Patient-matched analysis identifies deregulated networks in prostate cancer to guide personalized therapeutic intervention

Prostate cancer (PrCa) is the second most common malignancy in men. More than 50% of advanced prostate cancers display the TMPRSS2-ERG fusion. Despite extensive cancer genome/transcriptome data, little is known about the impact of mutations and altered transcription on regulatory networks in the PrCa of individual patients. Using patient-matched normal and tumor samples, we established somatic variations and differential transcriptome profiles of primary ERG-positive prostate cancers. Integration of protein-protein interaction and gene-regulatory network databases defined highly diverse patient-specific network alterations. Different components of a given regulatory pathway were altered by novel and known mutations and/or aberrant gene expression, including deregulated ERG targets, and were validated by using a novel in silico methodology. Consequently, different sets of pathways were altered in each individual PrCa. In a given PrCa, several deregulated pathways share common factors, predicting synergistic effects on cancer progression. Our integrated analysis provides a paradigm to identify druggable key deregulated factors within regulatory networks to guide personalized therapies.

The NANOTUMOR consortium - Towards the Tumor Cell Atlas

Cancer is a multi-step disease where an initial tumour progresses through critical steps shaping, in most cases, life-threatening secondary foci called metastases. The oncogenic cascade involves genetic, epigenetic, signalling pathways, intracellular trafficking and/or metabolic alterations within cancer cells. In addition, pre-malignant and malignant cells orchestrate complex and dynamic interactions with non-malignant cells and acellular matricial components or secreted factors within the tumour microenvironment that is instrumental in the progression of the disease. As our aptitude to effectively treat cancer mostly depends on our ability to decipher, properly diagnose and impede cancer progression and metastasis formation, full characterisation of molecular complexes and cellular processes at play along the metastasis cascade is crucial. For many years, the scientific community lacked adapted imaging and molecular technologies to accurately dissect, at the highest resolution possible, tumour and stromal cells behaviour within their natural microenvironment. In that context, the NANOTUMOR consortium is a French national multi-disciplinary workforce which aims at a providing a multi-scale characterisation of the oncogenic cascade, from the atomic level to the dynamic organisation of the cell in response to genetic mutations, environmental changes or epigenetic modifications. Ultimately, this program aims at identifying new therapeutic targets using innovative drug design.

CDYL2 Epigenetically Regulates MIR124 to Control NF-κB/STAT3-Dependent Breast Cancer Cell Plasticity

Epigenetic deregulation of gene transcription is central to cancer cell plasticity and malignant progression but remains poorly understood. We found that the uncharacterized epigenetic factor chromodomain on Y-like 2 (CDYL2) is commonly over-expressed in breast cancer, and that high CDYL2 levels correlate with poor prognosis. Supporting a functional role for CDYL2 in malignancy, it positively regulated breast cancer cell migration, invasion, stem-like phenotypes, and epithelial-to-mesenchymal transition. CDYL2 regulation of these plasticity-associated processes depended on signaling via p65/NF-κB and STAT3. This, in turn, was downstream of CDYL2 regulation of MIR124 gene transcription. CDYL2 co-immunoprecipitated with G9a/EHMT2 and GLP/EHMT1 and regulated the chromatin enrichment of G9a and EZH2 at MIR124 genes. We propose that CDYL2 contributes to poor prognosis in breast cancer by recruiting G9a and EZH2 to epigenetically repress MIR124 genes, thereby promoting NF-κB and STAT3 signaling, as well as downstream cancer cell plasticity and malignant progression.

•

Up-regulation of CDYL2 is common in breast cancer and correlates with poor prognosis

•

CDYL2 regulates enrichment of methyltransferases G9a and EZH2 at MIR124 genes

•

microRNA-124 regulation by CDYL2 impacts STAT3 and NF-κB signaling

•

CDYL2 regulation of EMT, migration, invasion, and stemness is STAT3/NF-κB dependent

POLYPHEMUS: R package for comparative analysis of RNA polymerase II ChIP-seq profiles by non-linear normalization

Chromatin immunoprecipitation coupled with massive parallel sequencing (ChIP-seq) is increasingly used to map protein–chromatin interactions at global scale. The comparison of ChIP-seq profiles for RNA polymerase II (PolII) established in different biological contexts, such as specific developmental stages or specific time-points during cell differentiation, provides not only information about the presence/accumulation of PolII at transcription start sites (TSSs) but also about functional features of transcription, including PolII stalling, pausing and transcript elongation. However, annotation and normalization tools for comparative studies of multiple samples are currently missing. Here, we describe the R-package POLYPHEMUS, which integrates TSS annotation with PolII enrichment over TSSs and coding regions, and normalizes signal intensity profiles. Thereby POLYPHEMUS facilitates to extract information about global PolII action to reveal changes in the functional state of genes. We validated POLYPHEMUS using a kinetic study on retinoic acid-induced differentiation and a publicly available data set from a comparative PolII ChIP-seq profiling in Caenorhabditis elegans. We demonstrate that POLYPHEMUS corrects the data sets by normalizing for technical variation between samples and reveal the potential of the algorithm in comparing multiple data sets to infer features of transcription regulation from dynamic PolII binding profiles.

Dissecting the retinoid-induced differentiation of F9 embryonal stem cells by integrative genomics

Retinoic acid (RA) triggers physiological processes by activating heterodimeric transcription factors (TFs) comprising retinoic acid receptor (RARα, β, γ) and retinoid X receptor (RXRα, β, γ). How a single signal induces highly complex temporally controlled networks that ultimately orchestrate physiological processes is unclear. Using an RA-inducible differentiation model, we defined the temporal changes in the genome-wide binding patterns of RARγ and RXRα and correlated them with transcription regulation. Unexpectedly, both receptors displayed a highly dynamic binding, with different RXRα heterodimers targeting identical loci. Comparison of RARγ and RXRα co-binding at RA-regulated genes identified putative RXRα-RARγ target genes that were validated with subtype-selective agonists. Gene-regulatory decisions during differentiation were inferred from TF-target gene information and temporal gene expression. This analysis revealed six distinct co-expression paths of which RXRα-RARγ is associated with transcription activation, while Sox2 and Egr1 were predicted to regulate repression. Finally, RXRα-RARγ regulatory networks were reconstructed through integration of functional co-citations. Our analysis provides a dynamic view of RA signalling during cell differentiation, reveals RAR heterodimer dynamics and promiscuity, and predicts decisions that diversify the RA signal into distinct gene-regulatory programs.