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Archive for November, 2012

struktura infliximabu

struktura infliximabu (Photo credit: Wikipedia)

Larry H Bernstein, MD, FCAP, Reporter

GI Disease, inflammation, elastase-inhibitor, membrane junctions and fatty acids

Sci Transl Med 2012; 4(158): 158ra144

Sci. Transl. Med. DOI: 10.1126/scitranslmed.3004212

RESEARCH ARTICLE
INFLAMMATORY BOWEL DISEASES
Food-Grade Bacteria Expressing Elafin Protect Against Inflammation and Restore Colon Homeostasis
Jean-Paul Motta1,2,3,*, Luis G. Bermúdez-Humarán4,*, Céline Deraison1,2,3, Laurence Martin1,2,3, Corinne Rolland1,2,3, Perrine Rousset1,2,3, Jérôme Boue1,2,3, Gilles Dietrich1,2,3, Kevin Chapman5, Pascale Kharrat4, Jean-Pierre Vinel3,6, Laurent Alric3,6, Emmanuel Mas1,2,3,7, Jean-Michel Sallenave8,9,10, Philippe Langella4,* and Nathalie Vergnolle1,2,3,5,†

1INSERM, U1043, Centre de Physiopathologie de Toulouse Purpan (CPTP), Toulouse F-31300, France.
2CNRS, U5282, Toulouse F-31300, France.
3CPTP, Université de Toulouse, Université Paul Sabatier (UPS), Toulouse F-31300, France.
4Institut National de la Recherche Agronomique (INRA), UMR 1319 Micalis, Commensal and Probiotics-Host Interactions Laboratory, Domaine de Vilvert, 78352 Jouy-en-Josas Cedex, France.
5Department of Physiology and Pharmacology, Faculty of Medicine, University of Calgary, Calgary, Alberta T2N 4N1, Canada.
6Pôle Digestif, CHU Purpan, Toulouse F-31059, France.
7Division of Gastroenterology, Hepatology and Nutrition, Children’s Hospital, Toulouse F-31059, France.
8Institut Pasteur, Unité de Défense Innée et Inflammation, Paris F-75015, France.
9INSERM U874, Paris F-75724, France.
10Universite Paris Diderot, Sorbonne Paris Cite, Cellule Pasteur F-75013, France.

ABSTRACT

Elafin, a natural protease inhibitor expressed in healthy intestinal mucosa, has pleiotropic anti-inflammatory properties in vitro and in animal models. We found that mucosal expression of Elafin is diminished in patients with inflammatory bowel disease (IBD). This defect is associated with increased elastolytic activity (elastase-like proteolysis) in colon tissue. We engineered two food-grade strains of lactic acid bacteria (LAB) to express and deliver Elafin to the site of inflammation in the colon to assess the potential therapeutic benefits of the Elafin-expressing LAB. In mouse models of acute and chronic colitis, oral administration of Elafin-expressing LAB decreased elastolytic activity and inflammation and restored intestinal homeostasis. Furthermore, when cultures of human intestinal epithelial cells were treated with LAB secreting Elafin, the inflamed epithelium was protected from increased intestinal permeability and from the release of cytokines and chemokines, both of which are characteristic of intestinal dysfunction associated with IBD. Together, these results suggest that oral delivery of LAB secreting Elafin may be useful for treating IBD in humans.

Copyright © 2012, American Association for the Advancement of Science
Citation: J.-P. Motta, L. G. Bermúdez-Humarán, C. Deraison, L. Martin, C. Rolland, P. Rousset, J. Boue, G. Dietrich, K. Chapman, P. Kharrat, J.-P. Vinel, L. Alric, E. Mas, J.-M. Sallenave, P. Langella, N. Vergnolle, Food-Grade Bacteria Expressing Elafin Protect Against Inflammation and Restore Colon Homeostasis. Sci. Transl. Med. 4, 158ra144 (2012).

Cytokines involved in IBD

Cytokines involved in IBD (Photo credit: Wikipedia)

Metabolism

Front. Physio., 10 October 2012 | doi: 10.3389/fphys.2012.00401
Outlook: membrane junctions enable the metabolic trapping of fatty acids by intracellular acyl-CoA synthetases
Joachim Füllekrug*, Robert Ehehalt and Margarete Poppelreuther
Molecular Cell Biology Laboratory, Internal Medicine IV, University of Heidelberg, Heidelberg, Germany
The mechanism of fatty acid uptake is of high interest for basic research and clinical interventions. Recently, we showed that mammalian long chain fatty acyl-CoA synthetases (ACS) are not only essential enzymes for lipid metabolism but are also involved in cellular fatty acid uptake. Overexpression, RNAi depletion or hormonal stimulation of ACS enzymes lead to corresponding changes of fatty acid uptake. Remarkably, ACS are not localized to the plasma membrane where fatty acids are entering the cell, but are found instead at the endoplasmic reticulum (ER) or other intracellular organelles like mitochondria and lipid droplets. This is in contrast to current models suggesting that ACS enzymes function in complex with transporters at the cell surface. Drawing on recent insights into non-vesicular lipid transport, we suggest a revised model for the cellular fatty acid uptake of mammalian cells which incorporates trafficking of fatty acids across membrane junctions. Intracellular ACS enzymes are then metabolically trapping fatty acids as acyl-CoA derivatives. These local decreases in fatty acid concentration will unbalance the equilibrium of fatty acids across the plasma membrane, and thus provide a driving force for fatty acid uptake.

English: Acyl-CoA from the cytosol to the mito...

English: Acyl-CoA from the cytosol to the mitochondrial matrix. Français : Transport de l’Acyl-CoA du Cytosol jusqu’à la matrice mitochondriale. (Photo credit: Wikipedia)

English: The mechanism for Long Chain Fatty Ac...

English: The mechanism for Long Chain Fatty Acyl-CoA Synthetase (Photo credit: Wikipedia)

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Xarelto (Rivaroxaban): Anticoagulant Therapy gains FDA New Indications and Risk Reduction for: (DVT) and (PE), while in use for Atrial fibrillation increase in Gastrointestinal (GI) Bleeding Reported

Reporter: Aviva Lev-Ari, PhD, RN

 

UPDATED on 8/17/2018

NOAC’s Brain Bleed Risk Outside Afib May Be Dose-Dependent

Higher risk seen only with higher rivaroxaban doses in meta-analysis

by Ashley Lyles, MedPage Today Intern 

The findings indicate the following risk of intracranial hemorrhage versus aspirin:

  • 10 mg of rivaroxaban taken once per day or 5 mg taken two times a day (three trials, OR 1.43, 95% CI 0.93-2.21)
  • 5 mg of apixaban twice daily (one trial, OR 0.84, 95% CI 0.38-1.88)

The study also showed that 15 mg to 20 mg of rivaroxaban each day was linked with an increased risk of fatal bleeding (two trials, OR 2.37, 95% CI 1.30-4.29). On the other hand, 10 mg of rivaroxaban each day or 5 mg taken twice a day (three trials, OR 1.47, 95% CI 0.72-2.97) and 5 mg of apixaban taken twice per day (one trial, OR 0.66, 95 % CI 0.19-2.35) were not linked with an increased risk.

Increased risk of major bleeding compared with aspirin was seen with 15 mg to 20 mg dose of rivaroxaban each day (two trials, OR 2.64, 95% CI 1.68-4.16) and a 10 mg dose of rivaroxaban once a day or 5 mg twice per day (three trials, OR 1.56, 95% CI 1.31-1.85).

Primary Source

JAMA Neurology

Source Reference: Huang W, et al “Association of intracranial hemorrhage risk with non–vitamin k antagonist oral anticoagulant use vs aspirin use a systematic review and meta-analysis” JAMA Neurology 2018; DOI: 10.1001.

SOURCE

https://www.medpagetoday.com/cardiology/strokes/74552?xid=nl_mpt_cardiodaily_2018-08-17&eun=g99985d0r&utm_source=Sailthru&utm_medium=email&utm_campaign=AHAWeekly_081718&utm_term=AHA%20Cardiovascular%20Daily%20-%20Active%20Users%20180%20days

 

UPDATED on 10/9/2017

Xarelto Flop in Stroke Prevention Trial; Syncope Device; Workout by Watching Hockey, Theater?

Recent developments of interest in cardiovascular medicine

  • by Crystal Phend,Senior Associate Editor, MedPage TodayOctober 09, 2017

https://www.medpagetoday.com/Cardiology/Prevention/68421

Rivaroxaban (Xarelto) flopped for preventing recurrent strokes and increased bleeding compared with aspirin in top-line results from the phase III NAVIGATE ESUS trial, Bayer and Janssen announced. (Genetic Engineering and Biotechnology News)

Xarelto (Rivaroxaban): Anticoagulant Therapy gains FDA New Indications and Risk Reduction for: (DVT) and (PE), while in use for Atrial fibrillation, increase in Gastrointestinal (GI) Bleeding Reported compared with Coumadin

Rivaroxaban Gains FDA Indications For The Treatment And Prevention Of DVT And PE

The FDA today expanded the indication for rivaroxaban (Xarelto, Johnson & Johnson) to include the treatment of deep vein thrombosis (DVT) and pulmonary embolism (PE) and to reduce the risk of recurrent DVT and PE.

The oral anticoagulant is already approved to reduce the post-surgical risk of DVT and PE  after hip and knee replacement surgery and to reduce the risk of stroke in people with atrial fibrillation. The new indication was granted under the FDA’s priority review program.

“Xarelto is the first oral anti-clotting drug approved to treat and reduce the recurrence of blood clots since the approval of warfarin nearly 60 years ago,” said Richard Pazdur,  director of the FDA’s Office of Hematology and Oncology Products, in an FDA press release.

Here is the FDA press release:

FDA expands use of Xarelto to treat, reduce recurrence of blood clots
The U.S. Food and Drug Administrationtoday expanded the approved use of Xarelto (rivaroxaban) to include treating deep vein thrombosis (DVT) or pulmonary embolism (PE), and to reduce the risk of recurrent DVT and PE following initial treatment.Blood clots occur when blood thickens and clumps together. DVT is a blood clot that forms in a vein deep in the body. Most deep vein blood clots occur in the lower leg or thigh. When a blood clot in a deep vein breaks off and travels to an artery in the lungs and blocks blood flow, it results in a potentially deadly condition called PE.Xarelto is already FDA-approved to reduce the risk of DVTs and PEs from occurring after knee or hip replacement surgery (July 2011), and to reduce the risk of stroke in people who have a type of abnormal heart rhythm called non-valvular atrial fibrillation (November 2011).

The FDA reviewed Xarelto’s new indication under the agency’s priority review program, which provides an expedited six-month review for drugs that offer major advances in treatment or that provide treatment when no adequate therapy exists.

“Xarelto is the first oral anti-clotting drug approved to treat and reduce the recurrence of blood clots since the approval of warfarin nearly 60 years ago,” said Richard Pazdur, M.D., director of the Office of Hematology and Oncology Products in the FDA’s Center for Drug Evaluation and Research.

Other drugs approved by FDA to treat or reduce the risk of blood clots include Lovenox (enoxaparin), generic versions of enoxaparin, Arixtra (fondaparinux), Fragmin (dalteparin), Coumadin (warfarin), and heparin.

The safety and effectiveness of Xarelto for the new indications were evaluated in three clinical studies. A total of 9,478 patients with DVT or PE were randomly assigned to receive Xarelto, a combination of enoxaparin and a vitamin K antagonist (VKA), or a placebo. The studies were designed to measure the number of patients who experienced recurrent symptoms of DVT, PE or death after receiving treatment.

Results showed Xarelto was as effective as the enoxaparin and VKA combination for treating DVT and PE. About 2.1 percent of patients treated with Xarelto compared with 1.8 percent to 3 percent of patients treated with the enoxaparin and VKA combination experienced a recurrent DVT or PE. Additionally, results from a third study showed extended Xarelto treatment reduced the risk of recurrent DVT and PE in patients. About 1.3 percent of patients treated with Xarelto compared with 7.1 percent of patients receiving placebo experienced a recurrent DVT or PE.

The major side effect observed with Xarelto is bleeding, similar to other anti-clotting drugs.

Xarelto is marketed by Raritan, N.J.-based Janssen Pharmaceuticals Inc.

For more information:

FDA: Office of Hematology and Oncology Products

FDA: Approved Drugs: Questions and Answers

The FDA, an agency within the U.S. Department of Health and Human Services, protects the public health by assuring the safety, effectiveness, and security of human and veterinary drugs, vaccines and other biological products for human use, and medical devices. The agency also is responsible for the safety and security of our nation’s food supply, cosmetics, dietary supplements, products that give off electronic radiation, and for regulating tobacco products.

 

SOURCE:

http://www.forbes.com/sites/larryhusten/2012/11/02/rivaroxaban-gains-fda-indications-for-the-treatment-and-prevention-of-dvt-and-pe/?goback=%2Egde_2069447_member_181862591

Cardiac Atrial Fibrillation

ATLANTA, Georgia — Patients with atrial fibrillation receiving anticoagulant therapy are more likely to experience gastrointestinal (GI) bleeding when treated with rivaroxaban than when treated with warfarin, according to a new analysis of data from ROCKET AF.

Christopher Nessel, MD, from research and development at Johnson & Johnson in Raritan, New Jersey, reported the findings here at CHEST 2012: American College of Chest Physicians Annual Meeting.

“Compared with warfarin, the risk of GI bleeding is increased with rivaroxaban, but the incidence of life-threatening or fatal GI bleeding is lower,” Dr. Nessel told Medscape Medical News. “A careful benefit/risk assessment is needed prior to prescribing rivaroxaban for high-risk patients,” he added.

The analysis examined the incidence and outcomes of GI hemorrhage in 14,264 patients with nonvalvular atrial fibrillation enrolled in ROCKET AF.

The patients were randomized to either rivaroxaban or dose-adjusted warfarin. All GI bleeding events were recorded during treatment and for 2 days after the last dose was administered. Severity of bleeding was defined by a corresponding drop in hemoglobin or transfusion of more than 2 units of red cells.

The composite principal safety end point for GI bleeding events (upper GI, lower GI, and rectal bleeding) occurred more frequently in the 394 patients receiving rivaroxaban than in the 290 receiving warfarin (3.61% vs 2.60% per year; hazard ratio [HR], 1.39; 95% confidence interval [CI], 1.19 to 1.61). Major bleeding was more frequent with rivaroxaban than with warfarin (2.00% vs 1.24% per year; HR, 1.61; 95% CI, 1.30 to 1.99), as was clinically relevant nonmajor bleeding (1.75% vs 1.39% per year; HR, 1.26; 95% CI, 1.20 to 1.55).

Patients who experienced major GI bleeding were more likely to have experienced GI bleeding in the past, to have mild anemia, to have a lower creatinine clearance, to be previous or current smokers, and to be older than patients who did not experience a GI bleeding during the trial (n = 13,552). They were also less likely to be female and to have previously experienced a stroke or transient ischemic attack.

The incidence of severe bleeding (transfusion of at least 4 units) was similar in the rivaroxaban and warfarin groups (49 vs 47). Six patients developed fatal bleeding: 1 in the rivaroxaban group and 5 in the warfarin group.

Data May Give Clinicians Pause When Considering Rivaroxaban

“The data presented extend the observations from the ROCKET AF clinical study,” Dr. Nessel said. “Specifically, the analyses identified characteristics of nonvalvular atrial fibrillation patients that may predispose them to the occurrence of GI hemorrhage. The data also indicated that the overall fatality rates for bleeds of this nature are very low.”

Independent commentator James Wisler, MD, from the division of cardiovascular disease at Duke University Medical Center in Durham, North Carolina, pointed out that this study underscores the importance of critically evaluating these newer anticoagulants when considering their use in a given patient.

“The decision regarding which anticoagulant to use for a given patient is complex, and risks and benefits need to be considered thoughtfully,” he told Medscape Medical News. He added that the results of this study might give some physicians pause about initiating a newer anticoagulant, such as rivaroxaban, in a given patient with atrial fibrillation and an unfavorable risk profile, such as those with a previous GI bleed.

“While the previously published results from ROCKET AF suggested that the risk profiles were similar between rivaroxaban and warfarin, these results demonstrate that there is indeed a subpopulation of patients who may be better served with warfarin than rivaroxaban,” he explained.

According to Dr. Wisler, both this analysis and the initial ROCKET AF study demonstrate that rivaroxaban is associated with fewer episodes of severe or fatal bleeding events, despite the increase in major and clinically relevant nonmajor bleeding observed in the specific subgroup of this study. “Currently, it is unclear why this discrepancy exists,” he added.

He recommends that clinicians take a careful patient history to assess bleeding risk factors when considering the initiation of a newer anticoagulant such as rivaroxaban.

“While perhaps more convenient and efficacious, certain patient populations, such as that evaluated in this study, may receive net harm from these newer agents,” he said.

SOURCE:

CHEST 2012: American College of Chest Physicians Annual Meeting. Presented October 22, 2012.

http://journal.publications.chestnet.org/issue.aspx?journalid=99&issueid=25283

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Author: Dr. Tilda Barliya PhD

 

One of the latest posts address to issue of immunoreactivity and nanotechnology and I wanted to take advantage of this stage to address this topic again. On the many, potentially good effects and goals of nanotechnology, we have emerging side effects and human health issues that needs to be addressed.

It is estimated that the average person in a developed country consumes between 10xE12 and 10xE14 man-made fine (diameter, 0.1–1 mm) to ultrafine (diameter, ,100 nm) particles every day. These dietary particles are mainly TiO2, silicates and aluminosilicates derived from food additives such as stabilizers and anticaking agents . Because most of these micro- and nanoparticles have negatively charged surfaces, they can bind to biomolecules in the gut lumen, absorb across the gastrointestinal tract and accumulate at the base of Peyer’s patches, where a large concentration of M cells are found. M cells transport microorganisms and particles from the gut lumen to immune cells across the intestinal epithelium, and are important for defending the body against ingested toxic substances and stimulating mucosal immunity.

In a research collaboration led by Michael Shuler, the Samuel B. Eckert Professor of Chemical Engineering and the James and Marsha McCormick Chair of Biomedical Engineering, studied how large doses of polystyrene nanoparticles — a common, FDA-approved material found in substances from food additives to vitamins — affected how well chickens absorbed iron, an essential nutrient, into their cells (http://www.nature.com/nnano/journal/v7/n4/full/nnano.2012.3.html).

The researchers tested both acute and chronic nanoparticle exposure using human gut cells in petri dishes as well as live chickens and reported matching results. They chose chickens because these animals absorb iron into their bodies similarly to humans, and they are also similarly sensitive to micronutrient deficiencies.

More so, the authors chose iron absorption as a subject because iron is an example of an essential nutrient that is transported across the intestinal epithelium by means of complex, highly regulated, protein-assisted vesicular and non-vesicular mechanisms.

The researchers used commercially available, 50-nanometer polystyrene carboxylated particles that are generally considered safe for human consumption. They found that following acute exposure, a few minutes to a few hours after consumption, both the absorption of iron in the in vitro cells and the chickens decreased. But following exposure of 2 milligrams per kilogram for two weeks — a slower, more chronic intake — the structure of the intestinal villi began to change and increase in surface area. This was an effective physiological remodeling that led to increased iron absorption.

The increased iron uptake by monolayers exposed to +50 nm particles is probably due to the increased tight junction permeability, as increased transcytosis of luminal material often accompanies tight junction dysfunction.

The in vivo experiments indicate that nanoparticle exposure causes a disruption in iron transport and that the intestinal villi remodel to increase the surface area available for absorption. This increased area compensates for the disruption in iron transport caused by the nanoparticles.

Ferritin levels were analysed in all samples to exclude pre-existing differences in iron status as a cause for differences in iron transport or uptake. Ferritin levels in all nanoparticle-exposed and control cultures were not significantly different. 

The authors concluded that The intestinal epithelial layer represents the initial gate that ingested nanoparticles must pass to reach the body. The polystyrene particles used in these experiments are generally considered non-toxic, but their interaction with a normal physiological process suggests a potential mechanism for a chronic, harmful, but subtle response.

Similar disruptions in nutrient absorption could be possible in relation to other inorganic elements such as calcium, copper and zinc, which require passive or active transport systems for them to be absorbed through the intestinal epithelium. Fat-soluble vitamins such as vitamins A, D, E and K are absorbed only after micellization by pancreatic lipase.

oral exposure to polystyrene nanoparticles can disrupt iron transport and chronic exposure can cause remodelling of the intestinal villi. Remodelling of the villi increases the surface area available for iron absorption. Nanoparticle size, concentration and charge can influence iron uptake and iron transport at doses that represent potential human exposure.

 

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Closing the Mammography gap

Author and Curator: Dror Nir, PhD

There are 40 million women seeking mammography breast-screening every year in the USA, out of which 15 million are women with heterogeneously dense or extremely dense breasts. USA epidemiology statistics show that 6 out of 7 missed cancers at mammography occur in women with dense breasts. It is also known that the majority of women presenting with mammography-dense breasts are below 45 years old.

The Oct. 22 issue of the American Journal of Roentgenology ( AJR) publishes results of a study showing that ultrasound is superior to mammography in evaluating symptomatic women 30-39 years of age [1].

This study was conducted by researchers at the Seattle Cancer Alliance and University of Washington. Patients were recruited between January 2002 and August 2006.   954 women ranging from 30 to 39 years old who presented for diagnostic breast imaging evaluation were  examined, and it was found that sensitivity (probability for cancer detection) of ultrasound was 95.7 percent compared to 60.9 percent for mammography. A very important result of this study is the calculated Negative Predictive Value (the probability to have negative pathology if the imaging-test is negative) which was similar for both modalities: 99.9% for ultrasound and 99.2% for mammography.

Show case in images (All images courtesy of the American Roentgen Ray Society.):

35-year-old woman who presented with a palpable left breast lump. Whole-breast craniocaudal (above left) and mediolateral oblique (above right) and spot-magnification craniocaudal (below left) and mediolateral (below right) mammographic images show no abnormality at area of clinical concern, marked by BB.

Zoom-in on the region of interest

Targeted ultrasound image above reveals solid mass with irregular shape and indistinct and angular margins. BI-RADS 5 assessment was made. Histopathology from ultrasound-guided core needle biopsy showed invasive ductal carcinoma.

In regards to which imaging modality should be used when screening such a population, the conclusion of the investigators is very clear: “Ultrasound has high sensitivity (95.7%) and high NPV (99.9%) in this setting and should be the primary imaging modality of choice. The added value of adjunct mammography is low.”

When reading this article I noted a gap to overcome if we want to successfully replace mammography with ultrasound. The Positive Predictive Value (the probability of  detecting a cancer) calculated for ultrasound in these study settings was lower than that calculated for mammography: 13.2% for ultrasound and 18.4% for mammography. This is because mammography detected one additional malignancy in an asymptomatic area in a 32-year-old woman who was subsequently found to have a BRCA2 gene mutation. Mammography could do that because it scans the whole breast, whereas the investigators in this study used ultrasound just for scanning the suspicious lumps. A solution is offered in using the recently introduced ultrasound modalities, which are able to perform automatic full breast ultrasound scans [2], preferably enhanced by real-time tissue characterisation capability – a technology I’m working to develop.

References:

  1. Accuracy and Value of Breast Ultrasound for Primary Imaging Evaluation of Symptomatic Women 30-39 Years of Age,Constance D. Lehman1,2Christoph I. Lee1,2Vilert A. Loving1,2, Michael S. Portillo1,2Sue Peacock1,2 and Wendy B. DeMartini1,2, Oct. 22 issue of the American Journal of Roentgenology
1 Department of Radiology, University of Washington School of Medicine, Seattle WA.
2 Seattle Cancer Care Alliance, G2-600, 825 Eastlake Ave E, Seattle, WA 98109.

2. Using Automated Breast Sonography as Part of a Multimodality Approach to Dense Breast Screening, Vincenzo Giuliano, MD, RDMS, RVT1, Concetta Giuliano, DO1, Journal of Diagnostic Medical SonographyJuly/August 2012 28: 159-165,

1Novasoutheastern University, Winter Springs, FL, USA
 
 
Written by: Dror Nir, PhD.

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Larry H Bernstein, MD, FCAP, Reporter

Laboratory

NIH-Funded Tissue Chips would Predict Drug Safety
Published: Friday, August 31, 2012
Last Updated: Friday, August 31, 2012

Researchers from Cornell University will develop microphysiological modules to model the nervous, circulatory and gastrointestinal tract systems.
Cornell’s Michael Shuler has received National Institutes of Health (NIH) funding to make 3-D chips with living cells and tissues that model the structure and function of human organs and help predict drug safety.

Shuler, the James and Marsha McCormick Chair of the Department of Biomedical Engineering, and James Hickman of the University of Central Florida (UCF) jointly received one of 17 NIH grants for tissue chip projects.

Shuler and Hickman’s grant of approximately $9 million over five years includes subcontracts to UCF, RegenMed, GE, Sanford-Burnham and Walter Reed Army Institute. It will support their work in microphysiological systems with functional readouts for drug candidate analysis during preclinical testing.

The researchers also plan to build a 10-organ system designed to be low-cost yet highly functional to use in drug discovery, toxicity and preclinical studies.

With the funds, the NIH is supporting bio-engineered devices that will be functionally relevant and will accurately reflect the complexity of a particular tissue, including genomic diversity, disease complexity and pharmacological response.

The NIH tissue chip projects will be tested with compounds known to be safe or toxic in humans to help identify the most reliable drug safety signals — ultimately advancing research to help predict the safety of drugs in a faster, more cost-effective way.

The initiative marks the first interagency collaboration, with the Defense Advanced Research Projects Agency, launched by the NIH’s recently created National Center for Advancing Translational Sciences. The NIH plans to commit up to $70 million over five years to the program

NIH Funds Development of Tissue Chips to Help Predict Drug Safety
Published: Wednesday, July 25, 2012
Last Updated: Wednesday, July 25, 2012

DARPA and FDA to collaborate on therapeutic development initiative.

Seventeen National Institutes of Health grants are aimed at creating 3-D chips with living cells and tissues that accurately model the structure and function of human organs such as the lung, liver and heart.

Once developed, these tissue chips will be tested with compounds known to be safe or toxic in humans to help identify the most reliable drug safety signals – ultimately advancing research to help predict the safety of potential drugs in a faster, more cost-effective way.

The initiative marks the first interagency collaboration launched by the NIH’s recently created National Center for Advancing Translational Sciences (NCATS).

Tissue chips merge techniques from the computer industry with modern tissue engineering by combining miniature models of living organ tissues on a transparent microchip.

Ranging in size from a quarter to a house key, the chips are lined with living cells and contain features designed to replicate the complex biological functions of specific organs.

NIH’s newly funded Tissue Chip for Drug Screening initiative is the result of collaborations that focus the resources and ingenuity of the NIH, Defense Advanced Research Projects Agency (DARPA) and U.S. Food and Drug Administration.

NIH’s Common Fund and National Institute of Neurological Disorders and Stroke led the trans-NIH efforts to establish the program. The NIH plans to commit up to $70 million over five years for the program.

“Serious adverse effects and toxicity are major obstacles in the drug development process,” said Thomas R. Insel, M.D., NCATS acting director.

Insel continued, “With innovative tools and methodologies, such as those developed by the tissue chip program, we may be able to accelerate the process by which we identify compounds likely to be safe in humans, saving time and money, and ultimately increasing the quality and number of therapies available for patients.”

More than 30 percent of promising medications have failed in human clinical trials because they are determined to be toxic despite promising pre-clinical studies in animal models.

Tissue chips, which are a newer human cell-based approach, may enable scientists to predict more accurately how effective a therapeutic candidate would be in clinical studies.

 

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Larry H Bernstein, MD, FCAP, Reporter

A Pot[age] to Die For

A Pot[age] to Die For (Photo credit: jazzijava)

Neurodegerative Disease
Tumeric-Derived Compound Curcumin May Treat Alzheimer’s
Curry chemical shows promise for treating the memory-robbing disease
By Lauren K. Wolf
Department: Science & Technology
News Channels: Biological SCENE
Keywords: alternative medicine, dietary supplements, curcumin, tumeric, Alzheimer’s disease

CURRY WONDER
Curcumin, derived from the rootstalk of the turmeric plant, not only gives Indian dishes their color but might treat Alzheimer’s.
Credit: Shutterstock
More than 5 million people in the U.S. currently live with Alzheimer’s disease. And according to the Alz­heimer’s Association, the situation is only going to get worse.
By 2050, the nonprofit estimates, up to 16 million Americans will have the memory-robbing disease. It will cost the U.S. $1.1 trillion annually to care for them unless a successful therapy is found.
Pharmaceutical companies have invested heavily in developing Alzheimer’s drugs, many of which target amyloid-β, a peptide that misfolds and clumps in the brains of patients. But so far, no amyloid-β-targeted medications have been successful. Expectation for the most advanced drugs—bapineu­zumab from Pfizer and Johnson & Johnson and solanezumab from Eli Lilly & Co.—are low on the basis of lackluster data from midstage clinical trials. That sentiment was reinforced last week when bapineuzumab was reported to have failed the first of four Phase III studies.
Even if these late-stage hopefuls do somehow work, they won’t come cheap, says Gregory M. Cole, a neuroscientist at the University of California, Los Angeles. These drugs “would cost patients tens of thousands of dollars per year,” he estimates. That hefty price tag stems from bapineuzumab and solanezumab being costly-to-manufacture monoclonal antibodies against amyloid-β.
“There’s a great need for inexpensive Alzheimer’s treatments,” as well as a backup plan if pharma fails, says Larry W. Baum, a professor in the School of Pharmacy at the Chinese University of Hong Kong. As a result, he says, a great many researchers have turned their attention to less pricy alternatives, such as compounds from plants and other natural sources.
Curcumin, a spice compound derived from the rootstalk of the turmeric plant (Curcuma longa), has stood out among some of the more promising naturally derived candidates.

When administered to mice that develop Alzheimer’s symptoms, curcumin decreases inflammation and reactive oxygen species in the rodents’ brains, researchers have found. The compound also inhibits the aggregation of troublesome amyloid-β strands among the animals’ nerve cells. But the development of curcumin as an Alzheimer’s drug has been stymied, scientists say, both by its low uptake in the body and a lack of funds for effective clinical trials—obstacles researchers are now trying to overcome.
In addition to contributing to curry dishes’ yellow color and pungent flavor, curcumin has been a medicine in India for thousands of years. Doctors practicing traditional Hindu medicine admire turmeric’s active ingredient for its anti-inflammatory properties and have used it to treat patients for ailments including digestive disorders and joint pain.
Only in the 1970s did Western researchers catch up with Eastern practices and confirm curcumin’s anti-inflammatory properties in the laboratory. Scientists also eventually determined that the polyphenolic compound is an antioxidant and has chemotherapeutic activity.

Bharat B. Aggarwal, a professor at the University of Texas M. D. Anderson Cancer Center, says curcumin is an example of a pleiotropic agent: It has a number of different effects and interacts with many targets and biochemical pathways in the body. He and his group have discovered that one important molecule targeted and subsequently suppressed by curcumin is NF-κB, a transcription factor that switches on the body’s inflammatory response when activated (J. Biol. Chem., DOI: 10.1074/jbc.270.42.24995).
Aside from NF-κB, curcumin seems to interact with several other molecules in the inflammatory pathway, a biological activity that Aggarwal thinks is advantageous. “All chronic diseases are caused by dysregulation of multiple targets,” he says. “Chemists don’t yet know how to design a drug that hits multiple targets.” With curcumin, “Mother Nature has already provided a compound that does so.”
Curcumin’s pleiotropy also brought it to the attention of UCLA’s Cole during the early 1990s while he was searching for possible Alzheimer’s therapeutics. “That was before we knew about amyloid-β” and its full role in Alzheimer’s, he says. “We were working on the disease from an oxidative damage and inflammation point of view—two processes implicated in aging.”
When Cole and his wife, Sally A. Frautschy, also at UCLA, searched the literature for compounds that could tackle both of these age-related processes, curcumin jumped out at them. It also didn’t hurt that the incidence of Alz­heimer’s in India, where large amounts of curcumin are consumed regularly, is lower than in other parts of the developing world (Lancet Neurol., DOI:10.1016/s1474-4422(08)70169-8).

In 2001, Cole, Frautschy, and colleagues published the first papers that demonstrated curcumin’s potential to treat neurodegenerative disease (Neurobiol. Aging, DOI: 10.1016/s0197-4580(01)00300-1; J. Neurosci.2001, 8370). The researchers studied the effects of curcumin on rats that had amyloid-β injected into their brains, as well as mice engineered to develop amyloid brain plaques. In both cases, curcumin suppressed oxidative tissue damage and reduced amyloid-β deposits.
Those results, Cole says, “turned us into curcuminologists.”
Although the UCLA team observed that curcumin decreased amyloid plaques in animal models, at the time, the researchers weren’t sure of the molecular mechanism involved.
Soon after the team’s first results were published, Cole recalls, a colleague brought to his attention the structural similarity between curcumin and the dyes used to stain amyloid plaques in diseased brain tissue. When Cole and Frautschy tested the spice compound, they saw that it, too, could stick to aggregated amyloid-β. “We thought, ‘Wow, not only is curcumin an antioxidant and an anti-inflammatory, but it also might be an anti-amyloid drug,’ ” he says.
In 2004, a group in Japan demonstrated that submicromolar concentrations of curcumin in solution could inhibit aggregation of amyloid-β and break up preformed fibrils of the stuff (J. Neurosci. Res., DOI: 10.1002/jnr.20025). Shortly after that, the UCLA team demonstrated the same (J. Biol. Chem., DOI: 10.1074/jbc.m404751200).
As an Alzheimer’s drug, however, it’s unclear how important it is that the spice compound inhibits amyloid-β aggregation, Cole says. “When you have something that’s so pleiotropic,” he adds, “it’s hard to know” which of its modes of action is most effective.
Having multiple targets may be what helps curcumin have such beneficial, neuroprotective effects, says David R. Schubert, a neurobiologist at the Salk Institute for Biological Studies, in La Jolla, Calif. But its pleiotropy can also be a detriment, he contends.
The pharmaceutical world, Schubert says, focuses on designing drugs aimed at hitting single-target molecules with high affinity. “But we don’t really know what ‘the’ target for curcumin is,” he says, “and we get knocked for it on grant requests.”
Another problem with curcumin is poor bioavailability. When ingested, UCLA’s Cole says, the compound gets converted into other molecular forms, such as curcumin glucuronide or curcumin sulfate. It also gets hydrolyzed at the alkaline and neutral pHs present in many areas of the body. Not much of the curcumin gets into the bloodstream, let alone past the blood-brain barrier, in its pure, active form, he adds.

Unfortunately, neither Cole nor Baum at the Chinese University of Hong Kong realized the poor bioavailability until they had each launched a clinical trial of curcumin. So the studies showed no significant difference between Alzheimer’s patients taking the spice compound and those taking a placebo (J. Clin. Psychopharma­col., DOI: 10.1097/jcp.0b013e318160862c).
“But we did show curcumin was safe for patients,” Baum says, finding a silver lining to the blunder. “We didn’t see any adverse effects even at high doses.”

Some researchers, such as Salk’s Schubert, are tackling curcumin’s low bioavailability by modifying the compound to improve its properties. Schubert and his group have come up with a molecule, called J147, that’s a hybrid of curcumin and cyclohexyl-bisphenol A. Like Cole and coworkers, they also came upon the compound not by initially screening for the ability to interact with amyloid-β, but by screening for the ability to alleviate age-related symptoms.

The researchers hit upon J147 by exposing cultured Alzheimer’s nerve cells to a library of compounds and then measuring changes to levels of biomarkers for oxidative stress, inflammation, and nerve growth. J147 performed well in all categories. And when given to mice engineered to accumulate amyloid-β clumps in their brains, the hybrid molecule prevented memory loss and reduced formation of amyloid plaques over time (PLoS One, DOI: 10.1371/journal.pone.0027865).

Other researchers have tackled curcumin’s poor bioavailability by reformulating it. Both Baum and Cole have encapsulated curcumin in nanospheres coated with either polymers or lipids to protect the compound from modification after ingestion. Cole tells C&EN that by packaging the curcumin in this way, he and his group have gotten micromolar quantities of it into the bloodstream of humans. The researchers are now preparing for a small clinical trial to test the formulation on patients with mild cognitive impairment, who are at an increased risk of developing Alzheimer’s.

An early-intervention human study such as this one comes with its own set of challenges, Cole says. People with mild cognitive impairment “have good days and bad days,” he says. A large trial over a long period would be the best way to get any meaningful data, he adds.  Such a trial can cost up to $100 million, a budget big pharma might be able to scrape together but that is far out of reach for academics funded by grants, Cole says. “If you’re down at the level of what an individual investigator can do, you’re running a small trial,” he says, “and even if the result is positive, it might be inconclusive” because of its small size or short duration. That’s one of the reasons the curcumin work is slow-going, Cole contends.
NIH-Funded Research Provides New Clues on How ApoE4 Affects Alzheimer’s Risk
Published: Tuesday, October 30, 2012
Last Updated: Tuesday, October 30, 2012

Researchers found that ApoE4 triggers an inflammatory reaction that weakens the blood-brain barrier.
Common variants of the ApoE gene are strongly associated with the risk of developing late-onset Alzheimer’s disease, but the gene’s role in the disease has been unclear.

Now, researchers funded by the National Institutes of Health have found that in mice, having the most risky variant of ApoE damages the blood vessels that feed the brain.

The researchers found that the high-risk variant, ApoE4, triggers an inflammatory reaction that weakens the blood-brain barrier, a network of cells and other components that lines brain’s brain vessels.

Normally, this barrier allows nutrients into the brain and keeps harmful substances out.

The study appears in Nature, and was led by Berislav Zlokovic, M.D., Ph.D., director of the Center for Neurodegeneration and Regeneration at the Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles.

“Understanding the role of ApoE4 in Alzheimer’s disease may be one of the most important avenues to a new therapy,” Dr. Zlokovic said. “Our study shows that ApoE4 triggers a cascade of events that damages the brain’s vascular system,” he said, referring to the system of blood vessels that supply the brain.

The ApoE gene encodes a protein that helps regulate the levels and distribution of cholesterol and other lipids in the body. The gene exists in three varieties.

ApoE2 is thought to play a protective role against both Alzheimer’s and heart disease, ApoE3 is believed to be neutral, and ApoE4 confers a higher risk for both conditions.

Outside the brain, the ApoE4 protein appears to be less effective than other versions at clearing away cholesterol; however, inside the brain, exactly how ApoE4 contributes to Alzheimer’s disease has been a mystery.

Dr. Zlokovic and his team studied several lines of genetically engineered mice, including one that lacks the ApoE gene and three other lines that produce only human ApoE2, ApoE3 or ApoE4. Mice normally have only a single version of ApoE.

The researchers found that mice whose bodies made only ApoE4, or made no ApoE at all, had a leaky blood-brain barrier. With the barrier compromised, harmful proteins in the blood made their way into the mice’s brains, and after several weeks, the researchers were able to detect loss of small blood vessels, changes in brain function, and a loss of connections between brain cells.

“The study demonstrates that damage to the brain’s vascular system may play a key role in Alzheimer’s disease, and highlights growing recognition of potential links between stroke and Alzheimer’s-type dementia,” said Roderick Corriveau, Ph.D., a program director at NIH’s National Institute of Neurological Disorders and Stroke (NINDS), which helped fund the research. “It also suggests that we might be able to decrease the risk of Alzheimer’s disease among ApoE4 carriers by improving their vascular health.”

The researchers also found that ApoE2 and ApoE3 help control the levels of an inflammatory molecule called cyclophilin A (CypA), but ApoE4 does not. Levels of CypA were raised about five-fold in blood vessels of mice that produce only ApoE4.

The excess CypA then activated an enzyme, called MMP-9, which destroys protein components of the blood-brain barrier. Treatment with the immunosuppressant drug cyclosporine A, which inhibits CypA, preserved the integrity of the blood-brain barrier and lessened damage to the brain.

An inhibitor of the MMP-9 enzyme had similar beneficial effects. In prior studies, inhibitors of this enzyme have been shown to reduce brain damage after stroke in animal models.

“These findings point to cyclophilin A as a potential new drug target for Alzheimer’s disease,” said Suzana Petanceska, Ph.D., a program director at NIH’s National Institute on Aging (NIA), which also funded Dr. Zlokovic’s study.

“Many population studies have shown an association between vascular risk factors in mid-life, such as high blood pressure and diabetes, and the risk for Alzheimer’s in late-life. We need more research aimed at deepening our understanding of the mechanisms involved and to test whether treatments that reduce vascular risk factors may be helpful against Alzheimer’s.”

Alzheimer’s disease is the most common cause of dementia in older adults, and affects more than 5 million Americans. A hallmark of the disease is a toxic protein fragment called beta-amyloid that accumulates in clumps, or plaques, within the brain.

Gene variations that cause higher levels of beta-amyloid are associated with a rare type of Alzheimer’s that appears early in life, between age 30 and 60.

However, it is the ApoE4 gene variant that is most strongly tied to the more common, late-onset type of Alzheimer’s disease. Inheriting a single copy of ApoE4 from a parent increases the risk of Alzheimer’s disease by about three-fold. Inheriting two copies, one from each parent, increases the risk by about 12-fold.

Dr. Zlokovic’s study and others point to a complex interplay between beta-amyloid and ApoE4. On the one hand, beta-amyloid is known to build up in and damage blood vessels and cause bleeding into the brain.

On the other hand, Dr. Zlokovic’s data suggest that ApoE4 can damage the vascular system independently of beta-amyloid. He theorizes that this damage makes it harder to clear beta-amyloid from the brain.

Some therapies under investigation for Alzheimer’s focus on destroying amyloid plaques, but therapies designed to compensate for ApoE4 might help prevent the plaques from forming, he said.

Compound Could Become Alzheimer’s Treatment
Thu, 10/11/2012 – 1:29pm
A new molecule designed to treat Alzheimer’s disease has significant promise and is potentially the safest to date, according to researchers.

Purdue University professor Arun Ghosh designed the molecule, which is a highly potent beta-secretase inhibitor with unique features that ensure it goes only to its target and does not affect healthy physiological processes, he said.

“This molecule maintains the disease-fighting properties of earlier beta-secretase inhibitors, but is much less likely to cause harmful side effects,” said Ghosh, the Ian P. Rothwell Distinguished Professor of Chemistry and Medicinal Chemistry and Molecular Pharmacology. “The selectivity we achieved is unprecedented, which gives it great promise for the long-term medication required to treat Alzheimer’s. Each time a treatment misses its disease target and instead interacts with a healthy cell or molecule, damage is done that we call toxicity. Even low levels of this toxicity could build up over years and years of treatment, and an Alzheimer’s patient would need to be treated for the rest of his or her life.”

The new molecule shows a 7,000-fold selectivity for its target enzyme, which far surpasses the benchmark of a 1,000-fold selectivity for a viable treatment molecule, and dwarfs the selectivity values in the hundreds for past beta-secretase inhibitors, he said. A paper detailing the work will be published in an upcoming Alzheimer’s research issue of the Journal of Medicinal Chemistry and is currently available online. The National Institutes of Health funded the research.

Beta-secretase inhibitors, which could allow for intervention in the early stages of Alzheimer’s disease, have promise as a potential treatment. Several drugs based on this molecular target have made it to clinical trials, including one based on a molecule Ghosh designed previously. These molecules prevent the first step in a chain of events that leads to the formation of amyloid plaque in the brain, fibrous clumps of toxic proteins that are believed to cause the disease’s devastating symptoms.

The National Institute on Aging estimates that 5.1 million Americans suffer from Alzheimer’s disease, which leads to dementia by affecting parts of the brain that control thought, memory and language.

“Alzheimer’s is a progressive disease that destroys the brain and also destroys the quality of life for those who suffer from it,” Ghosh said. “It eventually robs people of their ability to recognize their own spouse or child and to complete basic tasks necessary for independence, like getting dressed. It is a truly devastating disease for those who suffer from it and for their friends and loved ones.”

Earlier versions of the beta-secretase inhibitor were able to stop and even reverse the progression of amyloid plaques in tests on mice, but potency and selectivity are only two of the three pillars of a viable Alzheimer’s treatment, Ghosh said. It has yet to be shown whether this molecule possesses the third pillar, the ability to be turned into an easily administered drug that passes through the blood-brain barrier.

Ghosh collaborates with Jordan Tang, the J.G. Puterbaugh Chair in Medical Research at the Oklahoma Medical Research Foundation, who in 2000 identified beta-secretase and its role in the progression of Alzheimer’s. Later that year Ghosh designed his first molecule that bound to and inhibited the activity of the enzyme. He has strived to create the needed improvements ever since.

Ghosh bypasses the usual lengthy process of trial and error in finding useful inhibitor molecules by using a structure-based design strategy. He uses the structures of the inhibitor bound to the enzyme as a guide to what molecular features are important for desirable and undesirable characteristics. Then he removes, replaces and adds molecular groups to amplify the desirable and eliminate the undesirable.

“I believe structure-based design is vital to the development of new and improved medicine,” said Ghosh, who also is a member of the Purdue University Center for Cancer Research. “These strategies have the potential to eliminate enormous costs and time needed in traditional random screening protocols for drug development. Structure-based strategies allow us to design molecules that do precisely what we need them to do with fewer undesirable side effects.”

Tang performed the X-ray crystallography and captured the crystal structures to reveal important insights and serve as a guide for Ghosh’s designs.

“Developing inhibitors into clinically useful drugs is an evolutionary process,” Tang said. “We learn what works and what doesn’t along the way, and the knowledge permits us to do better in the next step. The miracles of modern medicine are built on top of excellent scientific findings. We try to do good science and know that the consequence will be a better chance for conquering diseases and improving lives.”

Beta-secretase belongs to a class of enzymes called aspartyl proteases. Research into beta-secretase inhibitors faced setbacks when other aspartyl proteases similar in structure, called memapsin 1 and cathepsin D, were discovered and found to be involved in many important physiological processes. Earlier designed beta-secretase inhibitors were found also to work against the biologically necessary enzymes.

Ghosh’s team focused on developing ways to make the inhibitor more selective so that it would avoid these other, physiologically important enzymes. They compared the structures of beta-secretase and memapsin 1 as they interacted with the inhibitor to find an active area unique only to beta-secretase. Then they added a functional molecular feature that targets and interacts with the unique area, making the inhibitor more attractive to beta-secretase and less attractive to the other enzymes.

“The added feature serves as a bait on the inhibitor molecule that entices beta-secretase and also grabs onto it tightly, greatly enhancing its selectivity,” he said. “This is a fundamental insight into the origins of selectivity and ways to increase it.”
Ghosh said this work highlights an important purpose of academic research.

“Academic research lays out and shares the fundamentals to advance drug discovery,” he said. “Advances in treatment are built upon the basic research happening at universities.”

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Word Cloud By Danielle Smolyar

Pancreatic stellate cell activation in chronic...

Pancreatic stellate cell activation in chronic pancreatitis and pancreatic cancer. Pancreatic stellate cells are activated by profibrogenic mediators, such as ethanol metabolites and cytokines/growth factors. Perpetuation of stellate cell activation under persisting pathological conditions results in pancreatic fibrosis. Jaster Molecular Cancer 2004 3:26 doi:10.1186/1476-4598-3-2 (Photo credit: Wikipedia)

Larry H. Bernstein, MD,  Reporter

Cancer-Causing Gene Alone Doesn’t Trigger Pancreatic Cancer, Mayo-led Study Finds
More than a cancer-causing gene is needed to trigger pancreatic cancer, a study led by Mayo Clinic, Jacksonville, Fla, has found.

A second factor creates a “perfect storm” that allows tumors to form, the researchers say. The study, published in a recent issue of Cancer Cell, overturns the current belief that a mutation in the KRAS oncogene is enough to initiate pancreatic cancer and unrestrained cell growth.

The findings uncover critical clues on how pancreatic cancer develops and why few patients benefit from current therapies. The findings also provide ideas about how to improve treatment and prevention of pancreatic cancer.

The research team, led by Howard C. Crawford, PhD, a cancer biologist at Mayo Clinic’s campus in Florida, and Jens Siveke, MD, at Technical University in Munich, Germany, found that for pancreatic cancer to form, mutated KRAS must recruit a second player: the epidermal growth factor receptor, or EGFR.A third genetic participant known as Trp53 makes pancreatic tumors very difficult to treat, the study showed.

The scientists also found that EGFR was required in pancreatic cancer initiated by pancreatic inflammation known as pancreatitis.

Imatinib May Help Treat Aggressive Lymphoma

Based on the results of a new study, researchers are developing a clinical trial to test imatinib (Gleevec) in patients with anaplastic large cell lymphoma (ALCL), an aggressive type of non-Hodgkin lymphoma that primarily affects children and young adults.

The researchers found that a protein called PDGFRB is important to the development of a common form of ALCL. PDGFRB, a growth factor receptor protein, is a target of imatinib. Imatinib had anticancer effects in both a mouse model of ALCL and a patient with the disease, Dr. Lukas Kenner of the Medical University of Vienna in Austria and his colleagues reported October 14 in Nature Medicine.

The authors decided to investigate the effect of imatinib after finding a link between PDGFRB and a genetic abnormality that is found in many patients with ALCL. Previous work had shown that this genetic change—a translocation that leads to the production of an abnormal fusion protein called NPM-ALK—stimulates the production of two proteins, transcription factors called JUN and JUNB.

In the new study, experiments in mice revealed that these proteins promote lymphoma development by increasing the levels of PDGFRB.

Because imatinib inhibits PDGFRB, the authors tested the effect of the drug in mice with the NPM-ALK change and found that it improved their survival. They also found that imatinib given together with the ALK inhibitor crizotinib (Xalkori) greatly reduced the growth of NPM-ALK-positive lymphoma cells in mice.

To test the treatment strategy in people, they identified a terminally ill patient with NPM-ALK-positive ALCL who had no other treatment options and agreed to try imatinib. The patient began to improve within 10 days of starting the therapy and has been free of the disease for 22 months, the authors reported.

The observation that inhibiting both ALK and PDGFRB “reduces lymphoma growth and alleviates relapse rates” led the authors to suggest that the findings might be relevant to lymphomas with PDGFRB but without the NPM-ALK protein. “Our findings suggest that imatinib is a potential therapeutic option for patients with crizotinib-resistant lymphomas.”

A planned clinical trial will be based on the expression of PDGFRB in tumors.

Researchers Identify Possible Biomarker for Early-Stage Lung Cancer

A protein that can be detected in blood samples may one day serve as a biomarker for early-stage lung cancer, according to new study results. The findings, published October 16 in the Proceedings of the National Academy of Sciences, suggest that measuring the levels of a variant form of the protein Ciz1 may help detect lung cancer early and noninvasively in high-risk individuals.

“We have struggled to find cancer biomarkers that are disease-specific, and this may be a step in the right direction,” said Dr. Sudhir Srivastava, chief of NCI’s Cancer Biomarkers Research Group. He called the study “promising” but noted that the results will need further validation.

Researchers led by Dr. Dawn Coverley of the University of York in the United Kingdom found that the “b-variant” form of Ciz1 was present in 34 of 35 lung tumors but not in adjacent tissue. Additional experiments showed that an antibody specific for this Ciz1 variant could detect the protein in small samples of blood from individuals with non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC).

In two independent sets of blood samples—from 170 and 160 patients, respectively—the researchers showed that variant Ciz1 levels above a certain threshold correctly identified 95 to 98 percent of lung cancer patients, with an overall specificity of 71 to 75 percent. Using the second set of samples, they showed that the level of variant Ciz1 could discriminate between patients with stage I NSCLC and age-matched heavy smokers without diagnosed cancer, individuals with benign lung nodules, and patients with inflammatory lung disease.

Although the high rate of false-positive test results seen with variant Ciz1 is a concern, the authors noted that a blood test for the Ciz1 variant might ultimately be shown to be useful when combined with low-dose helical computed tomography, also called spiral CT, for lung cancer screening. In that context, the test could confirm the presence of lung cancer in patients who have suspicious spiral CT results, reducing the need for invasive procedures to confirm a lung cancer diagnosis. And, if used before spiral CT, “the test could reduce the number of people who undergo imaging…[because] the false-negative rate is very low,” Dr. Coverley wrote in an e-mail message.

To assess variant Ciz1 levels, the researchers used a laboratory method known as Western blot analysis. However, this approach could not be routinely applied in a clinical context, the researchers acknowledged, so “a more streamlined method” for testing would need to be developed.

Supported in part by NCI Early Detection Research Network Grant U01CA086137.

NCI Reports

Study Looks at Terminal Cancer Patients’ Expectations of Chemotherapy

A majority of patients who opt to receive chemotherapy to treat newly diagnosed metastatic lung or colorectal cancer believe chemotherapy might cure their cancer, according to a recent survey. The survey results suggest that optimistic assumptions about the benefits of chemotherapy may hamper patients’ abilities to make informed treatment decisions that align with their preferences, said the researchers who led the study. The findings were published October 25 in the New England Journal of Medicine.

Dr. Jane Weeks of the Dana-Farber Cancer Institute and her colleagues interviewed 1,193 patients tracked by the prospective, observational Cancer Care Outcomes Research and Surveillance Consortium (CanCORS) study, 4 to 7 months after diagnosis. All of the patients had been diagnosed with stage IV lung or colorectal cancer and had chosen to receive chemotherapy. A surrogate was interviewed when a patient was too ill to participate. The survey asked patients how likely it was that chemotherapy would cure their disease, extend life, or relieve symptoms. The researchers also collected data on patients’ physical functioning, communication with their physicians, and social and demographic factors.

The majority of patients did not appear to understand that chemotherapy was very unlikely to cure their cancer (81 percent of those with colorectal cancer and 69 percent of those with lung cancer). Black, Hispanic, and Asian/Pacific Islander patients were more likely than white patients to believe that chemotherapy would cure them. Nevertheless, most patients believed that chemotherapy was more likely to extend their life than cure them.

Educational level, functional status, and the patient’s role in treatment decision making were not associated with inaccurate expectations about chemotherapy.

In an accompanying editorial, Drs. Thomas J. Smith of the Johns Hopkins Sidney Kimmel Cancer Center and Dan L. Longo of the National Institute on Aging wrote, “if patients actually have unrealistic expectations of a cure from a therapy that is administered with palliative intent, we have a serious problem of miscommunication that we need to address.”

This research was supported by grants from the National Institutes of Health (U01 CA093344, U01 CA093332, U01CA093324, U01 CA093348, U01 CA093329, U01 CA093339, and U01 CA093326).

 

Researchers Identify Possible Biomarker for Early-Stage Lung Cancer

A protein that can be detected in blood samples may one day serve as a biomarker for early-stage lung cancer, according to new study results. The findings, published October 16 in the Proceedings of the National Academy of Sciences, suggest that measuring the levels of a variant form of the protein Ciz1 may help detect lung cancer early and noninvasively in high-risk individuals.

“We have struggled to find cancer biomarkers that are disease-specific, and this may be a step in the right direction,” said Dr. Sudhir Srivastava, chief of NCI’s Cancer Biomarkers Research Group. He called the study “promising” but noted that the results will need further validation.

Researchers led by Dr. Dawn Coverley of the University of York in the United Kingdom found that the “b-variant” form of Ciz1 was present in 34 of 35 lung tumors but not in adjacent tissue. Additional experiments showed that an antibody specific for this Ciz1 variant could detect the protein in small samples of blood from individuals with non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC).

In two independent sets of blood samples—from 170 and 160 patients, respectively—the researchers showed that variant Ciz1 levels above a certain threshold correctly identified 95 to 98 percent of lung cancer patients, with an overall specificity of 71 to 75 percent. Using the second set of samples, they showed that the level of variant Ciz1 could discriminate between patients with stage I NSCLC and age-matched heavy smokers without diagnosed cancer, individuals with benign lung nodules, and patients with inflammatory lung disease.

Although the high rate of false-positive test results seen with variant Ciz1 is a concern, the authors noted that a blood test for the Ciz1 variant might ultimately be shown to be useful when combined with low-dose helical computed tomography, also called spiral CT, for lung cancer screening. In that context, the test could confirm the presence of lung cancer in patients who have suspicious spiral CT results, reducing the need for invasive procedures to confirm a lung cancer diagnosis. And, if used before spiral CT, “the test could reduce the number of people who undergo imaging…[because] the false-negative rate is very low,” Dr. Coverley wrote in an e-mail message.

To assess variant Ciz1 levels, the researchers used a laboratory method known as Western blot analysis. However, this approach could not be routinely applied in a clinical context, the researchers acknowledged, so “a more streamlined method” for testing would need to be developed.

Supported in part by NCI Early Detection Research Network Grant U01CA086137.

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Otto Warburg, A Giant of Modern Cellular Biology

Reporter: Larry H Bernstein, MD, FCAP

 

 

Otto Heinrich Warburg

Otto Heinrich Warburg (Photo credit: Wikipedia)

Otto Heinrich Warburg (October 8, 1883 – August 1, 1970), son of physicist Emil Warburg, was a German physiologist, medical doctor and Nobel laureate.

Otto Heinrich Warburg was born on October 8, 1883, in Freiburg, Baden. His father, the physicist Emil Warburg, was President of the Physikalische Reichsanstalt, Wirklicher Geheimer Oberregierungsrat. He was a member of the Warburg family, a prominent family and financial dynasty of German Jewish descent, noted for their varied accomplishments in physicsclassical musicart historypharmacologyphysiologyfinanceprivate equity and philanthropy. They are believed to be descended from the Venetian Jewish del Banco family, in the early 1500s one of the wealthiest Venetian families. The Warburgs fled from Italy to Warburg in Germany in the 16th century before moving to Altona, near Hamburg in the 17th century taking their surname from the city of Warburg. The brothers Moses Marcus Warburg(1763 – 1830) and Gerson Warburg (1765 – 1826) founded the M. M. Warburg & Co. banking company in 1798 that is still in existence.

Otto studied chemistry under the great Emil Fischer, and gained the degree, Doctor of Chemistry (Berlin), in 1906. He then studied under von Krehl and obtained the degree, Doctor of Medicine (Heidelberg), in 1911.

He served as an officer in the elite Uhlan (cavalry regiment) during the First World War, and won the Iron Cross (1st Class) for bravery. Warburg was one of the 20th century’s leading biochemists. [1] He won the Nobel Prize of 1931. In total, he was nominated an unprecedented three times for the Nobel prize for three separate achievements.
While working at the Marine Biological Station, Warburg performed research on oxygen consumption in sea urchin eggs after fertilization, and proved that upon fertilization, the rate of respiration increases by as much as sixfold. His experiments also proved iron is essential for the development of the larval stage.

In 1918, Warburg was appointed professor at the Kaiser Wilhelm Institute for Biology in Berlin-Dahlem (part of the Kaiser-Wilhelm-Gesellschaft). By 1931 he was named director of the Kaiser Wilhelm Institute for Cell Physiology, which was founded the previous year by a donation of the Rockefeller Foundation to the Kaiser Wilhelm Gesellschaft (since renamed the Max Planck Society).
Warburg’s early researches with Fischer were in the polypeptide field.

At Heidelberg he worked on the process of oxidation. His special interest in the investigation of vital processes by physical and chemical methods led to attempts to relate these processes to phenomena of the inorganic world. His methods involved detailed studies on the assimilation of carbon dioxide in plants, the metabolism of tumors, and the chemical constituent of the oxygen transferring respiratory ferment. Warburg was never a teacher, and he has always been grateful for his opportunities to devote his whole time to scientific research. His later researches at the Kaiser Wilhelm Institute have led to the discovery that the flavins and the nicotinamide were the active groups of the hydrogen-transferring enzymes.
This, together with the iron-oxygenase discovered earlier, gives a complete account of the oxidations and reductions in the living world. Warburg investigated the metabolism of tumors and the respiration of cells, particularly cancer cells, and in 1931 was awarded the Nobel Prize in Physiology for his “discovery of the nature and mode of action of the respiratory enzyme.”[2]

The award came after receiving 46 nominations over a period of nine years beginning in 1923, 13 of which were submitted in 1931, the year he won the prize. This discovery opened up new ways in the fields of cellular metabolism and cellular respiration. He hypothesized, among other things, that cancerous cells can live and develop, even in the absence of oxygen. Warburg also wrote about oxygen’s relationship to the pH of cancer cells’ internal environments, since fermentation was a major metabolic pathway of cancer cells.
Three scientists who worked in Warburg’s lab, including Sir Hans Adolf Krebs, went on to win the Nobel Prize. Among other discoveries, Krebs is credited with the identification of the citric acid cycle (or Szent györgyi-Krebs cycle).
In 1944, Warburg was nominated for a second Nobel Prize in Physiology by Albert Szent-Györgyi, for his work on nicotinamide, the mechanism and enzymes involved in fermentation, and the discovery of flavine (in yellow enzymes). Although he was considered a worthwhile candidate, he was not selected for the prize.

References

  1.  Krebs, HA (1972), “Warburg Heinrich Warburg. 1883-1970”, Biographical Memoirs of Fellows of the Royal Society (The Royal Society) 18: 628–699,doi:10.1098/rsbm.1972.0023
  2. ^ NobelPrize.org, The Nobel Prize in Physiology or Medicine 1931accessed April 20, 2007
  3.  Warburg O (1956), “On the origin of cancer cells”, Science 123 (3191): 309–14, doi:10.1126/science.123.3191.309PMID 13298683
  4. a b Kim JW, Dang CV (2006), “Cancer’s molecular sweet tooth and the Warburg effect”, Cancer Res. 66 (18): 8927–30, doi:10.1158/0008-5472.CAN-06-1501PMID 16982728
  5. Som P; Atkins HL; Bandoypadhyay D et al. (1 July 1980), “A fluorinated glucose analog, 2-fluoro-2-deoxy-D-glucose (F-18): nontoxic tracer for rapid tumor detection”, J. Nucl. Med. 21 (7): 670–5, PMID 7391842
  6. Chernow, Ron (1993), The Warburgs: The Twentieth-Century Odyssey of a Remarkable Jewish Family, New York, NY: Random House, ISBN 0-679-41823-7

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Recent Patents on Biomarkers.

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Reporter: Aviva Lev-Ari, PhD, RN

arrayMap: A Reference Resource for Genomic Copy Number Imbalances in Human Malignancies

Haoyang Cai#, Nitin Kumar#, Michael Baudis*Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland

Abstract Top

Background

The delineation of genomic copy number abnormalities (CNAs) from cancer samples has been instrumental for identification of tumor suppressor genes and oncogenes and proven useful for clinical marker detection. An increasing number of projects have mapped CNAs using high-resolution microarray based techniques. So far, no single resource does provide a global collection of readily accessible oncogenomic array data.

Methodology/Principal Findings

We here present arrayMap, a curated reference database and bioinformatics resource targeting copy number profiling data in human cancer. The arrayMap database provides a platform for meta-analysis and systems level data integration of high-resolution oncogenomic CNA data. To date, the resource incorporates more than 40,000 arrays in 224 cancer types extracted from several resources, including the NCBI’s Gene Expression Omnibus (GEO), EBI’s ArrayExpress (AE), The Cancer Genome Atlas (TCGA), publication supplements and direct submissions. For the majority of the included datasets, probe level and integrated visualization facilitate gene level and genome wide data review. Results from multi-case selections can be connected to downstream data analysis and visualization tools.

Conclusions/Significance

To our knowledge, currently no data source provides an extensive collection of high resolution oncogenomic CNA data which readily could be used for genomic feature mining, across a representative range of cancer entities. arrayMap represents our effort for providing a long term platform for oncogenomic CNA data independent of specific platform considerations or specific project dependence. The online database can be accessed at http//www.arraymap.org.

Citation: Cai H, Kumar N, Baudis M (2012) arrayMap: A Reference Resource for Genomic Copy Number Imbalances in Human Malignancies. PLoS ONE 7(5): e36944. doi:10.1371/journal.pone.0036944

Editor: Ying Xu, University of Georgia, United States of America

Received: January 10, 2012; Accepted: April 16, 2012; Published: May 18, 2012

Copyright: © 2012 Cai et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: HC is supported through a personal grant from the China Scholarship Council. NK and MB had received support through the Krebsliga Schweiz and the University of Zurich’s Research Priority Program Systems Biology. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

* E-mail: michael.baudis@imls.uzh.ch

# These authors contributed equally to this work.

Introduction Top

Genomic copy number abnormalities (CNAs) are a relevant feature in the development of basically all forms of human malignancies [1]. Many genomic imbalances are recurrent and display tumor-specific patterns [2],[3]. It is believed that these genomic instabilities reveal mutations in tumor suppressor genes and oncogenes which eventually result in a clone of fully malignant cells. Investigation of CNA hot spots (chromosomal loci frequently involved in CNA) has proven to be an effective methodology to identify novel cancer-causing genes [4][5]. On a systems level, CNA data along with expression or somatic mutation data is used to detect pathways altered in cancers and to deduce functional relevance of pathway members[6][7]. Since many CNAs have been attributed to specific tumor types or clinical risk profiles, in some entities copy number profiling is employed to characterize different biological as well as clinical subtypes with implications for treatment and individual prognosis. Subtype-associated CNA regions are used to predict causative genes, furthering understanding of biological differences and leading to discovery of new therapeutic targets [8][9].

Throughout the last two decades, molecular-cytogenetic techniques have been applied to scan genomic copy number profiles in virtually all types of human neoplasias. For whole genome analysis, these techniques predominantly consist of chromosomal and array comparative genomic hybridization (CGH), including CNA detection by cDNA and single nucleotide polymorphism (SNP) arrays [10][12]. While chromosomal CGH has a limited spatial resolution of several megabases, the resolution of recent array based technologies (aCGH) is mainly limited due to cost/benefit evaluations instead of technical obstacles. In this article, we use the terms “array CGH” and “aCGH” for all technical variants of whole genome copy number arrays. This includes e.g. single color arrays for which regional copy number normalization is performed through bioinformatics procedures applied to external references and internal data distribution.

The flood of new insights into structural genomic changes in health and disease has led to an increased interest in genomic data sets in genetic and cancer research. Several systematic studies of CNAs across many cancer types have been performed [13][14]. These efforts attempt a more complete understanding of functional effect of CNAs in the context of cancer.

The exponential increase of high resolution CNA datasets offers new challenges and opportunities for large-scale genomic data mining, data modeling and functional data integration. Several online resources have been developed, focusing on different aspects of data content as well as representation [6][15][19]. An overview of some of the prominent examples is given in Table 1. In principle, these databases facilitate access and utilization of CNA data. However, they are limited to specific aCGH platforms and/or single institutions as well as limited disease categories, or, as in the cases of GEO [15] and Ensembl ArrayExpress[16], mainly serve as raw data repositories. To the best of our knowledge, no single data source does yet provide an extensive collection of high resolution oncogenomic CNA data which readily could be used for genomic feature mining, across a representative range of cancer entities.

Table 1. Prominent online resources of genomic data.

doi:10.1371/journal.pone.0036944.t001

Here we present “arrayMap”, a web-based reference database for genomic copy number data sets in cancer. We have generated a pipeline to accumulate and process oncogenomic array data into a unified and structured format. The resource incorporates associated histopathological and clinical information where accessible.

So far, arrayMap contains more than 40,000 arrays on 224 cancer types from five main data sources: NCBI GEO, EBI ArrayExpress, The Cancer Genome Atlas, publication supplements and user submitted data. Samples of interest can be browsed, visualized and analyzed via an intuitive interface. Computational tools are provided for biostatistical data analysis such as CNA clustering for case specific or for subset data and basic clinical correlations. arrayMap is publicly available at www.arraymap.org.

Results Top

Data Content

Our combination of both “top-down” (publication driven) as well as “bottom-up” (array data driven) approaches allowed us to identify a comprehensive set of accessible aCGH based cancer CNA data sets and to estimate the ratio of accessible data of the overall published/deposited data.

As main result of the array data driven approach, we extracted 495 series comprising of 32002 arrays, generated on 237 platforms from NCBI’s GEO. Among those, raw data files of approximately 29000 whole genome arrays were suitable for inclusion into our data processing pipeline. When reviewing the content of AE, we found that the majority of AE cancer genome data sets were also submitted to GEO. At the time of writing, 11 datasets including 712 arrays not present in GEO had been processed based on AE specific series. Detailed information on the GEO/AE data sets is provided in Table S1.

The top-down procedure was based on our group’s continuous monitoring of cancer related articles utilizing genome copy number screening approaches, as established for our “Progenetix” project (www.progenetix.org[19]). The census date for the literature based data collection was August 15 2011. At this point, we had identified 931 articles discussing a total of 53213 genomic cancer CNA profiles based on aCGH techniques. Of these, 8728 cases out of 199 articles so far had been extracted from publication related sources (e.g. supplementary data tables) and annotated and made been accessible through Progenetix. This data included cases for which only supervised information but no probe data was available (e.g. author annotated Golden Path or cytogenetic CNA regions). Literature based data sets containing probe specific data or with the respective data presented to us by the authors (640 samples) were included into our arrayMap data processing pipeline.

The data content of arrayMap is summarized in Table 2. Current numbers on the website will include changes based on ongoing annotation efforts (i.e. addition of data sets, removal of low quality arrays).

Table 2. aCGH data integrated in arrayMap.

doi:10.1371/journal.pone.0036944.t002

As a by-product of our data collection and annotation efforts, we are able to provide estimates of content and trends for the platform usage and cancer entity coverage for the majority of published data. According to the assigned ICD-O 3 (International Classification of Diseases for Oncology, 3rd Edition) code and descriptive diagnostic text, breast carcinoma predominates as single largest clinical entity with 6459 arrays.Table S2 presents sample sets in arrayMap classified by ICD-O code.

The most widely available array CGH platforms are either based on large insert clones (BAC/P1 arrays) or based on shorter single-stranded DNA molecules (oligonucleotide arrays), which may or may not include single-nucleotide polymorphism specific probe sequences (SNP arrays). Also, although designed for gene expression profiling, cDNA arrays were used by several laboratories for measuring genomic copy number changes. Although all these platforms are considered suitable for whole genome CNA analysis, their probe densities and other parameters can affect specific features of the analysis results [20][23]Table S3 lists the general platform types and corresponding overall numbers of the data registered in arrayMap.

In reviewing the technical platform composition, two related trends become apparent (Figure 1). Originally developed in groups with expertise in molecular cytogenetics and cancer genome analysis, printed large insert clone arrays (BAC/P1) were the first whole genome CNA screening tools with a spatial resolution surpassing that of chromosomal CGH. Other groups re-employed cDNA arrays, developed for expression screening, for genomic hybridizations. However, over the last years one can observe the overwhelming use of various industrially produced oligonucleotide array platforms, which compensate their low single probe fidelity through a probe density at 1–3 orders of magnitude higher than common for BAC/P1 arrays. Another reason for the success of oligonucleotide arrays is the integration of SNP specific probes, which in principle allows to use of the same experiments for genetic association studies and the evaluation of copy number neutral loss of heterozygosity regions [12][24][25].

Figure 1. Distribution of resolutions and techniques of GEO platforms.

Each point represents a genomic array. The Y axis is labeled with probe number in log scale. The X axis denotes the time sequence of array data generation. From left to right are years from 2001 to 2011.

doi:10.1371/journal.pone.0036944.g001

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Data Access and Usage Scenarios

Based on our experience from the Progenetix project, a strong emphasis was put on a user friendly data interface. Here, we followed a “dual user type” scenario: Users without bioinformatics background should be able to intuitively visualize core data features as well as to perform standard analysis procedures, while for bioinformaticians the formatted database content should be accessible to use with their analysis tools of choice.

Query interface.

Data browsing in arrayMap is based on two types of query methods: search by experimental series metadata and search by sample features.

In the series query form, users can perform various search options by specifying (i) descriptive diagnosis text; (ii) disease classification (ICD-O 3 code(s)); (iii) disease locus (ICD topography code(s)); (iv) PubMed ID; (v) technique(s); (vi) series ID. For sample specific queries, additional features are available: sample ID; platform ID or description; and single or combined regional CNAs. Users can input gene name(s) in “regional CAN” search field. When at least two characters are entered into the field, suggestions based on a HUGO gene list are displayed for selection. Gene selections will be converted to genomic locations.

In the results table, associated array information is displayed. A number of links to additional and/or outside data is provided, according to the information available: the corresponding PubMed entries; the original GEO/AE accession display page for more complete information; the case and publication entries on the Progenetix website for further analysis; and importantly the array specific data visualization page.

Data download options.

On pages resulting from sample queries or sample data processing, users are presented with options to download sample data based on the current queryÕs return. Currently, three different file types are offered: JSON files, tab separated feature files and segments list files. These files enable bioinformaticians to perform further analyses based on their tools of choice. Particularly, the JSON format can be used for direct database import (e.g. MongoDB) or can be deparsed by common libraries (e.g. JSON.pm), or being read into web applications.

Array probe data visualization.

In the array plot interface, original plots of genomic array data sets can be searched and visualized (Figure S1). Default threshold parameters which were either provided with the data or assigned during the initial visualization will be loaded. In single array visualization, the general view of probe distribution and post-thresholding segmentation results are displayed for the whole genome as well as for each individual chromosome. If multiple arrays are retrieved, users can select sample data for downstream analysis procedures. Figure S2 shows the screenshot of single array visualization.

Users can segment the raw data values and re-plot the results after revising the following parameters:

  • Golden path edition, default HG18/NCBI Build 36. This is still the commonly used version of the human reference genome assembly. At the moment, coordinates of probes from all platforms were remapped to HG18. For the near future, we intend to allow for a selection of updated genome editions.
  • Chromosomes to plot, default 1 to 22. Single or all chromosomes can be selected for re-plotting. To avoid gender bias, most platforms do not contain probes in chromosome X and Y during the design.
  • Loss/gain thresholds. Cut-offs from which a segment is considered a genomic loss or gain. The optimum thresholds may vary between platforms.
  • Region size in kb. Sets a filter to remove CNA below (e.g. probable noise) or above (e.g. exclude non-focal CNA) a certain size range.
  • Minimal probe numbers for segments. This parameter can be used to limit the minimal number of probes required for a segment to be considered (e.g. to remove aberrant segmentation due to probe level noise). Empirical examples would be values of 2–3 for high quality BAC arrays and 6–10 for Affymetrix SNP 6 arrays.
  • Plot region. Single genomic region to be plotted, overriding the chromosome selection above. When selected, plots with this region will be generated for all current arrays. This is valuable to e.g. display the CNA status and copy number transition points for specific genes of interest (Figure S3).
Zoom-in visualization of focal CNA.

Figure 2 shows the visualization of focal genomic imbalances, e.g. to identify genes of interest targeted by focal CNA. The whole genome view of GSM535547 (human high grade glioma sample analyzed by Agilent Human Genome CGH Microarray 244A) shows a small regional deletion in chromosome 9p21. When plotting the approximate locus of the deletion (specified as chr9:21600000-22400000), genes, probes and chromosome bands in this zoomed in region are shown. Two genes, MTAP and CDKN2A can be seen as being localized in a potential homozygously deleted region. The focal deletion of these known tumor suppressor genes [26][27] points to their specific involvement in the glioblastoma sample analyzed here.

Figure 2. Zoom-in visualization of focal CNA.

(A) GSM535547 (human high grade glioma, Agilent CGH 244A) shows high quality of probe hybridization signal. CNAs are easy to distinguish. (B) When zoom-in the whole chromosome 9, an approximately 80 MB deletion is displayed, with two breakpoints located in p and q arm respectively. In addition, a small regional deletion in 9p21 is quite clear. Color bars in lower region of the panel represent 848 genes located in chromosome 9. (C) Zoom in the potential homozygously deleted region in 9p21 by specifying the exact region: chr9:21600000-22400000. The zoomed-in plot shows probes, chromosome band and two tumor suppressor genes, MTAP and CDKN2A. Gene name and location will be given while mouse hover. They link to UCSC genome browser with additional information.

doi:10.1371/journal.pone.0036944.g002

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Querying compound CNA.

The concept of focal CNA detection can be integrated with a global search for arrays containing gene specific regional imbalances. As an example, we demonstrate the search for arrays displaying imbalances in 4 gene loci associated with glioblastoma: EGFR, a transmembrane receptor and proto-oncogene [28]; PTEN, a tumor suppressor gene [29]; ASPM, frequently overexpressed in glioblastoma relative to normal brain tissue [30]; and CDKN2A (see above). In the “Search Samples” form, the “Match (Multiptle) Regions & Types” can be used to specify the genomic regions of those four genes including the expected CNA type: for EGFR (chr7:55054219-55242524:1), PTEN (chr10:89613175-89718511:-1), ASPM (chr1:195319885-195382287:1) and CDKN2A (chr9:21957751-21984490:-1), respectively. When executing the query, these regions were matched with the whole database and returned cases which have imbalances overlapping all these regions. When excluding controls and “worst quality” datasets, 303 out of 42421 arrays could be identified matching all four CNA regions. In addition to glioblastoma, several other types of cancer cases were among the results, including e.g. neuroblastomas, breast carcinomas, melanomas and lung carcinomas, which is in accordance with some previous observations [31][34]. CNA and associated data of those cases can be processed by online tools for further analysis and visualization (Figure S4) or downloaded for offline processing.

Copy number profiling of selected cancer entities.

One aim of arrayMap is to allow researchers to conveniently perform aCGH meta-analysis across different platforms. By selecting a single or several cancer entities e.g. based on their ICD entity codes or diagnostic keywords, users are able to generate disease specific CNA frequency profiles or to compare profiles of different cancer types.

As an example, we used ICD-O code 9440/3 (glioblastoma, NOS) to query the database. 1478 arrays from 25 publications were returned and passed to our suite of online analysis tools. Chromosomal ideograms and histograms were generated representing the frequency of copy number aberrations identified over the whole dataset (Figure 3A). In the overall aberration profile, the most common genomic imbalances included whole chromosome 7 gain and chromosome 10 loss, as well as focal gains e.g. on bands 1q21 and 17q21. In our example dataset, a prominent focal deletion hot-spot was centered around 9p21.3 (921 of 1478 arrays, 62.31%) which had been discussed previously [35]. The distribution of CNAs over the individual arrays was visualized through a matrix plot (Figure 3B). As additional information to the frequency histograms, this form of visualization facilitates e.g. the detection of CNA patterns among individual arrays as well as the concordance of individual CNAs (e.g. here the arm-level changes in chromosome 7 and 10).

Figure 3. Copy number profiling of glioblastoma.

(A) Chromosomal ideogram and histogram showing frequency of copy number aberrations. Percentage values corresponding to gains (yellow) and losses (blue) identified over the whole dataset. The most frequent imbalances include gain of chromosome 7 and loss of chromosome 10, 9p21.3. (B) Matrix plot of 1478 glioblastoma cases. The Y axis represents individual samples. The distribution of genomic copy number imbalances reveals the individual aberration patterns of glioblastoma. (C) Heatmap of regional CNA frequencies for 1478 arrays. The intensity of green and red color components correlates to the relative gain and loss frequencies, respectively. If dataset contains cancer subtypes, cancers with similar CNA frequency profiles will be clustered together, such that differences between subtypes will be revealed (e.g. see Figure S4H).

doi:10.1371/journal.pone.0036944.g003

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In the matrix plot, clicking on a certain segment would open the related view in the UCSC genome browser[36], for detailed information related to this genomic region (SVG plot only). The plot order of arrays can be re-sorted according to ICD morphology, ICD topography, clinical group or PubMed ID, which can be helpful in associating CNA patterns to external classification categories. For the selected classification criterium (default: ICD morphology), regional CNA frequencies for cases matching the different values will be visualized through a heatmap (Figure 3C); this feature is especially useful when comparing a number of different primary classification criteria.

An Overall Genomic Copy Number Profile of Cancer

Our high quality core dataset in arrayMap was used to generate an overall cancer copy number aberration profile based on 29,137 arrays (Figure 4). This data represented 177 cancer types according to ICD-O 3 code, with 59 types among them contained more than 50 arrays. Overall, one of the most common genomic alteration is copy-number gain of chromosome band 8q24, which is found in 30% of total samples. According to the COSMIC [37] database, the most significant cancer gene in this region is MYC. It is a well-documented oncogene codes for a transcription factor that is believed to regulate the expression of 15% of all genes, including genes involved in cell division, growth, and apoptosis [38][39]. Other common imbalances observed in at least 25% of oncogenomic arrays included gains of regions on e.g. 17q21 (29%), 1q21 (33%) and loss of regions on e.g. 8p23 (32%) and 9p21 (25%), including focal deletions of the CDKN2A/B locus (Figure 2).

Figure 4. The overall cancer copy number aberration profile consisted of 29137 arrays.

This plot represents 177 cancer types according to ICD-O 3 code. Percentage values in Y axis corresponding to numbers of gains (green) and losses (red) account for the whole dataset.

doi:10.1371/journal.pone.0036944.g004

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While the overall CNA frequency distribution points towards DNA features targeted in multiple entities, this information is insufficient for deriving molecular mechanisms associated with specific cancer types. The genomic heterogeneity of different neoplasias is reflected in the varying patterns of regional CNA frequencies. Based on our core dataset, we have generated a heatmap-style visualization of frequency profiles for all ICD-O entities containing more than 50 arrays (Figure S5). The striking patterning of the CNA profiles indicates the non-random occurrence of CNAs, and should be seen as an invitation to explore e.g. CNA similarities shared by separate histopathological entities, as a way to transpose knowledge about pathophysiological mechanisms.

Discussion Top

arrayMap was developed to facilitate the progress of oncogenomic research. Our aim is to provide high-quality genomic copy number profiles of human tumors, along with a set of tools for accessing and analyzing CNA data. The service has been implemented with a straightforward web interface, including search options for CNA features and clinical annotation data. All assembled datasets are processed into platform independent segmentation and, for the vast majority of arrays, probe level data files, and are presented in consistent formats. Importantly, the direct access to precomputed probe level data plots supports a rapid evaluation of experiments for features of interest. As a curated database using standardized annotation schemes (e.g. ICD classification), arrayMap facilitates the exploration of cancer type specific CNA data, as well as the statistical association of genomic features to clinical parameters.

arrayMap is a dynamic database that is being continuously expanded and improved. We will review the existing and newly published articles to update the database periodically. Over the past decade, we have witnessed a rapidly increasing number of aCGH publications, which gives us sufficient evidences to anticipate that cases in our database will continue to be deposited at a high rate. Although arrayMap is not a user driven repository, we welcome and support users interested in using the site for yet undisclosed data, if they agree on data sharing upon publication.

Although, in contrast to the continuous data from expression analysis, copy number analysis explores discrete value spaces (countable number of DNA copies, for segments defined by genomic base positions), interpretation of the data can vary due to different low level (e.g. signal/background correction) and higher level (e.g. segmentation algorithms, regional or size based filtering) procedures. In that respect, we have to emphasize that the results of our data processing and annotation procedures are open to scrutiny. We encourage a critical review of individual results, and are open for suggestions regarding improved processing procedures for specific platforms.

In this paper, we have provided example scenarios of using arrayMap on different levels, i.e. locus centric and for entity profiling. We believe that systematic analyses will help researchers to discover features which are indiscernible in individual studies, and thus bring new insights for understanding of disease pathology and the development of new therapeutic approaches [40][43]. We expect that researchers will integrate arrayMap data with their own analysis efforts, e.g. to increase sample size or for result verification purposes. We hope that this database will promote further evolution of microarray data meta-analysis. ArrayMap provides access to more than 200 tumor types, which makes it suitable for research across cancer entities. Furthermore, normal sample controls are of vital importance for genomic imbalances studies. ArrayMap includes more than 3000 normal samples from healthy individuals or from normal tissues of cancer patients. These data could be integrated as reference dataset e.g. to account for copy number variation data superimposed on the tumor profiling results.

In the near future, with the continuous accumulation of very high resolution CNA data from genomic arrays and next-generation sequencing experiments, it will become possible to integrate these data into systems biology methods to elucidate effects of genomic instability, and describe the results from more perspectives. Envisioned examples would be e.g. the identification of genes that are involved in metastasis and treatment response; identification of chromosomal breakpoints distribution in cancer; and modeling functional networks in cancer by systems biology approaches.

Methods Top

Dataset Collection

Raw experimental data from a variety of platforms and repositories were extracted. They were converted to an uniform format which is suited to our reanalysis and visualization system. After a series of parsing procedures, the called copy number data is stored in arrayMap. The flowchart of arrayMap data collection and analysis is as shown in Figure 5. Five main data sources are integrated into arrayMap:

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Figure 5. The flowchart of arrayMap data collection and analysis procedures.

Publicly available raw data or segmented data was collected from the respective data sources. Files were re-processed by distinct procedures, according to the different data types. Probe coordinates were remapped to the most commonly encountered human reference genome assembly (NCBI Build 36/hg18). All probe specific ratios were converted to log2 values. Thresholds for genomic gain and loss were obtained from the original publications or series annotations; if not available, empirical thresholds were assigned. A minimum of 2 probes was required for calling a CNA segment, with higher values used on high-density arrays and/or in cases of excessive probe level noise. Processed probe and segment information was converted to uniform formats and stored in per-sample text files, which are accessed through the arrayMap web applications.

doi:10.1371/journal.pone.0036944.g005

GEO/AE.

For extracting appropriate data Series from GEO/AE, two basic criteria have to be fulfilled. First, the raw data has to be from human malignancies analyzed by BAC, cDNA, aCGH or oligonucleotide arrays. Second, the array platform must be genome wide, with the optional omission of the sex chromosomes. Chromosome or region specific arrays were excluded because they were not able to reveal the whole genomic profile of the respective cancer. Associated clinical data was extracted if available.

TCGA.

Segmentation data with available clinical information was extracted and incorporated into the database. Due to data sharing restrictions, TCGA data is an exception in that, so far no probe level data is incorporated into arrayMap. This exception was accepted since users will be able to access individual TCGA datasets through the projects web portal at http://tcga-data.nci.nih.gov/tcga/.

Publications.

Many aCGH datasets can be found in the text or supplementary files of publications. In order to collect data from publications, we relied on our Progenetix projectÕs setup. Data in Progenetix is manually curated. The collection strategies are:

  • literature mining using complex search parameters through PubMed
  • identification of called aCGH data, in GP annotation or tabular format (article, supplementary tables)
  • evaluation of supplementary files for probe specific data tables
  • follow-up on article links outs, to repository entries or referenced datasets
User submission.

User submitted data was provided in a number of formats which were converted to the standard format as described. Although we accept and support private datasets, we insist on integration of at least the genomic and core clinical data (e.g. disease classifiers) upon publication of the datasets analysis results.

Dataset Analysis

Probe remapping.

A pipeline has been generated for determining the genomic positions for the tens to hundreds of thousands array probes with reference to a common genome Golden Path edition. For each array platform, the genome positions of probes were remapped to the current commonly used version of the human reference genome assembly (NCBI Build 36.1/hg18). Specific mapping procedures were employed for different types of probes. BAC clones were firstly remapped according to the clone sets information of Sanger/DECIPHER database [44]. If the probe position was not available, the UCSC Genome annotation database [36] (release hg18) was used for compensation. After these two steps, a mean of 98% of the BAC clones were remapped. For IMAGE clone sets, only the UCSC Genome annotation database was used. The average remapping rate of IMAGE clones was 91%. Affymetrix raw CEL data files were analyzed based on hg18 library files, namely the output segments have hg18 coordinates. The summary of the percentage of mapped probes is given in Table 3. The mapping details for each platform can be found in the (Table S4).

Table 3. Percentage of remapped probes according to platform types.

doi:10.1371/journal.pone.0036944.t003

Probe signal normalization.

The array data available was given in a variety of formats, most frequently as log2 ratio of probe hybridization intensity. In order to make data from different platforms directly comparable, all other types of normalized values were converted to log2. For dye swap experiments, reference/tumor intensity ratios data was “reversed” representing a tumor/reference value. For some two-color arrays for which only raw signal intensity were provided, the normalized log2 ratio for each probe was calculated by.

where T and T represent tumor sample intensity and tumor channel background intensity respectively, and R and R represent reference sample intensity and reference channel background intensity respectively. If multiple instances of the same clone exist, the average signal intensity of the certain clone was considered.

To call gains and losses according to normalized log2 ratio is an important step to identify copy number imbalances. For each re-analyzable dataset, related publications were explored to obtain original threshold descriptions. If this information was not available, empirical thresholds were assigned and resulting CNA calls were visually compared with probe value plots. Processing method and threshold information for each array are provided in the Table S5.

Affymetrix genotyping arrays.

For the widely used Affymetrix GenomeWide SNP arrays, raw CEL files were downloaded and underwent a massive re-analysis using the R package aroma.affymetrix [45] with the CRMAv.2 method [46]. During the processing step, approximately 50 normal sample arrays were employed as a reference set for each array type to reduce the noise level. Normal tissue arrays from different labs were extracted and used to build the reference dataset. In order to obtain high quality arrays, we excluded arrays which contain segments greater than 3 mega-bases, since copy number variations are always smaller than 3 mega-bases. The list of normal tissue reference arrays is giving in Table S6.

Quality control.

In our review of array data deposited in GEO or collected from publication supplements we encountered a large number of individual data sets with insufficient or limited probe quality. Also, for samples of unprocessed raw data (e.g. Affymetrix CEL files), we found that QC measures reported previously (e.g. call rate [47], NUSE [48], RLE [48]) only had a limited accuracy for detection of arrays with inadequate probe level data. Currently, the most viable strategy for quality assessment of processed, heterogeneous copy number arrays is the visual inspection of probe plotting and segmentation results through an experienced researcher. For the first arrayMap edition we generated a quality classification system, which contains a total of 4 categories based on inspections of genome-wide array plots:

  • Excellent. Probe signal distribution is significantly different between normal regions and imbalance regions. Signal baseline is distinct and unique, making segmentation threshold realistic appearing. Chromosomal changes are pretty clear.
  • Good. In general good quality. Probe signal may contain some noise, but tolerable. Chromosomal changes are distinguishable.
  • Hypersegmented. Serrated distribution of probe signal intensities, causing dozens of separate peaks and discontinuous segments. Chromosomal changes are always up to several hundreds and smaller than 5 mega-bases.
  • Noisy. Probe signal intensities are highly scattered, but well-distributed, with high standard deviation, resulting in the inability to differentiate copy number changes.

Depending on the intended research purpose this basic classification system can be used for a pre-analysis triage of copy number data. Applying stringent review criteria we identified a core dataset with “excellent” quality arrays accounting for approximately 60 percent of total arrays. We are currently working on a platform independent quality assessment system for genomic arrays, which will be implemented in future versions of the arrayMap resource.

Associated data.

For arrayMap, data is stored with separate datasets for each array. This is in contrast to the Progenetix database, for which technical replicates where available are combined into case specific CNA profiles. In arrayMap, technical replicates are assigned an identical case identifier to facilitate downstream statistical procedures including e.g. clinical data correlations. The assignment of the correct diagnostic entity to each sample is an essential step in generating a binding between genomic and associated data points. At the same time, to ensure annotation consistency and make the retrieval process more efficient, for all CNA profiles the following data points were manually collected from GEO/ArrayExpress and published papers if available.

  • Descriptive diagnostic text, as available through the original source
  • Diagnostic classification according to the International Classification of Diseases in Oncology (ICDO 3, morphology with code)
  • Tumor locus according to ICD (ICD topography with code)
  • Source of material (e.g. primary tumor, cell line, metastasis)
  • Clinical parameters where available, including age, gender, grade, clinical stage (TNM coded), recurrence/progression, time to recurrence/progression, death and followup
Web Server.

An online interface of arrayMap database was created using Perl common gateway interface (CGI) and R scripts running on Mac OS X Server. Sample and series data is stored using a MongoDB database eingine (http://www.mongodb.org). Precomputed array plots are stored as flat files, mostly in both SVG and PNG versions. The online release of the service has been optimized to be compatible with major browsers supporting current web standards (CSS2, HTML5, XML with inline SVG; e.g. Safari > = 3.0, Firefox > = 3.0, InternetExplorer > = 9, Google Chrome) with limited fallback support. Dynamic graphics provided in the array plot module were implemented as server side services by technologies including XML/XHTML, JavaScript, SVG and HTML5 Canvas.

For the future, we intend a quarterly database content revision to ensure inclusion of newly published articles and GEO/AE entries. Archived versions of the sample annotations will be made available upon special request. Additional feature and small data updates will be performed as seen necessary. The “News” page of Progenetix/arrayMap will be used for feature and content announcements.

Supporting Information Top

Figure S1.

Array data sets visualization. Original plots and optimized parameters for GSE21530 which contains 8 intimal sarcoma samples hybridized on Agilent CGH Microarray 244A platform. The normalized probe signal log2 ratios and post-thresholding segmentation results for each array are intuitively displayed. Genomic alterations are represented by horizontal green (gain) and red (loss) lines. Alterations defined here as regions with log2 ratio >0.15 or <−0.15. Simplified schemas of CNAs link to UCSC genome browser for further review.

(PDF)

Figure S2.

Screenshot of single array visualization. ArrayMap plots for GSM630977 (acute myelogenous leukemia). Besides the whole genome view, subviews of each chromosome are displayed as well. From these plots, different kinds of genetic variation events are clearly revealed, e.g. massive genomic rearrangement in chromosome 6; arm-level gain of chromosome 8q and 3MB focal change around 1p31.3. Through the “Plot Array Data” interface, users can segment the raw data values and re-plot the results with customized parameters.

(PDF)

Figure S3.

Plot single genomic region. In the “Plot Array Data” interface, input the precise location (chr5:1100000-1400000) in “Plot Region” field. Plots with this region were generated for all 8 arrays in the current series (GSE21530). In this region, there are 5 genes which are shown schematically as colored boxes. CNA status and copy number transition points for these genes are displayed.

(PDF)

Figure S4.

Compound CNA query. (A) Four gene loci associated with glioblastoma (EGFR, PTEN, ASPM and CDKN2A) were inserted into “Match (Multiple) Regions & Types” field. 303 out of 42421 arrays were returned. (B) Classification information of these 303 arrays were displayed and can be selected for the following analysis. (C) Statistical and plot parameters can be customized. Associated data was processed by online tools, and returned results included: (D) Chromosomal ideogram and (E) histogram, show frequency of copy number aberrations; (F) Matrix plot reveals the aberration pattern of selected arrays; (G) Array classification tree generated by hierarchical Ward clustering, arrays with similar frequency of CNA are part of the tree branch. (H) Heatmap of CNA frequencies clustered by clinical group.

(PDF)

Figure S5.

Heatmap of frequency profiles for 59 cancer types. Heatmap visualization of frequency profiles for all ICD-O entities containing more than 50 arrays in our core dataset. Region specific gain/loss frequencies were mapped to 1MB intervals. The intensity of colors (green: gains; losses: red) corresponds to the relative frequency of CNAs for each interval.

(PDF)

Table S1.

Entities extracted from NCBI GEO and EBI ArrayExpress.

(XLS)

Table S2.

Cancer entities grouped by ICD-O code.

(XLS)

Table S3.

Platform type distribution in arrayMap.

(XLS)

Table S4.

Probe remapping rate for platforms.

(XLS)

Table S5.

Processing method and threshold for calling genomic gains and losses.

(XLS)

Table S6.

Normal tissue reference arrays for Affymetrix platforms.

(XLS)

Acknowledgments Top

We want to thank Christian von Mering, Homayoun Bagheri, Henrik Bengtsson and Nuria Lopez-Bigas for helpful discussions.

Author Contributions Top

Conceived and designed the experiments: HC NK MB. Performed the experiments: HC MB. Analyzed the data: HC NK MB. Contributed reagents/materials/analysis tools: HC NK MB. Wrote the paper: HC MB.

References Top

  1. Stallings RL (2007) Are chromosomal imbalances important in cancer? Trends in genetics : TIG 23: 278–283. doi: 10.1016/j.tig.2007.03.009FIND THIS ARTICLE ONLINE
  2. Myllykangas S, Himberg J, Böhling T, Nagy B, Hollmén J, et al. (2006) DNA copy number amplification profiling of human neoplasms. Oncogene 25: 7324–7332. FIND THIS ARTICLE ONLINE
  3. Weir BA, Woo MS, Getz G, Perner S, Ding L, et al. (2007) Characterizing the cancer genome in lung adenocarcinoma. Nature 450: 893–898. FIND THIS ARTICLE ONLINE
  4. Wiedemeyer R, Brennan C, Heffernan TP, Xiao Y, Mahoney J, et al. (2008) Feedback circuit among INK4 tumor suppressors constrains human glioblastoma development. Cancer cell 13: 355–364.FIND THIS ARTICLE ONLINE
  5. Mullighan CG, Goorha S, Radtke I, Miller CB, Coustan-Smith E, et al. (2007) Genome-wide analysis of genetic alterations in acute lymphoblastic leukaemia. Nature 446: 758–764. FIND THIS ARTICLE ONLINE
  6. Cancer Genome Atlas Research Network (2008) Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455: 1061–1068. FIND THIS ARTICLE ONLINE
  7. Kan Z, Jaiswal BS, Stinson J, Janakiraman V, Bhatt D, et al. (2010) Diverse somatic mutation patterns and pathway alterations in human cancers. Nature 466: 869–873. FIND THIS ARTICLE ONLINE
  8. Bergamaschi A, Kim YH, Wang P, Sørlie T, Hernandez-Boussard T, et al. (2006) Distinct patterns of DNA copy number alteration are associated with different clinicopathological features and geneexpression subtypes of breast cancer. Genes, chromosomes & cancer 45: 1033–1040. FIND THIS ARTICLE ONLINE
  9. Hu X, Stern HM, Ge L, O’Brien C, Haydu L, et al. (2009) Genetic alterations and oncogenic pathways associated with breast cancer subtypes. Molecular cancer research : MCR 7: 511–522. FIND THIS ARTICLE ONLINE
  10. Kallioniemi A, Kallioniemi OP, Sudar D, Rutovitz D, Gray JW, et al. (1992) Comparative genomic hybridization for molecular cytogenetic analysis of solid tumors. Science (New York, NY) 258: 818–821. FIND THIS ARTICLE ONLINE
  11. Pollack JR, Perou CM, Alizadeh AA, Eisen MB, Pergamenschikov A, et al. (1999) Genome-wide analysis of DNA copy-number changes using cDNA microarrays. Nature genetics 23: 41–46. FIND THIS ARTICLE ONLINE
  12. Bignell GR, Huang J, Greshock J, Watt S, Butler A, et al. (2004) High-resolution analysis of DNA copy number using oligonucleotide microarrays. Genome research 14: 287–295. FIND THIS ARTICLE ONLINE
  13. Baudis M (2007) Genomic imbalances in 5918 malignant epithelial tumors: an explorative metaanalysis of chromosomal CGH data. BMC cancer 7: 226. FIND THIS ARTICLE ONLINE
  14. Alloza E, Al-Shahrour F, Cigudosa JC, Dopazo J (2011) A large scale survey reveals that chromosomal copy-number alterations significantly affect gene modules involved in cancer initiation and progression. BMC medical genomics 4: 37. FIND THIS ARTICLE ONLINE
  15. Barrett T, Troup DB, Wilhite SE, Ledoux P, Evangelista C, et al. (2011) NCBI GEO: archive for functional genomics data sets–10 years on. Nucleic acids research 39: D1005–10. FIND THIS ARTICLE ONLINE
  16. Parkinson H, Sarkans U, Kolesnikov N, Abeygunawardena N, Burdett T, et al. (2010) ArrayExpress update–an archive of microarray and high-throughput sequencing-based functional genomics experiments. Nucleic acids research 39: D1002–D1004. FIND THIS ARTICLE ONLINE
  17. Scheinin I, Myllykangas S, Borze I, Böhling T, Knuutila S, et al. (2008) CanGEM: mining gene copy number changes in cancer. Nucleic acids research 36: D830–5. FIND THIS ARTICLE ONLINE
  18. Cao Q, Zhou M, Wang X, Meyer CA, Zhang Y, et al. (2011) CaSNP: a database for interrogating copy number alterations of cancer genome from SNP array data. Nucleic acids research 39: D968–74.FIND THIS ARTICLE ONLINE
  19. Baudis M, Cleary ML (2001) Progenetix.net: an online repository for molecular cytogenetic aberration data. Bioinformatics (Oxford, England) 17: 1228–1229. FIND THIS ARTICLE ONLINE
  20. Baumbusch LO, Aarøe J, Johansen FE, Hicks J, Sun H, et al. (2008) Comparison of the Agilent, ROMA/NimbleGen and Illumina platforms for classification of copy number alterations in human breast tumors. BMC genomics 9: 379. FIND THIS ARTICLE ONLINE
  21. Curtis C, Lynch AG, Dunning MJ, Spiteri I, Marioni JC, et al. (2009) The pitfalls of platform comparison: DNA copy number array technologies assessed. BMC genomics 10: 588. FIND THIS ARTICLE ONLINE
  22. Greshock J, Feng B, Nogueira C, Ivanova E, Perna I, et al. (2007) A comparison of DNA copy number profiling platforms. Cancer research 67: 10173–10180. FIND THIS ARTICLE ONLINE
  23. Bengtsson H, Ray A, Spellman P, Speed TP (2009) A single-sample method for normalizing and combining full-resolution copy numbers from multiple platforms, labs and analysis methods. Bioinformatics (Oxford, England) 25: 861–867. FIND THIS ARTICLE ONLINE
  24. Heinrichs S, Look T (2007) Identification of structural aberrations in cancer by SNP array analysis. Genome biology. pp. 1–5.
  25. Carter NP (2007) Methods and strategies for analyzing copy number variation using DNA microarrays. Nature genetics 39: S16–S21. FIND THIS ARTICLE ONLINE
  26. Lubin M, Lubin A (2009) Selective killing of tumors deficient in methylthioadenosine phosphorylase: a novel strategy. PloS one 4: e5735. FIND THIS ARTICLE ONLINE
  27. Krasinskas AM, Bartlett DL, Cieply K, Dacic S (2010) CDKN2A and MTAP deletions in peritoneal mesotheliomas are correlated with loss of p16 protein expression and poor survival. Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc 23: 531–538. FIND THIS ARTICLE ONLINE
  28. Smith JS, Tachibana I, Passe SM, Huntley BK, Borell TJ, et al. (2001) PTEN mutation, EGFR amplification, and outcome in patients with anaplastic astrocytoma and glioblastoma multiforme. Journal of the National Cancer Institute 93: 1246–1256. FIND THIS ARTICLE ONLINE
  29. Li J (1997) PTEN, a Putative Protein Tyrosine Phosphatase Gene Mutated in Human Brain, Breast, and Prostate Cancer. Science (New York, NY) 275: 1943–1947. FIND THIS ARTICLE ONLINE
  30. Horvath S, Zhang B, Carlson M, Lu KV, Zhu S, et al. (2006) Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target. Proceedings of the National Academy of Sciences of the United States of America 103: 17402–17407. FIND THIS ARTICLE ONLINE
  31. Zhang W, Zhu J, Bai J, Jiang H, Liu F, et al. (2010) Comparison of the inhibitory effects of three transcriptional variants of CDKN2A in human lung cancer cell line A549. Journal of experimental & clinical cancer research : CR 29: 74. FIND THIS ARTICLE ONLINE
  32. van der Rhee JI, Krijnen P, Gruis NA, de Snoo FA, Vasen HFA, et al. (2011) Clinical and histologic characteristics of malignant melanoma in families with a germline mutation in CDKN2A. Journal of the American Academy of Dermatology.
  33. Bourdeaut F, Isidor B, Ferrand S, Thomas C, Moreau A, et al. (2011) Homozygous PTEN deletion in neuroblastoma arising in a child with Cowden syndrome. American journal of medical genetics Part A 155: 1763–1766. FIND THIS ARTICLE ONLINE
  34. Jin K, Kong X, Shah T, Penet MF, Wildes F, et al. (2011) Breast Cancer Special Feature: The HOXB7 protein renders breast cancer cells resistant to tamoxifen through activation of the EGFR pathway. Proceedings of the National Academy of Sciences of the United States of America.
  35. Wiltshire RN, Rasheed BK, Friedman HS, Friedman AH, Bigner SH (2000) Comparative genetic patterns of glioblastoma multiforme: potential diagnostic tool for tumor classification. Neurooncology 2: 164–173. FIND THIS ARTICLE ONLINE
  36. Fujita PA, Rhead B, Zweig AS, Hinrichs AS, Karolchik D, et al. (2011) The UCSC Genome Browser database: update 2011. Nucleic acids research 39: D876–82. FIND THIS ARTICLE ONLINE
  37. Forbes SA, Bindal N, Bamford S, Cole C, Kok CY, et al. (2011) COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer. Nucleic acids research 39: D945–50.FIND THIS ARTICLE ONLINE
  38. Gearhart J, Pashos EE, Prasad MK (2007) Pluripotency redux–advances in stem-cell research. The New England journal of medicine 357: 1469–1472. FIND THIS ARTICLE ONLINE
  39. Dalla-Favera R, Bregni M, Erikson J, Patterson D, Gallo RC, et al. (1982) Human c-myc onc gene is located on the region of chromosome 8 that is translocated in Burkitt lymphoma cells. Proceedings of the National Academy of Sciences of the United States of America Vol. 79: 7824–7827. FIND THIS ARTICLE ONLINE
  40. Climent J, Dimitrow P, Fridlyand J, Palacios J, Siebert R, et al. (2007) Deletion of chromosome 11q predicts response to anthracycline-based chemotherapy in early breast cancer. Cancer research 67: 818–826. FIND THIS ARTICLE ONLINE
  41. Chin K, DeVries S, Fridlyand J, Spellman PT, Roydasgupta R, et al. (2006) Genomic and transcriptional aberrations linked to breast cancer pathophysiologies. Cancer cell 10: 529–541. FIND THIS ARTICLE ONLINE
  42. Stevens KN, Fredericksen Z, Vachon CM, Wang X, Margolin S, et al. (2012) 19p13.1 is a triple negative-specific breast cancer susceptibility locus. Cancer research.
  43. Park NI, Rogan PK, Tarnowski HE, Knoll JHM (2012) Structural and genic characterization of stable genomic regions in breast cancer: Relevance to chemotherapy. Molecular oncology.
  44. Firth HV, Richards SM, Bevan AP, Clayton S, Corpas M, et al. (2009) DECIPHER: Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources. The American Journal of Human Genetics 84: 524–533. FIND THIS ARTICLE ONLINE
  45. Bengtsson H, Simpson K, Bullard J, Hansen K (2008) aroma.affymetrix: A genetic framework in R for analyzing small to very large Affymetrix data sets in bounded memory. Tech Report #745 Department of Statistics, University of California, Berkeley.
  46. Bengtsson H, Wirapati P, Speed TP (2009) A single-array preprocessing method for estimating fullresolution raw copy numbers from all Affymetrix genotyping arrays including GenomeWideSNP 5 & 6. Bioinformatics (Oxford, England) 25: 2149–2156. FIND THIS ARTICLE ONLINE
  47. Laurie CC, Doheny KF, Mirel DB, Pugh EW, Bierut LJ, et al. (2010) Quality control and quality assurance in genotypic data for genome-wide association studies. Genetic Epidemiology 34: 591–602.FIND THIS ARTICLE ONLINE
  48. F C, AL A, SA K, TP S, VL SM (2005) NUSE and RLE: Quality assessment of oligonucleotide microarray data to quantify systemic variation. 2005 Meeting of the Federation of Clinical Immunology Societies Boston, MA.

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http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0036944

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