The brain is a common site of metastatic disease in patients with breast cancer, which has few therapeutic options and dismal outcomes. The purpose of our study was to identify common and rare events that underlie breast cancer brain metastasis. We performed deep genomic profiling, which integrated gene copy number, gene expression and DNA methylation datasets on a collection of breast brain metastases. We identified frequent large chromosomal gains in 1q, 5p, 8q, 11q, and 20q and frequent broad-level deletions involving 8p, 17p, 21p and Xq. Frequently amplified and overexpressed genes included ATAD2, BRAF, DERL1, DNMTRB and NEK2A. The ATM, CRYAB and HSPB2 genes were commonly deleted and underexpressed. Knowledge mining revealed enrichment in cell cycle and G2/M transition pathways, which contained AURKA, AURKB and FOXM1. Using the PAM50 breast cancer intrinsic classifier, Luminal B, Her2+/ER negative, and basal-like tumors were identified as the most commonly represented breast cancer subtypes in our brain metastasis cohort. While overall methylation levels were increased in breast cancer brain metastasis, basal-like brain metastases were associated with significantly lower levels of methylation. Integrating DNA methylation data with gene expression revealed defects in cell migration and adhesion due to hypermethylation and downregulation of PENK, EDN3, and ITGAM. Hypomethylation and upregulation of KRT8 likely affects adhesion and permeability. Genomic and epigenomic profiling of breast brain metastasis has provided insight into the somatic events underlying this disease, which have potential in forming the basis of future therapeutic strategies.
Sarcomas are cancers that arise in soft tissues or bone and make up a small percentage of malignancies. In an effort to identify potential genetic targets for therapy, this study explores the genomic landscape of a metastatic undifferentiated pleomorphic sarcoma (UPS) with spindle cell morphology. Thick sections (50 µm) of formalin-fixed, paraffin-embedded tissue from a primary, recurrent, and metastatic tumor were collected and processed from a single patient for DNA content-based flow-sorting and analyses. Nuclei of diploid and aneuploid populations were sorted from the malignant tissues and their genomes interrogated with array comparative genomic hybridization. The third sample was highly degraded and did not contain any intact ploidy peaks in our flow assays. A 2.5N aneuploid population was identified in the primary and recurrent sample. We detected a series of shared and unique genomic aberrations in the sorted aneuploid populations. The patterns of aberrations suggest that two similar but independent clonal populations arose during the clinical history of this rare tumor. None of these aberrations were detected in the matching sorted diploid samples. The targeted regions of interest might play a role in UPS and may lead to clinical significance with further investigation.
Identifying similarities and differences in the molecular constitutions of various types of cancer is one of the key challenges in cancer research. The appearances of a cancer depend on complex molecular interactions, including gene regulatory networks and gene-environment interactions. This complexity makes it challenging to decipher the molecular origin of the cancer. In recent years, many studies reported methods to uncover heterogeneous depictions of complex cancers, which are often categorized into different subtypes. The challenge is to identify diverse molecular contexts within a cancer, to relate them to different subtypes, and to learn underlying molecular interactions specific to molecular contexts so that we can recommend context-specific treatment to patients.
Diabetic nephropathy is the most common cause of chronic kidney failure and end-stage renal disease in the Western World. One of the major characteristics of this disease is the excessive accumulation of extracellular matrix (ECM) in the kidney glomeruli. While both environmental and genetic determinants are recognized for their role in the development of diabetic nephropathy, epigenetic factors, such as DNA methylation, long non-coding RNAs, and microRNAs, have also recently been found to underlie some of the biological mechanisms, including ECM accumulation, leading to the disease. We previously found that a long non-coding RNA, the plasmacytoma variant translocation 1 (PVT1), increases plasminogen activator inhibitor 1 (PAI-1) and transforming growth factor beta 1 (TGF-?1) in mesangial cells, the two main contributors to ECM accumulation in the glomeruli under hyperglycemic conditions, as well as fibronectin 1 (FN1), a major ECM component. Here, we report that miR-1207-5p, a PVT1-derived microRNA, is abundantly expressed in kidney cells, and is upregulated by glucose and TGF-?1. We also found that like PVT1, miR-1207-5p increases expression of TGF-?1, PAI-1, and FN1 but in a manner that is independent of its host gene. In addition, regulation of miR-1207-5p expression by glucose and TGF?1 is independent of PVT1. These results provide evidence supporting important roles for miR-1207-5p and its host gene in the complex pathogenesis of diabetic nephropathy.
New anticancer agents that target a single cell surface receptor, up-regulated or amplified gene product, or mutated gene, have met with some success in treating advanced cancers. However, patients tumors still eventually progress on these therapies. If it were possible to identify a larger number of targetable vulnerabilities in an individuals tumor, multiple targets could be exploited with the use of specific therapeutic agents, thus possibly giving the patient viable therapeutic alternatives.
Aurora kinases are a family of mitotic kinases that play important roles in the tumorigenesis of a variety of cancers including pancreatic cancer. A number of Aurora kinase inhibitors (AKIs) are currently being tested in preclinical and clinical settings as anti-cancer therapies. However, the antitumor activity of AKIs in clinical trials has been modest. In order to improve the antitumor activity of AKIs in pancreatic cancer, we utilized a kinome focused RNAi screen to identify genes that, when silenced, would sensitize pancreatic cancer cells to AKI treatment. A total of 17 kinase genes were identified and confirmed as positive hits. One of the hits was the platelet-derived growth factor receptor, alpha polypeptide (PDGFRA), which has been shown to be overexpressed in pancreatic cancer cells and tumor tissues. Imatinib, a PDGFR inhibitor, significantly enhanced the anti-proliferative effect of ZM447439, an Aurora B specific inhibitor, and PHA-739358, a pan-Aurora kinase inhibitor. Further studies showed that imatinib augmented the induction of G2/M cell cycle arrest and apoptosis by PHA-739358. These findings indicate that PDGFRA is a potential mediator of AKI sensitivity in pancreatic cancer cells.
High-throughput RNA interference (HT-RNAi) is a powerful research tool for parallel, genome-wide, targeted knockdown of specific gene products. Such perturbation of gene product expression allows for the systematic query of gene function. The phenotypic results can be monitored by assaying for specific alterations in molecular and cellular endpoints, such as promoter activation, cell proliferation and survival. RNAi profiling may also be coupled with drug screening to identify molecular correlates of drug response. As with other genomic-scale data, methods of data analysis are required to handle the unique aspects of data normalization and statistical processing. In addition, novel techniques or knowledge-mining strategies are required to extract useful biological information from HT-RNAi data. Knowledge-mining strategies involve the novel application of bioinformatic tools and expert curation to provide biological context to genomic-scale data such as that generated from HT-RNAi data. Pathway-based tools, whether text-mining based or manually curated, serve an essential role in knowledge mining. These tools can be applied during all steps of HT-RNAi screen experiments including pre-screen knowledge gathering, assay development and hit confirmation and validation. Most importantly, pathway tools allow the interrogation of HT-RNAi data to identify and prioritize pathway-based biological information as a result of specific loss of gene function.
Breakthroughs in molecular profiling technologies are enabling a new data-intensive approach to biomedical research, with the potential to revolutionize how we study, manage, and treat complex diseases. The next great challenge for clinical applications of these innovations will be to create scalable computational solutions for intelligently linking complex biomedical patient data to clinically actionable knowledge. Traditional database management systems (DBMS) are not well suited to representing complex syntactic and semantic relationships in unstructured biomedical information, introducing barriers to realizing such solutions. We propose a scalable computational framework for addressing this need, which leverages a hypergraph-based data model and query language that may be better suited for representing complex multi-lateral, multi-scalar, and multi-dimensional relationships. We also discuss how this framework can be used to create rapid learning knowledge base systems to intelligently capture and relate complex patient data to biomedical knowledge in order to automate the recovery of clinically actionable information.
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