The lncRNA landscape of breast cancer reveals a role for DSCAM-AS1 in breast cancer progression.

Molecular classification of cancers into subtypes has resulted in an advance in our understanding of tumour biology and treatment response across multiple tumour types. However, to date, cancer profiling has largely focused on protein-coding genes, which comprise <1% of the genome. Here we leverage a compendium of 58,648 long noncoding RNAs (lncRNAs) to subtype 947 breast cancer samples. We show that lncRNA-based profiling categorizes breast tumours by their known molecular subtypes in breast cancer. We identify a cohort of breast cancer-associated and oestrogen-regulated lncRNAs, and investigate the role of the top prioritized oestrogen receptor (ER)-regulated lncRNA, DSCAM-AS1. We demonstrate that DSCAM-AS1 mediates tumour progression and tamoxifen resistance and identify hnRNPL as an interacting protein involved in the mechanism of DSCAM-AS1 action. By highlighting the role of DSCAM-AS1 in breast cancer biology and treatment resistance, this study provides insight into the potential clinical implications of lncRNAs in breast cancer

Cell Dispersal Influences Tumor Heterogeneity and Introduces a Bias in NGS Data Interpretation

Short and long distance cell dispersal can have a marked effect on tumor structure, high cellular motility could lead to faster cell mixing and lower observable intratumor heterogeneity. Here we evaluated a model for cell mixing that investigates how short-range dispersal and cell turnover will account for mutational proportions. We show that cancer cells can penetrate neighboring and distinct areas in a matter of days. In next generation sequencing runs, higher proportions of a given cell line generated frequencies with higher precision, while mixtures with lower amounts of each cell line had lower precision manifesting in higher standard deviations. When multiple cell lines were co-cultured, cellular movement altered observed mutation frequency by up to 18.5%. We propose that some of the shared mutations detected at low allele frequencies represent highly motile clones that appear in multiple regions of a tumor owing to dispersion throughout the tumor. In brief, cell movement will lead to a significant technical (sampling) bias when using next generation sequencing to determine clonal composition. A possible solution to this drawback would be to radically decrease detection thresholds and increase coverage in NGS analyses. © 2017 The Author(s)

Mapping genetic variations to three- dimensional protein structures to enhance variant interpretation: a proposed framework

The translation of personal genomics to precision medicine depends on the accurate interpretation of the multitude of genetic variants observed for each individual. However, even when genetic variants are predicted to modify a protein, their functional implications may be unclear. Many diseases are caused by genetic variants affecting important protein features, such as enzyme active sites or interaction interfaces. The scientific community has catalogued millions of genetic variants in genomic databases and thousands of protein structures in the Protein Data Bank. Mapping mutations onto three-dimensional (3D) structures enables atomic-level analyses of protein positions that may be important for the stability or formation of interactions; these may explain the effect of mutations and in some cases even open a path for targeted drug development. To accelerate progress in the integration of these data types, we held a two-day Gene Variation to 3D (GVto3D) workshop to report on the latest advances and to discuss unmet needs. The overarching goal of the workshop was to address the question: what can be done together as a community to advance the integration of genetic variants and 3D protein structures that could not be done by a single investigator or laboratory? Here we describe the workshop outcomes, review the state of the field, and propose the development of a framework with which to promote progress in this arena. The framework will include a set of standard formats, common ontologies, a common application programming interface to enable interoperation of the resources, and a Tool Registry to make it easy to find and apply the tools to specific analysis problems. Interoperability will enable integration of diverse data sources and tools and collaborative development of variant effect prediction methods

Evolution of sexually dimorphic pheromone profiles coincides with increased number of male‐specific chemosensory organs in Drosophila prolongata

All raw data will be accessible on Dryad with the accession https://doi.org/10.25338/B86K7V. PMCID: PMC6912897International audienceBinary communication systems that involve sex-specific signaling and sex-specific signal perception play a key role in sexual selection and in the evolution of sexually dimorphic traits. The driving forces and genetic changes underlying such traits can be investigated in systems where sex-specific signaling and perception have emerged recently and show evidence of potential coevolution. A promising model is found in Drosophila prolongata, which exhibits a species-specific increase in the number of male chemosensory bristles. We show that this transition coincides with recent evolutionary changes in cuticular hydrocarbon (CHC) profiles. Long-chain CHCs that are sexually monomorphic in the closest relatives of D. prolongata (D. rhopaloa, D. carrolli, D. kurseongensis, and D. fuyamai) are strongly male-biased in this species. We also identify an intraspecific female-limited polymorphism, where some females have male-like CHC profiles. Both the origin of sexually dimorphic CHC profiles and the female-limited polymorphism in D. prolongata involve changes in the relative amounts of three mono-alkene homologs, 9-tricosene, 9-pentacosene, and 9-heptacosene, all of which share a common biosynthetic origin and point to a potentially simple genetic change underlying these traits. Our results suggest that pheromone synthesis may have coevolved with chemosensory perception and open the way for reconstructing the origin of sexual dimorphism in this communication system

The lncRNA PCAT29 Inhibits Oncogenic Phenotypes in Prostate Cancer

UnlabelledLong noncoding RNAs (lncRNA) have recently been associated with the development and progression of a variety of human cancers. However, to date, the interplay between known oncogenic or tumor-suppressive events and lncRNAs has not been well described. Here, the novel lncRNA, prostate cancer-associated transcript 29 (PCAT29), is characterized along with its relationship to the androgen receptor. PCAT29 is suppressed by DHT and upregulated upon castration therapy in a prostate cancer xenograft model. PCAT29 knockdown significantly increased proliferation and migration of prostate cancer cells, whereas PCAT29 overexpression conferred the opposite effect and suppressed growth and metastases of prostate tumors in chick chorioallantoic membrane assays. Finally, in prostate cancer patient specimens, low PCAT29 expression correlated with poor prognostic outcomes. Taken together, these data expose PCAT29 as an androgen-regulated tumor suppressor in prostate cancer.ImplicationsThis study identifies PCAT29 as the first androgen receptor-repressed lncRNA that functions as a tumor suppressor and that its loss may identify a subset of patients at higher risk for disease recurrence. Visual Overview: http://mcr.aacrjournals.org/content/early/2014/07/31/1541-7786.MCR-14-0257/F1.large.jpg

Multigene profiling of CTCs in mCRPC identifies a clinically relevant prognostic signature

The trend toward precision-based therapeutic approaches dictated by molecular alterations offers substantial promise for men with metastatic castration-resistant prostate cancer (mCRPC). However, current approaches for molecular characterization are primarily tissue based, necessitating serial biopsies to understand changes over time and are limited by the challenges inherent to extracting genomic material from predominantly bone metastases. Therefore, a circulating tumor cell (CTC)-based assay was developed to determine gene expression across a panel of clinically relevant and potentially actionable prostate cancer-related genes. CTCs were isolated from the whole blood of mCRPC patients (n = 41) and multiplex qPCR was performed to evaluate expression of prostate cancer-related target genes (n = 78). A large fraction of patients (27/41, 66%) had detectable CTCs. Increased androgen receptor (AR) expression (70% of samples) and evidence of Wnt signaling (67% of samples) were observed. The TMPRSS2:ERG fusion was expressed in 41% of samples, and the aggressive prostate cancer-associated long noncoding RNA SChLAP1 was upregulated in 70%. WNT5a [HR 3.62, 95% confidence interval (CI), 1.63-8.05, P = 0.002], AURKA (HR 5.56, 95% CI, 1.79-17.20, P = 0.003), and BMP7 (HR 3.86, 95% CI, 1.60-9.32, P = 0.003) were independently predictive of overall survival (FDR < 10%) after adjusting for a panel of previously established prognostic variables in mCRPC (Halabi nomogram). A model including Halabi, WNT5a, and AURKA expression, termed the miCTC score, outperformed the Halabi nomogram alone (AUC = 0.89 vs. AUC = 0.70). Understanding the molecular landscape of CTCs has utility in predicting clinical outcomes in patients with aggressive prostate cancer and provides an additional tool in the arsenal of precision-based therapeutic approaches in oncology.Implications: Analysis of CTC gene expression reveals a clinically prognostic "liquid biopsy" signature in patients with metastatic castrate-resistance prostate cancer. Mol Cancer Res; 16(4); 643-54. ©2018 AACR