Danny Hillis: Understanding cancer through proteomics

57,327 views ・ 2011-03-16

TED


Please double-click on the English subtitles below to play the video.

00:15
I admit that I'm a little bit nervous here
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because I'm going to say some radical things,
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about how we should think about cancer differently,
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to an audience that contains a lot of people
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who know a lot more about cancer than I do.
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But I will also contest that I'm not as nervous as I should be
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because I'm pretty sure I'm right about this.
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(Laughter)
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And that this, in fact, will be
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the way that we treat cancer in the future.
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In order to talk about cancer,
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I'm going to actually have to --
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let me get the big slide here.
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First, I'm going to try to give you a different perspective of genomics.
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I want to put it in perspective of the bigger picture
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of all the other things that are going on --
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and then talk about something you haven't heard so much about, which is proteomics.
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Having explained those,
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that will set up for what I think will be a different idea
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about how to go about treating cancer.
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01:11
So let me start with genomics.
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It is the hot topic.
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01:15
It is the place where we're learning the most.
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This is the great frontier.
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But it has its limitations.
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And in particular, you've probably all heard the analogy
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that the genome is like the blueprint of your body,
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and if that were only true, it would be great,
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but it's not.
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It's like the parts list of your body.
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It doesn't say how things are connected,
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what causes what and so on.
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So if I can make an analogy,
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let's say that you were trying to tell the difference
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between a good restaurant, a healthy restaurant
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and a sick restaurant,
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and all you had was the list of ingredients
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that they had in their larder.
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So it might be that, if you went to a French restaurant
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and you looked through it and you found
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they only had margarine and they didn't have butter,
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you could say, "Ah, I see what's wrong with them.
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I can make them healthy."
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And there probably are special cases of that.
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You could certainly tell the difference
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between a Chinese restaurant and a French restaurant
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by what they had in a larder.
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So the list of ingredients does tell you something,
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and sometimes it tells you something that's wrong.
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If they have tons of salt,
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you might guess they're using too much salt, or something like that.
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But it's limited,
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because really to know if it's a healthy restaurant,
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you need to taste the food, you need to know what goes on in the kitchen,
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you need the product of all of those ingredients.
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So if I look at a person
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and I look at a person's genome, it's the same thing.
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The part of the genome that we can read
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is the list of ingredients.
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And so indeed,
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there are times when we can find ingredients
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that [are] bad.
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Cystic fibrosis is an example of a disease
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where you just have a bad ingredient and you have a disease,
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and we can actually make a direct correspondence
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between the ingredient and the disease.
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But most things, you really have to know what's going on in the kitchen,
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because, mostly, sick people used to be healthy people --
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they have the same genome.
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So the genome really tells you much more
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about predisposition.
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So what you can tell
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is you can tell the difference between an Asian person and a European person
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by looking at their ingredients list.
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But you really for the most part can't tell the difference
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between a healthy person and a sick person --
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except in some of these special cases.
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So why all the big deal
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about genetics?
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Well first of all,
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it's because we can read it, which is fantastic.
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It is very useful in certain circumstances.
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It's also the great theoretical triumph
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of biology.
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It's the one theory
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that the biologists ever really got right.
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It's fundamental to Darwin
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and Mendel and so on.
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And so it's the one thing where they predicted a theoretical construct.
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So Mendel had this idea of a gene
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as an abstract thing,
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and Darwin built a whole theory
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that depended on them existing,
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and then Watson and Crick
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actually looked and found one.
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So this happens in physics all the time.
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You predict a black hole,
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and you look out the telescope and there it is, just like you said.
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But it rarely happens in biology.
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So this great triumph -- it's so good,
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there's almost a religious experience
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in biology.
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And Darwinian evolution
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is really the core theory.
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So the other reason it's been very popular
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is because we can measure it, it's digital.
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And in fact,
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thanks to Kary Mullis,
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you can basically measure your genome in your kitchen
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with a few extra ingredients.
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So for instance, by measuring the genome,
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we've learned a lot about how we're related to other kinds of animals
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by the closeness of our genome,
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or how we're related to each other -- the family tree,
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or the tree of life.
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There's a huge amount of information about the genetics
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just by comparing the genetic similarity.
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Now of course, in medical application,
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that is very useful
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because it's the same kind of information
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that the doctor gets from your family medical history --
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except probably,
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your genome knows much more about your medical history than you do.
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And so by reading the genome,
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we can find out much more about your family than you probably know.
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And so we can discover things
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that probably you could have found
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by looking at enough of your relatives,
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but they may be surprising.
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I did the 23andMe thing
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and was very surprised to discover that I am fat and bald.
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(Laughter)
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But sometimes you can learn much more useful things about that.
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But mostly
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what you need to know, to find out if you're sick,
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is not your predispositions,
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but it's actually what's going on in your body right now.
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So to do that, what you really need to do,
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you need to look at the things
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that the genes are producing
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and what's happening after the genetics,
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and that's what proteomics is about.
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Just like genome mixes the study of all the genes,
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proteomics is the study of all the proteins.
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And the proteins are all of the little things in your body
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that are signaling between the cells --
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actually, the machines that are operating --
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that's where the action is.
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Basically, a human body
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is a conversation going on,
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both within the cells and between the cells,
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and they're telling each other to grow and to die,
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and when you're sick,
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something's gone wrong with that conversation.
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And so the trick is --
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unfortunately, we don't have an easy way to measure these
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like we can measure the genome.
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So the problem is that measuring --
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if you try to measure all the proteins, it's a very elaborate process.
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It requires hundreds of steps,
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and it takes a long, long time.
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And it matters how much of the protein it is.
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It could be very significant that a protein changed by 10 percent,
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so it's not a nice digital thing like DNA.
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And basically our problem is somebody's in the middle
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of this very long stage,
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they pause for just a moment,
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and they leave something in an enzyme for a second,
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and all of a sudden all the measurements from then on
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don't work.
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And so then people get very inconsistent results
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when they do it this way.
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People have tried very hard to do this.
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I tried this a couple of times
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and looked at this problem and gave up on it.
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I kept getting this call from this oncologist
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named David Agus.
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And Applied Minds gets a lot of calls
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from people who want help with their problems,
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and I didn't think this was a very likely one to call back,
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so I kept on giving him to the delay list.
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And then one day,
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I get a call from John Doerr, Bill Berkman
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and Al Gore on the same day
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saying return David Agus's phone call.
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(Laughter)
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So I was like, "Okay. This guy's at least resourceful."
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(Laughter)
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So we started talking,
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and he said, "I really need a better way to measure proteins."
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I'm like, "Looked at that. Been there.
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Not going to be easy."
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He's like, "No, no. I really need it.
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I mean, I see patients dying every day
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because we don't know what's going on inside of them.
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We have to have a window into this."
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And he took me through
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specific examples of when he really needed it.
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And I realized, wow, this would really make a big difference,
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if we could do it,
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and so I said, "Well, let's look at it."
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Applied Minds has enough play money
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that we can go and just work on something
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without getting anybody's funding or permission or anything.
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So we started playing around with this.
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And as we did it, we realized this was the basic problem --
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that taking the sip of coffee --
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that there were humans doing this complicated process
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and that what really needed to be done
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was to automate this process like an assembly line
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and build robots
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that would measure proteomics.
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And so we did that,
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and working with David,
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we made a little company called Applied Proteomics eventually,
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which makes this robotic assembly line,
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which, in a very consistent way, measures the protein.
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And I'll show you what that protein measurement looks like.
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Basically, what we do
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is we take a drop of blood
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out of a patient,
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and we sort out the proteins
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in the drop of blood
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according to how much they weigh,
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how slippery they are,
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and we arrange them in an image.
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And so we can look at literally
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hundreds of thousands of features at once
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out of that drop of blood.
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And we can take a different one tomorrow,
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and you will see your proteins tomorrow will be different --
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they'll be different after you eat or after you sleep.
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They really tell us what's going on there.
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And so this picture,
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which looks like a big smudge to you,
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is actually the thing that got me really thrilled about this
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and made me feel like we were on the right track.
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So if I zoom into that picture,
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I can just show you what it means.
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We sort out the proteins -- from left to right
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is the weight of the fragments that we're getting,
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and from top to bottom is how slippery they are.
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So we're zooming in here just to show you a little bit of it.
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And so each of these lines
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represents some signal that we're getting out of a piece of a protein.
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And you can see how the lines occur
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in these little groups of bump, bump, bump, bump, bump.
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And that's because we're measuring the weight so precisely that --
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carbon comes in different isotopes,
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so if it has an extra neutron on it,
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we actually measure it as a different chemical.
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So we're actually measuring each isotope as a different one.
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And so that gives you an idea
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of how exquisitely sensitive this is.
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So seeing this picture
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is sort of like getting to be Galileo
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and looking at the stars
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and looking through the telescope for the first time,
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and suddenly you say, "Wow, it's way more complicated than we thought it was."
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But we can see that stuff out there
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and actually see features of it.
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So this is the signature out of which we're trying to get patterns.
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So what we do with this
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is, for example, we can look at two patients,
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one that responded to a drug and one that didn't respond to a drug,
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and ask, "What's going on differently
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inside of them?"
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And so we can make these measurements precisely enough
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that we can overlay two patients and look at the differences.
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So here we have Alice in green
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and Bob in red.
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We overlay them. This is actual data.
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And you can see, mostly it overlaps and it's yellow,
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but there's some things that just Alice has
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and some things that just Bob has.
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And if we find a pattern of things
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of the responders to the drug,
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we see that in the blood,
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they have the condition
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that allows them to respond to this drug.
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We might not even know what this protein is,
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but we can see it's a marker
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for the response to the disease.
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So this already, I think,
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is tremendously useful in all kinds of medicine.
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But I think this is actually
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just the beginning
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of how we're going to treat cancer.
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So let me move to cancer.
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The thing about cancer --
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when I got into this,
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I really knew nothing about it,
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but working with David Agus,
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I started watching how cancer was actually being treated
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and went to operations where it was being cut out.
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And as I looked at it,
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to me it didn't make sense
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how we were approaching cancer,
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and in order to make sense of it,
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I had to learn where did this come from.
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We're treating cancer almost like it's an infectious disease.
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We're treating it as something that got inside of you
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that we have to kill.
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So this is the great paradigm.
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This is another case
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where a theoretical paradigm in biology really worked --
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was the germ theory of disease.
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So what doctors are mostly trained to do
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12:51
is diagnose --
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12:53
that is, put you into a category
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12:55
and apply a scientifically proven treatment
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for that diagnosis --
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and that works great for infectious diseases.
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13:02
So if we put you in the category
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of you've got syphilis, we can give you penicillin.
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13:07
We know that that works.
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If you've got malaria, we give you quinine
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or some derivative of it.
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13:13
And so that's the basic thing doctors are trained to do,
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13:16
and it's miraculous
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in the case of infectious disease --
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how well it works.
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13:23
And many people in this audience probably wouldn't be alive
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if doctors didn't do this.
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13:28
But now let's apply that
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to systems diseases like cancer.
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13:32
The problem is that, in cancer,
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there isn't something else
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that's inside of you.
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13:38
It's you; you're broken.
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13:40
That conversation inside of you
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got mixed up in some way.
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13:46
So how do we diagnose that conversation?
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Well, right now what we do is we divide it by part of the body --
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you know, where did it appear? --
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and we put you in different categories
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13:56
according to the part of the body.
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And then we do a clinical trial
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for a drug for lung cancer
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and one for prostate cancer and one for breast cancer,
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and we treat these as if they're separate diseases
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and that this way of dividing them
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had something to do with what actually went wrong.
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14:12
And of course, it really doesn't have that much to do
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with what went wrong
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because cancer is a failure of the system.
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14:19
And in fact, I think we're even wrong
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when we talk about cancer as a thing.
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I think this is the big mistake.
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I think cancer should not be a noun.
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We should talk about cancering
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as something we do, not something we have.
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And so those tumors,
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those are symptoms of cancer.
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14:39
And so your body is probably cancering all the time,
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but there are lots of systems in your body
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14:45
that keep it under control.
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14:47
And so to give you an idea
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of an analogy of what I mean
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by thinking of cancering as a verb,
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14:54
imagine we didn't know anything about plumbing,
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14:57
and the way that we talked about it,
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14:59
we'd come home and we'd find a leak in our kitchen
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15:02
and we'd say, "Oh, my house has water."
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15:06
We might divide it -- the plumber would say, "Well, where's the water?"
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15:09
"Well, it's in the kitchen." "Oh, you must have kitchen water."
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15:12
That's kind of the level at which it is.
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15:15
"Kitchen water,
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15:17
well, first of all, we'll go in there and we'll mop out a lot of it.
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15:19
And then we know that if we sprinkle Drano around the kitchen,
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15:22
that helps.
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15:25
Whereas living room water,
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15:27
it's better to do tar on the roof."
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15:29
And it sounds silly,
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but that's basically what we do.
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15:33
And I'm not saying you shouldn't mop up your water if you have cancer,
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15:36
but I'm saying that's not really the problem;
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15:39
that's the symptom of the problem.
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15:41
What we really need to get at
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is the process that's going on,
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15:45
and that's happening at the level
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of the proteonomic actions,
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15:49
happening at the level of why is your body not healing itself
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15:52
in the way that it normally does?
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15:54
Because normally, your body is dealing with this problem all the time.
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15:57
So your house is dealing with leaks all the time,
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16:00
but it's fixing them. It's draining them out and so on.
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16:04
So what we need
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16:07
is to have a causative model
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16:11
of what's actually going on,
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16:13
and proteomics actually gives us
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16:16
the ability to build a model like that.
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16:19
David got me invited
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to give a talk at National Cancer Institute
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16:23
and Anna Barker was there.
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And so I gave this talk
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16:29
and said, "Why don't you guys do this?"
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And Anna said,
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16:34
"Because nobody within cancer
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16:37
would look at it this way.
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16:39
But what we're going to do, is we're going to create a program
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16:42
for people outside the field of cancer
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to get together with doctors
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16:46
who really know about cancer
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and work out different programs of research."
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16:53
So David and I applied to this program
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16:55
and created a consortium
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at USC
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16:59
where we've got some of the best oncologists in the world
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17:02
and some of the best biologists in the world,
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17:05
from Cold Spring Harbor,
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Stanford, Austin --
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17:09
I won't even go through and name all the places --
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17:12
to have a research project
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17:15
that will last for five years
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17:17
where we're really going to try to build a model of cancer like this.
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17:20
We're doing it in mice first,
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17:22
and we will kill a lot of mice
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17:24
in the process of doing this,
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17:26
but they will die for a good cause.
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17:28
And we will actually try to get to the point
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17:31
where we have a predictive model
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17:33
where we can understand,
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17:35
when cancer happens,
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17:37
what's actually happening in there
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17:39
and which treatment will treat that cancer.
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17:42
So let me just end with giving you a little picture
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17:45
of what I think cancer treatment will be like in the future.
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17:48
So I think eventually,
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17:50
once we have one of these models for people,
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17:52
which we'll get eventually --
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17:54
I mean, our group won't get all the way there --
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17:56
but eventually we'll have a very good computer model --
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17:59
sort of like a global climate model for weather.
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18:02
It has lots of different information
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18:05
about what's the process going on in this proteomic conversation
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18:08
on many different scales.
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18:10
And so we will simulate
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18:12
in that model
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18:14
for your particular cancer --
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18:17
and this also will be for ALS,
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18:19
or any kind of system neurodegenerative diseases,
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18:22
things like that --
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we will simulate
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18:26
specifically you,
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18:28
not just a generic person,
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18:30
but what's actually going on inside you.
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18:32
And in that simulation, what we could do
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18:34
is design for you specifically
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18:36
a sequence of treatments,
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18:38
and it might be very gentle treatments, very small amounts of drugs.
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18:41
It might be things like, don't eat that day,
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18:44
or give them a little chemotherapy,
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18:46
maybe a little radiation.
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18:48
Of course, we'll do surgery sometimes and so on.
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But design a program of treatments specifically for you
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18:54
and help your body
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18:57
guide back to health --
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19:00
guide your body back to health.
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19:02
Because your body will do most of the work of fixing it
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if we just sort of prop it up in the ways that are wrong.
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19:09
We put it in the equivalent of splints.
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19:11
And so your body basically has lots and lots of mechanisms
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19:13
for fixing cancer,
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19:15
and we just have to prop those up in the right way
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and get them to do the job.
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19:20
And so I believe that this will be the way
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that cancer will be treated in the future.
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19:24
It's going to require a lot of work,
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a lot of research.
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19:28
There will be many teams like our team
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19:31
that work on this.
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But I think eventually,
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we will design for everybody
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a custom treatment for cancer.
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So thank you very much.
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(Applause)
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About this website

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