Distinct E-cadherin-based complexes regulate cell behaviour through miRNA processing or Src and p120 catenin activity.

E-cadherin and p120 catenin (p120) are essential for epithelial homeostasis, but can also exert pro-tumorigenic activities. Here, we resolve this apparent paradox by identifying two spatially and functionally distinct junctional complexes in non-transformed polarized epithelial cells: one growth suppressing at the apical zonula adherens (ZA), defined by the p120 partner PLEKHA7 and a non-nuclear subset of the core microprocessor components DROSHA and DGCR8, and one growth promoting at basolateral areas of cell-cell contact containing tyrosine-phosphorylated p120 and active Src. Recruitment of DROSHA and DGCR8 to the ZA is PLEKHA7 dependent. The PLEKHA7-microprocessor complex co-precipitates with primary microRNAs (pri-miRNAs) and possesses pri-miRNA processing activity. PLEKHA7 regulates the levels of select miRNAs, in particular processing of miR-30b, to suppress expression of cell transforming markers promoted by the basolateral complex, including SNAI1, MYC and CCND1. Our work identifies a mechanism through which adhesion complexes regulate cellular behaviour and reveals their surprising association with the microprocessor.

An integrated model of the transcriptome of HER2-positive breast cancer.

Our goal in these analyses was to use genomic features from a test set of primary breast tumors to build an integrated transcriptome landscape model that makes relevant hypothetical predictions about the biological and/or clinical behavior of HER2-positive breast cancer. We interrogated RNA-Seq data from benign breast lesions, ER+, triple negative, and HER2-positive tumors to identify 685 differentially expressed genes, 102 alternatively spliced genes, and 303 genes that expressed single nucleotide sequence variants (eSNVs) that were associated with the HER2-positive tumors in our survey panel. These features were integrated into a transcriptome landscape model that identified 12 highly interconnected genomic modules, each of which represents a cellular processes pathway that appears to define the genomic architecture of the HER2-positive tumors in our test set. The generality of the model was confirmed by the observation that several key pathways were enriched in HER2-positive TCGA breast tumors. The ability of this model to make relevant predictions about the biology of breast cancer cells was established by the observation that integrin signaling was linked to lapatinib sensitivity in vitro and strongly associated with risk of relapse in the NCCTG N9831 adjuvant trastuzumab clinical trial dataset. Additional modules from the HER2 transcriptome model, including ubiquitin-mediated proteolysis, TGF-beta signaling, RHO-family GTPase signaling, and M-phase progression, were linked to response to lapatinib and paclitaxel in vitro and/or risk of relapse in the N9831 dataset. These data indicate that an integrated transcriptome landscape model derived from a test set of HER2-positive breast tumors has potential for predicting outcome and for identifying novel potential therapeutic strategies for this breast cancer subtype.

Impact of library preparation on downstream analysis and interpretation of RNA-Seq data: comparison between Illumina PolyA and NuGEN Ovation protocol.

OBJECTIVES: The sequencing by the PolyA selection is the most common approach for library preparation. With limited amount or degraded RNA, alternative protocols such as the NuGEN have been developed. However, it is not yet clear how the different library preparations affect the downstream analyses of the broad applications of RNA sequencing. METHODS AND MATERIALS: Eight human mammary epithelial cell (HMEC) lines with high quality RNA were sequenced by Illumina's mRNA-Seq PolyA selection and NuGEN ENCORE library preparation. The following analyses and comparisons were conducted: 1) the numbers of genes captured by each protocol; 2) the impact of protocols on differentially expressed gene detection between biological replicates; 3) expressed single nucleotide variant (SNV) detection; 4) non-coding RNAs, particularly lincRNA detection; and 5) intragenic gene expression. RESULTS: Sequences from the NuGEN protocol had lower (75%) alignment rate than the PolyA (over 90%). The NuGEN protocol detected fewer genes (12-20% less) with a significant portion of reads mapped to non-coding regions. A large number of genes were differentially detected between the two protocols. About 17-20% of the differentially expressed genes between biological replicates were commonly detected between the two protocols. Significantly higher numbers of SNVs (5-6 times) were detected in the NuGEN samples, which were largely from intragenic and intergenic regions. The NuGEN captured fewer exons (25% less) and had higher base level coverage variance. While 6.3% of reads were mapped to intragenic regions in the PolyA samples, the percentages were much higher (20-25%) for the NuGEN samples. The NuGEN protocol did not detect more known non-coding RNAs such as lincRNAs, but targeted small and "novel" lincRNAs. CONCLUSION: Different library preparations can have significant impacts on downstream analysis and interpretation of RNA-seq data. The NuGEN provides an alternative for limited or degraded RNA but it has limitations for some RNA-seq applications.

Detection of redundant fusion transcripts as biomarkers or disease-specific therapeutic targets in breast cancer.

Fusion genes and fusion gene products are widely employed as biomarkers and therapeutic targets in hematopoietic cancers, but their applications have yet to be appreciated in solid tumors. Here, we report the use of SnowShoes-FTD, a powerful new analytic pipeline that can identify fusion transcripts and assess their redundancy and tumor subtype-specific distribution in primary tumors. In a study of primary breast tumors, SnowShoes-FTD was used to analyze paired-end mRNA-Seq data from a panel of estrogen receptor (ER)(+), HER2(+), and triple-negative primary breast tumors, identifying tumor-specific fusion transcripts by comparison with mRNA-Seq data from nontransformed human mammary epithelial cell cultures plus the Illumina Body Map data from normal tissues. We found that every primary breast tumor that was analyzed expressed one or more fusion transcripts. Of the 131 tumor-specific fusion transcripts identified, 86 were "private" (restricted to a single tumor) and 45 were "redundant" (distributed among multiple tumors). Among the redundant fusion transcripts, 7 were unique to ER(+) tumors and 8 were unique to triple-negative tumors. In contrast, none of the redundant fusion transcripts were unique to HER2(+) tumors. Both private and redundant fusion transcripts were widely expressed in primary breast tumors, with many mapping to genomic loci implicated in breast carcinogenesis and/or risk. Our finding that some fusion transcripts are tumor subtype-specific suggests that these entities may be critical determinants in the etiology of breast cancer subtypes, useful as biomarkers for tumor stratification, or exploitable as cancer-specific therapeutic targets.

Integrated analysis of gene expression, CpG island methylation, and gene copy number in breast cancer cells by deep sequencing.

We used deep sequencing technology to profile the transcriptome, gene copy number, and CpG island methylation status simultaneously in eight commonly used breast cell lines to develop a model for how these genomic features are integrated in estrogen receptor positive (ER+) and negative breast cancer. Total mRNA sequence, gene copy number, and genomic CpG island methylation were carried out using the Illumina Genome Analyzer. Sequences were mapped to the human genome to obtain digitized gene expression data, DNA copy number in reference to the non-tumor cell line (MCF10A), and methylation status of 21,570 CpG islands to identify differentially expressed genes that were correlated with methylation or copy number changes. These were evaluated in a dataset from 129 primary breast tumors. Gene expression in cell lines was dominated by ER-associated genes. ER+ and ER- cell lines formed two distinct, stable clusters, and 1,873 genes were differentially expressed in the two groups. Part of chromosome 8 was deleted in all ER- cells and part of chromosome 17 amplified in all ER+ cells. These loci encoded 30 genes that were overexpressed in ER+ cells; 9 of these genes were overexpressed in ER+ tumors. We identified 149 differentially expressed genes that exhibited differential methylation of one or more CpG islands within 5 kb of the 5' end of the gene and for which mRNA abundance was inversely correlated with CpG island methylation status. In primary tumors we identified 84 genes that appear to be robust components of the methylation signature that we identified in ER+ cell lines. Our analyses reveal a global pattern of differential CpG island methylation that contributes to the transcriptome landscape of ER+ and ER- breast cancer cells and tumors. The role of gene amplification/deletion appears to more modest, although several potentially significant genes appear to be regulated by copy number aberrations.