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A major challenge in the analysis of RNA-seq data at the transcript-level is accounting for the variability introduced during quantification of RNA sequencing reads. This variability is due to the high levels of sequence similarity among transcripts annotated to the same genomic locus and the mapping ambiguity resulting from the assignment of sequence reads to such transcripts. The quantification uncertainty associated with transcript-level estimated counts is intractable to measure analytically but represents an extra source of variation that seriously compromises differential transcript expression (DTE) analyses if standard statistical methods developed for gene-level analyses are used. Bootstrap counts, as provided by popular RNA-seq quantification tools, allow one to estimate the quantification uncertainty and account for such an effect in DTE analyses. We presentcatchSalmonandcatchKallisto, two functions included in the R/Bioconductor packageedgeR, that estimate the transcript-level quantification uncertainty, here termed mapping ambiguity overdispersion, using bootstrap counts. We discuss how the mapping ambiguity overdispersion can be effectively removed from the data in transcript-level analyses via count scaling, an approach that reduces the size of the estimated counts obtained from quantification tools to effective count sizes that reflect their true precision. The presented count scaling approach allows users to perform efficient DTE analyses within the efficientedgeRframework. A comprehensive simulation study and a DTE analysis of human lung adenocarcinoma cell lines are presented to illustrate the benefits of accounting for the mapping ambiguity overdispersion in transcript-level RNA-seq data analyses.

Mutations in genes encoding general transcription factors cause neurological disorders. Despite clinical prominence, the consequences of defects in the basal transcription machinery during brain development are unclear. We found that loss of the TATA-box binding protein-associated factor TAF8, a component of the general transcription factor TFIID, in the developing central nervous system affected the expression of many, but notably not all genes. Taf8 deletion caused apoptosis, unexpectedly restricted to forebrain regions. Nuclear levels of the transcription factor p53 were elevated in the absence of TAF8, as were the mRNAs of the pro-apoptotic p53 target genes Noxa, Puma and Bax. The cell death in Taf8 forebrain regions was completely rescued by additional loss of p53, but Taf8 and p53 brains failed to initiate a neuronal expression program. Taf8 deletion caused aberrant transcription of promoter regions and splicing anomalies. We propose that TAF8 supports the directionality of transcription and co-transcriptional splicing, and that failure of these processes causes p53-induced apoptosis of neuronal cells in the developing mouse embryo. [Image: see text]

The current lack of benchmark datasets with inbuilt ground-truth makes it challenging to compare the performance of existing long-read isoform detection and differential expression analysis workflows. Here, we present a benchmark experiment using two human lung adenocarcinoma cell lines that were each profiled in triplicate together with synthetic, spliced, spike-in RNAs (“sequins”). Samples were deeply sequenced on both Illumina short-read and Oxford Nanopore Technologies long-read platforms. Alongside the ground-truth available via the sequins, we createdin silicomixture samples to allow performance assessment in the absence of true positives or true negatives. Our results show that,StringTie2andbambuoutperformed other tools from the 6 isoform detection tools tested,DESeq2, edgeRandlimma-voomwere best amongst the 5 differential transcript expression tools tested and there was no clear front-runner for performing differential transcript usage analysis between the 5 tools compared, which suggests further methods development is needed for this application.

Breast cancer is a common and highly heterogeneous disease. Understanding the cellular diversity in the mammary gland and its surrounding micro-environment across different states can provide insight into the cancer development in human breast. Recently, a large-scale single-cell RNA expression atlas was constructed of the human breast spanning normal, preneoplastic and tumorigenic states. Single-cell expression profiles of nearly 430,000 cells were obtained from 69 distinct surgical tissue specimens from 55 patients. This article extends the study by providing downstream processed R data objects, complete cell annotation and R code to reproduce all the analyses. Details of all the bioinformatic analyses that produced the results described in the study are provided.

Bone marrow is a preferred metastatic site for multiple solid tumours and is associated with poor prognosis and significant morbidity. Accumulating evidence indicates that cancer cells colonise specialised niches within the bone marrow to support their long-term propagation, but the precise location and mechanisms that mediate niche interactions are unknown. Using breast cancer as a model of solid tumour metastasis to the bone marrow, we applied large-scale quantitative three-dimensional imaging to characterise temporal changes in the bone marrow microenvironment during disease progression. We show that mouse mammary tumour cells preferentially home to a pre-existing metaphyseal domain enriched for type H vessels. Metastatic lesion outgrowth rapidly remodelled the local vasculature through extensive sprouting to establish a tumour-supportive microenvironment. The evolution of this tumour microenvironment reflects direct remodelling of the vascular endothelium through tumour-derived granulocyte-colony stimulating factor (G-CSF) in a hematopoietic cell-independent manner. Therapeutic targeting of the metastatic niche by blocking G-CSF receptor inhibited pathological blood vessel remodelling and reduced bone metastasis burden. These findings elucidate a mechanism of ‘host’ microenvironment hijacking by mammary tumour cells to subvert the local microvasculature to form a specialised, pro-tumorigenic niche., The visualisation of the bone metastasis process in a spatial temporal manner is lacking. Here, the authors use three-dimensional quantitative imaging and show that mouse mammary tumour cells preferentially home to endothelial subtype type H vessels within the bone marrow and remodel this vasculature by producing granulocyte-colony stimulating factor.

Background Heterogeneity within the mouse mammary epithelium and potential lineage relationships have been recently explored by single-cell RNA profiling. To further understand how cellular diversity changes during mammary ontogeny, we profiled single cells from nine different developmental stages spanning late embryogenesis, early postnatal, prepuberty, adult, mid-pregnancy, late-pregnancy, and post-involution, as well as the transcriptomes of micro-dissected terminal end buds (TEBs) and subtending ducts during puberty. Methods The single cell transcriptomes of 132,599 mammary epithelial cells from 9 different developmental stages were determined on the 10x Genomics Chromium platform, and integrative analyses were performed to compare specific time points. Results The mammary rudiment at E18.5 closely aligned with the basal lineage, while prepubertal epithelial cells exhibited lineage segregation but to a less differentiated state than their adult counterparts. Comparison of micro-dissected TEBs versus ducts showed that luminal cells within TEBs harbored intermediate expression profiles. Ductal basal cells exhibited increased chromatin accessibility of luminal genes compared to their TEB counterparts suggesting that lineage-specific chromatin is established within the subtending ducts during puberty. An integrative analysis of five stages spanning the pregnancy cycle revealed distinct stage-specific profiles and the presence of cycling basal, mixed-lineage, and 'late' alveolar intermediates in pregnancy. Moreover, a number of intermediates were uncovered along the basal-luminal progenitor cell axis, suggesting a continuum of alveolar-restricted progenitor states. Conclusions This extended single cell transcriptome atlas of mouse mammary epithelial cells provides the most complete coverage for mammary epithelial cells during morphogenesis to date. Together with chromatin accessibility analysis of TEB structures, it represents a valuable framework for understanding developmental decisions within the mouse mammary gland.

To examine global changes in breast heterogeneity across different states, we determined the single‐cell transcriptomes of > 340,000 cells encompassing normal breast, preneoplastic BRCA1 +/– tissue, the major breast cancer subtypes, and pairs of tumors and involved lymph nodes. Elucidation of the normal breast microenvironment revealed striking changes in the stroma of post‐menopausal women. Single‐cell profiling of 34 treatment‐naive primary tumors, including estrogen receptor (ER)+, HER2+, and triple‐negative breast cancers, revealed comparable diversity among cancer cells and a discrete subset of cycling cells. The transcriptomes of preneoplastic BRCA1 +/– tissue versus tumors highlighted global changes in the immune microenvironment. Within the tumor immune landscape, proliferative CD8+ T cells characterized triple‐negative and HER2+ cancers but not ER+ tumors, while all subtypes comprised cycling tumor‐associated macrophages, thus invoking potentially different immunotherapy targets. Copy number analysis of paired ER+ tumors and lymph nodes indicated seeding by genetically distinct clones or mass migration of primary tumor cells into axillary lymph nodes. This large‐scale integration of patient samples provides a high‐resolution map of cell diversity in normal and cancerous human breast., A large‐scale gene expression resource integrates diverse tissue samples and reveals unexpected heterogeneity of breast cancer subtypes.

limma, edgeR and Glimma are R packages that are part of Bioconductor, the world's foremost software development project in statistical bioinformatics.They address one of the most fundamental of all genomic problems, that is detecting and interpreting differences in the abundance of genes or other genomic features between experimental conditions, cell types or disease states.

Summary: Humoral immune responses are tailored to the invading pathogen through regulation of key transcription factors and their networks. This is critical to establishing effective antibody-mediated responses, yet it is unknown how B cells integrate pathogen-induced signals to drive or suppress transcriptional programs specialized for each class of pathogen. Here, we detail the key role of the transcription factor c-Myb in regulating the T-bet-mediated anti-viral program. Deletion of c-Myb in mature B cells significantly increased serum IgG2c and CXCR3 expression by upregulating T-bet, normally suppressed during Th2-cell-mediated responses. Enhanced expression of T-bet resulted in aberrant plasma cell differentiation within the germinal center, mediated by CXCR3 expression. These findings identify a dual role for c-Myb in limiting inappropriate effector responses while coordinating plasma cell differentiation with germinal center egress. Identifying such intrinsic regulators of specialized antibody responses can assist in vaccine design and therapeutic intervention in B-cell-mediated immune disorders. : Piovesan et al. examine how B cells establish transcriptional programs that result in tailored responses to invading pathogens. The authors find that the transcription factor c-Myb represses the T-bet-mediated anti-viral program in B cells. c-Myb limits inappropriate effector responses while coordinating plasma cell differentiation with germinal center egress. Keywords: B cells, c-Myb, T-bet, immunoglobulin, CXCR3, plasma cell, germinal center

Primary triple negative breast cancers (TNBC) are prone to dissemination but sub-clonal relationships between tumors and resulting metastases are poorly understood. Here we use cellular barcoding of two treatment-naïve TNBC patient-derived xenografts (PDXs) to track the spatio-temporal fate of thousands of barcoded clones in primary tumors, and their metastases. Tumor resection had a major impact on reducing clonal diversity in secondary sites, indicating that most disseminated tumor cells lacked the capacity to ‘seed’, hence originated from ‘shedders’ that did not persist. The few clones that continued to grow after resection i.e. ‘seeders’, did not correlate in frequency with their parental clones in primary tumors. Cisplatin treatment of one BRCA1-mutated PDX model to non-palpable levels had a surprisingly minor impact on clonal diversity in the relapsed tumor yet purged 50% of distal clones. Therefore, clonal features of shedding, seeding and drug resistance are important factors to consider for the design of therapeutic strategies., Triple negative breast cancers (TNBC) disseminate and metastasise, but the clonal relationship of metastases to primary tumours is poorly understood. Here, the authors use cellular barcoding of TNBC patient-derived xenografts and track the fate of barcoded clones in primary tumours and their metastases, including after resection or chemotherapy.

Long-lived quiescent mammary stem cells (MaSCs) are presumed to coordinate the dramatic expansion of ductal epithelium that occurs through the different phases of postnatal development, but little is known about the molecular regulators that underpin their activation. We show that ablation of the transcription factor Foxp1 in the mammary gland profoundly impairs ductal morphogenesis, resulting in a rudimentary tree throughout life. Foxp1-deficient glands were highly enriched for quiescent Tspan8hi MaSCs, which failed to become activated even in competitive transplantation assays, thus highlighting a cell-intrinsic defect. Foxp1 deletion also resulted in aberrant expression of basal genes in luminal cells, inferring a role in cell-fate decisions. Notably, Foxp1 was uncovered as a direct repressor of Tspan8 in basal cells, and deletion of Tspan8 rescued the defects in ductal morphogenesis elicited by Foxp1 loss. Thus, a single transcriptional regulator Foxp1 can control the exit of MaSCs from dormancy to orchestrate differentiation and development.

Primary triple negative breast cancers (TNBC) are prone to dissemination but sub-clonal relationships between tumors and resulting metastases are poorly understood. Here we use cellular barcoding of two treatment-naïve TNBC patient-derived xenografts (PDXs) to track the spatio-temporal fate of thousands of barcoded clones in primary tumors, and their metastases. Tumor resection had a major impact on reducing clonal diversity in secondary sites, indicating that most disseminated tumor cells lacked the capacity to 'seed', hence originated from 'shedders' that did not persist. The few clones that continued to grow after resection i.e. 'seeders', did not correlate in frequency with their parental clones in primary tumors. Cisplatin treatment of one BRCA1-mutated PDX model to non-palpable levels had a surprisingly minor impact on clonal diversity in the relapsed tumor yet purged 50% of distal clones. Therefore, clonal features of shedding, seeding and drug resistance are important factors to consider for the design of therapeutic strategies.

The mammary epithelium comprises two primary cellular lineages, but the degree of heterogeneity within these compartments and their lineage relationships during development remain an open question. Here we report single-cell RNA profiling of mouse mammary epithelial cells spanning four developmental stages in the post-natal gland. Notably, the epithelium undergoes a large-scale shift in gene expression from a relatively homogeneous basal-like program in pre-puberty to distinct lineage-restricted programs in puberty. Interrogation of single-cell transcriptomes reveals different levels of diversity within the luminal and basal compartments, and identifies an early progenitor subset marked by CD55. Moreover, we uncover a luminal transit population and a rare mixed-lineage cluster amongst basal cells in the adult mammary gland. Together these findings point to a developmental hierarchy in which a basal-like gene expression program prevails in the early post-natal gland prior to the specification of distinct lineage signatures, and the presence of cellular intermediates that may serve as transit or lineage-primed cells., The mammary epithelium comprises two cell lineages but the heterogeneity amongst these during development is unclear. Here, the authors report single-cell RNA sequencing of the mouse mammary epithelium at four developmental stages, revealing diversity in both compartments and a transcriptional shift with puberty onset.

Lung squamous cell carcinoma (SqCC), the second most common subtype of lung cancer, is strongly associated with tobacco smoking and exhibits genomic instability. The cellular origins and molecular processes that contribute to SqCC formation are largely unexplored. Here we show that human basal stem cells (BSCs) isolated from heavy smokers proliferate extensively, whereas their alveolar progenitor cell counterparts have limited colony-forming capacity. We demonstrate that this difference arises in part because of the ability of BSCs to repair their DNA more efficiently than alveolar cells following ionizing radiation or chemical-induced DNA damage. Analysis of mice harbouring a mutation in the DNA-dependent protein kinase catalytic subunit (DNA-PKcs), a key enzyme in DNA damage repair by nonhomologous end joining (NHEJ), indicated that BSCs preferentially repair their DNA by this error-prone process. Interestingly, polyploidy, a phenomenon associated with genetically unstable cells, was only observed in the human BSC subset. Expression signature analysis indicated that BSCs are the likely cells of origin of human SqCC and that high levels of NHEJ genes in SqCC are correlated with increasing genomic instability. Hence, our results favour a model in which heavy smoking promotes proliferation of BSCs, and their predilection for error-prone NHEJ could lead to the high mutagenic burden that culminates in SqCC. Targeting DNA repair processes may therefore have a role in the prevention and therapy of SqCC., Author Summary Human lungs are constantly exposed to inhaled chemicals that have the potential to damage cellular DNA. Lung stem cells must therefore have the ability to repair DNA damage to survive and achieve tissue homeostasis. Lung airways are composed of different types of cells, including basal cells, which have been proposed to be the stem cells of the lung. Here, we show that lung basal stem cells have a superior ability to resolve DNA damage compared to alveolar progenitor cells, thus allowing these cells to survive and proliferate after injury. Accordingly, basal stem cells isolated from patients with a long history of tobacco smoking had remarkable proliferative potential compared to those extracted from never smokers. However, we demonstrate that basal stem cells predominately use nonhomologous end joining to repair DNA double-strand breaks, a notoriously error-prone pathway. A subset of polyploid basal stem cells was observed in cigarette-smoking patients, pointing to the accumulation of genetic instability in these long-lived cells. Gene expression analyses revealed that lung squamous cell carcinoma, a subtype of lung cancer that almost exclusively occurs in smokers, carries a transcriptional fingerprint of basal cells, suggesting that lung basal stem cells could be the cells of origin of this subtype of lung cancer. We postulate that further unravelling of DNA repair in lung cells may lead to potential therapeutic targets in the prevention or treatment of lung diseases.

Breast tumors are inherently heterogeneous, but the evolving cellular organization through neoplastic progression is poorly understood. Here we report a rapid, large-scale single-cell resolution 3D imaging protocol based on a one-step clearing agent that allows visualization of normal tissue architecture and entire tumors at cellular resolution. Imaging of multicolor lineage-tracing models of breast cancer targeted to either basal or luminal progenitor cells revealed profound clonal restriction during progression. Expression profiling of clones arising in Pten/Trp53-deficient tumors identified distinct molecular signatures. Strikingly, most clones harbored cells that had undergone an epithelial-to-mesenchymal transition, indicating widespread, inherent plasticity. Hence, an integrative pipeline that combines lineage tracing, 3D imaging, and clonal RNA sequencing technologies offers a comprehensive path for studying mechanisms underlying heterogeneity in whole tumors.

RNA sequencing (RNA-seq) has been rapidly adopted for the profiling of transcriptomes in many areas of biology, including studies into gene regulation, development and disease. Of particular interest is the discovery of differentially expressed genes across different conditions (e.g., tissues, perturbations), while optionally adjusting for other systematic factors that affect the data collection process. There are a number of subtle yet critical aspects of these analyses, such as read counting, appropriate treatment of biological variability, quality control checks and appropriate setup of statistical modeling. Several variations have been presented in the literature, and there is a need for guidance on current best practices. This protocol presents a "state-of-the-art" computational and statistical RNA-seq differential expression analysis workflow largely based on the free open-source R language and Bioconductor software and in particular, two widely-used tools DESeq and edgeR. Hands-on time for typical small experiments (e.g., 4-10 samples) can be

RNA sequencing (RNA-seq) is widely used to profile transcriptional activity in biological systems. Here we present an analysis pipeline for differential expression analysis of RNA-seq experiments using the Rsubread and edgeR software packages. The basic pipeline includes read alignment and counting, filtering and normalization, modelling of biological variability and hypothesis testing. For hypothesis testing, we describe particularly the quasi-likelihood features of edgeR. Some more advanced downstream analysis steps are also covered, including complex comparisons, gene ontology enrichment analyses and gene set testing. The code required to run each step is described, along with an outline of the underlying theory. The chapter includes a case study in which the pipeline is used to study the expression profiles of mammary gland cells in virgin, pregnant and lactating mice.

RNA sequencing (RNA-seq) is widely used to profile transcriptional activity in biological systems. Here we present an analysis pipeline for differential expression analysis of RNA-seq experiments using the Rsubread and edgeR software packages. The basic pipeline includes read alignment and counting, filtering and normalization, modelling of biological variability and hypothesis testing. For hypothesis testing, we describe particularly the quasi-likelihood features of edgeR. Some more advanced downstream analysis steps are also covered, including complex comparisons, gene ontology enrichment analyses and gene set testing. The code required to run each step is described, along with an outline of the underlying theory. The chapter includes a case study in which the pipeline is used to study the expression profiles of mammary gland cells in virgin, pregnant and lactating mice.

A flexible statistical framework is developed for the analysis of read counts from RNA-Seq gene expression studies. It provides the ability to analyse complex experiments involving multiple treatment conditions and blocking variables while still taking full account of biological variation. Biological variation between RNA samples is estimated separately from the technical variation associated with sequencing technologies. Novel empirical Bayes methods allow each gene to have its own specific variability, even when there are relatively few biological replicates from which to estimate such variability. The pipeline is implemented in the edgeR package of the Bioconductor project. A case study analysis of carcinoma data demonstrates the ability of generalized linear model methods (GLMs) to detect differential expression in a paired design, and even to detect tumour-specific expression changes. The case study demonstrates the need to allow for gene-specific variability, rather than assuming a common dispersion across genes or a fixed relationship between abundance and variability. Genewise dispersions de-prioritize genes with inconsistent results and allow the main analysis to focus on changes that are consistent between biological replicates. Parallel computational approaches are developed to make non-linear model fitting faster and more reliable, making the application of GLMs to genomic data more convenient and practical. Simulations demonstrate the ability of adjusted profile likelihood estimators to return accurate estimators of biological variability in complex situations. When variation is gene-specific, empirical Bayes estimators provide an advantageous compromise between the extremes of assuming common dispersion or separate genewise dispersion. The methods developed here can also be applied to count data arising from DNA-Seq applications, including ChIP-Seq for epigenetic marks and DNA methylation analyses.

Cytosine methylation is an important DNA epigenetic modification. In vertebrates, methylation occurs at CpG sites, which are dinucleotides where a cytosine is immediately followed by a guanine in the DNA sequence from 5' to 3'. When located in the promoter region of a gene, DNA methylation is often associated with transcriptional silencing of the gene. Aberrant DNA methylation is associated with the development of various diseases such as cancer. Bisulfite sequencing (BS-seq) is the current "gold-standard" technology for high-resolution profiling of DNA methylation. Reduced representation bisulfite sequencing (RRBS) is an efficient form of BS-seq that targets CpG-rich DNA regions in order to save sequencing costs. A typical bioinformatics aim is to identify CpGs that are differentially methylated (DM) between experimental conditions. This workflow demonstrates that differential methylation analysis of RRBS data can be conducted using software and methodology originally developed for RNA-seq data. The RNA-seq pipeline is adapted to methylation by adding extra columns to the design matrix to account for read coverage at each CpG, after which the RRBS and RNA-seq pipelines are almost identical. This approach is statistically natural and gives analysts access to a rich collection of analysis tools including generalized linear models, gene set testing and pathway analysis. The article presents a complete start to finish case study analysis of RRBS profiles of different cell populations from the mouse mammary gland using the Bioconductor package edgeR. We show that lineage-committed cells are typically hyper-methylated compared to progenitor cells and this is true on all the autosomes but not the sex chromosomes. We demonstrate a strong negative correlation between methylation of promoter regions and gene expression as measured by RNA-seq for the same cell types, showing that methylation is a regulatory mechanism involved in epithelial linear commitment.

Memory B cells and long-lived bone marrow-resident plasma cells maintain humoral immunity. Little is known about the intrinsic mechanisms that are essential for forming memory B cells or endowing them with the ability to rapidly differentiate upon reexposure while maintaining the population over time. Histone modifications have been shown to regulate lymphocyte development, but their role in regulating differentiation and maintenance of B-cell subsets during an immune response is unclear. Using stage-specific deletion of monocytic leukemia zinc finger protein (MOZ), a histone acetyltransferase, we demonstrate that mutation of this chromatin modifier alters fate decisions in both primary and secondary responses. In the absence of MOZ, germinal center B cells were significantly impaired in their ability to generate dark zone centroblasts, with a concomitant decrease in both cell-cycle progression and BCL-6 expression. In contrast, there was increased differentiation to IgM and low-affinity IgG1(+) memory B cells. The lack of MOZ affected the functional outcome of humoral immune responses, with an increase in secondary germinal centers and a corresponding decrease in secondary high-affinity antibody-secreting cell formation. Therefore, these data provide strong evidence that manipulating epigenetic modifiers can regulate fate decisions during humoral responses, and thus could be targeted for therapeutic intervention.

This article reviews the statistical theory underlying the edgeR software package for differential expression of RNA-seq data. Negative binomial models are used to capture the quadratic mean-variance relationship that can be observed in RNA-seq data. Conditional likelihood methods are used to avoid bias when estimating the level of variation. Empirical Bayes methods are used to allow gene-specific variation estimates even when the number of replicate samples is very small. Generalized linear models are used to accommodate arbitrarily complex designs. A key feature of the edgeR package is the use of weighted likelihood methods to implement a flexible empirical Bayes approach in the absence of easily tractable sampling distributions. The methodology is implemented in flexible software that is easy to use even for users who are not professional statisticians or bioinformaticians. The software is part of the Bioconductor project.

Over-expression of the c-MYC oncogene and Trp53 gene mutations are among the most common genetic alterations in human cancer and, when combined, result in highly aggressive malignant disease. Trp53 gene mutations produce over-expressed mutant TRP53 proteins that drive cancer growth through both loss of wild-type Trp53 tumor suppressor function and gain-of-function oncogenic properties. The Eμ-Myc mouse model provides a setting to study the functional interplay between c-Myc over-expression and mutant TRP53 proteins. Eμ-Myc transgenic mice carry a c-Myc transgene under the control of the immunoglobulin heavy chain gene enhancer (Eμ), recapitulating the chromosomal translocation underlying human Burkitt Lymphoma, and develop aggressive pre-B/B cell lymphoma with a high (~20%) spontaneous rate of Trp53 mutation. The effect of five mouse mutant TRP53 proteins (V170M, I192S, G280, R246Q, R270H) was initially examined in three settings (Trp53-/-, Trp53+/- and Trp53+/+;Eμ-Myc) using a hematopoietic stem and progenitor cell (HSPC) reconstitution model. Each mutant TRP53 protein studied corresponds to a commonly re-occurring Tp53 mutation in human cancer. Retroviral over-expression enabled the comparison of mutant-specific and genotype-specific features for each mutant TRP53 protein. Mutant TRP53 expression did not accelerate lymphoma development in mice receiving Trp53-/- or Trp53+/- HSPCs. However, mice reconstituted with Trp53+/- HSPCs expressing the TRP53 mutants displayed an altered tumor spectrum compared to mice reconstituted with control Trp53+/- HSPCs. In contrast, mutant TRP53 markedly accelerated lymphoma development in mice receiving Trp53+/+;Eμ-Myc HSPCs, highlighting a synergy between c-Myc over-expression and Trp53 mutations in neoplastic transformation. Furthermore, inducible mutant TRP53 expression demonstrated a dependency on sustained expression of mutant TRP53 in established MYC-driven lymphomas. Notably, none of the c-MYC plus mutant TRP53 driven lymphomas exhibited spontaneous endogenous Trp53 mutations. Despite the enhanced tumorigenesis, most established lymphomas from this model displayed sensitivity to TRP53-activating drugs consistent with a weak dominant negative effect over wild-type Trp53-induced apoptosis. Consistent with this finding, pre-malignant Trp53+/+;Eμ-Myc primary B-cells expressing mutant TRP53 were not protected against Trp53-induced apoptosis. Pre-malignant B-cells displayed a small increase in cell cycling and an expansion of the tumor-initiating pre/pro-B cell population. Most significantly, functional assessment of DNA damage in pre-malignant cells, using single cell gel electrophoresis (comet assay) and γ-H2AX staining, revealed increased DNA damage, suggesting an important role for defects in DNA repair during mutant TRP53-driven lymphoma development. To investigate the nature of the dominant negative effect, mutant TRP53 protein was exogenously expressed in mouse Eµ-Myc Trp53+/+ lymphoma cell lines. The impact of mutant TRP53 on the transcriptional function of the endogenous wild-type TRP53 protein was then studied using the TRP53-activating compound, nutlin-3a. Surprisingly, in established lymphoma cell lines, mutant TRP53 impaired nutlin-3a-induced apoptosis despite substantial induction of the critical pro-apoptotic effector, PUMA. To explore this finding further, we globally characterized the dominant negative effect, and assessed for mutant TRP53-specific transcriptional targets, by performing whole transcriptome sequence (RNAseq) analysis after treatment with nutlin-3a. Analysis of known wild-type Trp53 target genes (n=283) demonstrated that the induction of these genes as a group was repressed in the presence of the mutant TRP53 protein (ROAST test, p=6.7e-04). Remarkably, however, mutant TRP53 significantly repressed only 57% of the nutlin-3a-induced Trp53 target genes. Analysis of these strongly repressed genes highlighted the importance of several pathways, including metabolism, DNA damage repair and negative feedback loops in TRP53 signaling. This suggests a previously unrecognized selectivity of the dominant-negative-effect for certain p53 pathways that may be important in cancer initiation. Additional mutant TRP53-specific transcriptional targets were also identified and are under further investigation. Disclosures No relevant conflicts of interest to declare.

New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments. The voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline. This opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. Two case studies illustrate the use of linear modeling and gene set testing methods.

Introduction: MicroRNAs (miRNAs) have been implicated in governing lineage specification and differentiation in multiple organs; however, little is known about their specific roles in mammopoiesis. We have determined the global miRNA expression profiles of functionally distinct epithelial subpopulations in mouse and human mammary tissue, and compared these to their cognate transcriptomes and epigenomes. Finally, the human miRNA signatures were used to interrogate the different subtypes of breast cancer, with a view to determining miRNA networks deregulated during oncogenesis. Methods: RNA from sorted mouse and human mammary cell subpopulations was subjected to miRNA expression analysis using the TaqMan MicroRNA Array. Differentially expressed (DE) miRNAs were correlated with gene expression and histone methylation profiles. Analysis of miRNA signatures of the intrinsic subtypes of breast cancer in The Cancer Genome Atlas (TCGA) database versus those of normal human epithelial subpopulations was performed. Results: Unique miRNA signatures characterized each subset (mammary stem cell (MaSC)/basal, luminal progenitor, mature luminal, stromal), with a high degree of conservation across species. Comparison of miRNA and transcriptome profiles for the epithelial subtypes revealed an inverse relationship and pinpointed key developmental genes. Interestingly, expression of the primate-specific miRNA cluster (19q13.4) was found to be restricted to the MaSC/basal subset. Comparative analysis of miRNA signatures with H3 lysine modification maps of the different epithelial subsets revealed a tight correlation between active or repressive marks for the top DE miRNAs, including derepression of miRNAs in Ezh2-deficient cellular subsets. Interrogation of TCGA-identified miRNA profiles with the miRNA signatures of different human subsets revealed specific relationships. Conclusions: The derivation of global miRNA expression profiles for the different mammary subpopulations provides a comprehensive resource for understanding the interplay between miRNA networks and target gene expression. These data have highlighted lineage-specific miRNAs and potential miRNA-mRNA networks, some of which are disrupted in neoplasia. Furthermore, our findings suggest that key developmental miRNAs are regulated by global changes in histone modification, thus linking the mammary epigenome with genome-wide changes in the expression of genes and miRNAs. Comparative miRNA signature analyses between normal breast epithelial cells and breast tumors confirmed an important linkage between luminal progenitor cells and basal-like tumors. Conclusions: The derivation of global miRNA expression profiles for the different mammary subpopulations provides a comprehensive resource for understanding the interplay between miRNA networks and target gene expression. These data have highlighted lineage-specific miRNAs and potential miRNA–mRNA networks, some of which are disrupted in neoplasia. Furthermore, our findings suggest that key developmental miRNAs are regulated by global changes in histone modification, thus linking the mammary epigenome with genome-wide changes in the expression of genes and miRNAs. Comparative miRNA signature analyses between normal breast epithelial cells and breast tumors confirmed an important linkage between luminal progenitor cells and basal-like tumors. Refereed/Peer-reviewed