Natalie P. Thorne

MicroRNA expression profiling of human breast cancer identifies new markers of tumor subtype

BackgroundMicroRNAs (miRNAs), a class of short non-coding RNAs found in many plants and animals, often act post-transcriptionally to inhibit gene expression.ResultsHere we report the analysis of miRNA expression in 93 primary human breast tumors, using a bead-based flow cytometric miRNA expression profiling method. Of 309 human miRNAs assayed, we identify 133 miRNAs expressed in human breast and breast tumors. We used mRNA expression profiling to classify the breast tumors as luminal A, luminal B, basal-like, HER2+ and normal-like. A number of miRNAs are differentially expressed between these molecular tumor subtypes and individual miRNAs are associated with clinicopathological factors. Furthermore, we find that miRNAs could classify basal versus luminal tumor subtypes in an independent data set. In some cases, changes in miRNA expression correlate with genomic loss or gain; in others, changes in miRNA expression are likely due to changes in primary transcription and or miRNA biogenesis. Finally, the expression of DICER1 and AGO2 is correlated with tumor subtype and may explain some of the changes in miRNA expression observed.ConclusionThis study represents the first integrated analysis of miRNA expression, mRNA expression and genomic changes in human breast cancer and may serve as a basis for functional studies of the role of miRNAs in the etiology of breast cancer. Furthermore, we demonstrate that bead-based flow cytometric miRNA expression profiling might be a suitable platform to classify breast cancer into prognostic molecular subtypes.

A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis

DNA methylation is an indispensible epigenetic modification required for regulating the expression of mammalian genomes. Immunoprecipitation-based methods for DNA methylome analysis are rapidly shifting the bottleneck in this field from data generation to data analysis, necessitating the development of better analytical tools. In particular, an inability to estimate absolute methylation levels remains a major analytical difficulty associated with immunoprecipitation-based DNA methylation profiling. To address this issue, we developed a cross-platform algorithm—Bayesian tool for methylation analysis (Batman)—for analyzing methylated DNA immunoprecipitation (MeDIP) profiles generated using oligonucleotide arrays (MeDIP-chip) or next-generation sequencing (MeDIP-seq). We developed the latter approach to provide a high-resolution whole-genome DNA methylation profile (DNA methylome) of a mammalian genome. Strong correlation of our data, obtained using mature human spermatozoa, with those obtained using bisulfite sequencing suggest that combining MeDIP-seq or MeDIP-chip with Batman provides a robust, quantitative and cost-effective functional genomic strategy for elucidating the function of DNA methylation.

An integrated resource for genome-wide identification and analysis of human tissue-specific differentially methylated regions (tDMRs)

We report a novel resource (methylation profiles of DNA, or mPod) for human genome-wide tissue-specific DNA methylation profiles. mPod consists of three fully integrated parts, genome-wide DNA methylation reference profiles of 13 normal somatic tissues, placenta, sperm, and an immortalized cell line, a visualization tool that has been integrated with the Ensembl genome browser and a new algorithm for the analysis of immunoprecipitation-based DNA methylation profiles. We demonstrate the utility of our resource by identifying the first comprehensive genome-wide set of tissue-specific differentially methylated regions (tDMRs) that may play a role in cellular identity and the regulation of tissue-specific genome function. We also discuss the implications of our findings with respect to the regulatory potential of regions with varied CpG density, gene expression, transcription factor motifs, gene ontology, and correlation with other epigenetic marks such as histone modifications.

Global microRNA profiles in cervical squamous cell carcinoma depend on Drosha expression levels

Gain of chromosome 5p is seen in over 50% of advanced cervical squamous cell carcinomas (SCCs), although the genes responsible for the selective advantage provided by this abnormality are poorly understood. In the W12 cervical carcinogenesis model, we observed that 5p gain was rapidly selected over ∼15 population doublings and was associated with the acquisition of a growth advantage and invasiveness. The most significantly upregulated transcript following 5p gain was the microRNA (miRNA) processor Drosha. In clinically progressed cervical SCC, Drosha copy‐number gain was seen in 21/36 clinical samples and 8/10 cell lines and there was a significant association between Drosha transcript levels and copy‐number gain. Other genes in the miRNA processing pathway, DGCR8, XPO5 and Dicer, showed infrequent copy‐number gain and over‐expression. Drosha copy‐number and expression were not elevated in pre‐malignant cervical squamous intraepithelial lesions. Importantly, global miRNA profiling showed that Drosha over‐expression in cervical SCC appears to be of functional significance. Unsupervised principal component analysis of a mixed panel of cervical SCC cell lines and clinical specimens showed clear separation according to Drosha over‐expression. miRNAs most significantly associated with Drosha over‐expression are implicated in carcinogenesis in other tissues, suggesting that they regulate fundamental processes in neoplastic progression. Our evidence suggests that copy‐number driven over‐expression of Drosha and consequent changes in miRNAs are likely to be important contributors to the selective advantage provided by 5p gain in cervical neoplastic progression. Copyright

BioHMM: a heterogeneous hidden Markov model for segmenting array CGH data

SUMMARY We have developed a new method (BioHMM) for segmenting array comparative genomic hybridization data into states with the same underlying copy number. By utilizing a heterogeneous hidden Markov model, BioHMM incorporates relevant biological factors (e.g. the distance between adjacent clones) in the segmentation process.

Tissue-specific splicing factor gene expression signatures

The alternative splicing code that controls and coordinates the transcriptome in complex multicellular organisms remains poorly understood. It has long been argued that regulation of alternative splicing relies on combinatorial interactions between multiple proteins, and that tissue-specific splicing decisions most likely result from differences in the concentration and/or activity of these proteins. However, large-scale data to systematically address this issue have just recently started to become available. Here we show that splicing factor gene expression signatures can be identified that reflect cell type and tissue-specific patterns of alternative splicing. We used a computational approach to analyze microarray-based gene expression profiles of splicing factors from mouse, chimpanzee and human tissues. Our results show that brain and testis, the two tissues with highest levels of alternative splicing events, have the largest number of splicing factor genes that are most highly differentially expressed. We further identified SR protein kinases and small nuclear ribonucleoprotein particle (snRNP) proteins among the splicing factor genes that are most highly differentially expressed in a particular tissue. These results indicate the power of generating signature-based predictions as an initial computational approach into a global view of tissue-specific alternative splicing regulation.

Using array-comparative genomic hybridization to define molecular portraits of primary breast cancers

We analysed 148 primary breast cancers using BAC-arrays containing 287 clones representing cancer-related gene/loci to obtain genomic molecular portraits. Gains were detected in 136 tumors (91.9%) and losses in 123 tumors (83.1%). Eight tumors (5.4%) did not have any genomic aberrations in the 281 clones analysed. Common (more than 15% of the samples) gains were observed at 8q11–qtel, 1q21–qtel, 17q11–q12 and 11q13, whereas common losses were observed at 16q12–qtel, 11ptel–p15.5, 1p36–ptel, 17p11.2–p12 and 8ptel–p22. Patients with tumors registering either less than 5% (median value) or less than 11% (third quartile) total copy number changes had a better overall survival (log-rank test: P=0.0417 and P=0.0375, respectively). Unsupervised hierarchical clustering based on copy number changes identified four clusters. Women with tumors from the cluster with amplification of three regions containing known breast oncogenes (11q13, 17q12 and 20q13) had a worse prognosis. The good prognosis group (Nottingham Prognostic Index (NPI) ⩽3.4) tumors had frequent loss of 16q24–qtel. Genes significantly associated with estrogen receptor (ER), Grade and NPI were used to build k-nearest neighbor (KNN) classifiers that predicted ER, Grade and NPI status in the test set with an average misclassification rate of 24.7, 25.7 and 35.7%, respectively. These data raise the prospect of generating a molecular taxonomy of breast cancer based on copy number profiling using tumor DNA, which may be more generally applicable than expression microarray analysis.

A prospective evaluation of whole-exome sequencing as a first-tier molecular test in infants with suspected monogenic disorders.

Purpose:To prospectively evaluate the diagnostic and clinical utility of singleton whole-exome sequencing (WES) as a first-tier test in infants with suspected monogenic disease.Methods:Singleton WES was performed as a first-tier sequencing test in infants recruited from a single pediatric tertiary center. This occurred in parallel with standard investigations, including single- or multigene panel sequencing when clinically indicated. The diagnosis rate, clinical utility, and impact on management of singleton WES were evaluated.Results:Of 80 enrolled infants, 46 received a molecular genetic diagnosis through singleton WES (57.5%) compared with 11 (13.75%) who underwent standard investigations in the same patient group. Clinical management changed following exome diagnosis in 15 of 46 diagnosed participants (32.6%). Twelve relatives received a genetic diagnosis following cascade testing, and 28 couples were identified as being at high risk of recurrence in future pregnancies.Conclusions:This prospective study provides strong evidence for increased diagnostic and clinical utility of singleton WES as a first-tier sequencing test for infants with a suspected monogenic disorder. Singleton WES outperformed standard care in terms of diagnosis rate and the benefits of a diagnosis, namely, impact on management of the child and clarification of reproductive risks for the extended family in a timely manner.Genet Med 18 11, 1090–1096.

Pediatric Malignant Germ Cell Tumors Show Characteristic Transcriptome Profiles

Malignant germ cell tumors (GCT) of childhood are rare and heterogeneous neoplasms thought to arise from primordial germ cells. They vary substantially in their natural history and show important clinical differences from their adult counterparts. To address the biological basis for these observations, we have undertaken a comprehensive analysis of global gene expression patterns in pediatric malignant GCTs and compared these findings with published data on adult testicular GCTs (TGCT). Our study included 27 primary tumors and assessed the principal malignant histologic types of pediatric GCT, yolk sac tumor (YST; n = 18), and seminoma (n = 9). Analysis of Affymetrix U133A GeneChip data was performed using the statistical software environment R, including gene set enrichment analysis, with cross-validation at the RNA and protein level. Unsupervised analysis showed complete separation of YSTs and seminomas by global gene expression profiles and identified a robust set of 657 discriminatory transcripts. There was no segregation of tumors of the same histology arising at different sites or at different ages within the pediatric range. In contrast, there was segregation of pediatric malignant GCTs and adult malignant TGCTs, most notably for the YSTs. The pediatric seminomas were significantly enriched for genes associated with the self-renewing pluripotent phenotype, whereas the pediatric YSTs were significantly enriched for genes associated with a differentiation and proliferation phenotype. We conclude that histologic type is the key discriminator in pediatric malignant GCTs and that the observed clinical differences between malignant GCTs of children and adults are mirrored by significant differences in global gene expression.

MMASS: an optimized array-based method for assessing CpG island methylation.

We describe an optimized microarray method for identifying genome-wide CpG island methylation called microarray-based methylation assessment of single samples (MMASS) which directly compares methylated to unmethylated sequences within a single sample. To improve previous methods we used bioinformatic analysis to predict an optimized combination of methylation-sensitive enzymes that had the highest utility for CpG-island probes and different methods to produce unmethylated representations of test DNA for more sensitive detection of differential methylation by hybridization. Subtraction or methylation-dependent digestion with McrBC was used with optimized (MMASS-v2) or previously described (MMASS-v1, MMASS-sub) methylation-sensitive enzyme combinations and compared with a published McrBC method. Comparison was performed using DNA from the cell line HCT116. We show that the distribution of methylation microarray data is inherently skewed and requires exogenous spiked controls for normalization and that analysis of digestion of methylated and unmethylated control sequences together with linear fit models of replicate data showed superior statistical power for the MMASS-v2 method. Comparison with previous methylation data for HCT116 and validation of CpG islands from PXMP4, SFRP2, DCC, RARB and TSEN2 confirmed the accuracy of MMASS-v2 results. The MMASS-v2 method offers improved sensitivity and statistical power for high-throughput microarray identification of differential methylation.