Crispin J. Miller

Simpleaffy: a BioConductor package for Affymetrix Quality Control and data analysis

UNLABELLED Quality Control is a fundamental aspect of successful microarray data analysis. Simpleaffy is a BioConductor package that provides access to a variety of QC metrics for assessing the quality of RNA samples and of the intermediate stages of sample preparation and hybridization. Simpleaffy also offers fast implementations of popular algorithms for generating expression summaries and detection calls. AVAILABILITY Simpleaffy can be downloaded from http://www.bioconductor.org. SUPPLEMENTARY INFORMATION Additional information can be found on the supplementary website located at http://bioinformatics.picr.man.ac.uk.

Hypoxia-Mediated Down-Regulation of Bid and Bax in Tumors Occurs via Hypoxia-Inducible Factor 1-Dependent and -Independent Mechanisms and Contributes to Drug Resistance

ABSTRACT Solid tumors with disorganized, insufficient blood supply contain hypoxic cells that are resistant to radiotherapy and chemotherapy. Drug resistance, an obstacle to curative treatment of solid tumors, can occur via suppression of apoptosis, a process controlled by pro- and antiapoptotic members of the Bcl-2 protein family. Oxygen deprivation of human colon cancer cells in vitro provoked decreased mRNA and protein levels of proapoptotic Bid and Bad. Hypoxia-inducible factor 1 (HIF-1) was dispensable for the down-regulation of Bad but required for that of Bid, consistent with the binding of HIF-1α to a hypoxia-responsive element (positions −8484 to −8475) in the bid promoter. Oxygen deprivation resulted in proteosome-independent decreased expression of Bax in vitro, consistent with a reduction in global translation efficiency. The physiological relevance of Bid and Bax down-regulation was confirmed in tumors in vivo. Oxygen deprivation resulted in decreased drug-induced apoptosis and clonogenic resistance to agents with different mechanisms of action. The contribution of Bid and/or Bax down-regulation to drug responsiveness was demonstrated by the relative resistance of normoxic cells that had no or reduced expression of Bid and/or Bax and by the finding that forced expression of Bid in hypoxic cells resulted in increased sensitivity to the topoisomerase II inhibitor etoposide.

Eight-channel iTRAQ Enables Comparison of the Activity of Six Leukemogenic Tyrosine Kinases

There are a number of leukemogenic protein-tyrosine kinases (PTKs) associated with leukemic transformation. Although each is linked with a specific disease their functional activity poses the question whether they have a degree of commonality in their effects upon target cells. Exon array analysis of the effects of six leukemogenic PTKs (BCR/ABL, TEL/PDGFRβ, FIP1/PDGFRα, D816V KIT, NPM/ALK, and FLT3ITD) revealed few common effects on the transcriptome. It is apparent, however, that proteome changes are not directly governed by transcriptome changes. Therefore, we assessed and used a new generation of iTRAQ tagging, enabling eight-channel relative quantification discovery proteomics, to analyze the effects of these six leukemogenic PTKs. Again these were found to have disparate effects on the proteome with few common targets. BCR/ABL had the greatest effect on the proteome and had more effects in common with FIP1/PDGFRα. The proteomic effects of the four type III receptor kinases were relatively remotely related. The only protein commonly affected was eosinophil-associated ribonuclease 7. Five of six PTKs affected the motility-related proteins CAPG and vimentin, although this did not correspond to changes in motility. However, correlation of the proteomics data with that from the exon microarray not only showed poor levels of correlation between transcript and protein levels but also revealed alternative patterns of regulation of the CAPG protein by different oncogenes, illustrating the utility of such a combined approach.

Relation of a Hypoxia Metagene Derived from Head and Neck Cancer to Prognosis of Multiple Cancers

Affymetrix U133plus2 GeneChips were used to profile 59 head and neck squamous cell cancers. A hypoxia metagene was obtained by analysis of genes whose in vivo expression clustered with the expression of 10 well-known hypoxia-regulated genes (e.g., CA9, GLUT1, and VEGF). To minimize random aggregation, strongly correlated up-regulated genes appearing in >50% of clusters defined a signature comprising 99 genes, of which 27% were previously known to be hypoxia associated. The median RNA expression of the 99 genes in the signature was an independent prognostic factor for recurrence-free survival in a publicly available head and neck cancer data set, outdoing the original intrinsic classifier. In a published breast cancer series, the hypoxia signature was a significant prognostic factor for overall survival independent of clinicopathologic risk factors and a trained profile. The work highlights the validity and potential of using data from analysis of in vitro stress pathways for deriving a biological metagene/gene signature in vivo.

A comparison of massively parallel nucleotide sequencing with oligonucleotide microarrays for global transcription profiling

BackgroundRNA-Seq exploits the rapid generation of gigabases of sequence data by Massively Parallel Nucleotide Sequencing, allowing for the mapping and digital quantification of whole transcriptomes. Whilst previous comparisons between RNA-Seq and microarrays have been performed at the level of gene expression, in this study we adopt a more fine-grained approach. Using RNA samples from a normal human breast epithelial cell line (MCF-10a) and a breast cancer cell line (MCF-7), we present a comprehensive comparison between RNA-Seq data generated on the Applied Biosystems SOLiD platform and data from Affymetrix Exon 1.0ST arrays. The use of Exon arrays makes it possible to assess the performance of RNA-Seq in two key areas: detection of expression at the granularity of individual exons, and discovery of transcription outside annotated loci.ResultsWe found a high degree of correspondence between the two platforms in terms of exon-level fold changes and detection. For example, over 80% of exons detected as expressed in RNA-Seq were also detected on the Exon array, and 91% of exons flagged as changing from Absent to Present on at least one platform had fold-changes in the same direction. The greatest detection correspondence was seen when the read count threshold at which to flag exons Absent in the SOLiD data was set to t<1 suggesting that the background error rate is extremely low in RNA-Seq. We also found RNA-Seq more sensitive to detecting differentially expressed exons than the Exon array, reflecting the wider dynamic range achievable on the SOLiD platform. In addition, we find significant evidence of novel protein coding regions outside known exons, 93% of which map to Exon array probesets, and are able to infer the presence of thousands of novel transcripts through the detection of previously unreported exon-exon junctions.ConclusionsBy focusing on exon-level expression, we present the most fine-grained comparison between RNA-Seq and microarrays to date. Overall, our study demonstrates that data from a SOLiD RNA-Seq experiment are sufficient to generate results comparable to those produced from Affymetrix Exon arrays, even using only a single replicate from each platform, and when presented with a large genome.

Cancer-Associated Protein Kinase C Mutations Reveal Kinase’s Role as Tumor Suppressor

Protein kinase C (PKC) isozymes have remained elusive cancer targets despite the unambiguous tumor promoting function of their potent ligands, phorbol esters, and the prevalence of their mutations. We analyzed 8% of PKC mutations identified in human cancers and found that, surprisingly, most were loss of function and none were activating. Loss-of-function mutations occurred in all PKC subgroups and impeded second-messenger binding, phosphorylation, or catalysis. Correction of a loss-of-function PKCβ mutation by CRISPR-mediated genome editing in a patient-derived colon cancer cell line suppressed anchorage-independent growth and reduced tumor growth in a xenograft model. Hemizygous deletion promoted anchorage-independent growth, revealing that PKCβ is haploinsufficient for tumor suppression. Several mutations were dominant negative, suppressing global PKC signaling output, and bioinformatic analysis suggested that PKC mutations cooperate with co-occurring mutations in cancer drivers. These data establish that PKC isozymes generally function as tumor suppressors, indicating that therapies should focus on restoring, not inhibiting, PKC activity.

The utility of MAS5 expression summary and detection call algorithms

BackgroundUsed alone, the MAS5.0 algorithm for generating expression summaries has been criticized for high False Positive rates resulting from exaggerated variance at low intensities.ResultsHere we show, with replicated cell line data, that, when used alongside detection calls, MAS5 can be both selective and sensitive. A set of differentially expressed transcripts were identified that were found to be changing by MAS5, but unchanging by RMA and GCRMA. Subsequent analysis by real time PCR confirmed these changes. In addition, with the Latin square datasets often used to assess expression summary algorithms, filtered MAS5.0 was found to have performance approaching that of its peers.ConclusionWhen used alongside detection calls, MAS5 is a sensitive and selective algorithm for identifying differentially expressed genes.

CADRE: the Central Aspergillus Data REpository 2012

The Central Aspergillus Data REpository (CADRE; http://www.cadre-genomes.org.uk) is a public resource for genomic data extracted from species of Aspergillus. It provides an array of online tools for searching and visualising features of this significant fungal genus. CADRE arose from a need within the medical community to understand the human pathogen Aspergillus fumigatus. Due to the paucity of Aspergillus genomic resources 10 years ago, the long-term goal of this project was to collate and maintain Aspergillus genomes as they became available. Since our first release in 2004, the resource has expanded to encompass annotated sequence for eight other Aspergilli and provides much needed support to the international Aspergillus research community. Recent developments, however, in sequencing technology are creating a vast amount of genomic data and, as a result, we shortly expect a tidal wave of Aspergillus data. In preparation for this, we have upgraded the database and software suite. This not only enables better management of more complex data sets, but also improves annotation by providing access to genome comparison data and the integration of high-throughput data.

The removal of multiplicative, systematic bias allows integration of breast cancer gene expression datasets – improving meta-analysis and prediction of prognosis

BackgroundThe number of gene expression studies in the public domain is rapidly increasing, representing a highly valuable resource. However, dataset-specific bias precludes meta-analysis at the raw transcript level, even when the RNA is from comparable sources and has been processed on the same microarray platform using similar protocols. Here, we demonstrate, using Affymetrix data, that much of this bias can be removed, allowing multiple datasets to be legitimately combined for meaningful meta-analyses.ResultsA series of validation datasets comparing breast cancer and normal breast cell lines (MCF7 and MCF10A) were generated to examine the variability between datasets generated using different amounts of starting RNA, alternative protocols, different generations of Affymetrix GeneChip or scanning hardware. We demonstrate that systematic, multiplicative biases are introduced at the RNA, hybridization and image-capture stages of a microarray experiment. Simple batch mean-centering was found to significantly reduce the level of inter-experimental variation, allowing raw transcript levels to be compared across datasets with confidence. By accounting for dataset-specific bias, we were able to assemble the largest gene expression dataset of primary breast tumours to-date (1107), from six previously published studies. Using this meta-dataset, we demonstrate that combining greater numbers of datasets or tumours leads to a greater overlap in differentially expressed genes and more accurate prognostic predictions. However, this is highly dependent upon the composition of the datasets and patient characteristics.ConclusionMultiplicative, systematic biases are introduced at many stages of microarray experiments. When these are reconciled, raw data can be directly integrated from different gene expression datasets leading to new biological findings with increased statistical power.

Amplification protocols introduce systematic but reproducible errors into gene expression studies

The desire to perform microarray experiments with small starting amounts of RNA has led to the development of a variety of protocols for preparing and amplifying mRNA. This has consequences not only for the standardization of experimental design, but also for reproducibility and comparability between experiments. Here we investigate the differences between the Affymetrix standard and small sample protocols and address the data analysis issues that arise when comparing samples and experiments that have been processed in different ways. We show that data generated on the same platform using different protocols are not directly comparable. Further, protocols introduce systematic biases that can be largely accounted for by using the correct data analysis techniques.