Before and After: Comparison of Legacy and Harmonized TCGA Genomic Data Commons’ Data

Gao, Galen F.; Parker, Joel S.; Reynolds, Sheila M.; Silva, Tiago C.; Wang, Liang-Bo; Zhou, Wanding; Akbani, Rehan; Bailey, Matthew H.; Balu, Saianand; Berman, Benjamin P.; Brooks, Denise; Hu, Chen; Cherniack, Andrew D.; Demchok, John A.; Li, Ding; Felau, Ina; Gaheen, Sharon; Gerhard, Daniela S.; Heiman, David I.; Hernandez, Kyle M.; Hoadley, Katherine A.; Jayasinghe, Reyka G.; Kemal, Anab; Knijnenburg, Theo; Laird, Peter W.; Mensah, Michael K.A.; Mungall, Andrew J.; Robertson, A. Gordon; Shen, Hui; Tarnuzzer, Roy; Wang, Zhining; Wyczalkowski, Matthew A.; Yang, Liming; Zenklusen, Jean C.; Zhang, Zhenyu; Liang, Han; Noble, Michael S.

doi:10.1016/j.cels.2019.06.006

Cited by 130 publications

(114 citation statements)

References 49 publications

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“…The TCGA (The Cancer Genome Atlas Program) represents a joint venture of National Cancer Institute (NCI) and National Human Genome Research Institute (NGGRI) which began in 2006 as a pilot project with three cancer types (lung, ovarian and glioblastoma) which got expanded to present 33 tumor types encompassing a comprehensive dataset describing the molecular changes that occur in cancer [66]. Most samples in TCGA were originally aligned against the Genome Reference Consortium build GRCh37 (hg19) or the "legacy" dataset [67]. However, with advances in technology and drop in sequencing costs GDC (Genomic Data Commons -conceived by NCI) undertook harmonization effort to align the data to GRCh38 (hg38) build ("harmonized" dataset).…”

Section: Discussionmentioning

confidence: 99%

“…The work ow for generating RNA-Seq data in both legacy and harmonized dataset differs substantially [67] leading to introduction of bias between the hg19 and hg38 abundance estimates. However, Gao et al, [67] demonstrated that there exists excellent concordance between the two work ows in relation to BRCA PAM50 subtypes. Further, they reported that relative change between conditions is preserved across all subtypes of BRCA PAM50.…”

Section: Discussionmentioning

confidence: 99%

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Which Reference Genes to Choose for qPCR Normalization? A Comprehensive Analysis in MCF-7 Breast Cancer Cell Line

Jain¹,

Nitisa²,

Pirsko³

et al. 2020

Preprint

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Background MCF-7 cell line remains the most extensively studied patient derived model in breast cancer research, providing pivotal results that have over the decades translated to constantly improving patient care. Many research groups, in the past have identified suitable reference genes in various breast cancer cell lines including MCF-7. However, over the course of identification of stable internal controls, a comparative analysis comprising these genes together in a single study have not been previously undertaken. Further, very little is known about how these identified reference genes are expressed in MCF-7 sub-clones and when the cell line is cultured over multiple passages (p), given the heterogenic expression behavior often associated with MCF-7 cell line. We investigated the expression dynamics of 12 previously reported and suggested endogenous reference genes using RT-qPCR, available algorithms (NormFinder, geNorm, BestKeeper etc.) and TCGA transcriptomic analysis within identically cultured two sub-clones (culture A1 and A2) of MCF-7 cell line cultured over multiple passages. Further candidate reference genes were used to normalize 4 genes of interest (2 simulated and 2 backed by qPCR data) to make an evidence-based recommendation of the least variable reference genes that could be used in MCF-7 cell line. Results The analysis revealed the presence of differential reference gene expression within the sub-clones of MCF-7. In culture A1, GAPDH-CCSER2 were identified as least variable reference gene pair while for culture A2, GAPDH-RNA28S was recommended. However, upon validation using genes of interest, both these pairs were found to be unsuitable control pairs. Normalization with 3 genes and analyzing the combined dataset from culture A1 and A2 (supported by transcriptomic analysis), GAPDH-PCBP1-CCSER2 triplet was found to be least variable and hence potentially more well placed to handle any expression heterogeneity that may arise within sub-clones over multiple passages. Conclusions The variance in expression behavior amongst sub-clones shows the need for exercising caution while selecting and using reference genes for MCF-7. Further, using same reference genes amongst sub-clones can lead to misleading results arising from inaccurate normalization. GAPDH-PCBP1-CCSER2 triplet offers a reliable alternative to otherwise traditionally used internal controls in optimizing intra- and inter-assay gene expression differences.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Which Reference Genes to Choose for qPCR Normalization? A Comprehensive Analysis in MCF-7 Breast Cancer Cell Line

Jain¹,

Nitisa²,

Pirsko³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Nevertheless, there is still a considerable number of recently published studies, which make use of older sequencing data from a wide variety of sources [24][25][26] and we believe this will continue to be the case. Common incentives for reanalyzing genomic cohorts include re-mapping reads to a new reference genome version [27], periodic reanalysis of disease cohorts to diagnose more patients [28] or large meta-GWAS [29], aiming to achieve statistically signi cant results by increasing sample sizes.…”

Section: Discussionmentioning

confidence: 99%

Reliability of genomic variants across different next-generation sequencing platforms and bioinformatic processing pipelines

Gerber

Weißbach

Sys³

et al. 2020

Preprint

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Background Next Generation Sequencing (NGS) is the fundament of various studies providing insights into questions from biology and medicine. Nevertheless, integrating data from different experimental backgrounds can introduce strong biases. In order to methodically investigate the magnitude of systematic errors, we performed a cross-sectional observational study on a genomic cohort of 99 subjects each sequenced via (i) Illumina HiSeq X, (ii) Illumina HiSeq and (iii) Complete Genomics. Consequently, we systematically analyzed the heterogeneity between the sequencing cohorts with respect to genomic annotation and common filter criteria like minimum allele frequency (MAF). Results The number of detected variants/variant classes per individual was highly dependent on the sequencing technology. We observed a statistically significant overrepresentation of variants uniquely called by a single platform which indicates potential systematic biases. These variants were enriched in low complexity genomic regions and simple repeats. Furthermore, estimates of allele frequency were highly discrepant for a subset of variants in pairwise comparisons between different sequencing platforms. Applying common filters – such as MAF 5% and HWE- greatly reduced the heterogeneity between cohorts but still left discrepancies of several thousand variants after filtering.Conclusion We provide empirical evidence of systematic heterogeneity in variant calls between alternative experimental and data analysis setups. Our results highlight the potential benefit of reprocessing genomic data with harmonized pipelines when integrating data from different studies.

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“…The essential nature of DMG and DEG-DMG hub loci was investigated at the Genomic Data Commons Data Portal (https://portal.gdc.cancer. gov/), which contains numerous cancer datasets 42 . Screening the TCGA database revealed that all hubs identified in our analysis could undergo mutations classified as high-impact, affecting patient survival (Fig.…”

Section: Methylation Signal On Deg-dmgs Across Individuals Is Networkmentioning

confidence: 99%

Integrative Network Analysis of Differentially Methylated and Expressed Genes for Biomarker Identification in Leukemia

Sanchez

Mackenzie

2020

Sci Rep

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Genome-wide DNA methylation and gene expression are commonly altered in pediatric acute lymphoblastic leukemia (PALL). Integrated network analysis of cytosine methylation and expression datasets has the potential to provide deeper insights into the complex disease states and their causes than individual disconnected analyses. With the purpose of identifying reliable cancer-associated methylation signal in gene regions from leukemia patients, we present an integrative network analysis of differentially methylated (DMGs) and differentially expressed genes (DEGs). The application of a novel signal detection-machine learning approach to methylation analysis of whole genome bisulfite sequencing (WGBS) data permitted a high level of methylation signal resolution in cancer-associated genes and pathways. This integrative network analysis approach revealed that gene expression and methylation consistently targeted the same gene pathways relevant to cancer: Pathways in cancer, Ras signaling pathway, PI3K-Akt signaling pathway, and Rap1 signaling pathway, among others. Detected gene hubs and hub sub-networks were integrated by signature loci associated with cancer that include, for example, NOTCH1, RAC1, PIK3CD, BCL2, and EGFR. Statistical analysis disclosed a stochastic deterministic relationship between methylation and gene expression within the set of genes simultaneously identified as DEGs and DMGs, where larger values of gene expression changes were probabilistically associated with larger values of methylation changes. Concordance analysis of the overlap between enriched pathways in DEG and DMG datasets revealed statistically significant agreement between gene expression and methylation changes. These results support the potential identification of reliable and stable methylation biomarkers at genes for cancer diagnosis and prognosis.Network-based modeling approaches have the potential to integrate and improve the perception of complex disease states and their root causes. To date, network analysis provides reliable and cost effective approaches for early disease detection, prediction of co-occurring diseases and interactions, and drug design 1 . Although integrated genomic analysis of methylation and gene expression in leukemia has been reported 2-5 , meaningful assimilation of network analysis is still lacking.Our study investigates protein-protein interaction networks (PPI), which are exclusively focused on protein-protein associations and resulting cell activities. A PPI network can be defined as a (un)directed graph/ network holding vertices as proteins (or protein-coding genes) and edges as the interactions/association between them. Associations are meant to be specific and biologically meaningful, i.e., two proteins are connected by an edge if jointly contributing to a shared function, which does not necessarily reflect a physical binding interaction.Within the network, some proteins denote hubs interacting with numerous partners. Biologically, hubs are key elements on which functionality of the cellular proces...

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Before and After: Comparison of Legacy and Harmonized TCGA Genomic Data Commons’ Data

Cited by 130 publications

References 49 publications

Which Reference Genes to Choose for qPCR Normalization? A Comprehensive Analysis in MCF-7 Breast Cancer Cell Line

Which Reference Genes to Choose for qPCR Normalization? A Comprehensive Analysis in MCF-7 Breast Cancer Cell Line

Reliability of genomic variants across different next-generation sequencing platforms and bioinformatic processing pipelines

Integrative Network Analysis of Differentially Methylated and Expressed Genes for Biomarker Identification in Leukemia

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