TEDMED 2010
Danny Hillis: Understanding cancer through proteomics
丹尼 赫力斯：用蛋白质组学解读癌症
Filmed:
Readability: 3.6
465,363 views
丹尼 赫力斯讲述癌症医学的前沿：蛋白质组学，研究身体内的蛋白质。赫力斯向我们解释：基因组学显示了我们身体里的“佐料”， 而蛋白质组学向我们显示了用这些“佐料”有什么效果。理解我们身体里时时变化的蛋白质水平或许能帮我们了解癌症是怎么发生的。
Danny Hillis - Computer theorist
Inventor, scientist, author, engineer -- over his broad career, Danny Hillis has turned his ever-searching brain on an array of subjects, with surprising results. Full bio
Inventor, scientist, author, engineer -- over his broad career, Danny Hillis has turned his ever-searching brain on an array of subjects, with surprising results. Full bio
Double-click the English transcript below to play the video.
00:15
I admit that I'm a little bit nervous here
0
0
3000
我得承认我有点紧张，
00:18
because I'm going to say some radical things,
1
3000
3000
因为我将要谈谈一个很是激进的观点
00:21
about how we should think about cancer differently,
2
6000
3000
关于我们应该怎么从新的角度看癌症这个东西
00:24
to an audience that contains a lot of people
3
9000
2000
尤其是你们中很多人
00:26
who know a lot more about cancer than I do.
4
11000
3000
都是癌症专家，比我懂得多了。
00:30
But I will also contest that I'm not as nervous as I should be
5
15000
3000
但我也得承认我的弦绷得还不够紧，
00:33
because I'm pretty sure I'm right about this.
6
18000
2000
因为我挺自信我是对的。
00:35
(Laughter)
7
20000
2000
（笑声）
00:37
And that this, in fact, will be
8
22000
2000
我相信，事实上，（我的观点）
00:39
the way that we treat cancer in the future.
9
24000
3000
将会是未来我们治疗癌症的途经。
00:43
In order to talk about cancer,
10
28000
2000
要谈癌症前，
00:45
I'm going to actually have to --
11
30000
3000
我其实得——
00:48
let me get the big slide here.
12
33000
3000
让我展示这张图。
00:53
First, I'm going to try to give you a different perspective of genomics.
13
38000
3000
首先，我得让你们从另一个角度看基因组学。
00:56
I want to put it in perspective of the bigger picture
14
41000
2000
我希望把基因组学放在大环境中来看
00:58
of all the other things that are going on --
15
43000
3000
在不断变化的大环境中——
01:01
and then talk about something you haven't heard so much about, which is proteomics.
16
46000
3000
然后我会谈谈蛋白质组学，你们可能没怎么听过。
01:04
Having explained those,
17
49000
2000
讲了这两个之后，
01:06
that will set up for what I think will be a different idea
18
51000
3000
大家就好接受我的关于，
01:09
about how to go about treating cancer.
19
54000
2000
怎么治疗癌症的新观点了。
01:11
So let me start with genomics.
20
56000
2000
现在让我从基因组学开始。
01:13
It is the hot topic.
21
58000
2000
这可是热门科学。
01:15
It is the place where we're learning the most.
22
60000
2000
从中我们学到到最多，
01:17
This is the great frontier.
23
62000
2000
可谓科学前沿。
01:19
But it has its limitations.
24
64000
3000
但是它也有美中不足之处。
01:22
And in particular, you've probably all heard the analogy
25
67000
3000
特别是你们可能听过的一个比喻，
01:25
that the genome is like the blueprint of your body,
26
70000
3000
基因组学就像是你身体的蓝图，
01:28
and if that were only true, it would be great,
27
73000
2000
如果真是这样就太好了。
01:30
but it's not.
28
75000
2000
可惜不然。
01:32
It's like the parts list of your body.
29
77000
2000
基因组学好比你身体中的零件列表，
01:34
It doesn't say how things are connected,
30
79000
2000
但并没有说明每件之间是怎么连接的。
01:36
what causes what and so on.
31
81000
3000
什么是因，什么是果，等等。
01:39
So if I can make an analogy,
32
84000
2000
允许我也打个比方，
01:41
let's say that you were trying to tell the difference
33
86000
2000
就好比你想比较
01:43
between a good restaurant, a healthy restaurant
34
88000
3000
好吃又健康的餐馆
01:46
and a sick restaurant,
35
91000
2000
和差的餐馆，
01:48
and all you had was the list of ingredients
36
93000
2000
但你手里只有它们的佐料清单，
01:50
that they had in their larder.
37
95000
3000
它们贮藏室里有什么。
01:53
So it might be that, if you went to a French restaurant
38
98000
3000
就好像你去一个法国餐厅，
01:56
and you looked through it and you found
39
101000
2000
你查一个遍最后发现
01:58
they only had margarine and they didn't have butter,
40
103000
2000
它们只用人造黄油，不用天然黄油，
02:00
you could say, "Ah, I see what's wrong with them.
41
105000
2000
你可能会说：“嗯，我知道问题在哪里了，
02:02
I can make them healthy."
42
107000
2000
我能让它变成健康的餐馆。”
02:04
And there probably are special cases of that.
43
109000
2000
恐怕有时确实是这种情况。
02:06
You could certainly tell the difference
44
111000
2000
你可能很容易说出
02:08
between a Chinese restaurant and a French restaurant
45
113000
2000
中国餐馆和法国餐馆之间的区别，
02:10
by what they had in a larder.
46
115000
2000
就凭它们贮藏室里有什么。
02:12
So the list of ingredients does tell you something,
47
117000
3000
所以说佐料单确实提供一些信息，
02:15
and sometimes it tells you something that's wrong.
48
120000
3000
有时它能告诉你问题出在哪里。
02:19
If they have tons of salt,
49
124000
2000
好比它们有很多食盐，
02:21
you might guess they're using too much salt, or something like that.
50
126000
3000
你恐怕能猜出他们用盐太多之类的。
02:24
But it's limited,
51
129000
2000
但是只是一些信息。
02:26
because really to know if it's a healthy restaurant,
52
131000
2000
因为要确定一个餐馆是不是健康，
02:28
you need to taste the food, you need to know what goes on in the kitchen,
53
133000
3000
你得尝尝它们的食物，你得了解厨房里是怎么运作的，
02:31
you need the product of all of those ingredients.
54
136000
3000
你需要接触用所有这些佐料做成的产品。
02:34
So if I look at a person
55
139000
2000
所以如果你看一个人（是不是健康），
02:36
and I look at a person's genome, it's the same thing.
56
141000
3000
如果我只看这个人的基因组，就像（只看餐馆的佐料单）一样。
02:39
The part of the genome that we can read
57
144000
2000
我们能够从基因组看出来的，
02:41
is the list of ingredients.
58
146000
2000
也只是“佐料”列表而已。
02:43
And so indeed,
59
148000
2000
所以事实上，
02:45
there are times when we can find ingredients
60
150000
2000
有的时候我们能够看出
02:47
that [are] bad.
61
152000
2000
什么“佐料”不好。
02:49
Cystic fibrosis is an example of a disease
62
154000
2000
囊性纤维化就是这类病的一个例子，
02:51
where you just have a bad ingredient and you have a disease,
63
156000
3000
只要一个“佐料”坏了就能发病，
02:54
and we can actually make a direct correspondence
64
159000
3000
这里我们真能在基因和疾病间
02:57
between the ingredient and the disease.
65
162000
3000
建立直接的联系。
03:00
But most things, you really have to know what's going on in the kitchen,
66
165000
3000
但是大多数情况下，你真得知道厨房里是怎么回事，
03:03
because, mostly, sick people used to be healthy people --
67
168000
2000
因为绝大部分的病人都曾是健康的——
03:05
they have the same genome.
68
170000
2000
他们(得病前后的)基因组是不变的
03:07
So the genome really tells you much more
69
172000
2000
所以基因组真正告诉我们的，
03:09
about predisposition.
70
174000
2000
不过是易感性而已。
03:11
So what you can tell
71
176000
2000
光看“佐料单”你能够作出的结论
03:13
is you can tell the difference between an Asian person and a European person
72
178000
2000
只不过是这个人是亚洲人，
03:15
by looking at their ingredients list.
73
180000
2000
那个人是欧洲人而已。
03:17
But you really for the most part can't tell the difference
74
182000
3000
大多数的时候，病人和健康人之间的区别
03:20
between a healthy person and a sick person --
75
185000
3000
从基因组学是看不出来的——
03:23
except in some of these special cases.
76
188000
2000
除非很特别的情况下。
03:25
So why all the big deal
77
190000
2000
那为什么遗传学研究
03:27
about genetics?
78
192000
2000
这么重要呢？
03:29
Well first of all,
79
194000
2000
首先，
03:31
it's because we can read it, which is fantastic.
80
196000
3000
这是我们能够掌握的信息，很不容易的。
03:34
It is very useful in certain circumstances.
81
199000
3000
遗传信息在某些情况下特别有用。
03:37
It's also the great theoretical triumph
82
202000
3000
在生物学中，遗传学
03:40
of biology.
83
205000
2000
是生物理论研究上的巨大成功。
03:42
It's the one theory
84
207000
2000
它是个生物学上唯一的
03:44
that the biologists ever really got right.
85
209000
2000
所谓理论，能经得起推敲的。
03:46
It's fundamental to Darwin
86
211000
2000
它是达尔文学说的基础，
03:48
and Mendel and so on.
87
213000
2000
也是孟德尔学说和后续理论的基础。
03:50
And so it's the one thing where they predicted a theoretical construct.
88
215000
3000
他们预测出了这个理论构架。
03:54
So Mendel had this idea of a gene
89
219000
2000
孟德尔认为基因
03:56
as an abstract thing,
90
221000
3000
是抽象的。
03:59
and Darwin built a whole theory
91
224000
2000
达尔文把自己的整个学说
04:01
that depended on them existing,
92
226000
2000
建立在基因是实体的基础上。
04:03
and then Watson and Crick
93
228000
2000
接下来沃森和克瑞特
04:05
actually looked and found one.
94
230000
2000
观察发现了基因的存在。
04:07
So this happens in physics all the time.
95
232000
2000
这样的逻辑在物理学中常见。
04:09
You predict a black hole,
96
234000
2000
人们预测了黑洞的存在，
04:11
and you look out the telescope and there it is, just like you said.
97
236000
3000
之后用望远镜找，发现了之前预测的黑洞。
04:14
But it rarely happens in biology.
98
239000
2000
但是这个逻辑在生物学中很少见。
04:16
So this great triumph -- it's so good,
99
241000
3000
这就是为什么这个成功如此伟大——它如此伟大——
04:19
there's almost a religious experience
100
244000
2000
几乎称得上是生物学中的
04:21
in biology.
101
246000
2000
神迹
04:23
And Darwinian evolution
102
248000
2000
而其中达尔文的进化论
04:25
is really the core theory.
103
250000
3000
称得上是理论核心。
04:30
So the other reason it's been very popular
104
255000
2000
遗传学这么广为接受的另一个原因，
04:32
is because we can measure it, it's digital.
105
257000
3000
就是我们能够测量它，它是数字化的。
04:35
And in fact,
106
260000
2000
事实上，
04:37
thanks to Kary Mullis,
107
262000
2000
感谢凯瑞莫里斯（发明了聚合酶链反应PCR的生物学家）
04:39
you can basically measure your genome in your kitchen
108
264000
4000
你能事实上测量你的基因组，就在你自己的厨房里
04:43
with a few extra ingredients.
109
268000
3000
就靠几种材料。
04:46
So for instance, by measuring the genome,
110
271000
3000
举个例子，就靠着测量基因，
04:49
we've learned a lot about how we're related to other kinds of animals
111
274000
4000
我们已经深入理解了我们是怎么和其他动物同源的，
04:53
by the closeness of our genome,
112
278000
3000
就靠看我们和他们基因组间的相似性，
04:56
or how we're related to each other -- the family tree,
113
281000
3000
或者我们人类之间是怎么相联系的——家谱之类的，
04:59
or the tree of life.
114
284000
2000
或者是生物进化树。
05:01
There's a huge amount of information about the genetics
115
286000
3000
就靠着比较基因的相似性，
05:04
just by comparing the genetic similarity.
116
289000
3000
遗传学就能提供很多的信息。
05:07
Now of course, in medical application,
117
292000
2000
当然了，在医学应用方面，
05:09
that is very useful
118
294000
2000
这也是很有用的，
05:11
because it's the same kind of information
119
296000
3000
因为这是和医生从你的家族病史中
05:14
that the doctor gets from your family medical history --
120
299000
3000
得到的信息是类似的——
05:17
except probably,
121
302000
2000
只不过家族病史只是一个随机的子信息，
05:19
your genome knows much more about your medical history than you do.
122
304000
3000
你的基因其实能解释很多的你的病史，比你能解释的还要多。
05:22
And so by reading the genome,
123
307000
2000
所以通过解读基因，
05:24
we can find out much more about your family than you probably know.
124
309000
3000
我们能够比你还了解你的家庭。
05:27
And so we can discover things
125
312000
2000
我们还能发现新的信息
05:29
that probably you could have found
126
314000
2000
那些你应该早知道的
05:31
by looking at enough of your relatives,
127
316000
2000
凭着观察你的亲戚们，
05:33
but they may be surprising.
128
318000
3000
但这些事情可能还是会出乎你的意料。
05:36
I did the 23andMe thing
129
321000
2000
我做了类似的一个测试，
05:38
and was very surprised to discover that I am fat and bald.
130
323000
3000
很惊讶的发现我不但过胖还秃头。
05:41
(Laughter)
131
326000
7000
（笑声）
05:48
But sometimes you can learn much more useful things about that.
132
333000
3000
但有时你能得到很多有用的信息。
05:51
But mostly
133
336000
3000
多数情况下
05:54
what you need to know, to find out if you're sick,
134
339000
2000
发现疾病的必需的信息
05:56
is not your predispositions,
135
341000
2000
并不是你的易感性，
05:58
but it's actually what's going on in your body right now.
136
343000
3000
而是现时你身体的发生了什么。
06:01
So to do that, what you really need to do,
137
346000
2000
为了发现疾病，你真需要做的，
06:03
you need to look at the things
138
348000
2000
你真需要观察的，
06:05
that the genes are producing
139
350000
2000
是你的基因的产物，
06:07
and what's happening after the genetics,
140
352000
2000
是基因组学之后的一个层次。
06:09
and that's what proteomics is about.
141
354000
2000
这正是蛋白质组学所研究的。
06:11
Just like genome mixes the study of all the genes,
142
356000
3000
就像是基因组学研究所有的基因，
06:14
proteomics is the study of all the proteins.
143
359000
3000
蛋白质组学研究所有的蛋白质。
06:17
And the proteins are all of the little things in your body
144
362000
2000
这些蛋白质是你体内的小小物质
06:19
that are signaling between the cells --
145
364000
3000
它们在每个细胞间传递信息——
06:22
actually, the machines that are operating --
146
367000
2000
它们是真正操纵你身体的迷你机器。
06:24
that's where the action is.
147
369000
2000
它们是行动者。
06:26
Basically, a human body
148
371000
3000
基本上，人体
06:29
is a conversation going on,
149
374000
3000
是个
06:32
both within the cells and between the cells,
150
377000
3000
在细胞里和细胞间的持续对话，
06:35
and they're telling each other to grow and to die,
151
380000
3000
细胞们告诉对方该长大还是该消失。
06:38
and when you're sick,
152
383000
2000
当你生病时，
06:40
something's gone wrong with that conversation.
153
385000
2000
这种对话就出错了。
06:42
And so the trick is --
154
387000
2000
这里的微妙之处在于——
06:44
unfortunately, we don't have an easy way to measure these
155
389000
3000
不幸的是，我们没有像测试基因一样容易的方法，
06:47
like we can measure the genome.
156
392000
2000
来测试这些蛋白质。
06:49
So the problem is that measuring --
157
394000
3000
问题在于测试方法——
06:52
if you try to measure all the proteins, it's a very elaborate process.
158
397000
3000
如果你试图一起测试所有的蛋白质，这是个非常复杂的过程。
06:55
It requires hundreds of steps,
159
400000
2000
需要上百个步骤，
06:57
and it takes a long, long time.
160
402000
2000
需要很长的时间。
06:59
And it matters how much of the protein it is.
161
404000
2000
蛋白质的含量也很有关系。
07:01
It could be very significant that a protein changed by 10 percent,
162
406000
3000
十分之一的蛋白质的量变就很要命了，
07:04
so it's not a nice digital thing like DNA.
163
409000
3000
所以这并不是像基因一样是数码制的（分离系统）。
07:07
And basically our problem is somebody's in the middle
164
412000
2000
基本上我们的问题是，如果有人在测试蛋白质，
07:09
of this very long stage,
165
414000
2000
在长时间的操作中，
07:11
they pause for just a moment,
166
416000
2000
暂停了一下下，
07:13
and they leave something in an enzyme for a second,
167
418000
2000
把蛋白质留在蛋白酶中，就多一秒，
07:15
and all of a sudden all the measurements from then on
168
420000
2000
突然间所有的测量，从这一刻开始，
07:17
don't work.
169
422000
2000
就不再准确了。
07:19
And so then people get very inconsistent results
170
424000
2000
所以大家不断得到特别不一致的结果
07:21
when they do it this way.
171
426000
2000
因为他们是这么测量的。
07:23
People have tried very hard to do this.
172
428000
2000
大家做了很多努力来测量蛋白质，
07:25
I tried this a couple of times
173
430000
2000
我自己也做了几次实验
07:27
and looked at this problem and gave up on it.
174
432000
2000
试着克服这个问题，最后我放弃了。
07:29
I kept getting this call from this oncologist
175
434000
2000
后来我开始不断接到从大卫 艾格斯，
07:31
named David Agus.
176
436000
2000
一个癌症学家的电话。
07:33
And Applied Minds gets a lot of calls
177
438000
3000
总是有很多人给我们公司“Applied Minds”打电话
07:36
from people who want help with their problems,
178
441000
2000
不断有人要求我们帮忙，
07:38
and I didn't think this was a very likely one to call back,
179
443000
3000
我以为这个电话是不会再来的。
07:41
so I kept on giving him to the delay list.
180
446000
3000
所以我迟迟没有回他的电话。
07:44
And then one day,
181
449000
2000
直到有一天，
07:46
I get a call from John Doerr, Bill Berkman
182
451000
2000
我同一天内接到约翰 德尔，比尔 伯克曼，
07:48
and Al Gore on the same day
183
453000
2000
和埃尔 高尔的电话
07:50
saying return David Agus's phone call.
184
455000
2000
让我给大卫 艾格斯回电话。
07:52
(Laughter)
185
457000
2000
（笑声）
07:54
So I was like, "Okay. This guy's at least resourceful."
186
459000
2000
所以我想：“打就打，至少这个人聪明到会用关系网。”
07:56
(Laughter)
187
461000
4000
（笑声）
08:00
So we started talking,
188
465000
2000
这样我们开始对话，
08:02
and he said, "I really need a better way to measure proteins."
189
467000
3000
他说：“我迫切需要更好的技术来测量蛋白质。”
08:05
I'm like, "Looked at that. Been there.
190
470000
2000
我说：“我试了，也失败了。
08:07
Not going to be easy."
191
472000
2000
不是容易做的。”
08:09
He's like, "No, no. I really need it.
192
474000
2000
他说：“不一样，不一样，我是真需要。
08:11
I mean, I see patients dying every day
193
476000
4000
病人天天死在我眼前
08:15
because we don't know what's going on inside of them.
194
480000
3000
就因为我们不知道身体里面发生了什么。
08:18
We have to have a window into this."
195
483000
2000
我们一定要找到办法看透他们的身体。”
08:20
And he took me through
196
485000
2000
他还给我举了些例子，
08:22
specific examples of when he really needed it.
197
487000
3000
具体的病例和何时需要这个技术，
08:25
And I realized, wow, this would really make a big difference,
198
490000
2000
我才意识到，哇，如果我们能测量蛋白质的话，
08:27
if we could do it,
199
492000
2000
真的能改变命运。
08:29
and so I said, "Well, let's look at it."
200
494000
2000
于是我说：“好吧让我试试。”
08:31
Applied Minds has enough play money
201
496000
2000
我们公司有些积蓄，
08:33
that we can go and just work on something
202
498000
2000
是用来作初级测试的，
08:35
without getting anybody's funding or permission or anything.
203
500000
3000
不需要客户出钱或者授权。
08:38
So we started playing around with this.
204
503000
2000
于是我们就开始研发这个技术。
08:40
And as we did it, we realized this was the basic problem --
205
505000
3000
我们做的时候，意识到这里有个根源性的问题——
08:43
that taking the sip of coffee --
206
508000
2000
(实验员会)停下去喝口咖啡什么的，(导致实验中断)
08:45
that there were humans doing this complicated process
207
510000
2000
所以不该是靠人工来做这件事。
08:47
and that what really needed to be done
208
512000
2000
我们真正需要的
08:49
was to automate this process like an assembly line
209
514000
3000
是让机器做，就像是在流水线上一样，
08:52
and build robots
210
517000
2000
做出机器人
08:54
that would measure proteomics.
211
519000
2000
来替我们测试蛋白质。
08:56
And so we did that,
212
521000
2000
于是我们就这样做了。
08:58
and working with David,
213
523000
2000
和大卫合作，
09:00
we made a little company called Applied Proteomics eventually,
214
525000
3000
我们成立了一个小小的公司，定名为“蛋白组学应用公司”，
09:03
which makes this robotic assembly line,
215
528000
3000
专门做这些能够稳定测量蛋白质的
09:06
which, in a very consistent way, measures the protein.
216
531000
3000
机器人。
09:09
And I'll show you what that protein measurement looks like.
217
534000
3000
接下来我要介绍这个测量技术是什么样的。
09:13
Basically, what we do
218
538000
2000
基本上，我们所做的是
09:15
is we take a drop of blood
219
540000
2000
从病人身上
09:17
out of a patient,
220
542000
2000
取一滴血，
09:19
and we sort out the proteins
221
544000
2000
然后检测这滴血里的
09:21
in the drop of blood
222
546000
2000
所有的蛋白质
09:23
according to how much they weigh,
223
548000
2000
根据蛋白质的不同质量，
09:25
how slippery they are,
224
550000
2000
和蛋白质的不同粘性。
09:27
and we arrange them in an image.
225
552000
3000
我们给它们画个图，
09:30
And so we can look at literally
226
555000
2000
就能从这一滴血中
09:32
hundreds of thousands of features at once
227
557000
2000
同时看到
09:34
out of that drop of blood.
228
559000
2000
成百上千个不同的信息。
09:36
And we can take a different one tomorrow,
229
561000
2000
第二天我们还可以再检测一次，
09:38
and you will see your proteins tomorrow will be different --
230
563000
2000
你能看到第二天你的蛋白质组群是不同的——
09:40
they'll be different after you eat or after you sleep.
231
565000
3000
你吃东西或者睡觉都会改变它们。
09:43
They really tell us what's going on there.
232
568000
3000
它们是你身体里的实况报告。
09:46
And so this picture,
233
571000
2000
这就是一个图，
09:48
which looks like a big smudge to you,
234
573000
2000
看起来像是一大片污迹，
09:50
is actually the thing that got me really thrilled about this
235
575000
4000
正是让我觉得我们走对了路，
09:54
and made me feel like we were on the right track.
236
579000
2000
让我觉得无比震撼的。
09:56
So if I zoom into that picture,
237
581000
2000
如果我放大某个部分，
09:58
I can just show you what it means.
238
583000
2000
你就能看到我指的是什么。
10:00
We sort out the proteins -- from left to right
239
585000
3000
我们把蛋白质都分开了——从左到右，
10:03
is the weight of the fragments that we're getting,
240
588000
3000
是不同的蛋白片断的质量，
10:06
and from top to bottom is how slippery they are.
241
591000
3000
从上到下是它们的粘性。
10:09
So we're zooming in here just to show you a little bit of it.
242
594000
3000
我们放大图的这块，让你能看清很小的一点点。
10:12
And so each of these lines
243
597000
2000
这几条线里的每一条，
10:14
represents some signal that we're getting out of a piece of a protein.
244
599000
3000
都代表了这片蛋白的不同信息。
10:17
And you can see how the lines occur
245
602000
2000
你能看到它们是怎么分布的，
10:19
in these little groups of bump, bump, bump, bump, bump.
246
604000
4000
都是一小组一小组的，
10:23
And that's because we're measuring the weight so precisely that --
247
608000
3000
这是因为我们测量质量的方法精细到——
10:26
carbon comes in different isotopes,
248
611000
2000
能看到碳原子的不同同位素，
10:28
so if it has an extra neutron on it,
249
613000
3000
如果这个碳原子多一个少一个中子，
10:31
we actually measure it as a different chemical.
250
616000
4000
我们都能测得出来，把它们分开。
10:35
So we're actually measuring each isotope as a different one.
251
620000
3000
所以我们其实测量得到每个同位素。
10:38
And so that gives you an idea
252
623000
3000
这告诉我们
10:41
of how exquisitely sensitive this is.
253
626000
2000
这个技术有多灵敏。
10:43
So seeing this picture
254
628000
2000
我们看这张图片
10:45
is sort of like getting to be Galileo
255
630000
2000
就像是伽利略
10:47
and looking at the stars
256
632000
2000
看星星
10:49
and looking through the telescope for the first time,
257
634000
2000
第一次从望远镜中看到
10:51
and suddenly you say, "Wow, it's way more complicated than we thought it was."
258
636000
3000
你会感叹：“喔，这比我想象的复杂多了。”
10:54
But we can see that stuff out there
259
639000
2000
但我们能够看到这些区别，
10:56
and actually see features of it.
260
641000
2000
看到里面的信息。
10:58
So this is the signature out of which we're trying to get patterns.
261
643000
3000
这是个特例，我们能够通过它得到一个模式，
11:01
So what we do with this
262
646000
2000
方法是
11:03
is, for example, we can look at two patients,
263
648000
2000
比如，我们可以比较两个病人
11:05
one that responded to a drug and one that didn't respond to a drug,
264
650000
3000
一个对药物有阳性反应，另一个药物不起作用。
11:08
and ask, "What's going on differently
265
653000
2000
然后问：
11:10
inside of them?"
266
655000
2000
“他们身体内有什么不同？”
11:12
And so we can make these measurements precisely enough
267
657000
3000
通过精确的测量技术，
11:15
that we can overlay two patients and look at the differences.
268
660000
3000
我们可以比较来看两个人的蛋白质有什么不同。
11:18
So here we have Alice in green
269
663000
2000
像这里爱丽丝的是绿色的，
11:20
and Bob in red.
270
665000
2000
鲍勃的是红的，
11:22
We overlay them. This is actual data.
271
667000
3000
让我们比较两个结果，这是真的病人的结果。
11:25
And you can see, mostly it overlaps and it's yellow,
272
670000
3000
你能看到，绝大部分是一样的，显示黄色，
11:28
but there's some things that just Alice has
273
673000
2000
但有些蛋白是爱丽丝专有的，
11:30
and some things that just Bob has.
274
675000
2000
有的是鲍勃专有的。
11:32
And if we find a pattern of things
275
677000
3000
如果我们能发现在对药物有阳性反应的
11:35
of the responders to the drug,
276
680000
3000
病人的共性，
11:38
we see that in the blood,
277
683000
2000
我们从血液中能发现，
11:40
they have the condition
278
685000
2000
他们都有共同性
11:42
that allows them to respond to this drug.
279
687000
2000
让药物能对他们起作用，
11:44
We might not even know what this protein is,
280
689000
2000
我们可能不知道起作用的蛋白质的名字，
11:46
but we can see it's a marker
281
691000
2000
但我们能用它作为一个标志物，
11:48
for the response to the disease.
282
693000
2000
来标明对于疾病的反应。
11:53
So this already, I think,
283
698000
2000
所以这个已经是，我认为，
11:55
is tremendously useful in all kinds of medicine.
284
700000
3000
在医药学上极其有用的。
11:58
But I think this is actually
285
703000
2000
但我认为这其实只是
12:00
just the beginning
286
705000
2000
一个开始，
12:02
of how we're going to treat cancer.
287
707000
2000
将来我们要用它来治疗癌症。
12:04
So let me move to cancer.
288
709000
2000
让我来谈谈癌症。
12:06
The thing about cancer --
289
711000
2000
癌症——
12:08
when I got into this,
290
713000
2000
当我开始研究它，
12:10
I really knew nothing about it,
291
715000
2000
我什么都不知道，
12:12
but working with David Agus,
292
717000
2000
但是通过和大卫 艾格斯工作，
12:14
I started watching how cancer was actually being treated
293
719000
3000
我开始观察癌症是怎样被治疗的。
12:17
and went to operations where it was being cut out.
294
722000
3000
我还观察了手术，癌组织是怎么被取走的。
12:20
And as I looked at it,
295
725000
2000
当我研究癌症时，
12:22
to me it didn't make sense
296
727000
2000
对我来说，我们治疗癌症的方法
12:24
how we were approaching cancer,
297
729000
2000
并不正确。
12:26
and in order to make sense of it,
298
731000
3000
为了理解这个途径，
12:29
I had to learn where did this come from.
299
734000
3000
我得学习这些现今的治疗方法是怎么确定的。
12:32
We're treating cancer almost like it's an infectious disease.
300
737000
4000
我们治疗癌症，好像癌症是传染病一样，
12:36
We're treating it as something that got inside of you
301
741000
2000
我们治疗癌症像是癌症侵入了我们体内
12:38
that we have to kill.
302
743000
2000
我们得消灭敌人。
12:40
So this is the great paradigm.
303
745000
2000
这是为什么取走癌组织被认为是很好的模式。
12:42
This is another case
304
747000
2000
另一种情况，
12:44
where a theoretical paradigm in biology really worked --
305
749000
2000
这里生物的理论模式真的起作用了——
12:46
was the germ theory of disease.
306
751000
3000
就是疾病是细菌的理论。
12:49
So what doctors are mostly trained to do
307
754000
2000
医生都被训练
12:51
is diagnose --
308
756000
2000
来诊断疾病——
12:53
that is, put you into a category
309
758000
2000
就是把你放进一个类别里去——
12:55
and apply a scientifically proven treatment
310
760000
2000
给你用来治疗这个类别的人
12:57
for that diagnosis --
311
762000
2000
通常起作用的那个治疗方法。
12:59
and that works great for infectious diseases.
312
764000
3000
这通常对传染病是起作用的。
13:02
So if we put you in the category
313
767000
2000
如果我们把你放在这个类别中，
13:04
of you've got syphilis, we can give you penicillin.
314
769000
3000
就好像你得了梅毒，我们就给你青霉素。
13:07
We know that that works.
315
772000
2000
我们知道青霉素能治好你。
13:09
If you've got malaria, we give you quinine
316
774000
2000
就好像如果你得了疟疾，我们给你奎宁，
13:11
or some derivative of it.
317
776000
2000
或者相似的药物。
13:13
And so that's the basic thing doctors are trained to do,
318
778000
3000
因为这是通常医生被训练去做的。
13:16
and it's miraculous
319
781000
2000
对于传染病，
13:18
in the case of infectious disease --
320
783000
3000
这个非常管用——
13:21
how well it works.
321
786000
2000
就像是一个奇迹。
13:23
And many people in this audience probably wouldn't be alive
322
788000
3000
如果医生们不这样做，
13:26
if doctors didn't do this.
323
791000
2000
我们中的很多人恐怕活不到今天。
13:28
But now let's apply that
324
793000
2000
但是当我们把类似的治疗方法用于
13:30
to systems diseases like cancer.
325
795000
2000
像癌症那样的系统性疾病，
13:32
The problem is that, in cancer,
326
797000
2000
就有问题了。对于癌症，
13:34
there isn't something else
327
799000
2000
没有别的，
13:36
that's inside of you.
328
801000
2000
就是你出了问题。
13:38
It's you; you're broken.
329
803000
2000
是你，你有地方坏了，
13:40
That conversation inside of you
330
805000
4000
这是因为你体内的对话出了问题，
13:44
got mixed up in some way.
331
809000
2000
开始各处错误对话。
13:46
So how do we diagnose that conversation?
332
811000
2000
我们怎样解读这样的错误对话呢？
13:48
Well, right now what we do is we divide it by part of the body --
333
813000
3000
我们现在在做的是把癌症按照身体部分分类——
13:51
you know, where did it appear? --
334
816000
3000
你知道，按照癌症在什么地方发生——
13:54
and we put you in different categories
335
819000
2000
把你放进不同的类别里，
13:56
according to the part of the body.
336
821000
2000
那个身体部分的类别。
13:58
And then we do a clinical trial
337
823000
2000
接下来我们做医疗实验，
14:00
for a drug for lung cancer
338
825000
2000
比如对肺癌用一个药，
14:02
and one for prostate cancer and one for breast cancer,
339
827000
3000
对前列腺癌用一个药，对乳癌用一个药，
14:05
and we treat these as if they're separate diseases
340
830000
3000
我们治疗这些癌症，把它们当作是完全不同的病。
14:08
and that this way of dividing them
341
833000
2000
这样对癌症的分类方法
14:10
had something to do with what actually went wrong.
342
835000
2000
是真的按照出了什么问题来的。
14:12
And of course, it really doesn't have that much to do
343
837000
2000
当然了，其实这和到底什么出了问题
14:14
with what went wrong
344
839000
2000
并没有直接的关系。
14:16
because cancer is a failure of the system.
345
841000
3000
因为癌症是一个系统失灵。
14:19
And in fact, I think we're even wrong
346
844000
2000
事实上，我认为像这样把癌症当成是一件事来谈，
14:21
when we talk about cancer as a thing.
347
846000
3000
都是错误的。
14:24
I think this is the big mistake.
348
849000
2000
我认为这是个大错误。
14:26
I think cancer should not be a noun.
349
851000
4000
我看癌症，不是一个事物。
14:30
We should talk about cancering
350
855000
2000
我们应该用的词是“得癌”。
14:32
as something we do, not something we have.
351
857000
3000
是我们得的，不是我们有的。
14:35
And so those tumors,
352
860000
2000
那些癌组织，
14:37
those are symptoms of cancer.
353
862000
2000
只是“得癌”的一个症状。
14:39
And so your body is probably cancering all the time,
354
864000
3000
你的身体随时都在得癌，
14:42
but there are lots of systems in your body
355
867000
3000
但是绝大多数情况下，你的身体机构
14:45
that keep it under control.
356
870000
2000
能够不让它们发展。
14:47
And so to give you an idea
357
872000
2000
这里我给你一个概念，
14:49
of an analogy of what I mean
358
874000
2000
算是我的定义的一个比方，
14:51
by thinking of cancering as a verb,
359
876000
3000
想象癌症是一个过程，
14:54
imagine we didn't know anything about plumbing,
360
879000
3000
只要想象我们对下水管道一无所知，
14:57
and the way that we talked about it,
361
882000
2000
我们通常会这样描述：
14:59
we'd come home and we'd find a leak in our kitchen
362
884000
3000
我们回家，发现厨房有漏水，
15:02
and we'd say, "Oh, my house has water."
363
887000
4000
我们就说：“我们的房子有水。”
15:06
We might divide it -- the plumber would say, "Well, where's the water?"
364
891000
3000
我们能够粗粗分类——管道工会问：“哪里有水？”
15:09
"Well, it's in the kitchen." "Oh, you must have kitchen water."
365
894000
3000
“厨房里。”“那就是厨房水了。”
15:12
That's kind of the level at which it is.
366
897000
3000
这就是我们现在对癌症的认识。
15:15
"Kitchen water,
367
900000
2000
“厨房水”，
15:17
well, first of all, we'll go in there and we'll mop out a lot of it.
368
902000
2000
首先，我们要去厨房，把水拖干净，
15:19
And then we know that if we sprinkle Drano around the kitchen,
369
904000
3000
之后我们知道，在厨房里喷上
15:22
that helps.
370
907000
3000
管道清洁剂能起作用。
15:25
Whereas living room water,
371
910000
2000
如果是客厅水，
15:27
it's better to do tar on the roof."
372
912000
2000
“屋顶防潮剂能起作用。”
15:29
And it sounds silly,
373
914000
2000
这听起来可笑，
15:31
but that's basically what we do.
374
916000
2000
但是这是我们现在用的对策。
15:33
And I'm not saying you shouldn't mop up your water if you have cancer,
375
918000
3000
我不是说得了癌症后你不该除掉“厨房水”，
15:36
but I'm saying that's not really the problem;
376
921000
3000
我只是说那并不是问题的症结；
15:39
that's the symptom of the problem.
377
924000
2000
那只是问题的症状。
15:41
What we really need to get at
378
926000
2000
我们真的该解决的，
15:43
is the process that's going on,
379
928000
2000
是正在发生的症结。
15:45
and that's happening at the level
380
930000
2000
而且这个解决方案应该发生在
15:47
of the proteonomic actions,
381
932000
2000
蛋白质组互相作用的层次上，
15:49
happening at the level of why is your body not healing itself
382
934000
3000
发生在为什么你的身体不能自行治愈，
15:52
in the way that it normally does?
383
937000
2000
像它通常能做的？
15:54
Because normally, your body is dealing with this problem all the time.
384
939000
3000
通常你的身体每天都在解决这些问题。
15:57
So your house is dealing with leaks all the time,
385
942000
3000
所以说你的“房子”其实一直有漏水的问题，
16:00
but it's fixing them. It's draining them out and so on.
386
945000
4000
但是它在自己解决，自己排出漏水等等。
16:04
So what we need
387
949000
3000
我们需要的
16:07
is to have a causative model
388
952000
4000
是制定出一个症结模型
16:11
of what's actually going on,
389
956000
2000
来模拟问题是怎么发生的。
16:13
and proteomics actually gives us
390
958000
3000
蛋白质组学能够提供给我们
16:16
the ability to build a model like that.
391
961000
3000
建立起这样的模型的能力。
16:19
David got me invited
392
964000
2000
大卫请我去国家癌症研究院
16:21
to give a talk at National Cancer Institute
393
966000
2000
做个讲座，
16:23
and Anna Barker was there.
394
968000
3000
安娜 巴克也在那里。
16:27
And so I gave this talk
395
972000
2000
我做了讲座，
16:29
and said, "Why don't you guys do this?"
396
974000
3000
然后问他们：“为什么你们不按照这个思路做？”
16:32
And Anna said,
397
977000
2000
安娜说：
16:34
"Because nobody within cancer
398
979000
3000
“因为癌症学界没有人
16:37
would look at it this way.
399
982000
2000
能从这个角度看事情。
16:39
But what we're going to do, is we're going to create a program
400
984000
3000
但是我们想要做的，是成立一个计划署，
16:42
for people outside the field of cancer
401
987000
2000
让不在癌症学界工作的人们
16:44
to get together with doctors
402
989000
2000
来和真正对付癌症的
16:46
who really know about cancer
403
991000
3000
医生们合作，
16:49
and work out different programs of research."
404
994000
4000
发展出一个不同的研究方案。”
16:53
So David and I applied to this program
405
998000
2000
这样大卫和我就向这个计划署申请
16:55
and created a consortium
406
1000000
2000
在USC（南加州大学）成立了
16:57
at USC
407
1002000
2000
一个集团，
16:59
where we've got some of the best oncologists in the world
408
1004000
3000
在那里我们有世界顶级的癌症学家，
17:02
and some of the best biologists in the world,
409
1007000
3000
还有从Cold Spring Harbor（冷泉港），
17:05
from Cold Spring Harbor,
410
1010000
2000
Stanford（斯坦福），Austin（奥斯汀）等多处的
17:07
Stanford, Austin --
411
1012000
2000
一些世界级的生物学家——
17:09
I won't even go through and name all the places --
412
1014000
3000
我都列不出全部这些合作者们——
17:12
to have a research project
413
1017000
3000
来做这个研究项目。
17:15
that will last for five years
414
1020000
2000
在未来的五年，
17:17
where we're really going to try to build a model of cancer like this.
415
1022000
3000
我们将为癌症做一个症结模型。
17:20
We're doing it in mice first,
416
1025000
2000
我们正首先在小鼠身上做这个模型。
17:22
and we will kill a lot of mice
417
1027000
2000
在这个过程中，
17:24
in the process of doing this,
418
1029000
2000
我们需要使用很多小鼠，
17:26
but they will die for a good cause.
419
1031000
2000
但至少它们死得其所。
17:28
And we will actually try to get to the point
420
1033000
3000
之后我们会到达一个阶段，
17:31
where we have a predictive model
421
1036000
2000
是我们能有个预测出的模型，
17:33
where we can understand,
422
1038000
2000
在这个模型里我们是真的明白
17:35
when cancer happens,
423
1040000
2000
癌症是什么时候产生的，
17:37
what's actually happening in there
424
1042000
2000
里面是怎么回事，
17:39
and which treatment will treat that cancer.
425
1044000
3000
什么样的治疗方案能够奏效。
17:42
So let me just end with giving you a little picture
426
1047000
3000
这里让我稍稍描绘一下远景，来结束这个演讲
17:45
of what I think cancer treatment will be like in the future.
427
1050000
3000
谈谈我认为未来的癌症治疗方案是怎么一回事。
17:48
So I think eventually,
428
1053000
2000
我认为，总有一天，
17:50
once we have one of these models for people,
429
1055000
2000
当我们给每个病人都树立了正确的模型，
17:52
which we'll get eventually --
430
1057000
2000
总有一天——
17:54
I mean, our group won't get all the way there --
431
1059000
2000
光靠我们的研究队伍是不够的——
17:56
but eventually we'll have a very good computer model --
432
1061000
3000
但是最终我们会得到很好的计算模型——
17:59
sort of like a global climate model for weather.
433
1064000
3000
像是一个全球气候模型。
18:02
It has lots of different information
434
1067000
3000
这个模型包含了很多信息
18:05
about what's the process going on in this proteomic conversation
435
1070000
3000
描述蛋白质组间的对话，
18:08
on many different scales.
436
1073000
2000
从不同的精确度。
18:10
And so we will simulate
437
1075000
2000
这样我们就可以模拟
18:12
in that model
438
1077000
2000
为你身上的那种癌症
18:14
for your particular cancer --
439
1079000
3000
做出一个疾病模型来——
18:17
and this also will be for ALS,
440
1082000
2000
我们也可以为ALS（肌肉萎縮性側索硬化症）
18:19
or any kind of system neurodegenerative diseases,
441
1084000
3000
或者任何一种系统性的神经退化疾病做（这样的模型）
18:22
things like that --
442
1087000
2000
这类的疾病——
18:24
we will simulate
443
1089000
2000
我们会特别为你
18:26
specifically you,
444
1091000
2000
模拟一个治疗方案，
18:28
not just a generic person,
445
1093000
2000
不为其他任何人，
18:30
but what's actually going on inside you.
446
1095000
2000
而是根据你身体真的在发生什么，
18:32
And in that simulation, what we could do
447
1097000
2000
在这个程序里，我们能
18:34
is design for you specifically
448
1099000
2000
为你特别设计
18:36
a sequence of treatments,
449
1101000
2000
一系列的治疗方案
18:38
and it might be very gentle treatments, very small amounts of drugs.
450
1103000
3000
这些可以是非常轻微的治疗，非常微量的药量
18:41
It might be things like, don't eat that day,
451
1106000
3000
好比是，这天先别吃东西，
18:44
or give them a little chemotherapy,
452
1109000
2000
或者给一点点化疗，
18:46
maybe a little radiation.
453
1111000
2000
一点点放射性治疗，
18:48
Of course, we'll do surgery sometimes and so on.
454
1113000
3000
当然了，有时手术是不可避免的。
18:51
But design a program of treatments specifically for you
455
1116000
3000
但是我们能够为你量身定做治疗方案，
18:54
and help your body
456
1119000
3000
帮助你的身体，
18:57
guide back to health --
457
1122000
3000
领着它逐渐恢复健康——
19:00
guide your body back to health.
458
1125000
2000
领着你的身体恢复健康。
19:02
Because your body will do most of the work of fixing it
459
1127000
4000
因为你的身体会尽量自己恢复，
19:06
if we just sort of prop it up in the ways that are wrong.
460
1131000
3000
只要我们在它走错路的时候扶一把，
19:09
We put it in the equivalent of splints.
461
1134000
2000
只要我们能够提供支持
19:11
And so your body basically has lots and lots of mechanisms
462
1136000
2000
你的身体有很多的潜力，
19:13
for fixing cancer,
463
1138000
2000
自己治疗癌症。
19:15
and we just have to prop those up in the right way
464
1140000
3000
我们只需要关键时刻帮一把，
19:18
and get them to do the job.
465
1143000
2000
帮它回到正路上来。
19:20
And so I believe that this will be the way
466
1145000
2000
我相信这将会是
19:22
that cancer will be treated in the future.
467
1147000
2000
未来治疗癌症的途径。
19:24
It's going to require a lot of work,
468
1149000
2000
这将需要我们不断的努力，
19:26
a lot of research.
469
1151000
2000
很多很多的科研。
19:28
There will be many teams like our team
470
1153000
3000
需要其他的研究队伍，像我们队伍这样的
19:31
that work on this.
471
1156000
2000
一起进行这个研究。
19:33
But I think eventually,
472
1158000
2000
但我相信终有一天，
19:35
we will design for everybody
473
1160000
2000
我们能为每个人
19:37
a custom treatment for cancer.
474
1162000
4000
量身定做治疗癌症的方案。
19:41
So thank you very much.
475
1166000
2000
谢谢大家。
19:43
(Applause)
476
1168000
6000
（掌声）
ABOUT THE SPEAKER
Danny Hillis - Computer theoristInventor, scientist, author, engineer -- over his broad career, Danny Hillis has turned his ever-searching brain on an array of subjects, with surprising results.
Why you should listen
Danny Hillis is an inventor, scientist, author and engineer. While completing his doctorate at MIT, he pioneered the concept of parallel computers that is now the basis for graphics processors and cloud computing. He holds more than 300 US patents, covering parallel computers, disk arrays, forgery prevention methods, various electronic and mechanical devices, and the pinch-to-zoom display interface. He has recently been working on problems in medicine as well. He is also the designer of a 10,000-year mechanical clock, and he gave a TED Talk in 1994 that is practically prophetic. Throughout his career, Hillis has worked at places like Disney, and now MIT and Applied Invention, always looking for the next fascinating problem.
Danny Hillis | Speaker | TED.com