We present Model-based Analysis of ChIP-Seq data, MACS, which analyzes data generated by short read sequencers such as Solexa's Genome Analyzer. MACS empirically models the shift size of ChIP-Seq tags, and uses it to improve the spatial resolution of predicted binding sites. MACS also uses a dynamic Poisson distribution to effectively capture local biases in the genome, allowing for more robust predictions. MACS compares favorably to existing ChIP-Seq peak-finding algorithms, and is freely available.

/pdf/model-based-analysis-of-chip-seq-macs-11m6g885d3.pdf

Model-based Analysis of ChIP-Seq (MACS)

Ferroptosis: An Iron-Dependent Form of Nonapoptotic Cell Death

March 5, 2019 e1 WRITING GROUP MEMBERS Emelia J. Benjamin, MD, ScM, FAHA, Chair Paul Muntner, PhD, MHS, FAHA, Vice Chair Alvaro Alonso, MD, PhD, FAHA Marcio S. Bittencourt, MD, PhD, MPH Clifton W. Callaway, MD, FAHA April P. Carson, PhD, MSPH, FAHA Alanna M. Chamberlain, PhD Alexander R. Chang, MD, MS Susan Cheng, MD, MMSc, MPH, FAHA Sandeep R. Das, MD, MPH, MBA, FAHA Francesca N. Delling, MD, MPH Luc Djousse, MD, ScD, MPH Mitchell S.V. Elkind, MD, MS, FAHA Jane F. Ferguson, PhD, FAHA Myriam Fornage, PhD, FAHA Lori Chaffin Jordan, MD, PhD, FAHA Sadiya S. Khan, MD, MSc Brett M. Kissela, MD, MS Kristen L. Knutson, PhD Tak W. Kwan, MD, FAHA Daniel T. Lackland, DrPH, FAHA Tené T. Lewis, PhD Judith H. Lichtman, PhD, MPH, FAHA Chris T. Longenecker, MD Matthew Shane Loop, PhD Pamela L. Lutsey, PhD, MPH, FAHA Seth S. Martin, MD, MHS, FAHA Kunihiro Matsushita, MD, PhD, FAHA Andrew E. Moran, MD, MPH, FAHA Michael E. Mussolino, PhD, FAHA Martin O’Flaherty, MD, MSc, PhD Ambarish Pandey, MD, MSCS Amanda M. Perak, MD, MS Wayne D. Rosamond, PhD, MS, FAHA Gregory A. Roth, MD, MPH, FAHA Uchechukwu K.A. Sampson, MD, MBA, MPH, FAHA Gary M. Satou, MD, FAHA Emily B. Schroeder, MD, PhD, FAHA Svati H. Shah, MD, MHS, FAHA Nicole L. Spartano, PhD Andrew Stokes, PhD David L. Tirschwell, MD, MS, MSc, FAHA Connie W. Tsao, MD, MPH, Vice Chair Elect Mintu P. Turakhia, MD, MAS, FAHA Lisa B. VanWagner, MD, MSc, FAST John T. Wilkins, MD, MS, FAHA Sally S. Wong, PhD, RD, CDN, FAHA Salim S. Virani, MD, PhD, FAHA, Chair Elect On behalf of the American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee

Heart Disease and Stroke Statistics—2019 Update: A Report From the American Heart Association

Each chapter listed in the Table of Contents (see next page) is a hyperlink to that chapter. The reader clicks the chapter name to access that chapter.

Each chapter listed here is a hyperlink. Click on the chapter name to be taken to that chapter. 

Each year, the American Heart Association (AHA), in conjunction with the Centers for Disease Control and Prevention, the National Institutes of Health, and other government agencies, brings together in a single document the most up-to-date statistics related to heart disease, stroke, and the cardiovascular risk factors listed in the AHA’s My Life Check - Life’s Simple 7 (Figure1), which include core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure [BP], and glucose control) that contribute to cardiovascular health. The Statistical Update represents …

Heart Disease and Stroke Statistics-2018 Update: A Report From the American Heart Association.

We report the generation and analysis of functional data from multiple, diverse experiments performed on a targeted 1% of the human genome as part of the pilot phase of the ENCODE Project. These data have been further integrated and augmented by a number of evolutionary and computational analyses. Together, our results advance the collective knowledge about human genome function in several major areas. First, our studies provide convincing evidence that the genome is pervasively transcribed, such that the majority of its bases can be found in primary transcripts, including non-protein-coding transcripts, and those that extensively overlap one another. Second, systematic examination of transcriptional regulation has yielded new understanding about transcription start sites, including their relationship to specific regulatory sequences and features of chromatin accessibility and histone modification. Third, a more sophisticated view of chromatin structure has emerged, including its inter-relationship with DNA replication and transcriptional regulation. Finally, integration of these new sources of information, in particular with respect to mammalian evolution based on inter- and intra-species sequence comparisons, has yielded new mechanistic and evolutionary insights concerning the functional landscape of the human genome. Together, these studies are defining a path for pursuit of a more comprehensive characterization of human genome function.

/pdf/identification-and-analysis-of-functional-elements-in-1-of-2o640l8ikl.pdf

Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project

Genomic Predictors of Infield and Marginal Failure for Glioblastoma Treated With Concurrent Radiation Therapy and Temozolomide: A Step Towards Personalized Radiation Fields?

Liquid biopsies have become an integral part of cancer management as minimally invasive options to detect molecular and genetic changes. However, current options show poor sensitivity in peritoneal carcinomatosis (PC). Novel exosome-based liquid biopsies may provide critical information on these challenging tumors. In this initial feasibility analysis, we identified an exosome gene signature of 445 genes (ExoSig445) from colon cancer patients, including those with PC, that is distinct from healthy controls.Plasma exosomes from 42 patients with metastatic and non-metastatic colon cancer and 10 healthy controls were isolated and verified. RNAseq analysis of exosomal RNA was performed and differentially expressed genes (DEGs) were identified by the DESeq2 algorithm. The ability of RNA transcripts to discriminate control and cancer cases was assessed by principal component analysis (PCA) and Bayesian compound covariate predictor classification. An exosomal gene signature was compared with tumor expression profiles of The Cancer Genome Atlas.Unsupervised PCA using exosomal genes with greatest expression variance showed stark separation between controls and patient samples. Using separate training and test sets, gene classifiers were constructed capable of discriminating control and patient samples with 100% accuracy. Using a stringent statistical threshold, 445 DEGs fully delineated control from cancer samples. Furthermore, 58 of these exosomal DEGs were found to be overexpressed in colon tumors.Plasma exosomal RNAs can robustly discriminate colon cancer patients, including patients with PC, from healthy controls. ExoSig445 can potentially be developed as a highly sensitive liquid biopsy test in colon cancer. 

/pdf/plasma-exosome-gene-signature-differentiates-colon-cancer-29101d7p.pdf

Plasma Exosome Gene Signature Differentiates Colon Cancer from Healthy Controls

AACR Annual Meeting-- Apr 12-16, 2008; San Diego, CA

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RAB11FIP1 is one of several potential oncogenes located in the chromosome 8p11 amplicon which is amplified in 10-15% of human breast cancers (1, 2). It is over-expressed in a panel of breast cancer cell lines but not in MCF10A, a non-tumorigenic human breast epithelial cell line. However, its functions and mechanisms of action are not well characterized. We knocked down RAB11FIP1 with siRNA in a panel of cancer cell lines and observed significantly reduced cell viability. Data from cell-based assays suggest that this reduction in cell viability is caused by 2 mechanisms: increased apoptosis and cell cycle arrest at G2. Increased apoptosis was demonstrated by increased activation of Caspase 3, FOXO3a, PARP cleavage and nuclear condensation across a panel of breast and colon cancer cell lines. Knockdown of RAB11FIP1 also made cells more susceptible to SAHA-induced apoptosis, requiring a lower dose than wild-type cells. In addition, we observed increased activation of p53 and p21 in cells where RAB11FIP1 was knocked down, resulting in G2 arrest. Treatment with SAHA enhanced the p53 and p21 activation in a dose-dependent manner. While wild-type p53 was required for cell cycle arrest induced by RAB11FIP1 knock-down, it was not required for apoptosis induced by RAB11FIP1 knock-down, although presence of wild-type p53 enhanced the apoptotic response. In this paper, we will describe the pathways modulated by RAB11FIP1 and provide evidence supporting its candidacy as an oncogene. References: 1. Yang, Z. Q., Streicher, K. L., Ray, M. E., Abrams, J., and Ethier, S. P. Multiple interacting oncogenes on the 8p11-p12 amplicon in human breast cancer. Cancer Res, 66: 11632-11643, 2006. 2. Garcia, M. J., Pole, J. C., Chin, S. F., Teschendorff, A., Naderi, A., Ozdag, H., Vias, M., Kranjac, T., Subkhankulova, T., Paish, C., Ellis, I., Brenton, J. D., Edwards, P. A., and Caldas, C. A 1 Mb minimal amplicon at 8p11-12 in breast cancer identifies new candidate oncogenes. Oncogene, 24: 5235-5245, 2005.

Cell-based assays suggest role for RAB11FIP1 in cell cycle and apoptosis

DNA microarrays are ordered collections of tens to hundreds of thousands of DNA ‘probe’ sequences anchored to a solid substrate and capable of binding to, and thus detecting the presence of cDNA or cRNA ‘target’ sequences. Since their inception in the mid 1990’s, microarrays have evolved considerably. Originally developed to measure gene expression, they have since diverged into a variety of formats with a range of applications spanning comparative genomic hybridization [1], high through-put cell transfection [2] and even protein-protein interactions [3]. The most widely used array type is the expression microarray for measuring relative transcript levels [4]. The most popular formats of the expression array are the ‘spotted’ cDNA or long-oligonucleotide varieties (e.g., Amersham CodeLinkTM Bioarrays, Agilent inkjet-based arrays) and the ‘in situ’ oligonucleotide arrays (e.g., the Affymetrix GeneChip ) where the oligo probes are synthesized in situ. Both platforms estimate relative transcript levels from signal intensity measurements taken from individual probe spots where fluorescentlytagged target sequences have annealed [4]. Both are suited to rapid parallel analysis of multiple experimental samples, and are generally comparable in performance. Expression arrays are widely used to identify differentially expressed genes, uncover gene clusters with coordinated expression, and classify experimental samples according to their gene expression profiles in an effort to characterize and compare cellular states. Cancer

Expression profiling and breast cancer biology.

Management guidelines for peritoneal dissemination of appendiceal and other gastrointestinal malignancies are limited by the paucity of strong evidence from robustly designed randomized clinical trials. Cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC) are a treatment approach considered increasingly at centers with experience. The criteria for selection of patients who would benefit from this aggressive surgical intervention are based on consideration of clinicopathologic factors such as tumor grade and histologic subtype, surgeons’ experience, and patients’ general surgical candidacy. The heterogeneity of outcomes, however, reflects the limitations of conventional decision-making approaches. This decision-making is even more challenging when complete cytoreduction is unlikely or not feasible. Nevertheless, CRS/HIPEC still may be offered to select patients because the retrospective evidence supports long-term survival for a reasonable number of patients. In recent years, the authors’ group and others have tried to unravel the molecular landscape of appendiceal cancer to understand the genetic pathogenesis of the disease and identify genomic ‘‘signatures’’ as tools with prognostic and therapeutic implications at a personalized level. In a series of studies, the authors have reported the first gene expression-profiling of appendiceal cancer and its distinction from peritoneal dissemination of colorectal cancer, have identified 139 genes with prognostic values, and accordingly, have identified distinct molecular subtypes with prognostic values. They have identified an immune-expressing molecular subtype enriched with high expression of immune pathway markers and demonstrating a better prognosis than an oncogene-expressing subtype with a worse prognosis.

Lance D. Miller

Papers

Genomic Predictors of Infield and Marginal Failure for Glioblastoma Treated With Concurrent Radiation Therapy and Temozolomide: A Step Towards Personalized Radiation Fields?

Plasma Exosome Gene Signature Differentiates Colon Cancer from Healthy Controls

Cell-based assays suggest role for RAB11FIP1 in cell cycle and apoptosis

Expression profiling and breast cancer biology.

ASO Author Reflections: Molecular Profiling Can Provide Personalized Clinical Guidance in the Management of Peritoneal Malignancies.