Marco Chierici

The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance

The concordance of RNA-sequencing (RNA-seq) with microarrays for genome-wide analysis of differential gene expression has not been rigorously assessed using a range of chemical treatment conditions. Here we use a comprehensive study design to generate Illumina RNA-seq and Affymetrix microarray data from the same liver samples of rats exposed in triplicate to varying degrees of perturbation by 27 chemicals representing multiple modes of action (MOAs). The cross-platform concordance in terms of differentially expressed genes (DEGs) or enriched pathways is linearly correlated with treatment effect size (R20.8). Furthermore, the concordance is also affected by transcript abundance and biological complexity of the MOA. RNA-seq outperforms microarray (93% versus 75%) in DEG verification as assessed by quantitative PCR, with the gain mainly due to its improved accuracy for low-abundance transcripts. Nonetheless, classifiers to predict MOAs perform similarly when developed using data from either platform. Therefore, the endpoint studied and its biological complexity, transcript abundance and the genomic application are important factors in transcriptomic research and for clinical and regulatory decision making.

Comparison of RNA-seq and microarray-based models for clinical endpoint prediction

BackgroundGene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model.ResultsWe generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models.ConclusionsWe demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.

Assessing sources of inconsistencies in genotypes and their effects on genome-wide association studies with HapMap samples

The discordance in results of independent genome-wide association studies (GWAS) indicates the potential for Type I and Type II errors. We assessed the repeatibility of current Affymetrix technologies that support GWAS. Reasonable reproducibility was observed for both raw intensity and the genotypes/copy number variants. We also assessed consistencies between different SNP arrays and between genotype calling algorithms. We observed that the inconsistency in genotypes was generally small at the specimen level. To further examine whether the differences from genotyping and genotype calling are possible sources of variation in GWAS results, an association analysis was applied to compare the associated SNPs. We observed that the inconsistency in genotypes not only propagated to the association analysis, but was amplified in the associated SNPs. Our studies show that inconsistencies between SNP arrays and between genotype calling algorithms are potential sources for the lack of reproducibility in GWAS results.

Variability in GWAS analysis: the impact of genotype calling algorithm inconsistencies

The Genome-Wide Association Working Group (GWAWG) is part of a large-scale effort by the MicroArray Quality Consortium (MAQC) to assess the quality of genomic experiments, technologies and analyses for genome-wide association studies (GWASs). One of the aims of the working group is to assess the variability of genotype calls within and between different genotype calling algorithms using data for coronary artery disease from the Wellcome Trust Case Control Consortium (WTCCC) and the University of Ottawa Heart Institute. Our results show that the choice of genotyping algorithm (for example, Bayesian robust linear model with Mahalanobis distance classifier (BRLMM), the corrected robust linear model with maximum-likelihood-based distances (CRLMM) and CHIAMO (developed and implemented by the WTCCC)) can introduce marked variability in the results of downstream case–control association analysis for the Affymetrix 500K array. The amount of discordance between results is influenced by how samples are combined and processed through the respective genotype calling algorithm, indicating that systematic genotype errors due to computational batch effects are propagated to the list of single-nucleotide polymorphisms found to be significantly associated with the trait of interest. Further work using HapMap samples shows that inconsistencies between Affymetrix arrays and calling algorithms can lead to genotyping errors that influence downstream analysis.

Batch effects in the BRLMM genotype calling algorithm influence GWAS results for the Affymetrix 500K array

The Affymetrix GeneChip Human Mapping 500K array is common for genome-wide association studies (GWASs). Recent findings highlight the importance of accurate genotype calling algorithms to reduce the inflation in Type I and Type II error rates. Differential results due to genotype calling errors can introduce severe bias in case–control association study results. Using data from the Wellcome Trust Case Control Consortium, 1991 individuals with coronary artery disease (CAD) and 1500 controls from the UK Blood Services (NBS) were genotyped on the Affymetrix 500K array. Different batch sizes and compositions were used in the Bayesian Robust Linear Model with Mahalanobis distance classifier (BRLMM) genotype calling algorithm to assess the batch effect on downstream association analysis. Results show that composition (cases and controls genotyped simultaneously or separate) and size (number of individuals processed by BRLMM at a time) can create 2–3% discordance in the results for quality control and statistical analysis and may contribute to the lack of reproducibility between GWASs. The changes in batch size are largely responsible for differential single-nucleotide polymorphism results, yet we observe evidence of an interactive effect of batch size and composition that contributes to discordant results in the list of significantly associated loci.

Identification of GALNT14 as a novel neuroblastoma predisposition gene

Although several genes have been associated to neuroblastoma (NB) predisposition and aggressiveness, further genes are likely involved in the overall risk of developing this pediatric cancer. We thus carried out whole-exome sequencing on germline DNA from two affected second cousins and two unlinked healthy relatives from a large family with hereditary NB. Bioinformatics analysis revealed 6999 variations that were exclusively shared by the two familial NB cases. We then considered for further analysis all unknown or rare missense mutations, which involved 30 genes. Validation and analysis of these variants led to identify a GALNT14 mutation (c.802C > T) that properly segregated in the family and was predicted as functionally damaging by PolyPhen2 and SIFT. Screening of 8 additional NB families and 167 sporadic cases revealed this GALNT14 mutation in the tumors of two twins and in the germline of one sporadic NB patient. Moreover, a significant association between MYCN amplification and GALNT14 expression was observed in both NB patients and cell lines. Also, GALNT14 higher expression is associated with a worse OS in a public dataset of 88 NB samples (http://r2.amc.nl). GALNT14 is a member of the polypeptide N-acetylgalactosaminyl-transferase family and maps closely to ALK on 2p23.1, a region we previously discovered in linkage with NB in the family here considered. The aberrant function of GALNTs can result in altered glycoproteins that have been associated to the promotion of tumor aggressiveness in various cancers. Although rare, the recurrence of this mutation suggests GALNT14 as a novel gene potentially involved in NB predisposition.

Assessment of variability in GWAS with CRLMM genotyping algorithm on WTCCC coronary artery disease

The robustness of genome-wide association study (GWAS) results depends on the genotyping algorithms used to establish the association. This paper initiated the assessment of the impact of the Corrected Robust Linear Model with Maximum Likelihood Classification (CRLMM) genotyping quality on identifying real significant genes in a GWAS with large sample sizes. With microarray image data from the Wellcome Trust Case–Control Consortium (WTCCC), 1991 individuals with coronary artery disease (CAD) and 1500 controls, genetic associations were evaluated under various batch sizes and compositions. Experimental designs included different batch sizes of 250, 350, 500, 2000 samples with different distributions of cases and controls in each batch with either randomized or simply combined (4:3 case–control ratios) or separate case–control samples as well as whole 3491 samples. The separate composition could create 2–3% discordance in the single nucleotide polymorphism (SNP) results for quality control/statistical analysis and might contribute to the lack of reproducibility between GWAS. CRLMM shows high genotyping accuracy and stability to batch effects. According to the genotypic and allelic tests (P<5.0 × 10−7), nine significant signals on chromosome 9 were found consistently in all batch sizes with combined design. Our findings are critical to optimize the reproducibility of GWAS and confirm the genetic role in the pathophysiology of CAD.

PD-L1 Is a Therapeutic Target of the Bromodomain Inhibitor JQ1 and, Combined with HLA Class I, a Promising Prognostic Biomarker in Neuroblastoma

Purpose: This study sought to evaluate the expression of programmed cell death-ligand-1 (PD-L1) and HLA class I on neuroblastoma cells and programmed cell death-1 (PD-1) and lymphocyte activation gene 3 (LAG3) on tumor-infiltrating lymphocytes to better define patient risk stratification and understand whether this tumor may benefit from therapies targeting immune checkpoint molecules. Experimental Design: In situ IHC staining for PD-L1, HLA class I, PD-1, and LAG3 was assessed in 77 neuroblastoma specimens, previously characterized for tumor-infiltrating T-cell density and correlated with clinical outcome. Surface expression of PD-L1 was evaluated by flow cytometry and IHC in neuroblastoma cell lines and tumors genetically and/or pharmacologically inhibited for MYC and MYCN. A dataset of 477 human primary neuroblastomas from GEO and ArrayExpress databases was explored for PD-L1, MYC, and MYCN correlation. Results: Multivariate Cox regression analysis demonstrated that the combination of PD-L1 and HLA class I tumor cell density is a prognostic biomarker for predicting overall survival in neuroblastoma patients (P = 0.0448). MYC and MYCN control the expression of PD-L1 in neuroblastoma cells both in vitro and in vivo. Consistently, abundance of PD-L1 transcript correlates with MYC expression in primary neuroblastoma. Conclusions: The combination of PD-L1 and HLA class I represents a novel prognostic biomarker for neuroblastoma. Pharmacologic inhibition of MYCN and MYC may be exploited to target PD-L1 and restore an efficient antitumor immunity in high-risk neuroblastoma. Clin Cancer Res; 23(15); 4462–72. ©2017 AACR.

Monitoring Perinatal Gut Microbiota in Mouse Models by Mass Spectrometry Approaches: Parental Genetic Background and Breastfeeding Effects.

At birth, contact with external stimuli, such as nutrients derived from food, is necessary to modulate the symbiotic balance between commensal and pathogenic bacteria, protect against bacterial dysbiosis, and initiate the development of the mucosal immune response. Among a variety of different feeding patterns, breastfeeding represents the best modality. In fact, the capacity of breast milk to modulate the composition of infants’ gut microbiota leads to beneficial effects on their health. In this study, we used newborn mice as a model to evaluate the effect of parental genetic background (i.e., IgA-producing mice and IgA-deficient mice) and feeding modulation (i.e., maternal feeding and cross-feeding) on the onset and shaping of gut microbiota after birth. To investigate these topics, we used either a culturomic approach that employed Matrix Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry (MS), or bottom–up Liquid Chromatography, with subsequent MSMS shotgun metaproteomic analysis that compared and assembled results of the two techniques. We found that the microbial community was enriched by lactic acid bacteria when pups were breastfed by wild-type (WT) mothers, while IgA-deficient milk led to an increase in the opportunistic bacterial pathogen (OBP) population. Cross-feeding results suggested that IgA supplementation promoted the exclusion of some OBPs and the temporary appearance of beneficial species in pups fed by WT foster mothers. Our results show that both techniques yield a picture of microbiota from different angles and with varying depths. In particular, our metaproteomic pipeline was found to be a reliable tool in the description of microbiota. Data from these studies are available via ProteomeXchange, with identifier PXD004033.

Focal adhesion kinase depletion reduces human hepatocellular carcinoma growth by repressing enhancer of zeste homolog 2

Hepatocellular carcinoma (HCC) is the most common type of liver cancer in humans. The focal adhesion tyrosine kinase (FAK) is often over-expressed in human HCC and FAK inhibition may reduce HCC cell invasiveness. However, the anti-oncogenic effect of FAK knockdown in HCC cells remains to be clarified. We found that FAK depletion in HCC cells reduced in vitro and in vivo tumorigenicity, by inducing G2/M arrest and apoptosis, decreasing anchorage-independent growth, and modulating the expression of several cancer-related genes. Among these genes, we showed that FAK silencing decreased transcription and nuclear localization of enhancer of zeste homolog 2 (EZH2) and its tri-methylation activity on lysine 27 of histone H3 (H3K27me3). Accordingly, FAK, EZH2 and H3K27me3 were concomitantly upregulated in human HCCs compared to non-tumor livers. In vitro experiments demonstrated that FAK affected EZH2 expression and function by modulating, at least in part, p53 and E2F2/3 transcriptional activity. Moreover, FAK silencing downregulated both EZH2 binding and histone H3K27me3 levels at the promoter of its target gene NOTCH2. Finally, we found that pharmacological inhibition of FAK activity resembled these effects although milder. In summary, we demonstrate that FAK depletion reduces HCC cell growth by affecting cancer-promoting genes including the pro-oncogene EZH2. Furthermore, we unveil a novel unprecedented FAK/EZH2 crosstalk in HCC cells, thus identifying a targetable network paving the way for new anticancer therapies.