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Naval History in the years 1890-1920
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Presentation in Portsmouth June 10th, 2005
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Site Admin
Joined: 29 Oct 2004
Posts: 487
Location: Boston
Posted: Mon Jun 27, 2005 8:26 am
Post subject: Presentation in Portsmouth June 10th, 2005
I posted a few
from a presentation I did on June 10th for the Saint Barbara Association in HMS Excellent in Portsmouth.
The audience was 40 to 60 graduates of the RN's long gunnery program or the later "surface weapons" programs. I showed a 4 minute video of torpedo firing and then a 45 minute epic on dreadnought gunnery, narrating them live as they were projected in a lecture hall. Looking at a video of the presentation, I am self conscious of a rambling and poorly organized style, but the collateral effect was that the assault of information aroused many questions and comparisons to later systems used by the gunners who attended.
Of particular note to the era I focus on, Captain Jeremy Dreyer, grandson of Frederic (who invented the Dreyer tables) was present at my talk, as was Lord Fisher. After my talk was the annual dinner of the association at which I met Captain RP Dannreuther, who is the son of Commander Hubert Dannreuther who was the gunnery officer in HMS Invincible and one of her 6 survivors at Jutland.
This was the big calendar item for me in 2005, and I am happy to have met these practitioners of the art of naval gunnery and hope that I will continue to draw upon their knowledge and expertise in improving my work.
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Posted: Tue Jun 28, 2005 2:40 am
Post subject:
So I assume my Kaiser stood up to quite a bit of impacts before sinking right in front of the top brass of the Royal Navy?
I am glad you had a good time, Tone. I can tell you that for myself your website has been a lot of fun.
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Weird Guy
Joined: 26 Apr 2005
Posts: 18
Posted: Tue Jun 28, 2005 2:41 am
Post subject:
Kyle "Weird Guy" Wiegers.
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Site Admin
Joined: 29 Oct 2004
Posts: 487
Location: Boston
Posted: Tue Jun 28, 2005 8:23 am
Post subject:
Kyle... your model suffered tremendous punishment, and yet never sank, thanks to the ingenious construction of her German shipwrights (and the total lack of any damage modeling in the prototype).
She was featured in the both videos I showed, and your name appeared in the credits at the end of the first video (I think I forgot to put credits on the second one). There was only one brief moment, however, where the camera was on her decks; I wanted the people to get a sense of what it was like to be on the receiving end of the fire.
The AI of the sailors was in fine form for the gunnery exercise shown in the video. Though they sometimes get "stupid" owning to bugs, some of the mistakes they made were an intentional part of the code. For instance, at one point, a salvo scored a hit, but one or two shells fell short. The AI spotting officer called in an "up" correction because he had not seen the hit -- he had only seen the short shot(s). This was a common sort of error. The result, as you might imagine, was that the next salvo was long.
As I recall, the scenario was this: at a range averaging around 15,000 yards, own ship was zigzagging 2 points either side and target ship was steaming on a steady course. The range rate was set to some ridiculous value (a bug in someone's AI?) at the outset which quickly dragged the salvoes short and as this error was magnifying over time several salvoes were off for deflection. The result of THIS was that the spotting officer could not tell how short the fire was. Once the deflection was brought on, a lenthy series of "up" corrections was used until the salvoes crossed the target, and then the hits occurred but were unseen. A few corrections brought one more salvo on target.
Overall, as poor as the shooting seemed when you were there, the 9% hits realized was not at all bad. Of course, the simulation is not perfect by any stretch.
I hope to be in a position where I can again ask you for help in modeling, but in truth my simulation is likely to be "down" for months and months, as I need to move to a good platform for continued work.
I will keep you in touch with occasional updates!
FYI, here is the textual log of the gunnery run that served as the basis for the video I showed.
This firing exercise was at approximately 15000 yards against
a target steaming straight ahead at a constant speed while
own ship maneuvered.
2 of the 16 salvoes scored hits.
5 shells out of 58 fired struck the target
(2/4 in one salvo, and 3/3 in another)
The hitting percentage then, was 8.6%
Aboard HMS Queen Mary
Wind is 14.0 knots directed toward 103
Waves = 2.2m
18:30:05.6 Gunnery Officer: "Line up sights"
18:30:10.1 Gunnery Officer: "Line up receivers"
18:30:40.5 Gunnery Officer: "Target Green 45"
18:30:59.7 Gunnery Officer: "Turn 4 points to port"
18:31:01.6 Range cut "T" 14343.102 yards
18:31:05.6 Range cut "." 14617.464 yards
18:31:18.5 Bearing taken rel=59.5 gyro=159.1
18:31:37.9 Bearing taken rel=71.5 gyro=159.2
18:31:42.8 Range cut "T" 13906.852 yards
18:31:44.1 Gunnery Officer: "Open fire"
18:31:47.4 Range cut "." 14167.088 yards
18:31:50.6 Range Officer: "Set rate 850 Closing"
18:31:59.4 Bearing taken rel=79.5 gyro=159.2
18:32:06.3 Range Officer: "Set rate 600 Closing"
18:32:16.7 Spotting Officer: "Over"
18:32:18.6 Bearing taken rel=84.0 gyro=159.667
18:32:18.8 Spotting Officer: "Down 400, Right 2"
18:32:22.7 Gunnery Officer: "Enemy's course 90 to the left at 21 knots"
18:32:29.6 Range cut "T" 14082.337 yards
18:32:34.8 Range cut "." 14192.327 yards
18:32:48.7 Bearing taken rel=88.0 gyro=159.867
18:32:49.3 Range Officer: "Set rate 225 Closing"
18:32:54.6 Spotting Officer: "Too far left"
18:32:58.3 Spotting Officer: "Right 4"
18:33:04.9 Range Officer: "Set rate 150 Opening"
18:33:07.9 Bearing taken rel=89.5 gyro=159.133
18:33:10.2 Range cut "T" 14192.327 yards
18:33:19.8 Range cut "." 14317.007 yards
18:33:33.0 Spotting Officer: "Short"
18:33:33.4 Bearing taken rel=91.0 gyro=159.567
18:33:35.5 Spotting Officer: "Up 400, Right 4"
18:33:50.3 Range Officer: "Set rate 175 Opening"
18:33:51.4 Range cut "T" 14317.007 yards
18:33:52.6 Bearing taken rel=91.5 gyro=159.3
18:34:07.1 Range cut "." 14567.518 yards
18:34:12.9 Spotting Officer: "Short"
18:34:17.4 Spotting Officer: "Up 400, Right 2"
18:34:19.3 Bearing taken rel=92.25 gyro=158.717
18:34:35.0 Range cut "T" 14481.776 yards
18:34:35.3 Range Officer: "Set rate 300 Opening"
18:34:38.6 Bearing taken rel=92.75 gyro=159.683
18:34:52.2 Range cut "." 14892.29 yards
18:34:54.2 Spotting Officer: "Short"
18:34:58.9 Spotting Officer: "Up 800, Right 2"
18:35:01.4 Bearing taken rel=93.25 gyro=159.45
18:35:16.2 Range cut "T" 14857.064 yards
18:35:20.4 Range Officer: "Set rate 450 Opening"
18:35:20.6 Bearing taken rel=93.5 gyro=159.467
18:35:31.9 Range cut "." 14867.357 yards
18:35:35.6 Spotting Officer: "Over"
18:35:38.3 Spotting Officer: "Down 400, Right 2"
18:35:46.9 Bearing taken rel=94.0 gyro=160.2
18:36:00.6 Range cut "T" 15018.17 yards
18:36:06.3 Bearing taken rel=94.25 gyro=160.317
18:36:06.4 Range Officer: "Set rate 325 Opening"
(note: 2 shells of this 4-shell salvo hit, but the virtual
spotting officer did not see the hits. The human "player"
countermanded the errant spotting correction, but one salvo
had a bad correction applied)
18:36:19.3 Spotting Officer: "Short"
18:36:20.4 Range cut "." 15367.342 yards
18:36:20.6 Range Officer: "Set rate 300 Opening"
18:36:22.2 Gunnery Officer: "Hit"
18:36:23.6 Spotting Officer: "Up 200"
18:36:32.5 Bearing taken rel=94.75 gyro=160.683
18:36:38.9 Gunnery Officer: "Down 200"
18:36:51.7 Bearing taken rel=95.0 gyro=162.067
18:36:52.0 Range cut "T" 15032.283 yards
18:37:00.9 Spotting Officer: "Over"
18:37:06.0 Range cut "." 15367.41 yards
18:37:15.5 Range Officer: "Set rate 175 Opening"
18:37:17.1 Spotting Officer: "Can't see"
18:37:17.5 Bearing taken rel=95.25 gyro=161.883
18:37:35.7 Gunnery Officer: "Turn 4 points to starboard"
18:37:36.8 Bearing taken rel=95.5 gyro=163.6
18:37:41.0 Range cut "T" 15057.324 yards
(note: all 3 shells of a 3 shot salvo at this time hit the target)
18:37:51.0 Spotting Officer: "Hit"
18:37:51.5 Range Officer: "Set rate 175 Closing"
18:37:52.8 Range cut "." 15642.46 yards
18:38:04.5 Bearing taken rel=74.75 gyro=164.417
18:38:22.5 Range Officer: "Set rate 325 Opening"
18:38:23.2 Range cut "T" 15382.214 yards
18:38:23.8 Bearing taken rel=66.0 gyro=164.967
18:38:28.1 Spotting Officer: "Too far right"
18:38:32.2 Spotting Officer: "Left 8"
18:38:35.0 Range cut "." 15382.214 yards
18:38:37.5 Range Officer: "Set rate 200 Opening"
18:38:50.9 Bearing taken rel=59.0 gyro=165.0
18:39:07.5 Spotting Officer: "Can't see"
18:39:07.7 Range cut "T" 16167.132 yards
18:39:10.2 Bearing taken rel=56.25 gyro=166.183
18:39:23.2 Range Officer: "Set rate 550 Opening"
18:39:24.7 Range cut "." 15657.308 yards
18:39:36.3 Range Officer: "Set rate 225 Opening"
18:39:36.7 Bearing taken rel=54.25 gyro=166.45
18:39:38.5 Spotting Officer: "Can't see"
18:39:47.4 Range cut "T" 14851.277 yards
18:39:51.7 Range Officer: "Set rate 500 Opening"
18:39:56.0 Bearing taken rel=53.5 gyro=165.767
18:40:09.9 Spotting Officer: "Short"
18:40:11.4 Range cut "." 14892.471 yards
18:40:14.5 Spotting Officer: "Up 400"
18:40:25.9 Bearing taken rel=52.75 gyro=166.017
18:40:36.1 Range cut "T" 15567.989 yards
18:40:45.2 Bearing taken rel=52.5 gyro=165.2
18:40:50.8 Gunnery Officer: "Turn 4 points to port"
18:40:56.1 Spotting Officer: "Over"
18:40:58.2 Range cut "." 15407.321 yards
18:40:58.3 Spotting Officer: "Down 200"
18:40:59.3 Gunnery Officer: "Check"
18:41:11.9 Bearing taken rel=68.75 gyro=164.55
18:41:21.4 Range cut "T" 14706.927 yards
18:41:31.2 Bearing taken rel=80.0 gyro=165.133
18:41:43.2 Spotting Officer: "Too far left"
18:41:45.6 Range cut "." 14607.274 yards
18:41:46.6 Spotting Officer: "Right 4"
18:41:59.5 Bearing taken rel=89.25 gyro=164.85
18:42:01.5 Range cut "T" 14607.274 yards
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Weird Guy
Joined: 26 Apr 2005
Posts: 18
Posted: Wed Jun 29, 2005 5:30 am
Post subject:
Those lectures you gave, they were some sort of door prize to guests of the conference to attend as a reward, or what was their purpose if that is wrong?
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Site Admin
Joined: 29 Oct 2004
Posts: 487
Location: Boston
Posted: Wed Jun 29, 2005 8:23 am
Post subject:
My presentation was a preliminary "dog watch instruction" to an annual dinner of the organization. About 110 (of approx 800) members of the association attended the dinner, and about half of those attended my talk. I got the invitation to present last September, when the head of the organization saw me do a brief talk at the Fisher Conference.
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Weird Guy
Joined: 26 Apr 2005
Posts: 18
Posted: Thu Jul 07, 2005 3:52 am
Post subject:
Can I ask what the crowd thought of your computer demostrations?
Did anybody mention anything that surprised them?
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Site Admin
Joined: 29 Oct 2004
Posts: 487
Location: Boston
Posted: Thu Jul 07, 2005 9:39 am
Post subject:
I think there was a significant distance, technologically, between the systems I showed and what they had ever used. Some of the criticisms were of superficial bits easy for them to detect, but hard for me to research in advance (given all that I must juggle):
1. My new sailor models had small white stars inset in the corners of the flap of cloth that hangs between their shoulder blades. Several noted that those did not belong.
2. My ships all had a naval ensign or flag mounted on a jackstay on the prow. Several pointed out that such flags were never flown there, but were on the aft jackstay or at the head of a mast.
3. They wanted to correct my use of the term "step by step" transmitters by (if I recall correctly) M-type devices. This was merely a shift in terminology, I think, as "M-type devices" has never appeared in the contemporary literature I study, and "step by step" abounds. Just a new word was taken up to describe these devices at some post-war juncture.
Aside from that, they either failed to notice the occasional bits I knew were glaringly wrong (such as my turrets have a single sightsetting station that magically works to adjust all 4 sighting scopes, whereas a real turret would have had 4 such stations), or they lacked particular knowledge of the equipment and ships in the era I modeled, or assumed that omissions were bits I hoped to get to later (and of COURSE I want to get everything eventually!) Additionally, much of the ugly bits flew past very quickly --- I have editorial control, after all.
I have stopped work on the sim in its old form in order to concentrate on a new instantiation of the effort. It will be several to many months before ships and waves are seen again, but I can hope it will be shinier and in a form that I can eventually share it with others who'd like to tinker with it!
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Joined: 17 May 2005
Posts: 21
Posted: Wed Jul 27, 2005 10:07 pm
Post subject:
I'll say it once I'll say it again, I'm sooooo envious! That "psuedo-gunnery report" is pretty cool and I think you could turn that into a standar format say based on actual gunnery reports or action reports.
The more and more I read about this sim the more and more I
you on the right track. One area I really like is the communcation loops between the various FC stations. You can't really call it a simulation, particuarly the era your modeling, without those.
I can't wait.
BTW-got the automated announcement, works fine.
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Site Admin
Joined: 29 Oct 2004
Posts: 487
Location: Boston
Posted: Thu Jul 28, 2005 9:06 am
Post subject:
Thanks, Brad. The report as printed above reflects a split-purpose and some errors. I was torn between having the format be one that might have been logged on the ship and one that reflected the truth. The differences, of course, would be that errors in range and bearing should be manifest in the former but could be segregated in the latter (that is, observations with errors seen alongside actual data for comparison).
I may post my "analysis" video later, which comprises an orthographic overhead replay of a gunnery engagement, as it shows the actual course of firing and target ship over time, along with markers for range cuts, salvo aim points, shotfall splash markers, and a scrolling log of chatter with the spotting and rate corrections overlaid directly on the battle. Though it gets confusing, given how much is depicted, the best part of this is that an animated "ghost ship" is also drawn on the battle which represents the hypothesis on the firing ship of where the target ship is presumed to be. This really helps you see the divergence between the actual firing scenario and the one being modeled in the TS.
I'll post at least a few screenshots later, perhaps (though I've not run the old sim in 2 months).
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Site Admin
Joined: 29 Oct 2004
Posts: 487
Location: Boston
Posted: Wed Apr 19, 2006 1:29 pm
Post subject:
10 short months later, I got around to digitizing a video of the presentation.
It's a full hour, and 160 MB, but it should stream. I doubt many will want to see it, but it does show a slightly higher level of attainment in simulation than that seen in the videos already posted. Before downloading it, however, be aware that this is a video OF the presentation stage, and so the simulation may not be nearly as clear as the simulation videos already posted. I could post the videos being projected in this one, but they'd have no sound (I edited them for live narration).
I have to say -- I think my presentation skills were lacking, and that I drove into excessive depth of detail.
If you want to download to desktop before viewing, do the right-click+save as... thing.
The presentation is 46 minutes long, followed by 16 minutes of discussion before the tape ran out.
160 MB Quicktime Video
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Site Admin
Joined: 29 Oct 2004
Posts: 487
Location: Boston
Posted: Wed May 17, 2006 9:52 am
Post subject:
For the sake of completeness, I scanned the photo taken by the professional photographer at the event of Captain Raymond P Dannreuther and me right after dinner and posted it on the web page for the event.
Captain Dannreuther had just had his recent book on World War II naval operations in the Mediterranean,
Somerville's Force H: The Royal Navy's Gibraltar-Based Fleet, June 1940 To March 1942
published when we met.
His father was HMS Invincible's gunnery officer and a survivor of her destruction.
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I went to the Brion Gysin exhibition. I liked it. They have a working dream machine you can have a go on.
I went to the Brion Gysin exhibition. I liked it. They have a working dream machine you can have a go on.
I meant to go to the Joseph Cornell but sadly it coincided with a three week bender. The Daumier the RA put on was the last thing I saw there I think and that was one of the best things I've ever seen anywhere.
Actually I saw the kiefer but found it glib.
And what exhibitions have you seen lately? Would you recommend it? Anything you're looking forward to?
Is this an attempt to get me to rant about contemporary art again? I mean, you could havr just asked. You didn't need to start a new thread.
No, I know that rant, I've got it memorised. It's actually about going to exhibitions
I might have some new bits!
I doubt it. Your intellectual development stalled at the age of about 25. You haven't updated your software since. It's why you couldn't complete the Corbyn essay.
It's why I see Corbyn as a huge opportunity for you. I think it can give your brain a real kickstart
My intellectual development started at the age of 25.
Something has happened you didnt expect and you don't understand. That's great because your brain will have to adapt.
Taylor Parkes wrote a Corbyn essay for me. There was no need.
http://thequietus.com/articles/13940...-taylor-parkes ? Peter Capaldi regenerated from Malcolm Tucker into Jeremy Corbyn?
That detroit techno exhibition was frustrating. A chance to tell some really interesting stories but Instead they just pinned a few 12 inches to the wall and left it at that. Criminal really
I'll go to this. This thread has been a terrible failure.
I saw the Elton John collection of Man Ray and other photography- it was good fun, but I suspect a bit more middlebrow than what you're after. I like the smaller contained collections within the extension quite a lot, though I was doing the tourist thing with my gf so didn't really get to do my own thing.

subset: commoncrawl


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Jan. 22, 2011
LAS VEGAS, Nev. - The UCSB women's swim and dive team split a pair of dual meets on Saturday, defeating Northern Arizona, 182-98, while simultaneously falling to UNLV, 161-137, at the Rebel's Buchanan Natatorium. Meanwhile, the men were defeated, 160.5-99.5 by UNLV.
"There were a lot of good performances, but not enough of them," said head coach Gregg Wilson. "We had a good meet, but needed to have a really good meet."
The Gaucho women's meet was highlighted by standout performances from the freshmen.
Freshman Andrea Ward won the 1,000 freestyle in 10:14.97 and then took first in the 500 free later in the meet with a time of 5:00.84. Freshman Sophia Yamauchi was also a double event winner, taking first in the 100 breaststroke (1:02.88) and the 200 breaststroke (2:16.7).
Allie Knight also had a great meet. The freshman took second in the 100 backstroke with a time of 57.75 before taking first in the 200 back (2:02.78) and the 400 individual medley (4:27.92).
Freshman Mariah Tharp had a first-place finish in the 100 free. She touched the wall in 52.64 seconds.
The closest race of the afternoon was the women's 50 free, which was decided by .01 seconds. Kendall Neely was out-touched by Rachel Dixon and took second in 24.19 seconds.
Neely helped the Gaucho women end the meet on a high note as she, Tharp, Anne Peanasky and Yamauchi won the 200 free relay in 1:34.76.
On the men's side, it was the juniors that led the way. Junior Chris Peterson finished first in both the 50 (20.34) and the 100 free (44.44). His classmate, Kevin Ferguson, won the 200 back in 1:51.20 after being out-touched by UNLV's Cody Roberts in the 100 back by .07 seconds. Ferguson finished second in the event with a time of 49.81.
Junior Ryan Garcia was also impressive. He won the 200 butterfly in 1:53.11 as the Gauchos swept first through third in the event with Max Horrell and Randy Aakhus touching second and third, respectively. Garcia also placed second in the 200 back behind Ferguson with his time of 1:52.40 and then rounded out the meet with a second-place finish in the 400 IM (4:07.33).
UCSB will be back in action next Saturday, Jan. 29 when it hosts its final home meet of the season. The Gauchos will take-on Pacific and the UC Davis women in an 11 a.m. meet at Campus Pool.


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Monthly Archives: September 2013
Thought Process – Making of a Photograph.
Feel free to reach me @ Website | Facebook | 500px | Flickr | Instagram | Twitter "Shoot from the gut, edit with the brain" – Anders Petersen. Should have blogged on what I am about to a year back, but nevertheless, looks like this is the apt moment. This blog … Continue reading
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Losing a son is tough and Maxine Stradford said it's also tough knowing that the person who's been charged in his death hasn't been tried yet.
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Rapper 50 Cent appears to have made light of the domestic violence charges he is facing by posting a number of humorous pictures online to show fans he is not in jail.
The hip-hop star, real name Curtis Jackson, has been accused of attacking a former girlfriend at her home in California last month and he is facing five misdemeanor charges – one for domestic violence and four for vandalism – stemming from the alleged incident.
The "In Da Club" hit maker now appears to have addressed his legal drama in a series of posts on Instagram, publishing pictures of himself at home to prove he is "not in jail."
A picture that shows him in his kitchen is captioned, "I'm not in jail I'm in my kitchen fool," while another which shows him relaxing in his lounge is titled "I'm not in jail I'm on my Gucci couch."
The pictures also show him by his home swimming pool and in a casino, while a final image of him playing piano is captioned, "Ok I'm not in jail but if I go I'm taking this piano with me."
50 Cent will celebrate his 38th birthday on Saturday.


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|Head Coach
|Alma Mater:
|Graduating Year:
Bill Glassford led NU to its first Orange Bowl and second bowl appearance in 1954. He coached three All-Americans (Tom Novak, Bobby Reynolds, Jerry Minnick) in his seven years at Nebraska and added stability to a program that gone through six head coaches in a nine-year span before his hiring.
During his tenure, Nebraska played in the first night game in school history in 1951, and played in the first-ever televised broadcast of a college football game in 1953. He was inducted into the Nebraska Football Hall of Fame in 2002.
Glassford's first season at Nebraska in 1949 came on the heels of eight consecutive losing seasons by the Huskers under coaches Lawrence "Biff" Jones (1941), Glenn Presnell (1942), Adolph Lewandowski (1943-44), George "Potsy" Clark (1945, 1948) and Bernie Masterson (1946-47).
Glassford became Nebraska's sixth coach in nine seasons and provided immediate stability to the Huskers' program. He led NU to a 4-5 record in his first season that included a 3-3 Big Seven mark with wins over Kansas State, Iowa State and Colorado. The wins over the Cyclones and Buffaloes came in NU's final two games of the season and allowed the Huskers to build momentum heading into the 1950 campaign.
Nebraska posted a 6-2-1 record in 1950 and earned a No. 17 final national ranking from the Associated Press, the Huskers' first top-20 ranking since Jones coached NU to a final No. 7 ranking and the school's first-ever bowl appearance with an 8-2 mark following the Rose Bowl.
Nebraska's only losses in 1950 came at Colorado in the third game of the season and at No. 1 Oklahoma in the season finale. NU opened the season with a tie to Indiana before reeling off wins at Minnesota, a home shutout of Penn State and four straight Big Seven wins over Kansas, Missouri, Kansas State and Iowa State.
After coaching Nebraska to its best finish in a decade in 1950, Glassford became the first coach since Jones to stay with the Huskers for three straight seasons in 1951. Unfortunately, NU struggled to a 2-8 mark on the field and a 2-4 Big Seven record. One of NU's two victories on the season came with a 1-0 win by forfeit over Kansas State on Oct. 6.
The 1951 team also claimed the distinction of playing the first night game in school history in a 19-7 loss at Miami in the season finale on Nov. 30.
Glassford's 1952 club bounced back with NU's second winning season in three years with a final 5-4-1 record that included a 3-2-1 Big Seven mark. Nebraska opened the season with a 4-0 record after a 27-14 win over Kansas State on Oct. 11, but suffered a 10-0 loss at No. 19 Penn State on Oct. 18. After tying Colorado in Boulder, the Huskers suffered a 10-6 loss to Missouri before notching a 14-13 win over No. 7 Kansas on Nov. 8. NU then suffered back-to-back losses to Minnesota and Oklahoma to close the season.
Glassford led the Huskers to another historic milestone in 1953, when Nebraska met Oregon in the season opener in the first televised broadcast of a college football game in history. Unfortunately, NU suffered a 20-12 loss to the Ducks to open a 3-6-1 campaign.
Nebraska bounced back in 1954 with its third winning season under Glassford, finishing the year with a 6-5 overall mark and a 4-2 record in the Big Seven. The Huskers finished as the Big Seven runner-up, but advanced to the Orange Bowl at the end of the year under the conference's no-repeat rule with the bowl.
Although NU's season ended with a 34-7 loss to No. 14 Duke in Miami, the Huskers had earned just their second bowl bid in school history and made their first-ever trip to the Orange Bowl.
Glassford's seventh and final season at Nebraska ended with a 5-5 record in 1955, but the Huskers were an impressive 5-1 in the Big Seven with their only loss coming in the regular-season finale to No. 1 Oklahoma.
A native of Lancaster, Ohio, Glassford played guard for three years for coach Dr. Jock Sutherland at Pittsburgh. Glassford lettered at Pittsburgh in 1934, 1935 and 1936 and earned All-America honors as a senior. He was the captain of the Panther team that defeated Washington 21-0 in the 1937 Rose Bowl.
Glassford earned his bachelor's degree in business administration from Pittsburgh in 1937 and started his coaching career at Manhattan College in New York later that year. He spent three years at Manhattan before moving on to Carnegie Tech in Pittsburgh as the line coach under Dr. Edward Baker for two years. In 1942, Glassford was named the line coach at Yale.
He served in the Navy from 1943 to 1946, and coached the Miami Beach, Fla., Naval Air Station team during that time. He returned to Yale after his service in the Navy ended, but spent only a month at Yale before being named to his first head coaching job at New Hampshire in 1946.
Glassford led New Hampshire to Yankee Conference titles in 1946, 1947 and 1948. He led New Hampshire to an undefeated regular season in 1947, before his club lost to Toledo in the Glass Bowl.
James William Glassford was born March 8, 1914.


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Getty Images
When word broke Friday night of a deal in principle between the Ravens and quarterback Joe Flacco, agent Joe Linta issued a statement explaining that, if there are no issues with the language of the contract, Flacco would sign it.
Per a source with knowledge of the situation, Flacco is signing it. Tomorrow.
The six-year, $120.6 million deal pays out $51 million over the first two years, and $62 million over the first three, we're told.
In all, $52 million is guaranteed. It's currently unclear how much is fully guaranteed upon signing, and how much is guaranteed for injury only.
The agreement will be signed at 2:00 p.m. ET. Two hours before the window closes on using the franchise tag.


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You're the type who conscientiously recycles all your wine and champagne bottles. But what are you supposed to do with the corks? You hate to throw them out, but most municipalities don't recycle them. Well, if you are willing to make a trip to the post office, there's a place that is hankering to take those used corks off your hands.
Yemm & Hart Ltd., a small company in Missouri that focuses on green building materials, is accepting cork donations as it works on creating floor tiles made from recycled cork. Around 15 billion corks are produced each year, with some 60 percent coming from Portugal. According to Yemm & Hart, those cork oak savannas protect "biodiversity and certain wildlife that is endangered or on the brink of extinction."
The company said that the cork recycling is only part of the answer and that consumers should shy away from wine that uses synthetic corks. To participate in the program, send only your cork wine stoppers (no plastic or noncork materials are accepted) to:
Wine Cork Recycling
Yemm & Hart Ltd.
425 North Chamber Drive
Fredericktown, MO 63645


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- Research
- Open Access
An immune response gene expression module identifies a good prognosis subtype in estrogen receptor negative breast cancer
- Andrew E Teschendorff1Email author,
- Ahmad Miremadi2,
- Sarah E Pinder2,
- Ian O Ellis3 and
- Carlos Caldas1, 2Email author
© Teschendorff et al.; licensee BioMed Central Ltd. 2007
- Received: 14 March 2007
- Accepted: 2 August 2007
- Published: 02 August 2007
Estrogen receptor (ER)-negative breast cancer specimens are predominantly of high grade, have frequent p53 mutations, and are broadly divided into HER2-positive and basal subtypes. Although ER-negative disease has overall worse prognosis than does ER-positive breast cancer, not all ER-negative breast cancer patients have poor clinical outcome. Reliable identification of ER-negative tumors that have a good prognosis is not yet possible.
We apply a recently proposed feature selection method in an integrative analysis of three major microarray expression datasets to identify molecular subclasses and prognostic markers in ER-negative breast cancer. We find a subclass of basal tumors, characterized by over-expression of immune response genes, which has a better prognosis than the rest of ER-negative breast cancers. Moreover, we show that, in contrast to ER-positive tumours, the majority of prognostic markers in ER-negative breast cancer are over-expressed in the good prognosis group and are associated with activation of complement and immune response pathways. Specifically, we identify an immune response related seven-gene module and show that downregulation of this module confers greater risk for distant metastasis (hazard ratio 2.02, 95% confidence interval 1.2-3.4; P = 0.009), independent of lymph node status and lymphocytic infiltration. Furthermore, we validate the immune response module using two additional independent datasets.
We show that ER-negative basal breast cancer is a heterogeneous disease with at least four main subtypes. Furthermore, we show that the heterogeneity in clinical outcome of ER-negative breast cancer is related to the variability in expression levels of complement and immune response pathway genes, independent of lymphocytic infiltration.
- Breast Cancer
- Estrogen Receptor
- Additional Data File
- Lymphocytic Infiltration
- Intrinsic Subtype
It is widely recognized that estrogen receptor (ER)-positive (ER+) and ER-negative (ER-) breast cancers are two different disease entities. Generally, ER- tumours tend to be of high grade, are more frequently p53 mutated, and have worse prognosis compared with ER+ disease. Moreover, while ER+ disease can be treated with hormone therapy, the only targeted therapy available for ER- patients is a monoclonal antibody that binds to the ERBB2 receptor and that is effective only for those ER- tumours with HER2/ERBB2 over-expression.
In spite of these clinical advances, ER+ and ER- breast cancers remain heterogeneous diseases, and little is known regarding why patients with the same histopathologic characteristics may have widely different clinical outcomes [1]. This is particularly true for the basal subtype of ER- breast cancer, which is commonly defined by over-expression of cytokeratin markers (CK 5/6 and CK 14) and which is often also HER2 negative (HER2-) [2]. Most recent efforts to obtain a molecular understanding of the observed heterogeneity have focused on ER+ breast cancer, where gene expression signatures that are either prognostic or predictive of response to hormone therapy have been derived [3–10]. In contrast, few studies have thus far attempted to derive a prognostic signature within ER- breast cancer. Although cytokeratin markers have been shown to correlate with poor prognosis in breast cancer [11–14], an attempt to correlate basal markers with survival within ER- disease has shown that these markers were not predictive of outcome [15]. Based on our work presented here, it appears that the prognostic 'signal' in ER- breast cancer is much weaker than that in ER+ disease. This precludes the use of traditional supervised approaches, which assume a sufficiently low false discovery rate (FDR) for deriving gene expression based classifiers. A similar observation was reported by others [16].
Recently, we proposed a novel feature selection method (Profile Analysis using Clustering and Kurtosis [PACK]) [17], that selects genes using a pattern recognition method and that may significantly reduce the FDR. Using PACK in an integrated cohort of 186 ER- samples and 1,200 genes, we were able to identify distinct molecular subtypes, including a good prognosis subclass characterized by over-expression of immune response genes. However, these results were not validated in external cohorts.
The purpose of this work is to extend our preliminary findings [17] by applying PACK to the same three breast cancer datasets [5, 9, 18], but now using a much larger set of common genes (we rescued a significantly larger number of genes from our study [9] by imputing missing data, which led to a much larger number of overlapping genes, approximately 5,000, with the other two arrays.), and to further validate our findings using two external independent cohorts [7, 19]. More generally, our goal is to elucidate the molecular taxonomy of ER- breast cancer and, if possible, to find different prognostic subclasses and the corresponding prognostic markers.
The FDR is higher in ER-breast cancer
Finding ER-subclasses using PACK
Breast cancer datasets used
Cohort name
Oligo microarray platform
Death/distant metastasis (n)
Death/distant metastasis (n)
Distinct molecular subgroups of ER-breast cancer
Applying PAK to the integrated ER- data matrix of 5,007 genes, we found 813 genes with a negative kurtosis profile (Additional data file 1). Interestingly, applying the same analysis to ER+ breast cancer, we found only a much smaller number (193) of negative kurtosis profiles (Additional data file 2), despite there being roughly twice as many bimodal profiles in ER+ breast cancer (about 4500 in ER+ versus about 2,500 in ER-). We verified by explicit simulation that the significantly lower proportion of negative kurtosis profiles in ER+ disease could not be explained by the larger sample size in ER+ (527) compared with ER- disease (186; data not shown).
The other four groups were characterized mainly by absent or lower expression of these steroid response genes. One of these four clusters was characterized by over-expression of genes related to cell cycle and cell proliferation pathways (CC+), and another cluster also had over-expression of immune response genes (CC+/IR+). For the remaining two clusters, one was characterized by over-expression of extracellular matrix genes (ECM+), and the other was characterized by over-expression of immune response genes (IR+) only.
Relation to the intrinsic subtype classification
Distribution of ER- samples among clusters, prognostic groups and intrinsic subtypes
Luminal B
Luminal A
It further follows from these observations that ER- normal and luminal A samples were predominantly characterized by over-expression of ECM genes. ER- basal tumors, on the other hand, exhibited a more complex pattern and appeared to divide into at least four subgroups (CC+/IR+, CC+, ECM+, and IR+).
Classification of ER+ intrinsic subtypes, medullary breast cancer, and BRCA1 tumors into ER- subclasses
Luminal B
Luminal A
n classifiable
A subgroup of good prognosis in ER-breast cancer
We next considered whether the five identified clusters were associated with different prognostic groups. Because we had merged different cohorts, and it is questionable whether survival data can be also merged together, we decided to test first the clusters for association with clinical outcome by using a dichotomized outcome variable. Specifically, poor outcome was defined as any death or distant metastasis event, whereas good outcome was defined as a patient alive or with no distant metastasis. By studying the distribution of good and poor outcome events in the respective clusters, significant associations with prognosis could be found by means of Fisher's exact test (Table 2). Interestingly, this showed that the IR+ subgroup had better prognosis when compared with the ECM+ (P = 0.08), CC+ (P = 0.03), and SR+ (P = 0.005) subclasses.
Compared with the CC+/IR+ subgroup it also had better prognosis, although the difference was not statistically significant (P = 0.19). We thus combined the CC+/IR+ and IR+ subclasses together and evaluated the prognosis of this larger subclass relative to the rest of ER- samples. Consistent with our previous result [17] we found that the ER- tumors over-expressing immune response genes had better prognosis than ER- samples that did not (P = 0.02).
To further confirm our findings, we also generated Kaplan-Meier survival curves for the five identified subclasses using TTDM as the end-point (Figure 3b). This showed that the SR+ and CC+ subclasses had worst prognosis, whereas the IR+ subclass was the group with best prognosis. Specifically, relative to the IR+ subclass the SR+ subgroup had a hazard ratio (HR) of 3.70 (95% confidence interval [CI] 1.49 to 9.24; P = 0.005), whereas the CC+ subgroup had an HR of 2.75 (95% CI 1.07 to 7.05; P = 0.035). Similarly, relative to the CC+/IR+ subclass, the SR+ subgroup had an HR of 2.35 (95% CI 1.13 to 4.88; P = 0.02), whereas for the CC+ subgroup it did not quite reach statistical significance (HR 1.80, 95% CI 0.83 to 3.89; P = 0.13). We verified the statistical significance of these survival differences by 10,000 random permutations of the samples, which showed that the theoretical P value estimates above were essentially identical to the empirically derived P values.
Prognostic markers in ER-tumors are associated with immune response functions
In contrast, application of the same method to ER+ breast cancer yielded 29 genes (out of a possible 193) with P < 0.05 (Additional data file 2). In spite of there being only 29 genes in this list, there were many with mitotic cell cycle functions, notably UBE2C, MAD2L1, E2F1 and KIFC1, and GO enrichment analysis confirmed that the cell cycle GO was the most significantly enriched category followed by transcription regulatory activity. This result for ER+ breast cancer confirms findings reported elsewhere that poor prognosis in ER+ breast cancer is related mainly to over-expression of genes in cell cycle and cell proliferation pathways [3, 5, 7]. Notably, none of the identified prognostic genes were related to immune response functions.
Having identified a prognostic module of seven immune response genes (henceforth called the IR module), we next confirmed that clustering over this module resulted in clusters significantly associated with clinical outcome. Specifically, the partitioning around medoids (pam) clustering algorithm [29] with two centers predicted one cluster with 52 good outcome and 56 poor outcome patients, and another cluster with 56 good outcome and only 19 poor outcome events, which was highly significant under Fisher's exact test (P = 0.0004; Figure 3c). Kaplan-Meier and Cox regression analyses further confirmed that the group not over-expressing the seven-gene module (out of the seven genes the majority [six] are not over-expressed) had a greater risk for distant metastasis (HR 2.02, 95% CI 1.2 to 3.4; P = 0.009; Figure 3d).
Relation to STAT1and IFN cluster
Next, we investigated the relation of the good prognosis subgroup identified here with the novel IFN regulated cluster identified recently [23]. As shown previously [23], the IFN cluster, defined by over-expression of interferon regulated genes, including the transcriptional regulator STAT1, was most closely related to the basal subclass and had a prognostic performance similar to that of luminal B samples when compared with the luminal A and normal subtypes. Interestingly, when compared with the basal and HER2+ subclasses, the IFN group had better prognosis, although a formal comparison was not conducted by Hu and coworkers [23].
Among the 98 genes in the immune response gene cluster (Figure 3a and Additional data file 1), we identified a total of 14 with interferon related functions, including STAT1, SP110, NFKBI, IFI44, IFNGR1, ISGF3G, and IRF7. Interestingly, however, none of these genes showed association with prognosis except SP110. Thus, although it appears that our subclass is related to the IFN class discovered by Hu and coworkers [23], it is also distinct in that the associated prognostic markers are not in the IFN cluster.
Immune response module predicts outcome independently of lymph node status
Univariate and Multivariate Cox-regression model
ER- (n = 186)
ER+ (n = 527)
2.28 (1.36-3.85)
2.07 (1.47-2.90)
1.06 (0.66-1.70)
1.50 (0.72-3.14)
2.02 (1.19-3.41)
1.25 (0.91-1.71)
LNa + IRM
2.16 (1.28-3.64)
2.10 (1.49-2.96)
LN + IRMa
1.93 (1.14-3.26)
1.29 (0.94-1.76)
LIa + IRM
0.86 (0.32-2.28)
1.75 (0.41-7.47)
LI + IRMa
2.05 (0.71-5.97)
0.57 (0.27-1.19)
LNa + LI + IRM
1.79 (0.70-4.62)
1.48 (0.68-3.19)
LN + LIa + IRM
0.84 (0.51-1.38)
1.65 (0.38-7.07)
LN + LI + IRMa
2.22 (0.76-6.50)
0.57 (0.27-1.20)
Immune response module predicts outcome independently of lymphocytic infiltration
The upregulation of immune response genes in good prognosis tumors could be explained by the fact that these tumors elicit a stronger immune and inflammatory response, as measured for example by a higher degree of lymphocytic infiltration (LI). The association of high LI with good prognosis is well known [31–34], and although a few conflicting results have also been reported [35–37], we thought it natural to consider whether upregulation of the identified immune response module conferred good prognosis independently of LI. To this end, we scored the samples in the NCH cohort for LI (see Materials and methods, below) and combined these with the available LI score information from the NKI2 cohort, yielding a total of 50 scored samples. We found that although there were proportionally more tumors with high LI scores in the group over-expressing the immune response genes (specifically there were 11 high LI and 9 low LI samples versus 8 high LI and 22 low LI in the under-expressed group; Fisher test, P = 0.07), a multivariate Cox regression with TTDM as the outcome variable and the seven-gene IR module and LI score as predictors showed that the immune response module was still a strong predictor of clinical outcome, independent of LI (HR 2.05, 95% CI 0.71 to 5.97; P = 0.19; Table 4). (The P value is only marginally below statistical significance, which is most likely due to the relatively small sample size.) Supporting this result further, we did not find in ER- tumors any significant association between LI and clinical outcome (Table 4).
Relation to medullary and BRCA1 ER-breast cancer
To further confirm the independent prognostic power of the IR module from LI, we investigated the relationship of the identified good prognosis tumors with medullary breast cancers (MBCs), which are characterized by high LI scores and relatively good prognosis [38]. Thus, we considered to which ER- subclass the 22 MBC expression profiles from the report by Bertucci and coworkers [38] were most similar (Table 3). (For completeness and reference, we also performed the analysis for the 44 ductal breast cancers [DBCs], also profiled by Bertucci and coworkers [38].) This showed that of the 22 MBCs, 20 showed reasonably strong correlation to one of the ER- subclass centroids, 14 of which (70%) were most similar to the CC+/IR+ subtype, whereas only two (11%) were most similar to the IR+ subtype. In contrast, for the 33 DBC which could be classified, only four (12%) and three (9%) were most similar to the CC+/IR+ and IR+ subtypes. These results mirror the distribution of LI scores across the five ER- subclasses (Figure 3a and Table 2) and further confirms that the best prognostic group (IR+) is not related to MBC, whereas CC+/IR+ is.
A similar analysis was performed for BRCA1 mutant tumors. Of the 16 BRCA1 mutants from the NKI2 cohort [3], which were also deemed classifiable based on a 0.25 correlation threshold, 13 (81%) had expression profiles most similar to the CC+/IR+ subtype (Table 3). None showed similarity to the IR+ subclass. Therefore, this suggests that ER- BRCA1 mutants, in common with MBCs, are most similar to the CC+/IR+ subclass identified here.
External validation
External validation of immune response prognostic module: distribution of poor and good outcome patients in over-and-underexpressed subgroups
The prognostic value of the IR module is specific to ER-tumours
To confirm that the good prognosis conferred by activation of the IR module is specific to ER- breast cancer, we applied the same pam clustering algorithm over the seven genes to the integrated dataset of 527 ER+ breast tumors. This gave two clusters with unequal distributions of good and poor outcome samples (209 good and 99 poor for the cluster under-expressing the genes versus 163 good and 47 poor for the cluster over-expressing the genes; P = 0.02). Although this suggested to us that over-expression of this seven-gene module also conferred better prognosis in ER+ samples, the association was much weaker than for ER- samples. Univariate Cox regression with TTDM as the outcome variable confirmed that under-expression of this seven-gene module conferred a much greater risk for distant metastasis in ER- tumors (HR 2.02, 95% CI 1.2 to 3.4; P = 0.009) than in ER+ tumors (HR 1.25, 95% CI 0.9 to 1.7; P = 0.16; Table 4). It is also noteworthy that, in contrast to the ER- case, in the multivariate model setting for ER+ tumors, a low LI score and LN involvement were stronger predictors of TTDM than the seven-gene module (HR 1.65, 95% CI 0.4 to 7.1 for LI score and HR 1.48, 95% CI 0.7 to 3.2 for LN involvement, versus HR <1; Table 4). The specificity of our prognostic module to ER- breast cancer was confirmed by application of PAC, which showed that, with the exception of XCL2, none of the other six genes were individually prognostic.
In this context, it is worth noting once again the absence of immune response related genes among the 29 PAC derived prognostic genes in ER+ disease, which would suggest that a good prognosis IR related subtype is absent in ER+ breast cancer. To investigate this further we checked, by performing Wilcoxon rank sum tests on the 5,007 genes, that the absence of immune response GOs was not just an artefact of the small number of genes picked out by PACK. In fact, GO analysis (using GOTree Machine) of the top 500 genes obtained from this rank sum test (all with q-values < 0.05) showed that cell cycle and transcription regulator activity related GOs were the only categories with highly significant P values (uncorrected P < 10-6), and that the only enriched immune response related GO was that of humoral immune response, with 11 represented genes (including BLM, FADD, C3, C7, BCL2, NFKB1, and the IR module member TNFRSF17), and which was only marginally enriched (uncorrected P = 0.005). Although we verified that this humoral immune response module was associated with prognosis in the integrated ER+ cohort independent of LN status and LI (HR 2.26, 95% CI 0.97 to 5.26; P = 0.06) we were unable to validate this prognostic module in the external UPP and JRH-2 cohorts (Additional data file 8). Moreover, the co-regulation patterns for the genes in this module were less coherent than those for the IR module in ER- breast cancer (Figure 5 and Additional data file 8). Hence, independent of the methodology used, an IR related prognostic module in ER+ breast cancer could not be identified, which seems to suggest that a good prognosis subtype related to IR is specific to ER- disease.
A striking difference between ER+ and ER- disease is emerging at the level of mRNA expression. Although in ER+ disease a significant number of genes have been found that correlate with clinical outcome [5, 10, 18, 22], in ER- disease no such prognostic signatures have thus far been reported. Moreover, although in ER+ tumors subtypes of different prognostic risks, the luminal A and B subtypes, have been defined [21, 22], no such subdivisions have been noted for ER- breast cancer. It is known that the two main subtypes of ER- breast cancer (ER-/HER2+ and basals) have worse prognosis compared with the luminal A subtype, but no outcome differences between the ER-/HER2+ and basal subtypes have been observed [15, 21–23, 26].
We believe that these differences between ER+ and ER- disease are related to the different histopathologic characteristics of the tumors. The prognostic signatures derived for ER+ breast cancer are characterized by genes related to cell cycle and cell proliferation pathways, and are also highly correlated with the histologic grade of the tumors [7, 22]. It is not a coincidence that most luminal B tumours are of high grade, whereas the great majority of luminal A tumours are of low grade [7, 22]. It appears that there may be a whole plethora of diverse oncogenic pathways that drive the over-activation of cell cycle and cell growth pathways in poor prognosis tumors. This would explain the larger number of prognostic genes found in ER+ disease (the great majority of which are related to cell cycle ontologies) as well as the stronger prognostic signals (relatively large differences in log2 expression between poor and good prognosis tumours), an effect that is probably driven by oncogenic amplifications. This interpretation would also fit in well with our finding that most bimodal profiles in ER+ breast cancer have positive kurtosis values, because this could be a reflection of a more diverse range of small amplifier subgroups in ER+ breast cancer.
In contrast, most ER- tumours are of high grade, which would explain why any differences in clinical outcome within ER- disease are not related to differential activation of cell cycle pathways. Instead, the work presented here shows that differences in clinical outcome within ER- disease are mainly related to differentially expressed genes in the complement and immune response pathways, and that the association with prognosis can be independent of lymphocytic infiltration (LI) and LN status. In fact, for ER- tumors we observed that even though there were proportionally more high LI samples in the group over-expressing the IR module, these did not necessarily have better prognosis. The fact that LI could not explain the observed association of the IR module with outcome was supported further by our finding that medullary breast cancers, which are characterized by high LI scores, had expression profiles most similar to the CC+/IR+ subtype rather than the IR+ subtype, which had the best prognosis overall. The better prognosis of the CC+/IR+ subtype relative to CC+ and SR+ ER- breast cancer is therefore entirely consistent with the CC+/IR+ subclass being medullary breast cancers (MBCs), as MBC is known to have marginally better prognosis than other basal tumors [38]. On the other hand, the IR+ subclass, which had the best prognosis among the five ER- subclasses, was only marginally associated with high LI and was unrelated to MBC. Also consistent with these observations, it is important to note again the distinction between the identified seven-gene prognostic IR module and the 98-gene IR cluster that was derived from unsupervised hierarchical clustering. Clearly, we found a strong statistical association between high LI and over-expression of the 98-gene IR cluster. Specifically, there were 13 high LI and eight low LI samples in the combined IR+ and CC+/IR+ clusters relative to six high LI and 23 low LI samples in the rest of the cohort (Fisher test P = 0.007). In contrast, the association between high LI and over-expression of the IR module was much weaker (P = 0.07). Again, this suggests that a significant number of genes in the 98-gene IR cluster show expression variability that is not explained by LI. This is confirmed further by two recent studies that profiled breast cancer cell lines [27, 38], which showed that a considerable number of immune response related genes do exhibit significant variable expression across the basal cell subtype. Moreover, we found that two (SPP1 and HLA-F) of the three IR module genes that we could map to good quality probes in [27, 38] showed twofold changes across the eight basal cell lines.
Thus, these findings together suggest that a significant proportion of the expression of the IR module genes in the good prognosis tumors is tumor-intrinsic in origin. That tumor-intrinsic expression of IR genes can have an impact on prognosis of breast cancer patients is plausible in view of recent studies that show, for example, how amplification of kinase oncogenes can activate the nuclear factor-κB pathway, and hence immune response pathways, in both breast cancer cell lines and patient derived breast tumors [41]. Similarly, another recent study [42] used breast cancer cell lines to show how BRCA1/IFN-γ pathways may regulate target genes involved in innate immune response, providing another possible mechanism for tumor intrinsic IR gene expression variability.
In spite of identifying only a relatively small module of prognostic genes, we were nevertheless able to validate their prognostic potential in two external cohorts. It is likely that an integrative analysis similar to the one used here but applied to multiple cohorts that were all profiled on exactly the same genome-wide platform would allow further expansion of this module to include other members of the complement and immune response pathways. Interestingly, from the seven prognostic markers that composed the IR module, two have already been associated with clinical outcome in breast cancer. Specifically, C1QA, which is a gene involved in the classical complement pathway, was recently shown to harbor a single nucleotide polymorphism that correlated with distant metastasis in breast cancer [43]. Two recent studies also implicated SPP1 (osteopontin) in metastatic breast cancer [44, 45].
It is important also to note that the robustness of the identified prognostic markers is a consequence of the PACK methodology. Despite being a conservative procedure that filters out many true positives, PACK allows, by efficient removal of false positives, a more reliable identification of prognostic markers. We tested this further by applying two popular statistical tools, singular value decomposition (SVD) [46] and the shrunken centroids classifier (PAMR) [47], to the integrated ER- dataset to determine whether we could derive a similar if not identical prognostic IR module. Using SVD we found that none of the inferred SVD components showed a correlation with prognosis (Wilcoxon rank sum test P > 0.05), whereas at a FDR threshold of 0.3 PAMR yielded 21 prognostic genes, of which only two (the IR module member TNFRSF17 and KLRD1) had immune response related functions. Thus, in agreement with findings presented previously [17], this reinforces the advantage of PACK over other pattern recognition tools and supervised methods that do not use pattern recognition steps, such as for example those based on t-tests.
The absence of a prognostic IR module in ER+ breast cancer is intriguing. The seven-gene IR module was only marginally associated with prognosis in ER+ disease, and importantly this association was not independent of LI or LN status. Repeating the same unsupervised analysis (PAK) and semi-supervised analysis (PACK) in ER+ breast cancer also did not find a prognostic immune response module. By using a traditional supervised method, a prognostic IR module was identifiable but failed to validate in the two external cohorts. Thus, to determine fully whether such a robust prognostic IR module exists for ER+ breast cancer, it may be necessary to conduct larger integrative studies that use the same microarray platform so that the analysis can be performed over a larger set of common genes.
Besides identifying a module of genes that is prognostic in over 240 ER- breast tumors, PACK also provided us with a novel subclassification of ER- breast cancer. Specifically, clustering over PACK selected genes identified five different subtypes (CC+, CC+/IR+, IR+, ECM+, and SR+) characterized by the over-expression patterns of four distinct gene clusters, each enriched for IR, ECM, CC, and SR genes, respectively. Moreover, we related these subtypes to the gene expression based intrinsic subclasses. This showed that the basal subgroup was a heterogeneous group with at least four distinct subtypes (CC+, CC+/IR+, ECM+, and IR+), whereas the ER-/HER2+ subgroup showed strong overlap with the SR+ and IR+ subtypes.
While in ER+ breast cancer prognostic markers are associated mainly with cell cycle pathways, in ER- disease prognostic markers are associated with immune response pathways. In particular, we have identified a subclass of ER- tumors that over-express immune response genes and that has a good prognosis compared with the rest of ER- breast tumors, independently of LN status or LI. Furthermore, we have identified an associated module of complement and immune response genes that define prognostic markers valid in over 240 ER- samples.
Materials and methods
Datasets and gene annotation
The microarray breast cancer datasets considered in this work are described elsewhere [5, 7, 9, 18, 19]. For these cohorts we used the normalized data, which are available in the public domain (see [5, 7, 9, 18, 19]). The retrieved datasets were further normalized, if necessary, by transforming them onto a common log2 scale and shifting the median of each array to zero. We also created an automated computational pipeline (Perl scripts on a Linux platform) to crosslink the annotation provided for each dataset with UniGene. For some datasets, the linkage relied on Ensembl [48] external database identifiers. Thus each probe was associated with a universal gene name. This procedure generated a nonredundant set of gene identifiers for the subsequent integrative analysis.
PACK: profile analysis using clustering and kurtosis
The hypothesis underlying PACK [17] is that genes that are true biologic or clinical markers have expression profiles that are generated by a mixture of two or more underlying distributions, whereas spurious features are more likely to have profiles generated by a single distribution. The biologic validity of this assumption was proved through a FDR analysis [17].
PACK can be viewed as a semi-supervised algorithm, consisting of two main steps: a feature selection criterion and a supervised step, in which the selected features are correlated to a phenotype (Figure 2). It is important to note that PACK is a flexible modular algorithm in that the feature selection step can be applied on its own. In this case, there are two possible versions of the algorithm: PAC and PAK. The precise way in which these two algorithms are used in PACK will depend on the purpose of the exercise. Below, we describe the PACK strategy implemented in this paper, which is slightly different from that applied previously [17].
Feature selection with PAK: using negative kurtosis to find genes defining major subclasses
where a and b are the quadratic roots 2 ± √3. Thus, for e ≠ 0, the kurtosis is negative if and only if (2 - √3) < R < (2 + √3). This in turn requires the smallest cluster weight, πmin, to be in the range (approximately) of 0.22 <πmin < 0.5. It follows that for the case of approximately equal weights, where R ≅ 1 (πmin ≅ 0.5), the kurtosis is always negative and in the limit of large cluster separations (when e >> 1) the kurtosis decreases monotonically, asymptotically approaching the lower bound -2. Thus, kurtosis provides a useful measure for ranking genes based on how platykurtic their profiles are.
where and σ are the mean and standard deviation estimates of the profile. A standard error estimate of K was obtained by performing 10,000 random simulations, with n = 186 (number of ER- samples), which showed that the standard error estimate, 0.36, was essentially identical to the theoretical estimate, √(24/n) [50].
Two notes with the feature selection step are in order. First, the kurtosis threshold used to select features depends on how large the smallest subgroup must be. Generally, given the effective separation values that are typical for differential gene expression, we find that a zero kurtosis threshold (as used in this report) generally picks out subgroups within the individual gene expression profiles that are at least as large as 30% of the total sample size [17]. Second, in principle, genes defining major subclasses could be found using a clustering step to infer two clusters (PAC) and setting a lower bound threshold (for instance, 30%) on the size of the smallest cluster. However, this approach is computationally more expensive, because PAC attempts to estimate the optimal number of clusters in the profile. However, this model selection step is a necessary one to ensure that profiles for which there is no objective evidence of bimodality are excluded (see below).
PAC: identification of robust prognostic markers
Where πk are the weights of the components, (μk, σk) are the mean and standard deviation of the univariate gaussian k, and θ denotes the set of all parameters. In the above, CM denotes the maximum number of clusters that can be inferred, which in our application we set to 2. The optimal number of clusters, C, can be inferred using one of various approaches. One possibility is to use the EM algorithm to learn the parameters for the two different models C = 1 and C = 2, and perform model selection using the Bayesian Information Criterion (BIC) score [51, 52]. Alternatively, the optimal number of clusters, C, can be inferred using a lower bound on the model evidence, as provided by a variational Bayesian (VB) approach [39, 53, 54]. The results we report here were obtained using the VB algorithm for model selection. Thus, genes for which C = 1 were excluded from further analysis. Finally, association with the phenotype (here prognosis) was determined using Fisher's exact test to test whether poor outcome events were unevenly distributed across the two clusters.
Software packages used
All analyses were performed using the R statistical programming language [55]. The following add-on packages were used: vabayelMix for the PACK implementation, survival for the Cox regression models, qvalue for FDR estimation, and cluster for the partitioning around medoids (pam) clustering algorithm.
The SSP classifier
The classification of the samples in the NKI2, EMC, and NCH cohorts into the intrinsic subtypes was performed using the single sample predictor (SSP) [23] and was done for each cohort separately because this guaranteed a larger number of overlapping genes. In the SSP, samples were assigned the intrinsic subtype for which the corresponding Spearman rank correlation between the sample and SSP centroid was maximal [23].
The ER-subclass centroids
From the hierarchical clustering with Pearson correlation metric and complete linkage diagram (Figure 3a) we constructed mean centroids for each of the five subclasses. Classification of external samples to these centroids was performed using the nearest centroid criterion. Because these centroids were defined over ER- samples only, external samples (which may not be ER-) may not show strong correlation to any of these centroids. We thus validated, through 10,000 Monte Carlo (MC) randomisations, that samples with a maximal pearson coefficient larger than 0.25 were significantly correlated with the corresponding centroid (P < 0.0001). Samples with maximal correlation coefficients smaller than 0.25 were deemed to be unclassifiable.
Expression based basal and HER2+markers
The basal marker used in Figure 3 was derived by first mapping ten validated basal markers (CRYAB, ANXA8, LAMC2, LAMB3, ITGA6, KRT17, KRT15, KRT13, KRT6B, and KRT5) [27] onto the integrated data set of 5,007 genes. For each of these markers samples were ranked in order of decreasing expression or 'basalness'. For each sample in the integrated cohort an average rank was then computed over the ten basal markers. The average ranks were then rescaled onto the unit interval (0,1), with '1' indicating highest expression for basal markers. The marker for the ERBB2 subtype was obtained in an analogous manner using three genes in the ERBB2 amplicon (ERBB2, GRB7, and STARD3).
Lymphocyte infiltration scores
For the samples from our NCH cohort [9] we used the following scoring method. Lymphocytic infiltration (LI) was assessed in whole tumour sections from frozen sections stained with hematoxylin and eosin. The intensity of lymphocytic infiltrate was first graded semi-quantitatively as minimal or mild (1), moderate (2), and marked (3). The LI scores were then dichotomized (we considered mild and moderate as low LI and marked as high LI) to make them comparable with the binary LI scores used by van 't Veer and coworkers [3].
Additional data files
The following additional data are available with the online version of this manuscript. Additional data file 1 is a table showing the 813 genes with negative kurtosis expression profiles over 186 ER- tumors, together with the predicted number of clusters and Fisher's test P value with outcome as binary phenotype. Additional data file 2 is a table showing the 193 genes with negative kurtosis expression profiles over 527 ER+ samples, together with the predicted number of clusters and Fisher's test P value with outcome as binary phenotype. Additional data file 3 is a figure showing hierarchical clustering over 186 ER- breast cancers (gene annotated version). Additional data file 4 is a figure showing the distribution of basal and ERBB2 markers among ER- subtypes. Additional data file 5 is a table showing the centroids of gene expression for each of the five identified ER- subtypes. Additional data file 6 is a figure showing expression profiles of immune response module genes in ER- samples of the external UPP cohort. Additional data file 7 is a figure showing expression profiles of immune response module genes in ER- samples of the external JRH-2 cohort. Additional data file 8 is a figure showing the clustering of ER+ samples over the humoral immune response gene module in the two external UPP and JRH-2 cohorts.
This research was supported by grants from Cancer Research UK. We thank Ali Naderi for useful discussions.
Authors' Affiliations
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Union Point Museum
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Union Point GA 30667
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The former Union Point school house was built in the early 1900's. It was used as an education facility for almost seventy years. Pre-World War II, the building was used as a school for children in the day and a technical school in the evenings. Some men took welding classes and went on to serve in the military and at the Charleston Shipyard building ships for war. The building was recently restored by the City of Union Point and received an award for Excellence in Rehabilitation from the Georgia Trust in 2006. The rear two rooms are devoted to the historical museum and houses relics from the Confederate Wayside Home as well as displays from the Georgia Railroad and the Chipman Union Hosiery Mill which operated over 100 years before closing in 2001. The sister building that housed the high school was torn down in the 1960's, and the smaller white building was used as a lunchroom and a home economics class. The school gym is on the corner of Witcher Street and Hart Avenue, and it was used as a sight location in Kenny Rogers's movie, The Gambler. The gym is presently used as a part of the Greene County Recreational Department. The museum is opened by appointment and on special event days.
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Google has launched a new experimental version of their recently introduced SearchWiki interface which helps better target results. This latest Google Labs experiment introduces sound effects that make removing results from SERPs fun! When users participating in the experiment take aim at the "X" icon which removes results and and then click, they'll hear target practice like sound in addition to the SearchWiki animation. According to Google, the new sound effect was recorded by Co-Founder and President, Technology, Sergey Brin.
To check out the experiment visit:
UPDATE: seems this feature isn't as new as first believed......


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CHARLES LANE: Electric car fantasy runs out of juice
The Obama administration's electric-car fantasy finally may have died on the road between Newark, Del., and Milford, Conn.
The New York Times' John M. Broder reported Friday that the Tesla Model S electric car he was test-driving repeatedly ran out of juice, partly because cold weather reduces the battery's range by about 10 percent.
Broder's trip turned into a nightmare, including a stretch with the conked-out car riding the back of a flatbed truck.
Tesla chief executive Elon Musk fired back on Monday, tweeting that Broder's report is a "fake" and that "vehicle logs" show he "didn't actually charge to max & took a long detour."
The Times is standing by its story. My take is that even if Musk is 100 percent right and Broder is 100 percent wrong -- which I doubt -- Musk loses.
Who wants a $101,000 car that might die just because you feel like taking "a long detour"?
President Obama repeatedly declared that, with enough federal aid, we can put a million electric vehicles on the road by 2015. His administration has invested about $5 billion in grants, guaranteed loans -- including $465 million for Tesla -- and tax incentives to buyers.
Yet Americans bought just 71,000 plug-in hybrids or all-electric vehicles in the past two years, according to GreenCarReports.com. That's about a third as many as the Energy Department forecast in a 2011 report that attempted to explain why Obama's goal was not preposterous.
Federal billions cannot overcome the fact that electric vehicles and plug-in electric hybrids meet few, if any, of real consumers' needs. Compared with gas-powered cars, they deliver inferior performance at much higher cost. As an American Physical Society symposium on battery research concluded last June: "Despite their many potential advantages, all-electric vehicles will not replace the standard American family car in the foreseeable future."
If you don't believe the scientists, listen to Takeshi Uchiyamada, the "father" of the Toyota Prius: "Because of its shortcomings -- driving range, cost and recharging time -- the electric vehicle is not a viable replacement for most conventional cars."
Even Nissan chief Carlos Ghosn, whose commitment to the all-electric Leaf helped his firm get a $1.4 billion U.S. loan guarantee, has reduced his boosterism in the face of disappointing sales.
Nor do electric cars promise much in the way of greenhouse-gas reduction, as long as they rely on a power grid that is still mostly fired by fossil fuels.
As for Vice President Joe Biden's 2009 forecast of "billions and billions and billions of dollars in good, new jobs," the electric car factory at which he made that statement sits idle. Ditto the taxpayer-backed Michigan factory of battery maker LG Chem. Two Energy Department-funded lithium-ion battery makers have gone bankrupt.
There's simply no denying that the administration's electric-vehicle project was a mistake.
But it's worth asking precisely what kind of mistake (beyond eminently foreseeable and terribly expensive). As Bruce Springsteen once sang: "Is a dream a lie if it don't come true, or is it something worse?"
I accept the president's good intentions. He didn't set out to rip off the public. Nor was the electric-car dream a Democrats-only delusion. Several Republican pols shared it, too.
Rather, the debacle is a case study in unchecked righteousness. The administration assumed the worthiness and urgency of its goals. Americans should want electric cars, and therefore they would, apparently.
Energy Secretary Steven Chu, he of the Nobel Prize in physics, epitomized the regnant blend of sanctimony and technocratic hubris. He once told journalist Michael Grunwald that photosynthesis is "too damn inefficient," and that DOE might help correct that particular error of evolution.
The department has recently backed away from the million-car target, in favor of reducing battery costs to $300 per kilowatt hour by 2015 (from $650 today). Even this seems dubious, given the APS symposium's view that "only incremental improvements can be expected" in lithium-ion batteries.
Chu is on his way out but still dreaming. "For the engineers in the room or those who follow this, you might be saying to yourself, 'What are they smoking?' " he remarked at the Washington Auto Show. "We're not smoking anything. They are ambitious goals but they are achievable goals."
I might add that Chu does not own a car.
Charles Lane is a member of The Washington Post's editorial board.