Transcriptome analysis of mammary epithelial subpopulations identifies novel determinants of lineage commitment and cell fate

Background: Understanding the molecular control of cell lineages and fate determination in complex tissues is key to not only understanding the developmental biology and cellular homeostasis of such tissues but also for our understanding and interpretation of the molecular pathology of diseases such as cancer. The prerequisite for such an understanding is detailed knowledge of the cell types that make up such tissues, including their comprehensive molecular characterisation. In the mammary epithelium, the bulk of the tissue is composed of three cell lineages, namely the basal/myoepithelial, luminal epithelial estrogen receptor positive and luminal epithelial estrogen receptor negative cells. However, a detailed molecular characterisation of the transcriptomic differences between these three populations has not been carried out. Results: A whole transcriptome analysis of basal/myoepithelial cells, luminal estrogen receptor negative cells and luminal estrogen receptor positive cells isolated from the virgin mouse mammary epithelium identified 861, 326 and 488 genes as highly differentially expressed in the three cell types, respectively. Network analysis of the transcriptomic data identified a subpopulation of luminal estrogen receptor negative cells with a novel potential role as non-professional immune cells. Analysis of the data for potential paracrine interacting factors showed that the basal/myoepithelial cells, remarkably, expressed over twice as many ligands and cell surface receptors as the other two populations combined. A number of transcriptional regulators were also identified that were differentially expressed between the cell lineages. One of these, Sox6, was specifically expressed in luminal estrogen receptor negative cells and functional assays confirmed that it maintained mammary epithelial cells in a differentiated luminal cell lineage. Conclusion: The mouse mammary epithelium is composed of three main cell types with distinct gene expression patterns. These suggest the existence of a novel functional cell type within the gland, that the basal/myoepithelial cells are key regulators of paracrine signalling and that there is a complex network of differentially expressed transcription factors controlling mammary epithelial cell fate. These data will form the basis for understanding not only cell fate determination and cellular homeostasis in the normal mammary epithelium but also the contribution of different mammary epithelial cell types to the etiology and molecular pathology of breast disease

A Modified Method for Whole Exome Resequencing from Minimal Amounts of Starting DNA

Next generation DNA sequencing (NGS) technologies have revolutionized the pace at which whole genome and exome sequences can be generated. However, despite these advances, many of the methods for targeted resequencing, such as the generation of high-depth exome sequences, are somewhat limited by the relatively large amounts of starting DNA that are normally required. In the case of tumour analysis this is particularly pertinent as many tumour biopsies often return submicrogram quantities of DNA, especially when tumours are microdissected prior to analysis. Here, we present a method for exome capture and resequencing using as little as 50 ng of starting DNA. The sequencing libraries generated by this minimal starting amount (MSA-Cap) method generate datasets that are comparable to standard amount (SA) whole exome libraries that use three micrograms of starting DNA. This method, which can be performed in most laboratories using commonly available reagents, has the potential to enhance large scale profiling efforts such as the resequencing of tumour exomes

High-throughput RNA interference screening using pooled shRNA libraries and next generation sequencing

RNA interference (RNAi) screening is a state-of-the-art technology that enables the dissection of biological processes and disease-related phenotypes. The commercial availability of genome-wide, short hairpin RNA (shRNA) libraries has fueled interest in this area but the generation and analysis of these complex data remain a challenge. Here, we describe complete experimental protocols and novel open source computational methodologies, shALIGN and shRNAseq, that allow RNAi screens to be rapidly deconvoluted using next generation sequencing. Our computational pipeline offers efficient screen analysis and the flexibility and scalability to quickly incorporate future developments in shRNA library technology

Characterization of the genomic features and expressed fusion genes in micropapillary carcinomas of the breast

Micropapillary carcinoma ( MPC ) is a rare histological special type of breast cancer, characterized by an aggressive clinical behaviour and a pattern of copy number aberrations ( CNAs ) distinct from that of grade‐ and oestrogen receptor ( ER )‐matched invasive carcinomas of no special type ( IC‐NSTs ). The aims of this study were to determine whether MPCs are underpinned by a recurrent fusion gene(s) or mutations in 273 genes recurrently mutated in breast cancer. Sixteen MPCs were subjected to microarray‐based comparative genomic hybridization ( aCGH ) analysis and Sequenom OncoCarta mutation analysis. Eight and five MPCs were subjected to targeted capture and RNA sequencing, respectively. aCGH analysis confirmed our previous observations about the repertoire of CNAs of MPCs . Sequencing analysis revealed a spectrum of mutations similar to those of luminal B IC‐NSTs , and recurrent mutations affecting mitogen‐activated protein kinase family genes and NBPF10 . RNA ‐sequencing analysis identified 17 high‐confidence fusion genes, eight of which were validated and two of which were in‐frame. No recurrent fusions were identified in an independent series of MPCs and IC‐NSTs . Forced expression of in‐frame fusion genes ( SLC2A1–FAF1 and BCAS4–AURKA ) resulted in increased viability of breast cancer cells. In addition, genomic disruption of CDK12 caused by out‐of‐frame rearrangements was found in one MPC and in 13% of HER2 ‐positive breast cancers, identified through a re‐analysis of publicly available massively parallel sequencing data. In vitro analyses revealed that CDK12 gene disruption results in sensitivity to PARP inhibition, and forced expression of wild‐type CDK12 in a CDK12 ‐null cell line model resulted in relative resistance to PARP inhibition. Our findings demonstrate that MPCs are neither defined by highly recurrent mutations in the 273 genes tested, nor underpinned by a recurrent fusion gene. Although seemingly private genetic events, some of the fusion transcripts found in MPCs may play a role in maintenance of a malignant phenotype and potentially offer therapeutic opportunities. © 2014 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106752/1/path4325.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/106752/2/path4325-sup-0001-AppendixS1.pd

Genomic distance entrained clustering and regression modelling highlights interacting genomic regions contributing to proliferation in breast cancer

<p>Abstract</p> <p>Background</p> <p>Genomic copy number changes and regional alterations in epigenetic states have been linked to grade in breast cancer. However, the relative contribution of specific alterations to the pathology of different breast cancer subtypes remains unclear. The heterogeneity and interplay of genomic and epigenetic variations means that large datasets and statistical data mining methods are required to uncover recurrent patterns that are likely to be important in cancer progression.</p> <p>Results</p> <p>We employed ridge regression to model the relationship between regional changes in gene expression and proliferation. Regional features were extracted from tumour gene expression data using a novel clustering method, called genomic distance entrained agglomerative (GDEC) clustering. Using gene expression data in this way provides a simple means of integrating the phenotypic effects of both copy number aberrations and alterations in chromatin state. We show that regional metagenes derived from GDEC clustering are representative of recurrent regions of epigenetic regulation or copy number aberrations in breast cancer. Furthermore, detected patterns of genomic alterations are conserved across independent oestrogen receptor positive breast cancer datasets. Sequential competitive metagene selection was used to reveal the relative importance of genomic regions in predicting proliferation rate. The predictive model suggested additive interactions between the most informative regions such as 8p22-12 and 8q13-22.</p> <p>Conclusions</p> <p>Data-mining of large-scale microarray gene expression datasets can reveal regional clusters of co-ordinate gene expression, independent of cause. By correlating these clusters with tumour proliferation we have identified a number of genomic regions that act together to promote proliferation in ER+ breast cancer. Identification of such regions should enable prioritisation of genomic regions for combinatorial functional studies to pinpoint the key genes and interactions contributing to tumourigenicity.</p

Transcriptome analysis of embryonic mammary cells reveals insights into mammary lineage establishment

Introduction: The mammary primordium forms during embryogenesis as a result of inductive interactions between its constitutive tissues, the mesenchyme and epithelium, and represents the earliest evidence of commitment to the mammary lineage. Previous studies of embryonic mouse mammary epithelium indicated that, by mid-gestation, these cells are determined to a mammary cell fate and that a stem cell population has been delimited. Mammary mesenchyme can induce mammary development from simple epithelium even across species and classes, and can partially restore features of differentiated tissue to mouse mammary tumours in co-culture experiments. Despite these exciting properties, the molecular identity of embryonic mammary cells remains to be fully characterised. Methods: Here, we define the transcriptome of the mammary primordium and the two distinct cellular compartments that comprise it, the mammary primordial bud epithelium and mammary mesenchyme. Pathway and network analysis was performed and comparisons of embryonic mammary gene expression profiles to those of both postnatal mouse and human mammary epithelial cell sub-populations and stroma were made. Results: Several of the genes we have detected in our embryonic mammary cell signatures were previously shown to regulate mammary cell fate and development, but we also identified a large number of novel candidates. Additionally, we determined genes that were expressed by both embryonic and postnatal mammary cells, which represent candidate regulators of mammary cell fate, differentiation and progenitor cell function that could signal from mammary lineage inception during embryogenesis through postnatal development. Comparison of embryonic mammary cell signatures with those of human breast cells identified potential regulators of mammary progenitor cell functions conserved across species. Conclusions: These results provide new insights into genetic regulatory mechanisms of mammary development, particularly identification of novel potential regulators of mammary fate and mesenchymal-epithelial cross-talk. Since cancers may represent diseases of mesenchymal-epithelial communications, we anticipate these results will provide foundations for further studies into the fundamental links between developmental, stem cell and breast cancer biology

Comprehensive Genomic Analysis of a BRCA2 Deficient Human Pancreatic Cancer

Capan-1 is a well-characterised BRCA2-deficient human cell line isolated from a liver metastasis of a pancreatic adenocarcinoma. Here we report a genome-wide assessment of structural variations and high-depth exome characterization of single nucleotide variants and small insertion/deletions in Capan-1. To identify potential somatic and tumour-associated variations in the absence of a matched-normal cell line, we devised a novel method based on the analysis of HapMap samples. We demonstrate that Capan-1 has one of the most rearranged genomes sequenced to date. Furthermore, small insertions and deletions are detected more frequently in the context of short sequence repeats than in other genomes. We also identify a number of novel mutations that may represent genetic changes that have contributed to tumour progression. These data provide insight into the genomic effects of loss of BRCA2 function

SiGNet: A signaling network data simulator to enable signaling network inference

<div><p>Network models are widely used to describe complex signaling systems. Cellular wiring varies in different cellular contexts and numerous inference techniques have been developed to infer the structure of a network from experimental data of the network’s behavior. To objectively identify which inference strategy is best suited to a specific network, a gold standard network and dataset are required. However, suitable datasets for benchmarking are difficult to find. Numerous tools exist that can simulate data for transcriptional networks, but these are of limited use for the study of signaling networks. Here, we describe SiGNet (<u>Si</u>gnal <u>G</u>enerator for <u>Net</u>works): a Cytoscape app that simulates experimental data for a signaling network of known structure. SiGNet has been developed and tested against published experimental data, incorporating information on network architecture, and the directionality and strength of interactions to create biological data in silico. SiGNet is the first tool to simulate biological signaling data, enabling an accurate and systematic assessment of inference strategies. SiGNet can also be used to produce preliminary models of key biological pathways following perturbation.</p></div