H. B. Pearson, J. Li, V. S. Meniel, C. M. Fennell, P. Waring, K. G. Montgomery, R. J. Rebello, A. A. Macpherson, S. Koushyar, L. Furic, C. Cullinane, R. W. Clarkson, M. J. Smalley, K. J. Simpson, T. J. Phesse, P. R. Shepherd, P. O. Humbert, O. J. Sansom and W. A. Phillips
Genetic alterations that potentiate PI3K signaling are frequent in prostate cancer, yet how different genetic drivers of the PI3K cascade contribute to prostate cancer is unclear. Here, we report PIK3CA mutation/amplification correlates with poor survival of patients with prostate cancer. To interrogate the requirement of different PI3K genetic drivers in prostate cancer, we employed a genetic approach to mutate Pik3ca in mouse prostate epithelium. We show Pik3ca(H1047R) mutation causes p110alpha-dependent invasive prostate carcinoma in vivo Furthermore, we report that PIK3CA mutation and PTEN loss coexist in patients with prostate cancer and can cooperate in vivo to accelerate disease progression via AKT-mTORC1/2 hyperactivation. Contrasting single mutants that slowly acquire castration-resistant prostate cancer (CRPC), concomitant Pik3ca mutation and Pten loss caused de novo CRPC. Thus, Pik3ca mutation and Pten deletion are not functionally redundant. Our findings indicate that PIK3CA mutation is an attractive prognostic indicator for prostate cancer that may cooperate with PTEN loss to facilitate CRPC in patients.Significance: We show PIK3CA mutation correlates with poor prostate cancer prognosis and causes prostate cancer in mice. Moreover, PIK3CA mutation and PTEN loss coexist in prostate cancer and can cooperate in vivo to accelerate tumorigenesis and facilitate CRPC. Delineating this synergistic relationship may present new therapeutic/prognostic approaches to overcome castration/PI3K-AKT-mTORC1/2 inhibitor resistance. Cancer Discov; 8(6); 764-79. (c)2018 AACR.See related commentary by Triscott and Rubin, p. 682This article is highlighted in the In This Issue feature, p. 663.
Optical Mapping Reveals a Higher Level of Genomic Architecture of Chained Fusions in Cancer
E. K. F. Chan, D. L. Cameron, D. C. Petersen, R. J. Lyons, B. F. Baldi, A. T. Papenfuss, D. M. Thomas and V. M. Hayes
Genomic rearrangements are common in cancer, with demonstrated links to disease progression and treatment response. These rearrangements can be complex, resulting in fusions of multiple chromosomal fragments and generation of derivative chromosomes. Although methods exist for detecting individual fusions, they are generally unable to reconstruct complex chained events. To overcome these limitations, we adopted a new optical mapping approach, allowing megabase-length genome maps to be reconstructed and rearranged genomes to be visualized without loss of integrity. Whole-genome mapping (Bionano Genomics) of a well-studied highly rearranged liposarcoma cell line resulted in 3338 assembled consensus genome maps, including 72 fusion maps. These fusion maps represent 112.3 Mb of highly rearranged genomic regions, illuminating the complex architecture of chained fusions, including content, order, orientation, and size. Spanning the junction of 147 chromosomal translocations, we found a total of 28 Mb of interspersed sequences that could not be aligned to the reference genome. Traversing these interspersed sequences using short-read sequencing breakpoint calls, we were able to identify and place 399 sequencing fragments within the optical mapping gaps, thus illustrating the complementary nature of optical mapping and short-read sequencing. We demonstrate that optical mapping provides a powerful new approach for capturing a higher level of complex genomic architecture, creating a scaffold for renewed interpretation of sequencing data of particular relevance to human cancer.
Enhancer Transcription Reveals Subtype-Specific Gene Expression Programs Controlling Breast Cancer Pathogenesis
H. L. Franco, A. Nagari, V. S. Malladi, W. Li, Y. Xi, D. Richardson, K. L. Allton, K. Tanaka, J. Li, S. Murakami, K. Keyomarsi, M. T. Bedford, X. Shi, W. Li, M. C. Barton, S. Y. R. Dent and W. L. Kraus
Noncoding transcription is a defining feature of active enhancers, linking transcription factor (TF) binding to the molecular mechanisms controlling gene expression. To determine the relationship between enhancer activity and biological outcomes in breast cancers, we profiled the transcriptomes (using GRO-seq and RNA-seq) and epigenomes (using ChIP-seq) of 11 different human breast cancer cell lines representing five major molecular subtypes of breast cancer, as well as two immortalized ("normal") human breast cell lines. In addition, we developed a robust and unbiased computational pipeline that simultaneously identifies putative subtype-specific enhancers and their cognate TFs by integrating the magnitude of enhancer transcription, TF mRNA expression levels, TF motif P-values, and enrichment of H3K4me1 and H3K27ac. When applied across the 13 different cell lines noted above, the Total Functional Score of Enhancer Elements (TFSEE) identified key breast cancer subtype-specific TFs that act at transcribed enhancers to dictate gene expression patterns determining growth outcomes, including Forkhead TFs, FOSL1, and PLAG1. FOSL1, a Fos family TF, (1) is highly enriched at the enhancers of triple negative breast cancer (TNBC) cells, (2) acts as a key regulator of the proliferation and viability of TNBC cells, but not Luminal A cells, and (3) is associated with a poor prognosis in TNBC breast cancer patients. Taken together, our results validate our enhancer identification pipeline and reveal that enhancers transcribed in breast cancer cells direct critical gene regulatory networks that promote pathogenesis.
Bigscale: An Analytical Framework for Big-Scale Single-Cell Data
G. Iacono, E. Mereu, A. Guillaumet-Adkins, R. Corominas, I. Cusco, G. Rodriguez-Esteban, M. Gut, L. A. Perez-Jurado, I. Gut and H. Heyn
Single-cell RNA sequencing (scRNA-seq) has significantly deepened our insights into complex tissues, with the latest techniques capable of processing tens of thousands of cells simultaneously. Analyzing increasing numbers of cells, however, generates extremely large data sets, extending processing time and challenging computing resources. Current scRNA-seq analysis tools are not designed to interrogate large data sets and often lack sensitivity to identify marker genes. With bigSCale, we provide a scalable analytical framework to analyze millions of cells, which addresses the challenges associated with large data sets. To handle the noise and sparsity of scRNA-seq data, bigSCale uses large sample sizes to estimate an accurate numerical model of noise. The framework further includes modules for differential expression analysis, cell clustering, and marker identification. A directed convolution strategy allows processing of extremely large data sets, while preserving transcript information from individual cells. We evaluated the performance of bigSCale using both a biological model of aberrant gene expression in patient-derived neuronal progenitor cells and simulated data sets, which underlines the speed and accuracy in differential expression analysis. To test its applicability for large data sets, we applied bigSCale to assess 1.3 million cells from the mouse developing forebrain. Its directed down-sampling strategy accumulates information from single cells into index cell transcriptomes, thereby defining cellular clusters with improved resolution. Accordingly, index cell clusters identified rare populations, such as reelin (Reln)-positive Cajal-Retzius neurons, for which we report previously unrecognized heterogeneity associated with distinct differentiation stages, spatial organization, and cellular function. Together, bigSCale presents a solution to address future challenges of large single-cell data sets.
Detecting Differential Copy Number Variation between Groups of Samples
C. B. Lowe, N. Sanchez-Luege, T. R. Howes, S. D. Brady, R. R. Daugherty, F. C. Jones, M. A. Bell and D. M. Kingsley
We present a method to detect copy number variants (CNVs) that are differentially present between two groups of sequenced samples. We use a finite-state transducer where the emitted read depth is conditioned on the mappability and GC-content of all reads that occur at a given base position. In this model, the read depth within a region is a mixture of binomials, which in simulations matches the read depth more closely than the often-used negative binomial distribution. The method analyzes all samples simultaneously, preserving uncertainty as to the breakpoints and magnitude of CNVs present in an individual when it identifies CNVs differentially present between the two groups. We apply this method to identify CNVs that are recurrently associated with postglacial adaptation of marine threespine stickleback (Gasterosteus aculeatus) to freshwater. We identify 6664 regions of the stickleback genome, totaling 1.7 Mbp, which show consistent copy number differences between marine and freshwater populations. These deletions and duplications affect both protein-coding genes and cis-regulatory elements, including a noncoding intronic telencephalon enhancer of DCHS1 The functions of the genes near or included within the 6664 CNVs are enriched for immunity and muscle development, as well as head and limb morphology. Although freshwater stickleback have repeatedly evolved from marine populations, we show that freshwater stickleback also act as reservoirs for ancient ancestral sequences that are highly conserved among distantly related teleosts, but largely missing from marine stickleback due to recent selective sweeps in marine populations.
Integrated Analysis of Motif Activity and Gene Expression Changes of Transcription Factors
J. G. S. Madsen, A. Rauch, E. L. Van Hauwaert, S. F. Schmidt, M. Winnefeld and S. Mandrup
The ability to predict transcription factors based on sequence information in regulatory elements is a key step in systems-level investigation of transcriptional regulation. Here, we have developed a novel tool, IMAGE, for precise prediction of causal transcription factors based on transcriptome profiling and genome-wide maps of enhancer activity. High precision is obtained by combining a near-complete database of position weight matrices (PWMs), generated by compiling public databases and systematic prediction of PWMs for uncharacterized transcription factors, with a state-of-the-art method for PWM scoring and a novel machine learning strategy, based on both enhancers and promoters, to predict the contribution of motifs to transcriptional activity. We applied IMAGE to published data obtained during 3T3-L1 adipocyte differentiation and showed that IMAGE predicts causal transcriptional regulators of this process with higher confidence than existing methods. Furthermore, we generated genome-wide maps of enhancer activity and transcripts during human mesenchymal stem cell commitment and adipocyte differentiation and used IMAGE to identify positive and negative transcriptional regulators of this process. Collectively, our results demonstrate that IMAGE is a powerful and precise method for prediction of regulators of gene expression.
Enduring Epigenetic Landmarks Define the Cancer Microenvironment
R. Pidsley, M. G. Lawrence, E. Zotenko, B. Niranjan, A. Statham, J. Song, R. M. Chabanon, W. Qu, H. Wang, M. Richards, S. S. Nair, N. J. Armstrong, H. T. Nim, M. Papargiris, P. Balanathan, H. French, T. Peters, S. Norden, A. Ryan, J. Pedersen, J. Kench, R. J. Daly, L. G. Horvath, P. Stricker, M. Frydenberg, R. A. Taylor, C. Stirzaker, G. P. Risbridger and S. J. Clark
The growth and progression of solid tumors involves dynamic cross-talk between cancer epithelium and the surrounding microenvironment. To date, molecular profiling has largely been restricted to the epithelial component of tumors; therefore, features underpinning the persistent protumorigenic phenotype of the tumor microenvironment are unknown. Using whole-genome bisulfite sequencing, we show for the first time that cancer-associated fibroblasts (CAFs) from localized prostate cancer display remarkably distinct and enduring genome-wide changes in DNA methylation, significantly at enhancers and promoters, compared to nonmalignant prostate fibroblasts (NPFs). Differentially methylated regions associated with changes in gene expression have cancer-related functions and accurately distinguish CAFs from NPFs. Remarkably, a subset of changes is shared with prostate cancer epithelial cells, revealing the new concept of tumor-specific epigenome modifications in the tumor and its microenvironment. The distinct methylome of CAFs provides a novel epigenetic hallmark of the cancer microenvironment and promises new biomarkers to improve interpretation of diagnostic samples.
Critical Review: Involvement of Endoplasmic Reticulum Stress in the Aetiology of Alzheimer's Disease
S. Hashimoto and T. C. Saido
The endoplasmic reticulum (ER) stress response is regarded as an important process in the aetiology of Alzheimer's disease (AD). The accumulation of pathogenic misfolded proteins and the disruption of intracellular calcium (Ca(2+)) signalling are considered to be fundamental mechanisms that underlie the induction of ER stress, leading to neuronal cell death. Indeed, a number of studies have proposed molecular mechanisms linking ER stress to AD pathogenesis based on results from in vitro systems and AD mouse models. However, stress responsivity was largely different between each mouse model, even though all of these models display AD-related pathologies. While several reports have shown elevated ER stress responses in amyloid precursor protein (APP) and presenilin 1 (PS1) double-transgenic (Tg) AD mouse models, we and other groups, in contrast, observed no such ER stress response in APP-single-Tg or App-knockin mice. Therefore, it is debatable whether the ER stress observed in APP and PS1 double-Tg mice is due to AD pathology. From these findings, the roles of ER stress in AD pathogenesis needs to be carefully addressed in future studies. In this review, we summarize research detailing the relationship between ER stress and AD, and analyse the results in detail.