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Background: Data errors, including sample swapping and mis-labeling, are inevitable in the process of large-scale omics data generation. Data errors need to be identified and corrected before integrative data analyses where different types of data are merged on the basis of the annotated labels. Data with labeling errors dampen true biological signals. More importantly, data analysis with sample errors could lead to wrong scientific conclusions. We developed a robust probabilistic multi-omics data matching procedure, proMODMatcher, to curate data and identify and correct data annotation and errors in large databases., Results: Application to simulated datasets suggests that proMODMatcher achieved robust statistical power even when the number of cis-associations was small and/or the number of samples was large. Application of our proMODMatcher to multi-omics datasets in The Cancer Genome Atlas and International Cancer Genome Consortium identified sample errors in multiple cancer datasets. Our procedure was not only able to identify sample-labeling errors but also to unambiguously identify the source of the errors. Our results demonstrate that these errors should be identified and corrected before integrative analysis., Conclusions: Our results indicate that sample-labeling errors were common in large multi-omics datasets. These errors should be corrected before integrative analysis., (© The Author(s) 2019. Published by Oxford University Press.)

Glioblastoma (GBM) is the most aggressive and common form of brain cancer in adults. GBM is characterized by poor survival and remarkably high tumors heterogeneity (both intertumoral and intratumoral), and lack of effective therapies. Recent high-throughput data revealed heterogeneous genetic/genomic/epigenetic features and led to multiple methods aiming to classify tumors according to the key molecular events that drive the most aggressive cellular components so that targeted therapies can be developed for individual subtypes. However, GBM molecular subtypes have not led to improvement of patients outcomes. Targeted or tailored therapies for specific mutations or subtypes largely failed due to the complexities arising from intratumoral molecular heterogeneity. Most tumors develop resistance to treatment and soon recur. GBM stem cells (GSCs) have been identified. Recent single cell sequencing studies of GBM suggest that intratumoral cellular heterogeneity can be partially explained by tumor cell hierarchy arising from GBM stem cells. Therefore, the molecular subtypes based on patient derived GSCs may potentially lead to more effective subtype-specific treatments. In this paper, we review the molecular alterations of GBM and molecular subtyping methods as well as subtype plasticity in primary and recurrent tumors emphasizing the clinical relevance of potential targets for further drug development., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

A major challenge in inflammatory bowel disease (IBD) is the integration of diverse IBD data sets to construct predictive models of IBD. We present a predictive model of the immune component of IBD that informs causal relationships among loci previously linked to IBD through genome-wide association studies (GWAS) using functional and regulatory annotations that relate to the cells, tissues, and pathophysiology of IBD. Our model consists of individual networks constructed using molecular data generated from intestinal samples isolated from three populations of patients with IBD at different stages of disease. We performed key driver analysis to identify genes predicted to modulate network regulatory states associated with IBD, prioritizing and prospectively validating 12 of the top key drivers experimentally. This validated key driver set not only introduces new regulators of processes central to IBD but also provides the integrated circuits of genetic, molecular, and clinical traits that can be directly queried to interrogate and refine the regulatory framework defining IBD.

Errors in sample annotation or labeling often occur in large-scale genetic or genomic studies and are difficult to avoid completely during data generation and management. For integrative genomic studies, it is critical to identify and correct these errors. Different types of genetic and genomic data are inter-connected by cis-regulations. On that basis, we developed a computational approach, Multi-Omics Data Matcher (MODMatcher), to identify and correct sample labeling errors in multiple types of molecular data, which can be used in further integrative analysis. Our results indicate that inspection of sample annotation and labeling error is an indispensable data quality assurance step. Applied to a large lung genomic study, MODMatcher increased statistically significant genetic associations and genomic correlations by more than two-fold. In a simulation study, MODMatcher provided more robust results by using three types of omics data than two types of omics data. We further demonstrate that MODMatcher can be broadly applied to large genomic data sets containing multiple types of omics data, such as The Cancer Genome Atlas (TCGA) data sets.

Objective: Elucidating genetic factors of complex diseases is one of the most important challenges in biomedical research. Recently, a genetical genomics approach of mapping genotype to transcripts has been used in complex disease analysis. This approach treats messenger ribonucleic acid (mRNA) expression as a quantitative trait and identifies putative regulatory loci for the expression of an individual gene. However, the single-gene approach could not detect single nucleotide polymorphisms (SNP's) associated with the concerted activity of multiple genes. Since complex diseases result from the interactions of multiple genes, it is important to consider associations between genotype and multiple gene expression., Methods and Materials: We developed the differential allelic co-expression (DACE) that identifies regulatory loci that affect the inter-correlation structure of multiple genes or a gene set. We applied DACE to two benchmark datasets: the normal human lymphoblastoid cell dataset and the glioblastoma dataset. These datasets consist of both SNPs and mRNA expression profiles for each sample. When analyzing the lymphoblastoid cell dataset, principal component analysis (PCA) was compared with the DACE test., Results: While PCA identified associations found by single-gene analysis, the DACE test detected associations not identified by the single-gene approach. Using the DACE test, seven putative regulatory loci of immune-related pathways were identified in lymphoblastoid cells after controlling for family-wise error rate. In the glioblastoma dataset, DACE identified 4582 SNPs associated with six pathways. In 231 of the 4582 SNPs, patient survival length was correlated significantly with the SNP genotype. This finding suggests that our integrative approach may provide a biological explanation for the putative relationship between sequence level variation and disease outcome, via expression of a functional pathway., Conclusion: The DACE test shows promise for finding regulatory relationships between a genomic locus and sets of genes which may be related to disease outcome.

Ion channels and transporters have increasingly recognized roles in cancer progression through the regulation of cell proliferation, migration, and death. Glioblastoma stem-like cells (GSCs) are a source of tumor formation and recurrence in glioblastoma multiforme, a highly aggressive brain cancer, suggesting that ion channel expression may be perturbed in this population. However, little is known about the expression and functional relevance of ion channels that may contribute to GSC malignancy. Using RNA sequencing, we assessed the enrichment of ion channels in GSC isolates and non-tumor neural cell types. We identified a unique set of GSC-enriched ion channels using differential expression analysis that is also associated with distinct gene mutation signatures. In support of potential clinical relevance, expression of selected GSC-enriched ion channels evaluated in human glioblastoma databases of The Cancer Genome Atlas and Ivy Glioblastoma Atlas Project correlated with patient survival times. Finally, genetic knockdown as well as pharmacological inhibition of individual or classes of GSC-enriched ion channels constrained growth of GSCs compared to normal neural stem cells. This first-in-kind global examination characterizes ion channels enriched in GSCs and explores their potential clinical relevance to glioblastoma molecular subtypes, gene mutations, survival outcomes, regional tumor expression, and experimental responses to loss-of-function. Together, the data support the potential biological and therapeutic impact of ion channels on GSC malignancy and provide strong rationale for further examination of their mechanistic and therapeutic importance. [ABSTRACT FROM AUTHOR]

Multiple myeloma (MM) is the second most prevalent hematological cancer. MM is a complex and heterogeneous disease, and thus, it is essential to leverage omics data from large MM cohorts to understand the molecular mechanisms underlying MM tumorigenesis, progression, and drug responses, which may aid in the development of better treatments. In this study, we analyzed gene expression, copy number variation, and clinical data from the Multiple Myeloma Research Consortium (MMRC) dataset and constructed a multiple myeloma molecular causal network (M3CN). The M3CN was used to unify eight prognostic gene signatures in the literature that shared very few genes between them, resulting in a prognostic subnetwork of the M3CN, consisting of 178 genes that were enriched for genes involved in cell cycle (fold enrichment = 8.4, p value = 6.1 × 10−26). The M3CN was further used to characterize immunomodulators and proteasome inhibitors for MM, demonstrating the pleiotropic effects of these drugs, with drug-response signature genes enriched across multiple M3CN subnetworks. Network analyses indicated potential links between these drug-response subnetworks and the prognostic subnetwork. To elucidate the structure of these important MM subnetworks, we identified putative key regulators predicted to modulate the state of these subnetworks. Finally, to assess the predictive power of our network-based models, we stratified MM patients in an independent cohort, the MMRF-CoMMpass study, based on the prognostic subnetwork, and compared the performance of this subnetwork against other signatures in the literature. We show that the M3CN-derived prognostic subnetwork achieved the best separation between different risk groups in terms of log-rank test p-values and hazard ratios. In summary, this work demonstrates the power of a probabilistic causal network approach to understanding molecular mechanisms underlying the different MM signatures. [ABSTRACT FROM AUTHOR]