants contributed to 20/47 (43%) of our solved cases. Five genes that are novel, or were novel at the time of discovery, were identified, whilst a further three genes are putative novel disease genes with evidence of causality. We identified variants of uncertain significance in a further fourteen candidate genes. The phenotypic spectrum associated with RMND1 was expanded to include polymicrogyria. Two patients with secondary findings in FBN1 and KCNQ1 were confirmed to have previously unidentified Marfan and long QT syndromes, respectively, and were referred for further clinical interventions. Clinical diagnoses were changed in six patients and treatment adjustments made for eight individuals, which for five patients was considered life-saving.CONCLUSIONS: Genome sequencing is increasingly being considered as a first-line genetic test in routine clinical settings and can make a substantial contribution to rapidly identifying a causal aetiology for many patients, shortening their diagnostic odyssey. We have demonstrated that structural, splice site and intronic variants make a significant contribution to diagnostic yield and that comprehensive analysis of the entire genome is essential to maximise the value of clinical genome sequencing.</p

A Large Fraction of Extragenic RNA Pol II Transcription Sites Overlap Enhancers

A substantial fraction of extragenic Pol II transcription sites coincides with transcriptional enhancers, which may be relevant for functional annotation of mammalian genomes