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VLAN Trunking Protocol (VTP) Overview – Series 4
What do you think what happens? If a new Cisco Catalyst® switch is placed between any two VTP domains, the Cisco Catalyst® switch will join the VTP domain identified by the first VTP update it receives. Just check the below example.
The VTP domain can be changed to another in a Cisco Catalyst® switch by using the CLI prompt, this is the only way you can change.
Review for CCENT 640-822 Network Simulator
CCENT 640-822 Network Simulator is a simply great simulator for the CCNA Certification aspirers covering all the ICND1 topics.
The state-of-the-art, interactive simulation software enables you to practice your networking skills with 98 structured labs designed to help you learn by doing, which is the most effective method of learning.
The CCENT 640-822 Network Simulator is divided into three categories, Skill Builder, Configuration Scenario and Troubleshooting Scenario. The best part of the CCENT 640-822 Network Simulator is its very fast consumes less CPU processing power & it's a java based application. The lab scenarios included in the CCENT 640-822 Network Simulator are far more complex, challenging you to learn how to perform realistic network configuration and troubleshooting tasks. The CCENT 640-822 Network Simulator is best for the CCNA Certifications aspirers especially for the guys who have less exposure to real environment.
The best thing about the CCENT 640-822 Network Simulator is the organization of labs. The Skill builder consists of three parts; even the Configuration Scenario consists of three parts where as Troubleshooting scenario has one part. These labs are quite handy and useful. The moment you open any lab the corresponding pdf file opens with the lab instructions. You are supposed to answer few questions in the labs, the LAB topology is explained well.
I would strongly recommend the CCENT 640-822 Network Simulator for anyone who is depended on the self study. CCENT 640-822 Network Simulator is available only as a download, and is a subset of the CCENT 640-822 Network Simulator, covering all the ICND1 topics.
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Cisco Edge Quest 2 Tournament: Play to Win from February 23 Through March 6, 2009
The power is at your fingertips. Play, challenge a friend, and see if you score high enough to win. The top three eligible scorers at the end of each week could win great prizes.
Play Now: www.cisco.com/go/edgequest
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Cisco Edge Quest 2 is an online game that is fast and fun. Players are encouraged to release the power of the Cisco ASR 9000 Series Aggregation Services Routers by maneuvering a craft through various levels in order to handle the increasing demands on IP networks. It is fast and fun, and the learning comes through playing.
The Edge Quest 2 tournament starts on Monday, February 23, 2009 and ends on Friday, March 6, 2009. Play as many times as you want to master the game: your top score might qualify you to win prizes worth up to $500. Prizes will be awarded to each week's top eligible scorers.
For official terms and conditions for participation in the Cisco Edge Quest 2 Tournament: visit www.cisco.com/go/edgequest.
Don¹t miss this opportunity to play, learn, and network. Also, you can practice your skills and challenge your friends with the new Edge Quest 2 iPhone and Facebook applications.
Cisco Saudi -Expo 2009 was amazing and helpful
As I posted before about the Cisco Saudi – Expo 2009 which was held at King Fahd Cultural Center, Riyadh ,Saudi Arabia on 9 & 10th February 2009. The conference was opened by Dr Mohammed Al-Suwaiyel, President of King Abdullah College of Science and Technology, and a keynote from Dr Badr Al Badr, managing director of Cisco Saudi Arabia.
The two-day event for customers and partners included sessions on a number of emerging technologies, including unified communications, collaboration, managed services, and Cisco's Connected Home and Smart Education systems.The two-day event for customers and partners included sessions on a number of emerging technologies, including unified communications, collaboration, managed services, and Cisco's Connected Home and Smart Education systems.
The technical presentations were simply great I would say they were meeting the Networkers standards.
Some of the topics covered as follows which can be downloaded.
VLAN Trunking Protocol (VTP) Overview – Series 3
In Cisco Catalyst® switches the VTP domain can be added through CLI configuration or else it can be learnt from an adjacent VTP Cisco Catalyst® switch. Let's see this from the below example
Switch C is added to the network connecting via a trunk port to Switch B with no VTP domain configured, in this mode when the Switch C receives the first VTP update from the adjacent Switch B , it will become the part of the VTP domain ITKE as identified in the VTP update.
Cisco's innovative EnergyWise technology improves Green Practices
If you all are looking for a complete green solution for your Data Center using Cisco Catalyst® switches, there is good news with Cisco EnergyWise software. The most interesting fact of the innovative Cisco EnergyWise technology for Cisco Catalyst® switches that measures, reports and reduces the energy consumption of Internet Protocol (IP) devices such as phones, laptops and access points. Cisco also announced industry partner solutions along with an intelligent middleware acquisition that will ultimately enable the management of power consumption for entire building systems such as lights, elevators, and air conditioning and heating.
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- Cisco EnergyWise was purpose-built in alignment with Cisco's vision of how networking technology can support a low-carbon economy, cut energy use and promote overall environmental sustainability.
- Cisco EnergyWise is part of the company's technology roadmap for changing the value-chain of information technology (IT) by placing the intelligent network squarely at the center of how companies reduce greenhouse gas (GhG) emissions across their entire organization.
- Cisco EnergyWise will roll out in three phases to improve IT and building system energy utilization:
- In the first phase (February 2009), Network Control, Cisco EnergyWise will be supported on Catalyst switches and manage the energy consumption of IP devices such as phones, video surveillance cameras and wireless access points.
- In the next phase (Summer 2009), IT Control, there will be expanded industry support of EnergyWise on devices such as personal computers (PCs), laptops and printers.
- In the final phase (Early 2010), Building Control, Cisco EnergyWise will be extended to the management of building system assets such as heating, ventilation and air conditioning (HVAC), elevators, lights, employee badge access systems, fire alarm systems and security systems.
- Cisco EnergyWise provides real-time, granular measurement capabilities to give chief information officers better visibility into energy savings across an entire organization as well as specific places in the network such as the campus, branch office and data center .
- Cisco EnergyWise supports currently deployed Catalyst switches worldwide with a free software upgrade and new Catalyst switches beginning in February 2009.
- Cisco is working with Schneider Electric for building utility management, SolarWinds for network monitoring, and Verdiem for monitoring PC power in order to extend EnergyWise as a platform for power management across IT and the building systems of an organization by early 2010.
- Cisco also announced the acquisition of Richards-Zeta's intelligent middleware to provide simplified and cost-effective interoperability and integration between building infrastructure, IT applications and Cisco EnergyWise.
- The new Cisco Green Business Value Calculator provides customers with projected cost and GhG emission-saving results from adopting Cisco EnergyWise and other green technologies such as Cisco TelePresenceTM, Cisco WebExTM and Cisco Unified Communications.
- Cisco also introduced adaptive power management functionality in the Cisco Wireless Control System to save power by enabling customers to turn off redundant radios during off hours.
Supporting Quotes:
- "Cisco believes that information technology has the power to transform the way the world manages its environmental and energy challenges. With EnergyWise, Cisco is uniquely positioned to help our customers gain a networkwide view of energy consumption that encompasses not only device-level power, but in the future entire network efficiency, building operations and business practices across an entire organization," said Judy Lin, senior vice president of the Ethernet Switching technology group for Cisco.
- "With the organizationwide visibility that Cisco EnergyWise provides, we anticipate potential annual reduced energy savings of 15 to 20 percent conservatively across our portfolio of commercial office buildings. This could equate to energy savings of 19 to 33 cents a square foot, which over the course of a year could be thousands of dollars in energy related savings," said Jeff Ton, vice president of IT for Lauth Property Group.
- "As a global specialist in energy management, we help our customers to make the most of their energy," said Jean-Pascal Tricoire, CEO of Schneider Electric. "Today, through the monitoring and control of energy usage with energy efficiency products, Schneider Electric's solutions deliver up to 30 percent energy savings to residential, commercial and industrial buildings. Using the network as a platform through Cisco EnergyWise, we will be able to bring additional reductions in energy consumption, offering our customers an efficient, simple, and reliable infrastructure."
- "With SURVEYOR, Verdiem's commitment to reducing the environmental impact of technology has already helped our customers save up to 60 percent of their PC networks' energy consumption. Verdiem is proud to be working towards a Cisco EnergyWise enterprise network energy management agreement because not only is EnergyWise good corporate citizenship, but in the future it will also help improve business performance and most importantly, gives corporations a reliable way to meet their sustainability goals," said Jeremy Jaech, Verdiem's CEO.
- "SolarWinds provides powerful, simple and affordable network management solutions to thousands of IT organizations around the world. Within the year, the integration of Cisco EnergyWise and SolarWinds Orion Network Performance Monitor will allow network engineers to easily manage energy use alongside the other metrics they monitor on a daily basis," said Kenny Van Zant, chief product strategist.
Supporting Resources:
- Video on EnergyWise with John Chambers
- Check out the Cisco EnergyWise Demo
- Read the "Cisco Ecolibrium" blog post
- Customer video with leading United Kingdom retailer John Lewis Partnership
- Customer video with Friends Provident, a financial services company in the United Kingdom
- Customer video with the University of Plymouth in the United Kingdom
VLAN Trunking Protocol (VTP) Overview – Series 2
In my previous post I just introduced the VLAN trunking protocol; let's talk about the VTP domain.
The VTP domain consists of a group of logical group of Cisco Catalyst Switches that are part of the same VTP management domain. A Cisco Catalyst Switch can belong only to one VTP domain at any time,
The VTP domain is a logical group of switches that will share VLAN information. Each switch can belong only to one VTP domain in addition to sharing the VLAN information with all other Cisco Catalyst Switches in the same VTP domain. Cisco Catalyst Switches in different VTP domains drops any VTP updates received from Cisco Catalyst Switches.
VLAN Trunking Protocol (VTP) Overview – Series 1
What is a VLAN Trunking Protocol (VTP)?
VLAN Trunking Protocol (VTP) is a Cisco proprietary Layer 2 messaging protocol that manages the addition, deletion, and renaming of VLANs for the Cisco Catalyst Switches in the same VLAN Trunking Protocol (VTP) domain. VLAN Trunking Protocol (VTP) enables Cisco Catalyst Switches to exchange and maintain consistent VLAN information amongst a group of Cisco Catalyst Switches.
For example, information for the VLAN 50 defined in Cisco Catalyst Switch A is propagated via VTP updates to all other Cisco Catalyst Switches (Switches B, C and D) in the same VTP domain, the other Cisco Catalyst Switches B, C and D will all end up adding VLAN 50 in their local VLAN data base. VTP is available on most of the Cisco Catalyst Family products.
The comparable IEEE standard in use by other manufacturers is GVRP.
CCNA, Administrative Distance for the Dynamic Routing Protocols
If you are preparing for your CCNA then it's better to know about the dynamic routing protocols. Cisco Routers support many dynamic routing protocols for learning routes in a complex network topology. The routing protocols supported are the following:
Each routing protocol has its own Administrative Distance (AD), which is a value representing the trustworthiness of the specific routing protocol. If a router learns a destination route from two or more routing protocols, it selects the route from the protocol having the smallest Administrative Distance.
The below table shows the default Administrative Distance (AD) values for each routing protocol:
Routing Protocol
AD Value
Now, if the router learns a specific destination route from both OSPF and RIP, it will select the route learned from OSPF because this protocol has a smaller AD compared to RIP (110 instead of 120).

subset: commoncrawl


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Green Bay Packers rookie cornerback Casey Hayward (29) defends against Cleveland Browns receiver Greg Little during a preseason game at Lambeau Field on Thursday, Aug. 16, 2012.
In some ways, Casey Hayward's rookie season with the Green Bay Packers brings him back to his freshman year at Vanderbilt.
Like that first season with the Commodores in 2008, the 23-year-old cornerback is working on special teams, edging his nose into defensive subpackages and practicing in the same secondary as Packers rookie safety Sean Richardson, with whom he played for four years at Vanderbilt.
More than anything, the 5-foot-11, 192-pound cornerback is out to prove he belongs and show that a kid from Perry, Ga., with no family ties to professional football, can take his game national. ...


text:
Originally posted by iskander
MiG-25 btw, with its "old" 50s technologies, scored a low altitude kill on F-18 by a missile designed to track bombers, all while the Hornet was beaming, dumping chaff and jamming with modern ECM.
Originally posted by iskander
As long as we are on the same page, considering the proposed shift from asymmetrical warfare toward horizontal integration, this will still be on the topic
Naturally the only positive prevention method is to prevent deployment, but our flawed nature (short sided policies) brought us to the brink time and time again.
Originally posted by grunt2
the mig downed the f18 with an IR acrid not radar, was a face to face, the mig outurned the hornet and shoot the missile
[edit on 6-12-2005 by grunt2]
Originally posted by iskander
If Tomcats indeed fired entirely out of "anger" as BigTrain suggested, while being outside the engaging envelope, it simply shows irresponsible waste of military resources, and/or poor training, which I'm sure was not the case.
Originally posted by BigTrain
It is physically impossibe for a Mig 25 to "out-turn an f-18"


text:
superb Central Otago pinot; delicious ripe, gamey, rose-scented spice2010 Cuvée de Richard, France. Majestic Wine, £4.79
herb-and-celery-stashed zesty, fruity Gascon white2010 Neblina Cabernet Sauvignon, Chile. Majestic, 2 @ £4.49
Terrific velvety, cassis-laden Chilean cabernet2006 Vasse Felix Cabernet Sauvignon, Australia. M&S £12.99 until April 3
fine, ripe, blackcurranty cabernet from the Margaret River wine pioneer2009 Vin d'Alsace Gewürztraminer France. Marks & Spencer, £8.99
Seductive, spicy; classic rose petal and lychee-layered fruit
Lightly spicy Alsatian blend2009 Auzells Costers del Segre (£12.49 Oddbins)
Catalan blend; fine perfume and breeding2009 Chapel Down Flint Dry (£8.99 Waitrose)
English pears and aniseed balls
typically bold Aussie production, but an elegant structureMarqués del Romeral Reserva 2005. £7.99 until 3 April, Marks & Spencer
Full-bodied, rich, mellow, lots of crunchy fruit and vanillaStellar Organics Sauvignon Blanc 2010. £4.48, Asda until Tues, £5.99, Booths, Nisa
crisply refreshing wine, full of zesty green fruit and vegetal flavours
succulence to the juicy red fruit; effortless smoothnessPaul Mas Gardemiel Vineyard Malbec, France (£8.99, or 2 @ £6.49, Majestic)
stonking good value: lots of classically plummy fruit; chunky stuffPuklavec & Friends Sauvignon Blanc, Slovenia 2009 (£6.74 until 5 April, Waitrose)
pure, limpid and aromatic version of sauvignon, with zippy acidity
lush, slightly smoky. Will age another eight to 10 yearsYabby Lake 2008 Pinot Noir (£24.05, swig.co.uk; 14% abv)
gloriously ripeYabby Lake Block 5 (£39, swig.co.uk, Harvey Nichols; 14% abv)
stellarJacob's Creek Reserve Pinot Noir 2009 (£9.99 yourfavouritewines.com, £10.25 Sainsbury)
dark and dusky, rather than jammy, and would be great with crispy duckPaxton Quandong Farm Shiraz 2009 (£21.50 Noel Young, £22.95 Jeroboams)
intensely vibrant, unfiltered
Classy, complex; intense, rounded flavours of lemon with orange2010 Fragoso Merlot Mendoza, Argentina. £4.99 until 3 April, M&S
light, fresh, juicy red with attractive cherry flavours2010 McPherson Basilisk Chardonnay, Australia. £9.99, Rhymers Fayre Melrose
Smooth, creamy and rich with generous tropical fruit and a touch of spice
off-dry and with a juicy flavour of apples and quincesLa Croisade Réserve Grenache 2009, France (Majestic, two for £9.98)
packed with sweetly ripe red plums and perfumed with the scent of clovesConcha y Toro Late Harvest Sauvignon Blanc 2007 (Tesco, £6.29 37.5cl)
Subtle, sweet stuff, crisp with lemon and grapefruit zest


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More Ideas & Inspiration
These Valentine's Day Cards are Eco-Fabulous
Botanical PaperWorks Eco-Valentines are featured on Ecofabulous.com. Read more
Make Your Own Favor Pillow Boxes with Eco-Friendly Paper
Eco-friendly paper is beautiful and can be used in many ways beyond making cards. Why not use it to make a perfect package for small or fragile objects? Take a handmade gift and wrap it with some handmade wrapping paper. The lucky recipient will be overjoyed knowing that you p... Read more
Homemade Valentine's Day Gifts with Seed Paper
Valentine's Day is here and many are making personalized gifts and cards for their sweethearts. The typical giving of chocolate and flowers is still lovely, but we all know the best gifts are ones that come from the heart and not neccesarily in a heart. Just take a look at thi... Read more
The True Meaning of Eco-Friendly
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A Blooming Business - The Botanical PaperWorks Story
Entrepreneurs are like gardeners. A gardener can plant a seed of a creative idea and over time a seedling begins to sprout and reaches toward the sun. With the proper mix of a green thumb, ample sweat of the brow, and fertilizer, a gardener can turn that little seed into a thr... Read more
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Canadian seed paper
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|Reviews for Scars
| am chapter 1 . 5/13/2011
hey I love this story 3 I also like the way you write can you please! do me a faver write a Guys/Guildias ending were Guys is like the new Myuca please do it for a fan
| FreeBelovedArmy chapter 1 . 11/1/2010
Aww, I really loved that! 3
| Lucrecia LeVrai chapter 1 . 6/25/2009
Ah, Vallewida and Evan, my favorite characters, put in a fic together... and the fic's made of my favorite genres, too. Needless to say, I liked this bitter, poetic, thought-provoking drabble. I only wished it was much longer - because there's never enough good Enzai stuff out there.
| Gone4everAndaDay chapter 1 . 3/22/2009
Wa! So good! I love reading Enzai fanfic's but I can't seem to find any other then yours! T~T
our writing style is brilliant, seriously! And I really hope you continue writing your Enzai one-shots!


text:
Formula One statistics for Sunday's German Grand Prix at Nuerburgring:
Ferrari have won 216 races in Formula One, McLaren 171, Williams 113 and champions Red Bull 21.
Michael Schumacher holds the record for victories by a driver, with 91. Ferrari's Fernando Alonso has the second most among current drivers (27).
Alonso's win at Silverstone this month took the Spaniard level with Britain's Jackie Stewart in equal fifth place in the all-time lists.
Ferrari have now won at least one race in every season since 1993.
Red Bull's Sebastian Vettel has won 16, McLaren's Lewis Hamilton 15, Williams' Rubens Barrichello 11 and McLaren's Jenson Button 10.
Vettel has won six of the nine races this season and nine of the last 13. Schumacher holds the record for most wins in a season (13 with Ferrari in 2004).
Only three drivers have won seven races in a season and not gone on to take the title: Schumacher (2006), Kimi Raikkonen (2005) and Alain Prost (1988 and 1984)
- - - -
Ferrari's British Grand Prix win came on the 60th anniversary of their first at the same circuit in 1951.
With Daniel Ricciardo making his debut for HRT at Silverstone, Australia had two drivers on the F1 starting grid for the first time since 1977 when Vern Schuppan and Larry Perkins were team mates at Surtees and Alan Jones was at Shadow.
Vettel's start to the season is the best ever by a driver. He has dropped just 21 of a possible 225 points so far.
Vettel has been on the podium for 11 races in a row. Schumacher holds the record of 19 (2001/02).
Vettel has been on pole in seven of the nine races this season. Red Bull have been on pole in all of them, with Australian Mark Webber taking the top slot in Spain and Britain.
The record for poles in a single season by a driver is 14 (Nigel Mansell, 1992)
Vettel has 22 career poles. Schumacher a record 68.
Red Bull's run of poles extends back to last season and the team have started the last 10 races from that position.
The record for successive poles by a constructor was set by Williams with 24 in 1992/93.
McLaren's last pole position was with Hamilton in Canada in June 2010. Ferrari's last was with Alonso in Singapore last September.
Vettel has qualified on the front row for the last 14 races. The record of 24 front row starts still belongs to the late Brazilian Ayrton Senna (1988/89).
Ferrari's Felipe Massa is the only driver to have been outqualified by his team mate in every race so far in 2011.
Webber has set the fastest lap in four of the season's nine races so far.
Since 1995, the driver on pole position at the Nuerburgring has gone on to win the race only three times (2001, 2004, 2009).
The last German driver to win in Germany was Michael Schumacher at Hockenheim in 2006. Schumacher also won at Nuerburgring that year.
Germany has six Formula One drivers (Vettel, Nico Rosberg, Schumacher, Nick Heidfeld, Adrian Sutil and Timo Glock) more than any other nation. Only Vettel and Schumacher have won races.
Vettel has now finished his last 11 races in the points. Schumacher holds the record of 24 successive scoring finishes.
Team Lotus, Hispania (HRT) and Virgin Racing have yet to score a point in Formula One since their debuts in Bahrain last year.


text:
One of the primary objectives in cancer research is to identify causal genomic alterations, such as somatic copy number variation (CNV) and somatic mutations, during tumor development. Many valuable studies lack genomic data to detect CNV; therefore, methods that are able to infer CNVs from gene expression data would help maximize the value of these studies.
We developed a framework for identifying recurrent regions of CNV and distinguishing the cancer driver genes from the passenger genes in the regions. By inferring CNV regions across many datasets we were able to identify 109 recurrent amplified/deleted CNV regions. Many of these regions are enriched for genes involved in many important processes associated with tumorigenesis and cancer progression. Genes in these recurrent CNV regions were then examined in the context of gene regulatory networks to prioritize putative cancer driver genes. The cancer driver genes uncovered by the framework include not only well-known oncogenes but also a number of novel cancer susceptibility genes validated via siRNA experiments.
To our knowledge, this is the first effort to systematically identify and validate drivers for expression based CNV regions in breast cancer. The framework where the wavelet analysis of copy number alteration based on expression coupled with the gene regulatory network analysis, provides a blueprint for leveraging genomic data to identify key regulatory components and gene targets. This integrative approach can be applied to many other large-scale gene expression studies and other novel types of cancer data such as next-generation sequencing based expression (RNA-Seq) as well as CNV data.
Keywords:breast cancer; copy number variation; gene regulatory networks; oncogenes
Tumors arise from the activation of oncogenes along with the inactivation of tumor suppressor genes via somatic gene mutations or copy number variation (CNV). Identification of the genetic/genomic changes that drive biological processes associated with cancer onset or progression assists in the development of therapeutics targeting the affected proteins or their downstream consequences[1-4]. Although extensive gene expression studies have been conducted for identifying tumor signature genes associated with poor outcome[5,6], the reproducibility of these signatures is low[7,8], posing a major challenge for identifying the causal genetic/genomic variations.
Genome-wide DNA copy number variation (CNV) has been increasingly used for identifying biomarkers and targets in cancer research[9-11]. Unfortunately, the CGH, SNP genotype, and DNA sequencing data typically used to detect CNV are not available for many published, large-scale studies. Therefore, the development of methods to infer CNV from non-genetic data collected in these studies would serve to enhance their scientific value. Strong correlations between CNV and gene expression have been observed[11,12] and initially suggested the possibility of detecting CNV directly from gene expression data. Recently, the Analysis of Copy number alteration by Expression (ACE) algorithm was developed to identify amplified or deleted chromosome regions based on gene expression data[13]. While this approach demonstrated the utility of leveraging expression data to identify candidate CNV regions and genes whose expressions might be affected by the candidate CNV, the identified regions were often large and harbored many genes. Furthermore, no objective mechanism was employed to distinguish cancer driver genes from passenger genes within a putative CNV region.
Here we describe a novel Wavelet based Analysis of Copy number alteration by Expression (WACE) algorithm and combine it with Bayesian networks via a key driver analysis to provide a systematic and unified approach for distinguishing cancer driver genes from passenger genes residing in inferred copy number variation sites.
Preprocessing data
We collected four independent breast cancer datasets, NKI[14], Wang[6], Miller[15], and Christos[8]. NKI samples were profiled on the Agilent Human 25 K platform comprised of 24,496 non-control oligonucletoide probes while the other studies were carried out using the Affymetrix HG133A platform, compromised of 22,282 probe sets. Each of the four microarray datasets was adjusted for estrogen and progesterone receptor (ER/PR) status as well as age to avoid their influence. The data were fit using a robust linear regression model (rlm function from R statistical package), and the residuals with respect to the model fit were carried forward in all subsequence analyses as the gene expression traits. In all analyses except ACE, the expression of individual gene/probe was used. In the subsequent analysis, for a gene with multiple probes we used the average expression profile to represent its expression so as to eliminate the weight effect due to multiple data points on a single location.
We also downloaded the gene expression and aCGH data from the Stanford University Breast Cancer Study http://smd.stanford.edu/ webcite[16]. Again the expression profiles of multiple probes representing the same gene were consolidated by average. The expression and aCGH data were adjusted for array batch, ER/PR status and age to avoid their influence.
A Unified Framework for Expression based CNV Inference and Causal Gene Identification
We developed a framework for integrating CNV inference and gene regulatory network analysis to provide a systematic and unified approach to prioritizing genes residing in inferred CNV (ICNV) regions (Figure 1). Under this framework, we first developed a wavelet-based ACE algorithm (WACE) to more efficiently and accurately detect amplified/deleted regions of the genome in cancer samples. For a given gene expression study the samples are first classified into two groups based on phenotypes such as poor versus good outcome and the two corresponding subsets of gene expression data are then taken as input for WACE to infer significantly amplified or deleted CNV regions. Meanwhile, a regulatory network is constructed using the Bayesian network reconstruction method. Finally, genes on the inferred CNV regions are input into a key driver analysis to identify hub genes in the network as potential cancer driver genes. When multiple datasets are available, all ICNV regions are aligned to form recurrent CNV regions and multiple regulatory networks are combined into a single network. The recurrent CNV regions and the combined network then go through the key driver analysis to identify putative regulators.
Figure 1. A framework for integrating wavelet based CNV inference and gene network analysis. The samples in a given gene expression study are first partitioned into two groups based on phenotypes such as poor versus good outcome, followed by differential expression analysis (t-test) to yields expression scores (ES t-statistics). Wavelet analysis is then performed on ES' ordered by gene chromosomal locations to detect significant consecutive regions (called inferred CNV regions). Using the same gene expression data, a gene regulatory network (Bayesian network) is constructed. Finally, the inferred CNV regions and the Bayesian network are input to the key driver analysis to identify potential cancer driver genes.
Wavelet based Analysis of Copy number alteration by Expression (WACE)
WACE algorithm is described in detail in the Method of Additional File 1 and summarized by a diagram in Figure 2. In brief, for a given dataset, the gene expression traits were first ordered according to their physical location on chromosomes. The expression scores (ES, t-statistics) were computed for each gene with respect to the good and bad tumor outcome, and were then subjected to wavelet transform to obtain the neighboring scores (NS). To evaluate the significance of the NS's on each individual chromosome, we empirically approximated its null distribution by applying wavelet transform to "random" ES's based on randomizing the sample class labels with respect to the expression vectors, repeating this process 1000 times. The false discovery rate for each observed NS was computed as the fraction of random NS's that were greater than (less than) or equal to the observed value if NS > 0 (NS < 0). After evaluating the statistical significance of NS, an ICNV region on a chromosome was identified if it harbored at least n consecutive positive/negative NS's at a false discovery rate < 0.01. The n value, which varied from 5 to 10, was proportional to the scaling level used in wavelet transform which in turn relate to the gene/probe density of the microarray platform. A high scaling level of wavelet transform increases the NS magnitude of neighbor points around a single differentiated gene, and thus makes them become statistically significant, which might in turn falsely identify a region as ICNV if n is small (Additional File 1, Figure S1B). Therefore, n value is determined by the scaling level used in wavelet transform. The higher scaling level requires large n. We have tested different n values from 5 to 10 for a given s = 5 in the Stanford data which is comprised of both aCGH and expression data. We have found that n = 10 provides the best overlap between aCGH-and expression-based expression. Similarly, n = 5 which corresponds to s = 3, provides robust results for identifying recurrent ICNV regions. Finally, ICNV regions in multiple datasets were aligned to determine the recurrent CNV regions. The selection of filter function and scaling level for wavelet transform, as well as the validation of the method, is discussed in detail in Additional File 1.
Additional file 1. Supplementary Information. Supplementary methods and results.
Format: DOC Size: 1.1MB Download file
This file can be viewed with: Microsoft Word Viewer
Figure 2. Outline of the WACE algorithm. For a given gene expression dataset, the samples are classified into two groups based on phenotypes such as poor versus good outcome and the genes are ordered based on their physical location on chromosomes. Expression scores (ES, t-statistics) for all the genes are computed and then subjected to wavelet transform to obtain smoothed ES, called neighboring score (NS). The significance (false discovery rate, FDR) of NS on each individual chromosome is empirically approximated based on its null distribution by performing the same wavelet transform on "random" ES's based on the randomized samples. A segment containing at least n consecutive positive/negative NS with FDR ≤ 0.01 is defined as an inferred CNV region. ICNV regions from multiple datasets are finally aligned to determine the recurrent regions of CNV.
Distinguishing cancer driver genes from passenger genes via reconstruction of Bayesian networks
CNV regions harbor many genes but only a small portion of them are cancer causal genes. A key challenge in cancer genomics is to distinguish the cancer driver genes, which are causal for oncogenesis and whose variations confer growth advantage on cancer cells, from the passenger genes that are physically located close to the driver genes in CNV regions[17]. Several methods have been proposed to indirectly identify driver genes by intersecting genes associated with CNV regions and gene coexpression networks[18] or protein-protein interaction data[19]. In our study, gene expression of two neighbor genes in an inferred CNV region (shown in Figure 3) are correlated to the inferred CNV (as implicated by the WACE method). To distinguish the potential cancer drivers, we tested whether they can causally regulate other genes instead. As shown in Figure 3, two candidate genes C1 and C2 are cis regulated by a common CNV. To determine whether a downstream gene G is regulated by C1 or/and C2 or neither is equivalent to select among competing models:
Figure 3. Cancer driver genes versus passenger genes. Two candidate genes C1 and C2 are located on an inferred CNV region, and are likely cis regulated by a CNV. To distinguish potential drivers from passengers, we test whether they can causally regulate other genes. To determine whether a downstream gene G is regulated by C1 or/and C2 or neither is equivalent to selection among four competing models. Given that many other potential genes or factors affect the expression level of gene G, the models conditioning on all other factors are needed to evaluate, i.e., a Bayesian network reconstruction process.
where eq. (1) represents G independent of C1 and C2, eq.s (2) - (4) represent G regulated by C1 , C2, both C1 and C2, respectively. As there are many other potential genes or factors that can affect the expression levels of gene G, the models eq. (1) - (4) are needed to condition on all other such factors. Thus, the model selection process is equivalent to a Bayesian network reconstruction process, which will be discussed subsequently.
Bayesian network is a probabilistic representation of the gene regulatory network and has shown superior performance in integrating genetic data into gene causal networks predictive of complex phenotypes[20-25]. 6,312, 6,349, 6,268 and 5,802 differentially regulated genes for NKI[14], Wang[6], Miller[15], and Christos[8], respectively, were selected as input into a Bayesian network reconstruction software program based on a previously described algorithm[24]. Markov Chain Monte Carlo (MCMC) simulation was then employed to identify the most plausible structures. For each seed, 15*n2 iterations of MCMC were run on average, where n is the number of nodes. The Bayesian Information Criterion (BIC) scores were used as the optimization criteria. One thousand Bayesian networks were reconstructed using different random seeds to start the reconstruction process. From the resulting set of 1000 networks generated by this process, edges that appeared in greater than 30% of the networks were used to define a consensus network. The cutoff of 30% was based on a simulation study[26]. When summarizing 1000 constructed structures from random seeds, the histogram of the number of link occurrences among 1000 structures shows a bimodal distribution, where 30% is the best value to separate the two modes and also has the best recall-precision tradeoff [26]. Edges in this consensus network were removed if 1) the edge was involved in a loop, and 2) the edge was the most weakly supported of all edges making up the loop.
Key Driver Analysis (KDA)
One primary goal of gene network analysis is to identify key regulatory components, or key drivers, of sub-networks with respect to varying biological contexts [25,27]. The KDA takes as input a set of genes (G) and a gene causal (directed) network N. The objective is to identify the key regulators for the gene sets with respect to the given network. Candidate drivers are identified as follows. We first compute the size of the h-layer neighborhood (HLN) for each node. The range of h is from 1 to the diameter of the network N. Specifically, for a given node g, the size of its HLN is the number of its downstream nodes that are within h edges away from g. For the given network N, let μ be an array of the sizes of HLNs and d be an array of the out-degrees for all the nodes. The nodes are nominated as candidate drivers if their sizes of their HLN are greater than , where is the mean of μ is and σ(μ) is the standard deviation of μ. The candidate drivers without any parent node (i.e., root nodes) are nominated as global drivers while the rest are local regulators. Let be the mean of d and σ(d) be the standard deviation of d. We also promote hub nodes as global drivers, i.e., the nodes with out-degrees above are designated as global drivers. These criteria identify genes with number of downstream nodes or number of outlinks significantly above the average.
By definition, each driver modulates a set of genes, i.e., its downstream nodes. Previous work has shown that a gene's function can be predicted by its neighbor genes in networks[28]. Moreover, a series of validation experiments show that the downstream nodes of a driver predicted by Bayesian networks significantly overlap with its knockout signature[25].
siRNA Screen and Cell Viability Assays
Cell lines and siRNA library
Four different breast cancer cell lines, ER-positive MCF7 and ZR-75-1; and ER-negative MDA-MB-231and MDA-MB-468[29], were obtained from the American Type Culture Collection, Rockville, MD (Catalogue numbers HTB-22, HTB-26, HTB-132 and CRL-1500, respectively). The siRNA library targets ~2,400 unique human genes, with three siRNAs per gene, as described previously[30]. The library represents genes comprising kinases, membrane proteins, enzymes, components of major cellular pathways including cell cycle, transcription regulation, and signal transduction, etc. [31]. siRNA sequences were designed with an algorithm developed to increase efficiency of the siRNAs for silencing while minimizing their off-target effects [32]. siRNAs were ordered from Sigma-Proligo (The Woodlands, TX).
siRNA screen and cell viability assays
siRNA screens were performed as described previously[30] and cells were transfected using RNAiMAX (Invitrogen, Carlsbad, CA). Cell viability assay was determined using AlamarBlue reagent (BioSource International, Camarillo, CA). The fluorescence signal was corrected for background (no cells). Cell growth was expressed as % viability relative to the median value of wells transfected with an siRNA to Luciferase.
As detailed in the Materials and Methods, the proposed unified framework for expression based CNV inference and causal gene identification is comprised of three major components, wavelet based CNV inference, causal network construction and key driver identification.
Performance Comparison of WACE and GACE
The original ACE approach for identifying amplified or deleted chromosome regions used the simple Gaussian transform to smooth the data and then identified the significantly abnormal regions comprised of over- or under-expressed genes via a permutation test[13]. Although this approach, named GACE, was able to narrow down genes whose expression might be affected by the local CNV, it often systematically overestimated the size of the identified regions which were typically re-arrangements of small sequences. We improved GACE by introducing: i) a wavelet based smoothing technique and ii) a new statistical method for assessing significance of putative CNV regions. Wavelet transform is a more sophisticated filtering technique and has become a cutting-edge technology in signal and image processing because of its superior ability to accurately deconstruct and reconstruct finite, non-periodic and/or non-stationary signals[33]. This led to a wavelet-based ACE algorithm (WACE) to efficiently and accurately narrow down amplified/deleted regions of the genome in cancer samples.
To access the performance of WACE, first we compared it with the existing method GACE based on a previously published breast cancer study [16], which consisted of gene expression and aCGH data, as well as clinical data relating to tumor progression (referred to here as BCS1). A detailed comparison is described in Additional File 1, WACE and GACE Comparison (Section 2.1). Figure S4 to S6 in Additional File 1 illustrated the comparison results between two methods based on (i) the correlation coefficient between the expression- and aCGH-based NS profiles, and (ii) re-identifying CNVs based on gene expression, respectively. Here we summarize the findings: (i) WACE uncovered almost three times as many expression ICNV regions overlapping with the aCGH ICNV regions compared to GACE, and (ii) these two sets of regions identified by WACE were better correlated with each other than those identified by GACE.
We also verified the cis-effect of CNV on gene expression by considering the correlation between the aCGH and gene expression data of each gene in BCS1. The expression of the genes located in the overlapped CNV regions was highly correlated to the corresponding aCGH data. For instance, WACE identified in both expression and aCGH data a DNA amplification region at 20q13, and the gene expression and aCGH NS profiles were highly correlated (r = 0.55).
Amplified regions associated with poor outcome affect cell cycle
We applied WACE to the aforementioned four breast cancer microarray studies [6,8,14,15] to infer CNV regions associated with metastasis. Each dataset was independently analyzed by WACE and the identified regions were then aligned to locate the recurrent regions of ICNVs. To calculate the ES's for each dataset, the samples were classified into two groups, the patients with metastases within 5 years and those with no metastasis after more than 5 years of follow-up.
Figure 4 shows the NS profiles on chromosome 8. WACE identified the amplified chromosome 8q21-q23 region (around 100 Mb and harboring the MTDH gene) in three of the four datasets. The amplification of this region has previously been experimentally verified [13]. It also identified CNV in the 8p21-p12 and 8q24.3 cytobands associated with tumor outcome. The 8q24 cytoband, which was consistently detected as an amplified region by WACE, was found to be the most frequently amplified region by genome-wide array CGH[9,12], where the expression of the underlying genes also reflected the change in DNA copy number[12]. Moreover, this region also includes the well-known oncogene MYC.
Figure 4. NS profiles on chromosome 8 from the four independent breast cancer studies using WACE. Red lines encode identified CNV regions. MYC neighborhood region (121-133 Mb) is detected to be amplified in two studies by WACE but only in one study by GACE.
The recurrent regions of CNV were defined as the union of the regions in which the abnormal gain/loss events were identified in at least two out of the four studies. WACE found 109 recurrent regions covering 2,560 genes (Table 1 and Additional File 1, Table S1). Table 1 lists the recurrent regions of amplification that were consistently found by WACE in all four datasets. Several regions on chromosome 8, 11, and 20 identified here have been previously identified via analysis of CGH arrays applied to breast cancer tumors [10]. These results further confirm the accuracy of WACE. We note that many of the genes in the inferred recurrent regions were oncogenes (19 of total 84 oncogenes according to Panther database) or significantly affected the cell viability based on siRNA experiments (Figure 5).
Table 1. The inferred recurrent regions of CNV in the four breast cancer datasets.
Figure 5. Validation of predicted key drivers by testing the enrichment of siRNA hit signatures in various gene sets. Two siRNA hit signatures, V1 (an across cell line signature with 216 genes) and V2 (with 484 genes as a union of the signatures across cell lines and from the individual cell lines), were tested for enrichment in the following gene sets: the genes not on the recurrent regions (nonRR), the genes on the amplified ICNV regions (RR Gain), the non-driver genes (non Drivers) on the amplified regions, the drivers (global and local), the local drivers and the global drivers. The drivers and non-drivers were based on the key driver analysis on the amplified regions and the Bayesian network. A) and B) shows the fold enrichment of V1 and V2 signatures in the gene sets, respectively. The genes on the recurrent ICNV regions are more than twice more likely to enriched for the siRNA signatures than the genes not on the recurrent regions while the global drivers has the highest likelihood (7.8 and 5.9), followed by the drivers (6.8 and 5.4), the local drivers (4.8 and 4.4), and the non drivers (2 and 1.7).
These regions were tested for gene enrichment using the gene ontology (GO) categories, KEGG, Panther, and GeneGo pathways. The most enriched categories include nucleosome assembly (P < 7.24E-22), chromatin assembly (P < 1.02E-20), systemic lupus erythematosus (P < 9.03E-18), chromatin assembly or disassembly (P < 4.57E-17), DNA packaging (P < 1.29E-16) and protein-DNA complex assembly (P < 1.92E-11). Cell cycle and cholesterol biosynthetic process pathways are also enriched in the regions. All the enriched categories are shown Additional File 1, Table S3.
ICNV regions versus aCGH based regions
We further examined to what extent the recurrent CNVs inferred by gene expression reflect the recurrent CNVs based on an independent aCGH data in BCS1. To ensure an objective comparison, we used a popular CNV detection method, the circular binary segmentation (CBS) algorithm (implemented in the DNAcopy package from Bioconductor) on the aCGH data. Forty six or 68.7% of the 67 CBS based aCGH CNV regions in BCS1 significantly (gene-based FET P = 3.1e-13) overlapped with the 109 recurrent ICNV regions detected by WACE on the four gene expression studies. The overlapped regions on chromosome 8, 11, and 20 have been previously identified via analysis of CGH arrays applied to breast cancer tumors [10]. These results demonstrate a good correspondence between the inferred CNV regions and the experimental results and thus confirm the accuracy of WACE.
Breast Cancer Gene Regulatory Networks
Four whole-genome gene regulatory networks were first constructed by the Bayesian network reconstruction method [26] for the aforementioned four breast cancer studies (NKI, Christos, Miller, and Wang) and then were combined by union of directed links to form a single network, which consisted of 10,118 genes and 20,732 directed links. Previous works have shown that genes controlling many other genes in gene regulatory networks are more likely regulatory genes regulating their downstream genes[24,25,27]. The top 5 genes with the most out links are ARF1, FOXA1, ESR1, HIF1A and UBE2C. ARF1 is involved in the activation of the PI3K/AKT pathways which regulate cell survival and proliferation[34]. HIF1A plays an essential role in cellular and systemic homeostatic response to hypoxia was recently found to regulate the metastasis of breast tumor to lung[35]. FOXA1, ESR1 and UBE2C, are known as breast cancer prognosis biomarkers[36]. The global features of the combined network are described in detail in Additional File 1, Properties of Breast Cancer Bayesian Networks (Section 3).
Key Driver Analysis
We assume that the cancer driver genes have broad impacts on global gene expression while passenger genes have small impacts [37]. To quantify impacts of genes residing in the recurrent ICNVs detected across multiple data sets, we count numbers of downstream genes that are regulated by each candidate gene locally or globally in the combined network. Genes are grouped into global drivers, local drivers and passengers according to the rank of these numbers (see Methods for details). In the key driver analysis, we focus on the genes residing in amplified regions which are regarded as a hallmark of dominant cancer driver genes[17].
For the genes in the recurrent regions of amplification (RR Gain), we identified 44 global drivers and 24 local drivers. Known breast cancer susceptibility genes like TPX2, AURKA, TK1 and BIRC5 are among the top 7 global drivers (the other 3 top drivers are GINS2, COX4NB and NUP93). Other known breast cancer gene target such as ERBB2, E2F1 and MTDH are in the global driver list. Many of them such as aurora kinases AURKA and survivin (BIRC5) have been targets for anticancer drugs [38-40].
To access the robustness of predicted key drivers, we performed the KDA on the networks (named as the 3N networks) as combinations of any three of the four Bayesian networks. Over 79% of the drivers based on the 3N networks are the drivers based on the combination of all the four BNs. On the other hand, the four driver sets based on the 3N networks significantly overlap with each other (with FET p-values < 1E-14) and they share at least 41% of their members. Therefore, the drivers uncovered are fairly robust. It is of note that the network based on all the four networks leads to many more drivers than any 3N network, indicating the great advantage of the combination of all the networks.
Validation of key drivers via in vitro siRNA knockdown experiments
We assessed the effect of knocking down the genes located on the amplified recurrent ICNV regions by siRNA on breast cancer cell viability because they were enriched for gene sets involved in cell cycle and metabolic regulation. In the previously described siRNA experiments, the median of the viability of the siRNA transfected cells was 64.30%, with a standard deviation of 15.28%, compared to the control cells transfected with an siRNA to luciferase. We used a viability cutoff value of 41.38% (1.5× standard deviation below the median) to determine viability signature genes that, when silenced, significantly decrease cell viability. This resulted in the identification of a 216 gene signature (V1) with a significant effect on cell viability in multiple cell lines. Based on a cutoff of 1.5× standard deviation below the median, we also derived a signature for each cell line. The four signatures from individual cell lines were then combined with V1 to derive a combined signature V2 with 484 genes. The genes in V1 and V2 and their viability scores are shown in Additional File 2, Table S4.
Additional file 2. Supplementary Table S4. siRNA screen signatures and the associated viability scores.
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We assessed whether V1 and V2 signatures are significantly enriched in different gene sets including the global drivers, the local drivers, the drivers (global and local), the non-driver genes and the genes not on the recurrent regions (nonRR). Figure 5 shows the fold enrichment of each gene set for the siRNA hit signatures V1 and V2. When compared with the genes not on the recurrent ICNV regions, the genes on these regions are about twice (i.e. 2.1 and 1.8 vs. 0.9) more likely to fall into V1 and V2, respectively. Also the global drivers are most likely to be siRNA hits in both V1 and V2, followed by local drivers and non-drivers. For example, the global drivers, the local drivers and the non drivers are 7.8 (P < 5.8e-4), 4.8 (P < 1.9e-2) and 1.97 (P < 2.5e-6) times more likely to overlap with V1, respectively than the genes on the human genome. When the genes on the recurrent ICNV regions were considered as background, the global drivers were 4.2 (P < 0.0055) and 3.5 (P < 0.003) times more likely to be in V1 and V2, respectively, and the local drivers are less significantly enriched for the signatures.
Seven drivers (ATP6V1C1, BIRC5, DDX19A, DDX28, E2F1, PSMC5 and TPX2) are in V1. The protein Survivin is encoded by BIRC5 (located in 17q25) and is well-known for its role in mitotic regulation and apoptosis, and has been developed as a target for cancer treatment [39]. Silencing BIRC5 decreased the cellular viability by 64%. TPX2 is located on 20q11 and the cell viability dropped by 64% when it was silenced. Figure 6 shows the subnetwork associated with the amplified recurrent ICNV regions. The global and local drivers are highlighted in different sizes. The genes found in the signature V1 are highlighted in green.
Figure 6. A regulatory network for the genes on the amplified recurrent ICNV regions. The key driver analysis was applied to the genes on the regions and the combined Bayesian network to identify potential key drivers. The largest nodes in the network are the global drivers and the medium size nodes are local drivers while the non-drivers are the smallest nodes. The genes found in the siRNA signature V1 are highlighted in green.
As mentioned earlier, AURKA and TPX2 are the top key regulators of the recurrent ICNV regions. On the other hand, 107 of the genes on the recurrent regions overlapped with V2 and they include TACC3, ECT2, CDC6, NOTCH1, and PIK3CA etc. Here we found a molecular mechanism that linked TACC3 and TPX2 through AURKA. TACC3, located on an amplified region on 4p16, recently emerged as an important gene in the stabilization of the mitotic spindle[41], and is up-regulated in several cancer cell lines including thyroid and lung cancers[42-44]. Cell viability was reduced by 59.80% in the TACC3 siRNA transfected cells. AURKA resides on 20q13, which was also identified as a recurrent region of amplification. It has previously been shown that TPX2 is one of several activators for AURKA [45], which in turn controls the localization of TACC3 to the spindles [46]. Furthermore, TPX2 acts as an allosteric activator of Aurora A, and together they inhibit the activity of tumor suppressor p53 in Xenopus Oocytes (Pascreau et al. 2009). The molecular interaction between the hubs located on different recurrent regions might suggest the trans-association between regions. We expect more mechanisms of this kind will be unraveled through the integration of WACE and network analysis.
These experimental results demonstrate the importance of the recurrent ICNV regions and the predicted key driver genes, especially global drivers in terms of significant impact on cancer cell viability, and thus validate our procedures for inferring CNV regions and identifying cancer driver genes in these inferred CNV regions.
We developed a novel Wavelet based algorithm to Analyze Copy number alteration based on Expression (WACE) and further combined this analysis with gene regulatory network analysis to identify the cancer driver genes in the chromosome regions with genomic alterations. The performance of these two methods was evaluated in Additional File 1, Section 2. In brief, WACE improved on the state-of-the-art algorithm (GACE) by (i) reducing the over-smoothing effect of the Gaussian transform and retaining the local patterns, and (ii) eliminating the type I and II errors induced by the non-zero centered null distribution (Additional File 1, Figure S2). WACE demonstrated superior performance over GACE on several large scale gene expression datasets (see Additional File 1, Section 2). By analyzing multiple datasets, we were able to identify recurrent ICNVs that harbor many cancer-related genes, suggesting genes in these regions are worthy of further study.
Integration of the recurrent regions identified by WACE and the gene regulatory network analysis uncovered not only many well-known oncogenes like BIRC5and E2F1, but also a number of novel cancer susceptibility genes such as ECT2, TPX2 and TACC3, which are involved in cell cycle and ECM regulation. Many of the cancer driver genes we predicted were subsequently validated via siRNA experiments. Table S5 in Additional File 1 includes the four known and 45 novel breast cancer genes that are in the recurrent regions of amplification and validated by the siRNA experiments across multiple breast cancer cell lines.
The importance of the identified recurrent regions of amplification (ARR) and the inferred drivers are further justified by another independent dataset from Cancer Gene Census http://www.sanger.ac.uk/genetics/CGP/Census/ webcite. Among the 19 known breast cancer genes compiled by Cancer Gene Census, 5 are located in the ARRs while 14 are outside the ARRs, indicating that the known breast cancer driver genes are 3.5-fold overrepresented in the ARRs when compared to the genes not on these regions. Among the 5 census genes located in the ARRs, one (ERBB2) is the global key driver and the rest four are not the drivers, representing 25- and 2-fold enrichments, respectively, compared to randomly selected human genes.
There are multiple molecular factors contributing to poor outcome, including cell proliferation, metastasis and cell stemness etc. Our assumption is that CNV significantly contributes to the disease outcome though the separation of the samples into good and bad outcome groups tends to identify metastasis genes. We don't need to pre-specify through what molecular mechanism that CNV drives disease outcome, which can be inferred in a data-driven manner. To further understand the functions of the ARRs, we performed gene co-expression network analysis[47,48] on the four breast cancer datasets to identify gene modules (comprised of highly interacted genes). A module enriched for cell cycle genes (named as cell cycle module) and conserved across the four corresponding networks is most predictive of survival in all the four studies, indicating cell proliferation contributes to breast cancer outcome. The ARRs are highly enriched for the genes of the cell cycle module (P < 1.2e-14). This is further supported by the fact that the inferred key drivers include not only known breast cancer metastasis genes (MTDH[13] and ERBB2[49]) but also known cancer cell cycle genes such as TPX2, AURKA, E2F1 and BIRC5. As shown in Figure 6, these known cell cycle and metastasis genes interact with each other and with many other drivers to co-regulate the genes on the AARs. Furthermore, AARs are enriched (2.3 fold-enrichment, P < 2.6e-2) for a cell cycle prognostic signature identified as metastasis markers[50], and two (AURKA and BIRC5) of the thirty-three signature genes are the global key drivers (32 fold-enrichment, P < 1.8e-3). Therefore, cell viability assay, used to test cell proliferation, at least partially validated our drivers. The future experiments will consider cell mobility assay to test how these inferred key drivers impact cancer cell mobility that drives metastasis.
A recent paper by Akavia et al. describes a method to predict cancer driver genes by integrating CNA and gene expression data[51]. Akavia et al's approach bears certain similarity as ours. Both approaches first define gene groups of interest, then identify genes in the groups with cis-CNV as candidate driver genes. As we discussed in the main text, expression levels of genes in the same CNV segments are likely to be correlated so that they are assigned into the group. Candidate genes (with cis-CNV) in the same co-expression module are equivalent so that we cannot rank which candidate genes are more likely to be drivers. Akavia et al. used a literature based method to rank candidate genes. Basically, they chose genes with known connections to cancers. However, our approach is completely data driven. We first constructed causal networks to dissect how genes in the regions of interest are regulated and related to each other and then we ranked candidate genes based on the causal networks, which is the key driver analysis described in the main text. As we mentioned in the previous discussion, majority of the drivers identified by our approach are novel and thus they won't be picked up by Akavia et al's literature based approach.
Jornsten et al. recently developed the EPoC method to integrate CNA and gene expression variations[52]. EPoC focuses on CNA driving gene expression changes but disregards the indirect changes through more hubs. Only genes with CNA can be cancer driver genes based on the EPoC model. However, our Bayesian network approach focuses on how genes are causally related to each other. In our case, predicted cancer driver genes may or may not have copy number alterations. Theoretically, the EPoC method can also derive gene-gene relationship as Jorsten et al. indicated that gene-gene relationship matrix A is the inversion of the CNA-gene relationship matrix G (G = A-1). However, they showed that there was no prediction power using the A matrix. One reason is that in their Lesso formulation for G matrix, they explicitly assumed CNAs are hubs so that G-1 is not exactly A. We have showed that gene-gene networks based on our Bayesian network approach have prediction power[24-27]. Thus, our method can be complimentary to the EPoC method.
The impact of CNV on gene expression has been extensively studied[53,54]. In our analysis, we focused on genes with cis-regulated CNVs. CNVs may result partial deletions of genes and the functional changes of the affected genes in turn cause expression changes of downstream genes. Gene regulatory networks may be able to capture the effect of these cis-regulated CNVs on the trans-regulated ones. In our model, we explicitly assume CNVs cause gene expression changes. There are many mechanisms that CNV can be arisen[55]. It is possible that expression changes of genes in DNA repair processes affect CNV. More sophisticated causal models, such as dynamic Bayesian network, are needed to capture these causal relationships. It is of note that there is a limitation of this transcriptome-based approach. We may miss kinases or enzymes that drive cancer progression and metastasis if these kinases' or enzymes' activity changes are mainly due to protein level changes. Complementary proteomic approaches are needed to complement this approach.
The framework where the wavelet analysis of copy number alteration based on expression coupled with the gene regulatory network analysis, provides a blueprint for leveraging genomic data to identify key regulatory components and gene targets. To our knowledge, this is the first effort to systematically identify and validate drivers for expression based CNV regions in breast cancer. This integrative approach can be applied to many other large-scale gene expression studies and other novel types of cancer data such as next-generation sequencing based expression (RNA-Seq) as well as CNV data.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
BZ, JZ and ES conceived and designed the experiments. LT and BZ designed the data analysis scheme. CZ, TX, JL, HD and JZ collected samples and preprocessed the data. LT, BZ, ZZ, CZ, TX, JL and JZ analyzed the data. ZZ performed validation experiments. LT, BZ, ZZ, JZ and ES wrote the paper. BZ coordinated the study. All the authors read and approved the final manuscript.
This study was funded in part by Merck & Co.
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text:
– Thanks to Ricky V. for the following WWE SmackDown spoilers from tonight's tapings in Denver, Colorado…
* ZackDown opens up with Zack Ryder coming out as the General Manager. He tries to get the world's biggest fist pump going with the whole crowd doing it. Alberto Del Rio comes out and runs him down. Ryder makes Sin Cara vs. Del Rio and that match is up first.
* Alberto Del Rio beat Sin Cara with the armbar.
* Backstage segment with Big Show insulting Zack Ryder and The Great Khali. Show vs. The Great Khali is announced.
* Primo and Epico defeated Darren Young and Titus O'Neil.
* Backstage segment with Ryder and Sheamus. Chris Jericho comes in and Jericho vs. Sheamus is announced for tonight's main event.
* Cody Rhodes and Dolph Ziggler defeated Christian and Santino Marella.
* Big Show quickly knocked out The Great Khali to win their match.
* Curt Hawkins and Tyler Reks come out and does Ryback. They talk strategy but Ryback gets the win over Reks in a competitive match.
* Damien Sandow defeated Justin Gabriel.
* Sheamus beat Chris Jericho in the match of the night that went over 15 minutes. Sheamus hit a Brogue Kick after lots of back and forth action.
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text:
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Sunlight MTN Elects Inaugural Mini-MayorMon. Nov. 30, 2015
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Resorts Invite Guests to Celebrate the Winter Holidays on the Slopes of Colorado Ski CountrySun. Nov. 29, 2015
Festive Events, Promotions, and Santa Sightings in Store for Holidays in the High Country As the days grow shorter and the snow gets deeper, Colorado Ski Country USA (CSCUSA) resorts are ready t...Read More ››