Genetic mapping of species differences via in vitro crosses in mouse embryonic stem cells.

Discovering the genetic changes underlying species differences is a central goal in evolutionary genetics. However, hybrid crosses between species in mammals often suffer from hybrid sterility, greatly complicating genetic mapping of trait variation across species. Here, we describe a simple, robust, and transgene-free technique to generate "in vitro crosses" in hybrid mouse embryonic stem (ES) cells by inducing random mitotic cross-overs with the drug ML216, which inhibits the DNA helicase Bloom syndrome (BLM). Starting with an interspecific F1 hybrid ES cell line between the Mus musculus laboratory mouse and Mus spretus (∼1.5 million years of divergence), we mapped the genetic basis of drug resistance to the antimetabolite tioguanine to a single region containing hypoxanthine-guanine phosphoribosyltransferase (Hprt) in as few as 21 d through "flow mapping" by coupling in vitro crosses with fluorescence-activated cell sorting (FACS). We also show how our platform can enable direct study of developmental variation by rederiving embryos with contribution from the recombinant ES cell lines. We demonstrate how in vitro crosses can overcome major bottlenecks in mouse complex trait genetics and address fundamental questions in evolutionary biology that are otherwise intractable through traditional breeding due to high cost, small litter sizes, and/or hybrid sterility. In doing so, we describe an experimental platform toward studying evolutionary systems biology in mouse and potentially in human and other mammals, including cross-species hybrids.

FGFR2 risk SNPs confer breast cancer risk by augmenting oestrogen responsiveness.

The fibroblast growth factor receptor 2 (FGFR2) locus is consistently the top hit in genome-wide association studies for oestrogen receptor-positive (ER(+)) breast cancer. Yet, its mode of action continues to be controversial. Here, we employ a systems biology approach to demonstrate that signalling via FGFR2 counteracts cell activation by oestrogen. In the presence of oestrogen, the oestrogen receptor (ESR1) regulon (set of ESR1 target genes) is in an active state. However, signalling by FGFR2 is able to reverse the activity of the ESR1 regulon. This effect is seen in multiple distinct FGFR2 signalling model systems, across multiple cells lines and is dependent on the presence of FGFR2. Increased oestrogen exposure has long been associated with an increased risk of breast cancer. We therefore hypothesized that risk variants should reduce FGFR2 expression and subsequent signalling. Indeed, transient transfection experiments assaying the three independent variants of the FGFR2 risk locus (rs2981578, rs35054928 and rs45631563) in their normal chromosomal context show that these single-nucleotide polymorphisms (SNPs) map to transcriptional silencer elements and that, compared with wild type, the risk alleles augment silencer activity. The presence of risk variants results in lower FGFR2 expression and increased oestrogen responsiveness. We thus propose a molecular mechanism by which FGFR2 can confer increased breast cancer risk that is consistent with oestrogen exposure as a major driver of breast cancer risk. Our findings may have implications for the clinical use of FGFR2 inhibitors.

A novel patient stratification strategy to enhance the therapeutic efficacy of dasatinib in glioblastoma.

BACKGROUND: Glioblastoma is the most common primary malignancy of the central nervous system with a dismal prognosis. Genomic signatures classify isocitrate dehydrogenase 1 (IDH)-wildtype glioblastoma into three subtypes: proneural, mesenchymal, and classical. Dasatinib, an inhibitor of proto-oncogene kinase Src (SRC), is one of many therapeutics which, despite promising preclinical results, have failed to improve overall survival in glioblastoma patients in clinical trials. We examined whether glioblastoma subtypes differ in their response to dasatinib and could hence be evaluated for patient enrichment strategies in clinical trials. METHODS: We carried out in silico analyses on glioblastoma gene expression (TCGA) and single-cell RNA-Seq data. In addition, in vitro experiments using glioblastoma stem-like cells (GSCs) derived from primary patient tumors were performed, with complementary gene expression profiling and immunohistochemistry analysis of tumor samples. RESULTS: Patients with the mesenchymal subtype of glioblastoma showed higher SRC pathway activation based on gene expression profiling. Accordingly, mesenchymal GSCs were more sensitive to SRC inhibition by dasatinib compared to proneural and classical GSCs. Notably, SRC phosphorylation status did not predict response to dasatinib treatment. Furthermore, serpin peptidase inhibitor clade H member 1 (SERPINH1), a collagen-related heat-shock protein associated with cancer progression, was shown to correlate with dasatinib response and with the mesenchymal subtype. CONCLUSION: This work highlights further molecular-based patient selection strategies in clinical trials and suggests the mesenchymal subtype as well as SERPINH1 to be associated with response to dasatinib. Our findings indicate that stratification based on gene expression subtyping should be considered in future dasatinib trials.

PFKFB4 interacts with FBXO28 to promote HIF-1α signaling in glioblastoma.

Glioblastoma is a highly aggressive brain tumor for which there is no cure. The metabolic enzyme 6-Phosphofructo-2-Kinase/Fructose-2,6-Biphosphatase 4 (PFKFB4) is essential for glioblastoma stem-like cell (GSC) survival but its mode of action is unclear. Understanding the role of PFKFB4 in tumor cell survival could allow it to be leveraged in a cancer therapy. Here, we show the importance of PFKFB4 for glioblastoma growth in vivo in an orthotopic patient derived mouse model. In an evaluation of patient tumor samples of different cancer entities, PFKFB4 protein was found to be overexpressed in prostate, lung, colon, mammary and squamous cell carcinoma, with expression level correlating with tumor grade. Gene expression profiling in PFKFB4-silenced GSCs revealed a downregulation of hypoxia related genes and Western blot analysis confirmed a dramatic reduction of HIF (hypoxia inducible factor) protein levels. Through mass spectrometric analysis of immunoprecipitated PFKFB4, we identified the ubiquitin E3 ligase, F-box only protein 28 (FBXO28), as a new interaction partner of PFKFB4. We show that PFKFB4 regulates the ubiquitylation and subsequent proteasomal degradation of HIF-1α, which is mediated by the ubiquitin ligase activity of FBXO28. This newly discovered function of PFKFB4, coupled with its cancer specificity, provides a new strategy for inhibiting HIF-1α in cancer cells.

A Set of Cell Lines Derived from a Genetic Murine Glioblastoma Model Recapitulates Molecular and Morphological Characteristics of Human Tumors.

Glioblastomas (GBM) are the most aggressive tumors affecting the central nervous system in adults, causing death within, on average, 15 months after diagnosis. Immunocompetent in-vivo models that closely mirror human GBM are urgently needed for deciphering glioma biology and for the development of effective treatment options. The murine GBM cell lines currently available for engraftment in immunocompetent mice are not only exiguous but also inadequate in representing prominent characteristics of human GBM such as infiltrative behavior, necrotic areas, and pronounced tumor heterogeneity. Therefore, we generated a set of glioblastoma cell lines by repeated in vivo passaging of cells isolated from a neural stem cell-specific Pten/p53 double-knockout genetic mouse brain tumor model. Transcriptome and genome analyses of the cell lines revealed molecular heterogeneity comparable to that observed in human glioblastoma. Upon orthotopic transplantation into syngeneic hosts, they formed high-grade gliomas that faithfully recapitulated the histopathological features, invasiveness and immune cell infiltration characteristic of human glioblastoma. These features make our cell lines unique and useful tools to study multiple aspects of glioblastoma pathomechanism and to test novel treatments in an intact immune microenvironment.

Master regulators of FGFR2 signalling and breast cancer risk.

The fibroblast growth factor receptor 2 (FGFR2) locus has been consistently identified as a breast cancer risk locus in independent genome-wide association studies. However, the molecular mechanisms underlying FGFR2-mediated risk are still unknown. Using model systems we show that FGFR2-regulated genes are preferentially linked to breast cancer risk loci in expression quantitative trait loci analysis, supporting the concept that risk genes cluster in pathways. Using a network derived from 2,000 transcriptional profiles we identify SPDEF, ERα, FOXA1, GATA3 and PTTG1 as master regulators of fibroblast growth factor receptor 2 signalling, and show that ERα occupancy responds to fibroblast growth factor receptor 2 signalling. Our results indicate that ERα, FOXA1 and GATA3 contribute to the regulation of breast cancer susceptibility genes, which is consistent with the effects of anti-oestrogen treatment in breast cancer prevention, and suggest that fibroblast growth factor receptor 2 signalling has an important role in mediating breast cancer risk.

RedeR: R/Bioconductor package for representing modular structures, nested networks and multiple levels of hierarchical associations.

Visualization and analysis of molecular networks are both central to systems biology. However, there still exists a large technological gap between them, especially when assessing multiple network levels or hierarchies. Here we present RedeR, an R/Bioconductor package combined with a Java core engine for representing modular networks. The functionality of RedeR is demonstrated in two different scenarios: hierarchical and modular organization in gene co-expression networks and nested structures in time-course gene expression subnetworks. Our results demonstrate RedeR as a new framework to deal with the multiple network levels that are inherent to complex biological systems. RedeR is available from http://bioconductor.org/packages/release/bioc/html/RedeR.html.