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Emerging evidence suggests that the prognosis of patients with lung adenocarcinoma can be determined from germline variants and transcript levels in nontumoral lung tissue. Gene expression data from noninvolved lung tissue of 483 lung adenocarcinoma patients were tested for correlation with overall survival using multivariable Cox proportional hazard and multivariate machine learning models. For genes whose transcript levels are associated with survival, we used genotype data from 414 patients to identify germline variants acting as cis-expression quantitative trait loci (eQTLs). Associations of eQTL variant genotypes with gene expression and survival were tested. Levels of four transcripts were inversely associated with survival by Cox analysis (CLCF1, hazard ratio [HR] = 1.53; CNTNAP1, HR = 2.17; DUSP14, HR = 1.78; and MT1F: HR = 1.40). Machine learning analysis identified a signature of transcripts associated with lung adenocarcinoma outcome that was largely overlapping with the transcripts identified by Cox analysis, including the three most significant genes (CLCF1, CNTNAP1, and DUSP14). Pathway analysis indicated that the signature is enriched for ECM components. We identified 32 cis-eQTLs for CNTNAP1, including 6 with an inverse correlation and 26 with a direct correlation between the number of minor alleles and transcript levels. Of these, all but one were prognostic: the six with an inverse correlation were associated with better prognosis (HR 1) while the others were associated with worse prognosis. Our findings provide supportive evidence that genetic predisposition to lung adenocarcinoma outcome is a feature already present in patients' noninvolved lung tissue.

This file describes the legend of supplemental figures. It also include the supplemental Table S1

This figure includes distribution of PD-1+ and LAG-3+ lymphocytes in lymphoid aggregates (A). B-D Kaplan-Meier curves of OS according to the density of PD-1+ or LAG-3+ lymphocytes alone (B), or in combination with PD-L1+ tumor cells (C), or HLA-I+ tumor cells (D). E-H, Kaplan-Meier curves of RFS (E and G) and EFS (F and H) according to the combined PD-L1+ and HLA-I+ tumor-cell densities with tumor infiltrating PD-1+ lymphocytes (E and F) or LAG-3+ lymphocytes (G and H).

Tumor-infiltrating lymphocytes play an essential role in improving clinical outcome of neuroblastoma (NB) patients, but their relationship with other tumor-infiltrating immune cells in the T cell-inflamed tumors remains poorly investigated. Here we show that dendritic cells (DCs) and natural killer (NK) cells are positively correlated with T-cell infiltration in human NB, both at transcriptional and protein levels, and associate with a favorable prognosis. Multiplex imaging displays DC/NK/T cell conjugates in the tumor microenvironment of low-risk NB. Remarkably, this connection is further strengthened by the identification of gene signatures related to DCs and NK cells able to predict survival of NB patients and strongly correlate with the expression of PD-1 and PD-L1. In summary, our findings unveil a key prognostic role of DCs and NK cells and indicate their related gene signatures as promising tools for the identification of clinical biomarkers to better define risk stratification and survival of NB patients., Tumour-infiltrating lymphocytes play a crucial role in neuroblastoma, but their relationship to other immune cells is poorly understood. Here the authors identify the cellular and gene signatures of intratumoural dendritic cells and natural killer cells that predict the clinical outcome of neuroblastoma.

Background Oncopanel genomic testing, which identifies important somatic variants, is increasingly common in medical practice and especially in clinical trials. Currently, there is a paucity of reliable genomic reference samples having a suitably large number of pre-identified variants for properly assessing oncopanel assay analytical quality and performance. The FDA-led Sequencing and Quality Control Phase 2 (SEQC2) consortium analyze ten diverse cancer cell lines individually and their pool, termed Sample A, to develop a reference sample with suitably large numbers of coding positions with known (variant) positives and negatives for properly evaluating oncopanel analytical performance. Results In reference Sample A, we identify more than 40,000 variants down to 1% allele frequency with more than 25,000 variants having less than 20% allele frequency with 1653 variants in COSMIC-related genes. This is 5–100× more than existing commercially available samples. We also identify an unprecedented number of negative positions in coding regions, allowing statistical rigor in assessing limit-of-detection, sensitivity, and precision. Over 300 loci are randomly selected and independently verified via droplet digital PCR with 100% concordance. Agilent normal reference Sample B can be admixed with Sample A to create new samples with a similar number of known variants at much lower allele frequency than what exists in Sample A natively, including known variants having allele frequency of 0.02%, a range suitable for assessing liquid biopsy panels. Conclusion These new reference samples and their admixtures provide superior capability for performing oncopanel quality control, analytical accuracy, and validation for small to large oncopanels and liquid biopsy assays.

SO_SCPLOWTUDYC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWQUESTIONC_SCPLOWCan small genetic variants detected in the whole genome sequencing of spontaneously aborted euploid embryos give insight into possible causes of pregnancy loss? SO_SCPLOWUMMARYC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWANSWERC_SCPLOWBy filtering and prioritizing genetic variants it is possible to identify genomic variants putatively responsible for miscarriage. WO_SCPLOWHATC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWISC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWKNOWNC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWALREADYC_SCPLOWMiscarriage is often caused to chromosomal aneuploidies of the gametes but it can also have other genetic causes like small mutations, both de novo or inherited from parents. The analysis of genomic sequences of miscarried embryos has mostly focused on rare variation, and been carried out using criteria and methods that are difficult to reproduce. The role of small mutations has been scantily investigated so far. SO_SCPLOWTUDYC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWDESIGNC_SCPLOW, O_SCPLOWSIZEC_SCPLOW, O_SCPLOWDURATIONC_SCPLOWThis is a monocentric observational study. The study includes the data analysis of 46 embryos obtained from women experiencing pregnancy loss recruited by the University of Ferrara from 2017 to 2018. The study was approved by the Ethical committee of Emilia-Romagna (CE/FE 170475). PO_SCPLOWARTICIPANTSC_SCPLOW/O_SCPLOWMATERIALSC_SCPLOW, O_SCPLOWSETTINGC_SCPLOW, O_SCPLOWMETHODSC_SCPLOWThe participants are forty-six women, mostly European (87%) diagnosed with first (n=25, av.age 32.7) or recurrent (n=21, av.age 36.5) miscarriage. Embryonic DNA was prepared form chorionic villi and used to select euploid embryos using quantitative PCR, comparative genomic hybridiztion and shallow sequencing of random genomic regions. Euploid embryos were whole-genome sequenced at 30X using Illumina short-reads technology and genomic sequences were used to identify genetic variants. Variants were annotated integrating information from Ensembl100 and literature knowledge on genes associated with embryonic development, miscarriages, lethality, cell cycle. Following annotation, variants were filtered to prioritize putatively detrimental variants in genes that are relevant for embryonic development using a pipeline that we developed. The code is available on gitHub (ezcn/grep). MO_SCPLOWAINC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWRESULTSC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWANDC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWTHEC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWROLEC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWOFC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWCHANCEC_SCPLOWOur pipeline prioritized 439 putatively causative single nucleotide polymorphisms among 11M variants discovered in ten embryos. By systematic investigation of all coding regions, 47 genes per embryo were selected. Among them STAG2, known in literature for its role in congenital and developmental disorders as well as in cancer, TLE4 a key gene in embryonic development, expressed in both embryonic and extraembryonic tissues in the Wnt and Notch signalling pathways, and FMNL2, involved in cell motility with a major role in driving cell migration. Our analysis is fully reproducible (our code is open-source), and we take measures to increase its robustness to false positives by excluding genes with >5% chance to be selected in a control population. LO_SCPLOWIMITATIONSC_SCPLOW, O_SCPLOWREASONSC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWFORC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWCAUTIONC_SCPLOWThis pilot study has major limitations in sample size and lack of integration of the parental genomic information. Despite being encouraging, the results need to be interpreted with caution as functional analyses are required to validate the hypotheses that have been generated. Although we have developed a robust and scalable methodology for prioritizing genetic variants, we have not yet extended it beyond the coding regions of the genome. WO_SCPLOWIDERC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWIMPLICATIONSC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWOFC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWTHEC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWFINDINGSC_SCPLOWThis pilot study demonstrate that analysis of genome sequencing can help to clarify the causes of idiopathic miscarriages and provides initial results from the analysis of ten euploid embryos, discovering plausible candidate genes and variants. This study provides guidance for a larger study. Results of this and following wider studies can be used to test genetic predisposition to miscarriages in parents that are planning to conceive or undergoing preimplantation genetic testing. In a wider context, the results of this study might be relevant for genetic counseling and risk management in miscarriages SO_SCPLOWTUDYC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWFUNDINGC_SCPLOW/O_SCPLOWCOMPETINGC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWINTERESTC_SCPLOWO_SCPCAP(C_SCPCAPO_SCPLOWSC_SCPLOWO_SCPCAP)C_SCPCAPA.C. is a full time employee of Igenomix. A.D.M. was employee of Igenomix while working on this project. I.D.B., P.D.A., G.E., S.D.B. are full time employees of the MeriGen Research. All other authors declare that they have no conflicts of interest.

Over the last two decades, molecular biology has been changed by the introduction of high-throughput technologies. Data sharing requirements have prompted the establishment of persistent data archives. A standardized approach for recording and managing these data was first proposed in the Minimal Information About a Microarray Experiment (MIAME) guidelines. The Minimal Information about a high throughput nucleotide Sequencing Experiment (MINSEQE) proposal was introduced in 2008 as a logical extension of the guidelines to next-generation sequencing (NGS) technologies used for transcriptome analysis.We present a historical snapshot of the data-sharing situation focusing on transcriptomics data from both microarray and RNA-sequencing experiments published between 2009 and 2013, a period during which RNA-seq studies became increasingly popular for transcriptome analysis. We assess how much data from RNA-seq based experiments is actually available in persistent data archives, compared to data derived from microarray based experiments, and evaluate how these types of data differ. Based on this analysis, we provide recommendations to improve RNA-seq data availability, reusability, and reproducibility.

We introduce here ML4Tox, a framework offering Deep Learning and Support Vector Machine models to predict agonist, antagonist, and binding activities of chemical compounds, in this case for the estrogen receptor ligand-binding domain. The ML4Tox models have been developed with a 10 × 5-fold cross-validation schema on the training portion of the CERAPP ToxCast dataset, formed by 1677 chemicals, each described by 777 molecular features. On the CERAPP "All Literature" evaluation set (agonist: 6319 compounds; antagonist 6539; binding 7283), ML4Tox significantly improved sensitivity over published results on all three tasks, with agonist: 0.78 vs 0.56; antagonist: 0.69 vs 0.11; binding: 0.66 vs 0.26.

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.

Immunotherapy by using immune checkpoint inhibitors (ICI) has dramatically improved the treatment options in various cancers, increasing survival rates for treated patients. Nevertheless, there are heterogeneous response rates to ICI among different cancer types, and even in the context of patients affected by a specific cancer. Thus, it becomes crucial to identify factors that predict the response to immunotherapeutic approaches. A comprehensive investigation of the mutational and immunological aspects of the tumor can be useful to obtain a robust prediction. By performing a pan-cancer analysis on gene expression data from the Cancer Genome Atlas (TCGA, 8055 cases and 29 cancer types), we set up and validated a machine learning approach to predict the potential for positive response to ICI. Support vector machines (SVM) and extreme gradient boosting (XGboost) models were developed with a 10&times, 5-fold cross-validation schema on 80% of TCGA cases to predict ICI responsiveness defined by a score combining tumor mutational burden and TGF- &beta, signaling. On the remaining 20% validation subset, our SVM model scored 0.88 accuracy and 0.27 Matthews Correlation Coefficient. The proposed machine learning approach could be useful to predict the putative response to ICI treatment by expression data of primary tumors.

Almost every clinical specialty will use artificial intelligence in the future. The first area of practical impact is expected to be the rapid and accurate interpretation of image streams such as radiology scans, histo-pathology slides, ophthalmic imaging, and any other bioimaging diagnostic systems, enriched by clinical phenotypes used as outcome labels or additional descriptors. In this study, we introduce a machine learning framework for automatic image interpretation that combines the current pattern recognition approach (“radiomics”) with Deep Learning (DL). As a first application in cancer bioimaging, we apply the framework for prognosis of locoregional recurrence in head and neck squamous cell carcinoma (N=298) from Computed Tomography (CT) and Positron Emission Tomography (PET) imaging. The DL architecture is composed of two parallel cascades of Convolutional Neural Network (CNN) layers merging in a softmax classification layer. The network is first pretrained on head and neck tumor stage diagnosis, then finetuned on the prognostic task by internal transfer learning. In parallel, radiomics features (e.g., shape of the tumor mass, texture and pixels intensity statistics) are derived by predefined feature extractors on the CT/PET pairs. We compare and mix deep learning and radiomics features into a unifying classification pipeline (RADLER), where model selection and evaluation are based on a data analysis plan developed in the MAQC initiative for reproducible biomarkers. On the multimodal CT/PET cancer dataset, the mixed deep learning/radiomics approach is more accurate than using only one feature type, or image mode. Further, RADLER significantly improves over published results on the same data.

Although pediatric malignant extracranial germ-cell tumors (meGCTs) are among the most chemosensitive solid tumors, a group of patients relapse and die of disease. To identify new markers predicting clinical outcome, we examined the prognostic relevance of tumor-infiltrating T lymphocytes (TILs) and the expression of PD-1 and PD-L1 in a cohort of pediatric meGCTs by in situ immunohistochemistry. MeGCTs were variously infiltrated by T cell-subtypes according to the tumor subtype, tumor location and age at diagnosis. We distinguished three different phenotypes: i) tumors not infiltrated by T cells (immature teratomas and half of the yolk sac tumors), ii) tumors highly infiltrated by CD8(+) T cells expressing PD-1, which identifies activated tumor-reactive T cells (seminomas and dysgerminomas), iii) tumors highly infiltrated by CD8(+) T cells within an immunosuppressive tumor microenvironment characterized by CD4(+)FOXP3(+) Treg cells and PD-L1-expressing tumor cells (embryonal carcinomas, choriocarcinomas and the remaining yolk sac tumors). Tumor subtypes belonging mixed meGCTs were variously infiltrated, suggesting the coexistence of multiple immune microenvironments either facilitating or precluding the entry of T cells. These findings support the hypothesis that TILs influence the development of meGCTs and might be of clinical relevance to improve risk stratification and the treatment of pediatric patients.

Artificial Intelligence is exponentially increasing its impact on healthcare. As deep learning is mastering computer vision tasks, its application to digital pathology is natural, with the promise of aiding in routine reporting and standardizing results across trials. Deep learning features inferred from digital pathology scans can improve validity and robustness of current clinico-pathological features, up to identifying novel histological patterns, e.g., from tumor infiltrating lymphocytes. In this study, we examine the issue of evaluating accuracy of predictive models from deep learning features in digital pathology, as an hallmark of reproducibility. We introduce the DAPPER framework for validation based on a rigorous Data Analysis Plan derived from the FDA’s MAQC project, designed to analyze causes of variability in predictive biomarkers. We apply the framework on models that identify tissue of origin on 787 Whole Slide Images from the Genotype-Tissue Expression (GTEx) project. We test three different deep learning architectures (VGG, ResNet, Inception) as feature extractors and three classifiers (a fully connected multilayer, Support Vector Machine and Random Forests) and work with four datasets (5, 10, 20 or 30 classes), for a total of 53, 000 tiles at 512 × 512 resolution. We analyze accuracy and feature stability of the machine learning classifiers, also demonstrating the need for diagnostic tests (e.g., random labels) to identify selection bias and risks for reproducibility. Further, we use the deep features from the VGG model from GTEx on the KIMIA24 dataset for identification of slide of origin (24 classes) to train a classifier on 1, 060 annotated tiles and validated on 265 unseen ones. The DAPPER software, including its deep learning pipeline and the Histological Imaging—Newsy Tiles (HINT) benchmark dataset derived from GTEx, is released as a basis for standardization and validation initiatives in AI for digital pathology.

We introduce the CDRP (Concatenated Diagnostic-Relapse Prognostic) architecture for multi-task deep learning that incorporates a clinical algorithm, e.g., a risk stratification schema to improve prognostic profiling. We present the first application to survival prediction in High-Risk (HR) Neuroblastoma from transcriptomics data, a task that studies from the MAQC consortium have shown to remain the hardest among multiple diagnostic and prognostic endpoints predictable from the same dataset. To obtain a more accurate risk stratification needed for appropriate treatment strategies, CDRP combines a first component (CDRP-A) synthesizing a diagnostic task and a second component (CDRP-N) dedicated to one or more prognostic tasks. The approach leverages the advent of semi-supervised deep learning structures that can flexibly integrate multimodal data or internally create multiple processing paths. CDRP-A is an autoencoder trained on gene expression on the HR/non-HR risk stratification by the Children's Oncology Group, obtaining a 64-node representation in the bottleneck layer. CDRP-N is a multi-task classifier for two prognostic endpoints, i.e., Event-Free Survival (EFS) and Overall Survival (OS). CDRP-A provides the HR embedding input to the CDRP-N shared layer, from which two branches depart to model EFS and OS, respectively. To control for selection bias, CDRP is trained and evaluated using a Data Analysis Protocol (DAP) developed within the MAQC initiative. CDRP was applied on Illumina RNA-Seq of 498 Neuroblastoma patients (HR: 176) from the SEQC study (12,464 Entrez genes) and on Affymetrix Human Exon Array expression profiles (17,450 genes) of 247 primary diagnostic Neuroblastoma of the TARGET NBL cohort. On the SEQC HR patients, CDRP achieves Matthews Correlation Coefficient (MCC) 0.38 for EFS and MCC = 0.19 for OS in external validation, improving over published SEQC models. We show that a CDRP-N embedding is indeed parametrically associated to increasing severity and the embedding can be used to better stratify patients' survival.

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.

Background: Convolutional Neural Networks can be effectively used only when data are endowed with an intrinsic concept of neighbourhood in the input space, as is the case of pixels in images. We introduce here Ph-CNN, a novel deep learning architecture for the classification of metagenomics data based on the Convolutional Neural Networks, with the patristic distance defined on the phylogenetic tree being used as the proximity measure. The patristic distance between variables is used together with a sparsified version of MultiDimensional Scaling to embed the phylogenetic tree in a Euclidean space. Results: Ph-CNN is tested with a domain adaptation approach on synthetic data and on a metagenomics collection of gut microbiota of 38 healthy subjects and 222 Inflammatory Bowel Disease patients, divided in 6 subclasses. Classification performance is promising when compared to classical algorithms like Support Vector Machines and Random Forest and a baseline fully connected neural network, e.g. the Multi-Layer Perceptron. Conclusion: Ph-CNN represents a novel deep learning approach for the classification of metagenomics data. Operatively, the algorithm has been implemented as a custom Keras layer taking care of passing to the following convolutional layer not only the data but also the ranked list of neighbourhood of each sample, thus mimicking the case of image data, transparently to the user. Keywords: Metagenomics; Deep learning; Convolutional Neural Networks; Phylogenetic trees, Presented at BMTL 2017, Naples

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.

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 (R-2 approximate to 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.

We present primary results from the Sequencing Quality Control (SEQC) project, coordinated by the US Food and Drug Administration. Examining Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms at multiple laboratory sites using reference RNA samples with built-in controls, we assess RNA sequencing (RNA-seq) performance for junction discovery and differential expression profiling and compare it to microarray and quantitative PCR (qPCR) data using complementary metrics. At all sequencing depths, we discover unannotated exon-exon junctions, with >80% validated by qPCR. We find that measurements of relative expression are accurate and reproducible across sites and platforms if specific-filters are used. In contrast, RNA-seq and microarrays do not provide accurate absolute measurements, and gene-specific biases are observed for all examined platforms, including qPCR. Measurement performance depends on the platform and data analysis pipeline, and variation is large for transcript-level profiling. The complete SEQC data sets, comprising >100 billion reads (10Tb), provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings.

The presence of tumor-infiltrating T cells is crucial for the development of immunotherapies and the prediction of clinical response for many human cancers. Recently, we have shown that tumor-infiltrating T cells have a prognostic value greater than, and independent of, the criteria currently used to stage neuroblastoma, the most common pediatric extra-cranial solid tumor accounting for 15% of childhood cancer-related death. We demonstrated that expression of PD-L1 in tumor cells is inversely correlated with that of HLA class I, and that when combined they represent a promising prognostic biomarker in neuroblastoma. To further dissect the immune heterogeneity of neuroblastoma microenviroment, we evaluated the density of infiltrating dendritic cells (iDC), which are crucial for drug-induced anticancer immune response, in the same cohort of NB samples characterized for type, density, and composition of tumor T lymphocytes and expression of tumor PD-L1 and HLA class I. As for T cells, high density of iDCs was correlated with the presence of infiltrating T cells, expression of tumor HLA class I and favorable clinical outcome, suggesting that iDCs may be critical for robust tumor control by improving prediction of patient survival. Citation Format: Ombretta Melaiu, Renata Boldrini, Marco Chierici, Cesare Furlanello, Doriana Fruci. Clinical relevance of tumor-infiltrating immune cells in neuroblastoma [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2017 Oct 1-4; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2018;6(9 Suppl):Abstract nr B37.

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.

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 (P5.0 x 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.

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.

The discordance in results between independent genome-wide association studies (GWAS) indicates the potential for Type I and Type II errors. To identify the causes of variability underlying lack of reproducibility, here we present the results of a repeatability experiment on GWAS on a cohort of 1991 coronary artery disease individuals and 1500 controls (National Blood Service) provided by the Wellcome Trust Case Control Consortium. As part of the MicroArray Quality Control project, we identified quality control (QC) and association analysis steps with a major impact on the identification of candidate markers for possible classifiers. Different experimental conditions were used with the CHIAMO calling algorithm to assess the effects of batch size and case-control composition on downstream association analysis. Results showed that both composition and size create discordant single-nucleotide polymorphism (SNP) results for QC and statistical analysis and may contribute to the lack of reproducibility in GWAS. An interactive effect of batch size and composition contributes to discordant results in significantly associated loci. About 800 significant SNPs (Cochran-Armitage trend test, P

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.

Background Gene 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. Results We 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. Conclusions We 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. Electronic supplementary material The online version of this article (doi:10.1186/s13059-015-0694-1) contains supplementary material, which is available to authorized users.