Showing papers by "Garrick Wallstrom published in 2014"
TL;DR: 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.
Abstract: 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.
101 citations
TL;DR: The vaccine potential of MV are highly immunogenic without adjuvants and elicit immune responses comparable to those achieved with BCG in protection against M. tuberculosis, and establish a new type of vaccine formulation.
Abstract: Pathogenic and nonpathogenic species of bacteria and fungi release membrane vesicles (MV), containing proteins, polysaccharides, and lipids, into the extracellular milieu. Previously, we demonstrated that several mycobacterial species, including bacillus Calmette-Guerin (BCG) and Mycobacterium tuberculosis, release MV containing lipids and proteins that subvert host immune response in a Toll-like receptor 2 (TLR2)-dependent manner (R. Prados-Rosales et al., J. Clin. Invest. 121:1471–1483, 2011, doi:10.1172/JCI44261). In this work, we analyzed the vaccine potential of MV in a mouse model and compared the effects of immunization with MV to those of standard BCG vaccination. Immunization with MV from BCG or M. tuberculosis elicited a mixed humoral and cellular response directed to both membrane and cell wall components, such as lipoproteins. However, only vaccination with M. tuberculosis MV was able to protect as well as live BCG immunization. M. tuberculosis MV boosted BCG vaccine efficacy. In summary, MV are highly immunogenic without adjuvants and elicit immune responses comparable to those achieved with BCG in protection against M. tuberculosis. IMPORTANCE This work offers a new vaccine approach against tuberculosis using mycobacterial MV. Mycobacterium MV are a naturally released product combining immunogenic antigens in the context of a lipid structure. The fact that MV do not need adjuvants and elicit protection comparable to that elicited by the BCG vaccine encourages vaccine approaches that combine protein antigens and lipids. Consequently, mycobacterium MV establish a new type of vaccine formulation.
87 citations
07 Apr 2014
TL;DR: These efforts to determine the proportion of GenBank records with “insufficient” geographic metadata for seven well-studied viruses are described and the performance of four different Named Entity Recognition systems for automatically extracting related entities using a manually created gold-standard is evaluated.
Abstract: Zoonotic viruses represent emerging or re-emerging pathogens that pose significant public health threats throughout the world It is therefore crucial to advance current surveillance mechanisms for these viruses through outlets such as phylogeography Despite the abundance of zoonotic viral sequence data in publicly available databases such as GenBank, phylogeographic analysis of these viruses is often limited by the lack of adequate geographic metadata However, many GenBank records include references to articles with more detailed information and automated systems may help extract this information efficiently and effectively In this paper, we describe our efforts to determine the proportion of GenBank records with "insufficient" geographic metadata for seven well-studied viruses We also evaluate the performance of four different Named Entity Recognition (NER) systems for automatically extracting related entities using a manually created gold-standard
14 citations
Patent•
30 Jan 2014TL;DR: In this article, protein microarrays displaying full-length candidate antigens were developed and sequentially screening to select candidate autoantibody biomarkers to limit the false discovery rate inherent to large-scale proteomic screening.
Abstract: Methods for identifying antigens as potential biomarkers for the early detection of ovarian cancer, as well as kits for utilizing said antigens as biomarkers and in personalized medicine/therapeutics assessment. Protein microarrays displaying full-length candidate antigens were developed and sequentially screening to select candidate autoantibody biomarkers to limit the false discovery rate inherent to large-scale proteomic screening.
6 citations
01 Jun 2014
TL;DR: The authors proposed an automated system for extracting this information using Natural Language Processing (NLP) methods and evaluated four different named entity recognition (NER) systems which may help in the automatic extraction of information from related articles that can be used to improve the GenBank geographic metadata.
Abstract: Zoonotic viruses, viruses that are transmittable between animals and humans, represent emerging or re-emerging pathogens that pose significant public health threats throughout the world. It is therefore crucial to advance current surveillance mechanisms for these viruses through outlets such as phylogeography. Phylogeographic techniques may be applied to trace the origins and geographical distribution of these viruses using sequence and location data, which are often obtained from publicly available databases such as GenBank. Despite the abundance of zoonotic viral sequence data in GenBank records, phylogeographic analysis of these viruses is greatly limited by the lack of adequate geographic metadata. Although more detailed information may often be found in the related articles referenced in these records, manual extraction of this information presents a severe bottleneck. In this work, we propose an automated system for extracting this information using Natural Language Processing (NLP) methods. In order to validate the need for such a system, we first determine the percentage of GenBank records with “insufficient” geographic metadata for seven well-studied zoonotic viruses. We then evaluate four different named entity recognition (NER) systems which may help in the automatic extraction of information from related articles that can be used to improve the GenBank geographic metadata. This includes a novel dictionary-based location tagging system that we introduce in this paper.
2 citations
TL;DR: This work describes the first immunoproteomic screen for AAb biomarkers of BLBC, a rare aggressive subtype of breast cancer with high recurrence rates and poor prognosis, using sero-proteomic technology, called Nucleic Acid Programmable Protein Arrays (NAPPA), which tests the immune responses to thousands of proteins simultaneously.
Abstract: Background: Basal-like breast cancer (BLBC) is a rare aggressive subtype of breast cancer (∼10-20% of all BC) with high recurrence rates and poor prognosis. BLBC disproportionately affects African American and younger women, has a high growth rate and occurs at young ages. These factors render mammographic screening challenging, necessitating the development of new methods for BLBC9s early detection. Proteins released from small early tumors may only be present intermittently and get diluted to tiny concentrations in the blood, making them difficult to use as biomarkers. However, they can induce an autoantibody (AAb) response, which can amplify the signal and persist in the blood even if the antigen is gone. Accordingly, we assessed whether circulating AAbs could act as early detection biomarkers of BLBC. We have developed a sero-proteomic technology, called Nucleic Acid Programmable Protein Arrays (NAPPA), which tests the immune responses to thousands of proteins simultaneously. Results: Plasma samples were from the Polish Breast Cancer Study. BLBC patients were classified by immunohistochemical staining of ER-, PR-, HER2-, and either EGFR+ or CK5/6+. Case and control plasma were probed against ∼10,000 full-length human proteins on NAPPA to discover informative antigens, which were selected based on the following statistical metrics: 1) sensitivity at 95% specificity; 2) area under receiver operating characteristic curve (AUC); 3) partial AUC above 95% specificity; 4) Welch9s t test. We then identified candidate AAbs in a training study combining the 800 top antigens on a single array. These arrays were screened with an independent training set of plasma from 50 each of BLBC cases and healthy controls, and 30 other BC cases. Preliminary analyses suggest 5 candidate biomarkers with sensitivities ranging from 26-32% at 94% specificity. The antigen list will be refined using AUC, and the sensitivity and partial AUC at 95%, 90% and 80% specificity levels. Permutation-based false discovery rates will be used to assess statistical significance, and the antigens will be combined into a biomarker panel using a random forests algorithm. Finally, we will test the individual markers and the antigen panel in a blinded independent validation set of 50 cases, 50 controls and 30 other BC cases. These tests will use cutoffs and algorithm determined entirely from the training data. We are assessing whether the candidate biomarkers are specific for cancer vs. controls and BLBC vs. other molecular subtypes of breast cancer. Summary: This describes the first immunoproteomic screen for AAb biomarkers of BLBC. Using protein microarrays displaying more than 10,000 folded unique human proteins, controls, BLBC and non-BLBC cases were tested in independent discovery and training stages identifying AAbs with potential diagnostic value. Blinded validation studies are in progress. Citation Format: Jie Wang, Jonine D. Figueroa, Garrick Wallstrom, Joshua Sampson, Eliseo Mendoza Garcia, Jason Steel, Jin Park, Karen S. Anderson, Louise Brinton, Montserrat Garcia-Closas, Jolanta Lissowska, Mark E. Sherman, Ji Qiu, Joshua LaBaer. Autoantibody biomarker discovery in basal-like breast cancer using nucleic acid programmable protein array. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 874. doi:10.1158/1538-7445.AM2014-874
1 citations