Danny Hillis: 了解癌症透過蛋白質組學

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http://dotsub.com/view/805361cf-4156-4c4a-884d-6f3327276f71
Danny Hillis: 了解癌症透過蛋白質組學
我得承认我有点紧张， 因为我将要谈谈一个很是激进的观点 关于我们应该怎么从新的角度看癌症这个东西 尤其是你们中很多人 都是癌症专家，比我懂得多了。 但我也得承认我的弦绷得还不够紧， 因为我挺自信我是对的。 （笑声） 我相信，事实上，（我的观点） 将会是未来我们治疗癌症的途经。 要谈癌症前， 我其实得—— 让我展示这张图。 首先，我得让你们从另一个角度看基因组学。 我希望把基因组学放在大环境中来看 在不断变化的大环境中—— 然后我会谈谈蛋白质组学，你们可能没怎么听过。 讲了这两个之后， 大家就好接受我的关于， 怎么治疗癌症的新观点了。
现在让我从基因组学开始。 这可是热门科学。 从中我们学到到最多， 可谓科学前沿。 但是它也有美中不足之处。 特别是你们可能听过的一个比喻， 基因组学就像是你身体的蓝图， 如果真是这样就太好了。 可惜不然。 基因组学好比你身体中的零件列表， 但并没有说明每件之间是怎么连接的。 什么是因，什么是果，等等。 允许我也打个比方， 就好比你想比较 好吃又健康的餐馆 和差的餐馆， 但你手里只有它们的佐料清单， 它们贮藏室里有什么。 就好像你去一个法国餐厅， 你查一个遍最后发现 它们只用人造黄油，不用天然黄油， 你可能会说：“嗯，我知道问题在哪里了， 我能让它变成健康的餐馆。” 恐怕有时确实是这种情况。 你可能很容易说出 中国餐馆和法国餐馆之间的区别， 就凭它们贮藏室里有什么。 所以说佐料单确实提供一些信息， 有时它能告诉你问题出在哪里。 好比它们有很多食盐， 你恐怕能猜出他们用盐太多之类的。 但是只是一些信息。 因为要确定一个餐馆是不是健康， 你得尝尝它们的食物，你得了解厨房里是怎么运作的， 你需要这些佐料的最终产品。
所以如果你看一个人（是不是健康）， 如果我只看这个人的基因组，就像（只看餐馆的佐料单）一样。 我们能够从基因组看出来的， 也只是“佐料”列表而已。 所以事实上， 有的时候我们能够看出 什么“佐料”不好。 囊性纤维化就是这类病的一个例子， 只要一个“佐料”坏了就能发病， 这里我们真能在基因和疾病间 建立直接的联系。 但是大多数情况下，你真得知道厨房里是怎么回事， 因为绝大部分的病人都曾是健康的—— 这些人的基因没有变。 所以基因组真正告诉我们的， 不过是易感性而已。 光看“佐料单”你能够作出的结论 只不过是这个人是亚洲人， 那个人是欧洲人而已。 大多数的时候，病人和健康人之间的区别 从基因组学是看不出来的—— 除非很特别的情况下。
那为什么遗传学研究 这么重要呢？ 首先， 这是我们能够掌握的信息，很不容易的。 遗传信息在某些情况下特别有用。 在生物学中，遗传学 是生物理论研究上的巨大成功。 它是个生物学上唯一的 所谓理论，能经得起推敲的。 它是达尔文学说的基础， 也是孟德尔学说和后续理论的基础。 他们预测出了这个理论构架。 孟德尔认为基因 是抽象的。 达尔文把自己的整个学说 建立在基因是实体的基础上。 接下来沃森和克瑞特 观察发现了基因的存在。 这样的逻辑在物理学中常见。 人们预测了黑洞的存在， 之后用望远镜找，发现了之前预测的黑洞。 但是这个逻辑在生物学中很少见。 这就是为什么这个成功如此伟大——它如此伟大—— 几乎称得上是生物学中的 神迹 而其中达尔文的进化论 称得上是理论核心。
遗传学这么广为接受的另一个原因， 就是我们能够测量它，它是数字化的。 事实上， 感谢凯瑞莫里斯（发明了聚合酶链反应PCR的生物学家） 你能事实上测量你的基因组，就在你自己的厨房里 就靠几种材料。 举个例子，就靠着测量基因， 我们已经深入理解了我们是怎么和其他动物同源的， 就靠看我们和他们基因组间的相似性， 或者我们人类是怎么相联系的——家谱之类的， 或者是生物进化树。 就靠着比较基因的相似性， 遗传学就能提供很多的信息。 当然了，在医学应用方面， 这也是很有用的， 因为这是和医生从你的家族病史中 得到的信息是类似的—— 只不过家族病史只是一个随机的子信息， 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。 所以通过解读基因， 我们能够比你还了解你的家庭。 我们还能发现新的信息 那些你应该早知道的 凭着观察你的亲戚们， 但这些事情可能还是会出乎你的意料。 我做了类似的一个测试， 很惊讶的发现我不但过胖还秃头。 （笑声） 但有时你能得到很多有用的信息。
多数情况下 发现疾病的必需的信息 并不是你的易感性， 而是现时你身体的发生了什么。 为了发现疾病，你真需要做的， 你真需要观察的， 是你的基因的产物， 是基因组学之后的一个层次。 这正是蛋白质组学所研究的。 就像是基因组学研究所有的基因， 蛋白质组学研究所有的蛋白质。 这些蛋白质是你体内的小小物质 它们在每个细胞间传递信息—— 它们是真正操纵你身体的迷你机器。 它们是行动者。 基本上，人体 是个 在细胞里和细胞间的持续对话， 细胞们告诉对方该长大还是该消失。 当你生病时， 这种对话就出错了。 这里的微妙之处在于—— 不幸的是，我们没有像测试基因一样容易的方法， 来测试这些蛋白质。
问题在于测试方法—— 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。 需要上百个步骤， 需要很长的时间。 蛋白质的含量也很有关系。 十分之一的蛋白质的量变就很要命了， 所以这并不是像基因一样是数码制的（分离系统）。 基本上我们的问题是，如果有人在测试蛋白质， 在长时间的操作中， 暂停了一下下， 把蛋白质留在蛋白酶中，就多一秒， 突然间所有的测量，从这一刻开始， 就不再准确了。 所以大家不断得到特别不一致的结果 因为他们是这么测量的。 大家做了很多努力来测量蛋白质， 我自己也做了几次实验 试着克服这个问题，最后我放弃了。
后来我开始不断接到从大卫 艾格斯， 一个癌症学家的电话。 我们公司“Applied Minds”总是需求不断的， 不断有人要求我们帮忙， 我以为这个电话是不会再来的。 所以我迟迟没有回他的电话。 直到有一天， 我同一天内接到约翰 德尔，比尔 伯克曼， 和埃尔 高尔的电话 让我给大卫 艾格斯回电话。 （笑声） 所以我想：“打就打，至少这个人聪明到会用关系网。” （笑声） 这样我们开始对话， 他说：“我迫切需要更好的技术来测量蛋白质。” 我说：“我试了，也失败了。 不是容易做的。” 他说：“我了，但是我是真需要。 病人天天死在我眼前 就因为我们不知道身体里面发生了什么。 我们一定要找到办法看透他们的身体。” 他还给我举了些例子， 有些病人是如何需要这个技术， 我才意识到，哇，如果我们能测量蛋白质的话， 真的能改变命运。 于是我说：“好吧让我试试。”
我们公司有些积蓄， 是用来作初级测试的， 不需要客户出钱或者授权。 于是我们就开始研发这个技术。 我们做的时候，意识到这里有个根源性的问题—— 非常基本——（这句不知道怎么翻译） 就是不该是靠人工来做这件事。 我们真正需要的 是让机器做，就像是在流水线上一样， 做出机器人 来替我们测试蛋白质。 于是我们就这样做了。 和大卫合作， 我们成立了一个小小的公司，定名为“蛋白组学应用公司”， 专门做这些能够稳定测量蛋白质的 机器人。 接下来我要介绍这个测量技术是什么样的。
基本上，我们所做的是 从病人身上 取一滴血， 然后检测这滴血里的 所有的蛋白质 根据蛋白质的不同质量， 和蛋白质的不同粘性。 我们给它们画个图， 就能从这一滴血中 同时看到 成百上千个不同的信息。 第二天我们还可以再检测一次， 你能看到第二天你的蛋白质组群是不同的—— 你吃东西或者睡觉都会改变它们。 它们是你身体里的实况报告。 这就是一个图， 看起来像是一大片污迹， 正是让我觉得我们走对了路， 让我觉得无比震撼的。 如果我放大某个部分， 你就能看到我指的是什么。 我们把蛋白质都分开了——从左到右， 是不同的蛋白片断的质量， 从上到下是它们的粘性。 我们放大图的这块，让你能看清很小的一点点。 这几条线里的每一条， 都代表了这片蛋白的不同信息。 你能看到它们是怎么分布的， 都是一小组一小组的， 这是因为我们测量质量的方法精细到—— 能看到碳原子的不同同位素， 如果这个碳原子多一个少一个中子， 我们都能测得出来，把它们分开。 所以我们其实测量得到每个同位素。
这告诉我们 这个技术有多灵敏。 我们看这张图片 就像是伽利略 看星星 第一次从望远镜中看到 你会感叹：“喔，这比我想象的复杂多了。” 但我们能够看到这些区别， 看到里面的信息。 这是个特例，我们能够通过它得到一个模式， 方法是 比如，我们可以比较两个病人 一个对药物有阳性反应，另一个药物不起作用。 然后问： “他们身体内有什么不同？” 通过精确的测量技术， 我们可以比较来看两个人的蛋白质有什么不同。
像这里爱丽丝的是绿色的， 鲍勃的是红的， 让我们比较两个结果，这是真的病人的结果。 你能看到，绝大部分是一样的，显示黄色， 但有些蛋白是爱丽丝专有的， 有的是鲍勃专有的。 如果我们能发现在对药物有阳性反应的 病人的共性， 我们从血液中能发现， 他们都有共同性 让药物能对他们起作用， 我们可能不知道起作用的蛋白质的名字， 但我们能用它作为一个标志物， 来标明对于疾病的反应。 所以这个已经是，我认为， 在医药学上极其有用的。 但我认为这其实只是 一个开始， 将来我们要用它来治疗癌症。 让我来谈谈癌症。
癌症—— 当我开始研究它， 我什么都不知道， 但是通过和大卫 艾格斯工作， 我开始观察癌症是怎样被治疗的。 我还观察了手术，癌组织是怎么被取走的。 当我研究癌症时， 对我来说，我们治疗癌症的方法 并不正确。 为了理解这个途径， 我得学习这些现今的治疗方法是怎么确定的。 我们治疗癌症，好像癌症是传染病一样， 我们治疗癌症像是癌症侵入了我们体内 我们得消灭敌人。 这是为什么取走癌组织被认为是很好的模式。 另一种情况， 这里生物的理论模式真的起作用了—— 就是疾病是细菌的理论。 医生都被训练 来诊断疾病—— 就是把你放进一个类别里去—— 给你用来治疗这个类别的人 通常起作用的那个治疗方法。 这通常对传染病是起作用的。 如果我们把你放在这个类别中， 就好像你得了梅毒，我们就给你青霉素。 我们知道青霉素能治好你。 就好像如果你得了疟疾，我们给你奎宁， 或者相似的药物。 因为这是通常医生被训练去做的。 对于传染病， 这个非常管用—— 就像是一个奇迹。 如果医生们不这样做， 我们中的很多人恐怕活不到今天。
但是当我们把类似的治疗方法用于 像癌症那样的系统性疾病， 就有问题了。对于癌症， 没有别的， 就是你出了问题。 是你，你有地方坏了， 这是因为你体内的对话出了问题， 开始各处错误对话。 我们怎样解读这样的错误对话呢？ 我们现在在做的是把癌症按照身体部分分类—— 你知道，按照癌症在什么地方发生—— 把你放进不同的类别里， 那个身体部分的类别。 接下来我们做医疗实验， 比如对肺癌用一个药， 对前列腺癌用一个药，对乳癌用一个药， 我们治疗这些癌症，把它们当作是完全不同的病。 这样对癌症的分类方法 是真的按照出了什么问题来的。 当然了，其实这和到底什么出了问题 并没有直接的关系。 因为癌症是一个系统失灵。 事实上，我认为像这样把癌症当成是一件事来谈， 都是错误的。 我认为这是个大错误。 我看癌症，不是一个事物。 我们应该用的词是“得癌”。 是我们得的，不是我们有的。 那些癌组织， 只是“得癌”的一个症状。 你的身体随时都在得癌， 但是绝大多数情况下，你的身体机构 能够不让它们发展。
这里我给你一个概念， 算是我的定义的一个比方， 想象癌症是一个过程， 只要想象我们对下水管道一无所知， 我们通常会这样描述： 我们回家，发现厨房有漏水， 我们就说：“我们的房子有水。” 我们能够粗粗分类——管道工会问：“哪里有水？” “厨房里。”“那就是厨房水了。” 这就是我们现在对癌症的认识。 “厨房水”， 首先，我们要去厨房，把水拖干净， 之后我们知道，在厨房里喷上 管道清洁剂能起作用。 如果是客厅水， “屋顶防潮剂能起作用。” 这听起来可笑， 但是这是我们现在用的对策。 我不是说得了癌症后你不该除掉“厨房水”， 我只是说那并不是问题的症结； 那只是问题的症状。
我们真的该解决的， 是正在发生的症结。 而且这个解决方案应该发生在 蛋白质组互相作用的层次上， 发生在为什么你的身体不能自行治愈， 像它通常能做的？ 通常你的身体每天都在解决这些问题。 所以说你的“房子”其实一直有漏水的问题， 但是它在自己解决，自己排出漏水等等。 我们需要的 是发展出一个症结模型 来模拟问题是怎么发生的。 蛋白质组学能够提供给我们 建立起这样的模型的能力。
大卫请我去国家癌症研究院 做个讲座， 安娜 巴克也在那里。 我做了讲座， 然后问他们：“为什么你们不按照这个思路做？” 安娜说： “因为癌症学界没有人 能从这个角度看事情。 但是我们想要做的，是成立一个计划署， 让不在癌症学界工作的人们 来和真正对付癌症的 医生们合作， 发展出一个不同的研究方案。” 这样大卫和我就向这个计划署申请 在USC（南加州大学）成立了 一个集团， 在那里我们有世界顶级的癌症学家， 还有从Cold Spring Harbor（冷泉港）， Stanford（斯坦福），Austin（奥斯汀）等多处的 一些世界级的生物学家—— 我都列不出全部这些合作者们—— 来做这个研究项目。 在未来的五年， 我们将为癌症做一个症结模型。 我们正首先在小鼠身上做这个模型。 在这个过程中， 我们需要使用很多小鼠， 但至少它们死得其所。 之后我们会到达一个阶段， 是我们能有个预测出的模型， 在这个模型里我们是真的明白 癌症是什么时候产生的， 里面是怎么回事， 什么样的治疗方案能够奏效。
这里让我稍稍描绘一下远景，来结束这个演讲 谈谈我认为未来的癌症治疗方案是怎么一回事。 我认为，总有一天， 当我们给每个病人都树立了正确的模型， 总有一天—— 光靠我们的研究队伍是不够的—— 但是最终我们会得到很好的计算模型—— 像是一个全球气候模型。 这个模型包含了很多信息 描述蛋白质组间的对话， 从不同的精确度。 这样我们就可以模拟 为你身上的那种癌症 做出一个疾病模型来—— 我们也可以为ALS（肌肉萎縮性側索硬化症） 或者任何一种系统性的神经退化疾病做（这样的模型） 这类的疾病—— 我们会特别为你 模拟一个治疗方案， 不为其他任何人， 而是根据你身体真的在发生什么，
在这个程序里，我们能 为你特别设计 一系列的治疗方案 这些可以是非常轻微的治疗，非常微量的药量 好比是，这天先别吃东西， 或者给一点点化疗， 一点点放射性治疗， 当然了，有时手术是不可避免的。 但是我们能够为你量身定做治疗方案， 帮助你的身体， 领着它逐渐恢复健康—— 领着你的身体恢复健康。 因为你的身体会尽量自己恢复， 只要我们在它走错路的时候扶一把， 只要我们能够提供支持 你的身体有很多的潜力， 自己治疗癌症。 我们只需要关键时刻帮一把， 帮它回到正路上来。
我相信这将会是 未来治疗癌症的途径。 这将需要我们不断的努力， 很多很多的科研。 需要其他的研究队伍，像我们队伍这样的 一起进行这个研究。 但我相信终有一天， 我们能为每个人 量身定做治疗癌症的方案。
谢谢大家。
（掌声）



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Danny Hillis: Understanding cancer through proteomics
I admit that I'm a little bit nervous here because I'm going to say some radical things about how we should think about cancer differently to an audience that contains a lot of people who know a lot more about cancer than I do. But I will also contest that I'm not as nervous as I should be because I'm pretty sure I'm right about this. (Laughter) And that this, in fact, will be the way that we treat cancer in the future. In order to talk about cancer, I'm going to actually have to -- let me get the big slide here. First, I'm going to try to give you a different perspective of genomics. I want to put it in perspective of the bigger picture of all the other things that are going on -- and then talk about something you haven't heard so much about, which is proteomics. Having explained those, that will set up for what I think will be a different idea about how to go about treating cancer.

So let me start with genomics. It is the hot topic. It is the place where we're learning the most. This is the great frontier. But it has its limitations. And in particular, you've probably all heard the analogy that the genome is like the blueprint of your body. And if that were only true, it would be great, but it's not. It's like the parts list of your body. It doesn't say how things are connected, what causes what and so on. So if I can make an analogy, let's say that you were trying to tell the difference between a good restaurant, a healthy restaurant, and a sick restaurant, and all you had was the list of ingredients that they had in their larder. So it might be that, if you went to a French restaurant and you looked through it and you found they only had margarine and they didn't have butter, you could say, "Ah, I see what's wrong with them. I can make them healthy." And there probably are special cases of that. You could certainly tell the difference between a Chinese restaurant and a French restaurant by what they had in a larder. So the list of ingredients does tell you something, and sometimes it tells you something that's wrong. If they have tons of salt, you might guess they're using too much salt, or something like that. But it's limited, because really to know if it's a healthy restaurant, you need to taste the food, you need to know what goes on in the kitchen, you need the product of all of those ingredients.

So if I look at a person and I look at a person's genome, it's the same thing. The part of the genome that we can read is the list of ingredients. And so indeed, there are times when we can find ingredients that [are] bad. Cystic fibrosis is an example of a disease where you just have a bad ingredient and you have a disease, and we can actually make a direct correspondence between the ingredient and the disease. But most things, you really have to know what's going on in the kitchen, because, mostly, sick people used to be healthy people -- they have the same genome. So the genome really tells you much more about predisposition. So what you can tell is you can tell the difference between an Asian person and a European person by looking at their ingredients list. But you really for the most part can't tell the difference between a healthy person and a sick person -- except in some of these special cases.

So why all the big deal about genetics? Well first of all, it's because we can read it, which is fantastic. It is very useful in certain circumstances. It's also the great theoretical triumph of biology. It's the one theory that the biologists ever really got right. It's fundamental to Darwin and Mendel and so on. And so it's the one thing where they predicted a theoretical construct. So Mendel had this idea of a gene as an abstract thing. And Darwin built a whole theory that depended on them existing. And then Watson and Crick actually looked and found one. So this happens in physics all the time. You predict a blackhole, and you look out the telescope and there it is, just like you said. But it rarely happens in biology. So this great triumph -- it's so good -- there's almost a religious experience in biology. And Darwinian evolution is really the core theory.

So the other reason it's been very popular is because we can measure it, it's digital. And in fact, thanks to Kary Mullis, you can basically measure your genome in your kitchen with a few extra ingredients. So for instance, by measuring the genome, we've learned a lot about how we're related to other kinds of animals by the closeness of our genome, or how we're related to each other -- the family tree, or the tree of life. There's a huge amount of information about the genetics just by comparing the genetic similarity. Now of course, in medical application, that is very useful because it's the same kind of information that the doctor gets from your family medical history -- except probably, your genome knows much more about your medical history than you do. And so by reading the genome, we can find out much more about your family than you probably know. And so we can discover things that probably you could have found by looking at enough of your relatives, but they may be surprising. I did the 23andMe thing and was very surprised to discover that I am fat and bald. (Laughter) But sometimes you can learn much more useful things about that.

But mostly what you need to know to find out if you're sick is not your predispositions, but it's actually what's going on in your body right now. So to do that, what you really need to do, you need to look at the things that the genes are producing and what's happening after the genetics. And that's what proteomics is about. Just like genome mixes the study of all the genes, proteomics is the study of all the proteins. And the proteins are all of the little things in your body that are signaling between the cells -- actually the machines that are operating. That's where the action is. Basically, a human body is a conversation going on, both within the cells and between the cells, and they're telling each other to grow and to die. And when you're sick, something's gone wrong with that conversation. And so the trick is -- unfortunately, we don't have an easy way to measure these like we can measure the genome.

So the problem is that measuring -- if you try to measure all the proteins, it's a very elaborate process. It requires hundreds of steps, and it takes a long, long time. And it matters how much of the protein it is. It could be very significant that a protein changed by 10 percent, so it's not a nice digital thing like DNA. And basically our problem is somebody's in the middle of this very long stage, they pause for just a moment, and they leave something in an enzyme for a second, and all of a sudden all the measurements from then on don't work. And so then people get very inconsistent results when they do it this way. People have tried very hard to do this. I tried this a couple of times and looked at this problem and gave up on it.

I kept getting this call from this oncologist named David Agus. And Applied Minds gets a lot of calls from people who want help with their problems, and I didn't think this was a very likely one to call back, so I kept on giving him to the delay list. And then one day, I get a call from John Doerr, Bill Berkman and Al Gore on the same day saying return David Agus's phone call. (Laughter) So I was like, "Okay. This guy's at least resourceful." (Laughter) So we started talking, and he said, "I really need a better way to measure proteins." I'm like, "Looked at that. Been there. Not going to be easy." He's like, "No, no. I really need it. I mean, I see patients dying every day because we don't know what's going on inside of them. We have to have a window into this." And he took me through specific examples of when he really needed it. And I realized, wow, this would really make a big difference, if we could do it. And so I said, "Well, let's look at it."

Applied Minds has enough play money that we can go and just work on something without getting anybody's funding or permission or anything. So we started playing around with this. And as we did it, we realized this was the basic problem -- that taking the sip of coffee -- that there were humans doing this complicated process and that, what really needed to be done, was to automate this process like an assembly line and build robots that would measure proteomics. And so we did that. And working with David, we made a little company called Applied Proteomics eventually, which makes this robotic assembly line, which, in a very consistent way, measures the protein. And I'll show you what that protein measurement looks like.

Basically, what we do is we take a drop of blood out of a patient, and we sort out the proteins in the drop of blood according to how much they weigh, how slippery they are, and we arrange them in an image. And so we can look at literally hundreds of thousands of features at once out of that drop of blood. And we can take a different one tomorrow, and you will see your proteins tomorrow will be different -- they'll be different after you eat or after you sleep. They really tell us what's going on there. And so this picture, which looks like a big smudge to you, is actually the thing that got me really thrilled about this and made me feel like we were on the right track. So if I zoom into that picture, I can just show you what it means. We sort out the proteins -- from left to right is the weight of the fragments that we're getting. And from top to bottom is how slippery they are. So we're zooming in here just to show you a little bit of it. And so each of these lines represents some signal that we're getting out of a piece of a protein. And you can see how the lines occur in these little groups of bump, bump, bump, bump, bump. And that's because we're measuring the weight so precisely that -- carbon comes in different isotopes, so if it has an extra neutron on it, we actually measure it as a different chemical. So we're actually measuring each isotope as a different one.

And so that gives you an idea of how exquisitely sensitive this is. So seeing this picture is sort of like getting to be Galileo and looking at the stars and looking through the telescope for the first time, and suddenly you say, "Wow, it's way more complicated than we thought it was." But we can see that stuff out there and actually see features of it. So this is the signature out of which we're trying to get patterns. So what we do with this is, for example, we can look at two patients, one that responded to a drug and one that didn't respond to a drug, and ask, "What's going on differently inside of them?" And so we can make these measurements precisely enough that we can overlay two patients and look at the differences.

So here we have Alice in green and Bob in red. We overlay them. This is actual data. And you can see, mostly it overlaps and it's yellow, but there's some things that just Alice has and some things that just Bob has. And if we find a pattern of things of the responders to the drug, we see that in the blood, they have the condition that allows them to respond to this drug. We might not even know what this protein is, but we can see it's a marker for the response to the disease. So this already, I think, is tremendously useful in all kinds of medicine. But I think this is actually just the beginning of how we're going to treat cancer. So let me move to cancer.

The thing about cancer -- when I got into this, I really knew nothing about it, but working with David Agus, I started watching how cancer was actually being treated and went to operations where it was being cut out. And as I looked at it, to me it didn't make sense how we were approaching cancer. And in order to make sense of it, I had to learn where did this come from. We're treating cancer almost like it's an infectious disease. We're treating it as something that got inside of you that we have to kill. So this is the great paradigm. This is another case where a theoretical paradigm in biology really worked -- was the germ theory of disease. So what doctors are mostly trained to do is diagnose -- that is put you into a category -- and apply a scientifically proven treatment for that diagnosis. And that works great for infectious diseases. So if we put you in the category of you've got syphilis, we can give you penicillin. We know that that works. If you've got malaria, we give you quinine, or some derivative of it. And so that's the basic thing doctors are trained to do. And it's miraculous in the case of infectious disease -- how well it works. And many people in this audience probably wouldn't be alive if doctors didn't do this.

But now let's apply that to systems diseases like cancer. The problem is that, in cancer, there isn't something else that's inside of you. It's you, you're broken. That conversation inside of you got mixed up in some way. So how do we diagnose that conversation? Well right now what we do is we divide it by part of the body -- you know, where did it appear -- and we put you in different categories according to the part of the body. And then we do a clinical trial for a drug for lung cancer and one for prostate cancer and one for breast cancer, and we treat these as if they're separate diseases and that this way of dividing them had something to do with what actually went wrong. And of course, it really doesn't have that much to do with what went wrong. Because cancer is a failure of the system. And in fact, I think we're even wrong when we talk about cancer as a thing. I think this is the big mistake. I think cancer should not be a noun. We should talk about cancering as something we do, not something we have. And so those tumors, those are symptoms of cancer. And so your body is probably cancering all the time. But there are lots of systems in your body that keep it under control.

And so to give you an idea of an analogy of what I mean by thinking of cancering as a verb, imagine we didn't know anything about plumbing, and the way that we talked about it, we'd come home and we'd find a leak in our kitchen and we'd say, "Oh, my house has water." We might divide it -- the plumber would say, "Well, where's the water?" "Well, it's in the kitchen." "Oh, you must have kitchen water." That's kind of the level at which it is. "Kitchen water? Well, first of all, we'll go in there and we'll mop out a lot of it. And then we know that if we sprinkle Draino around the kitchen, that helps. Whereas living room water, it's better to do tar on the roof." And it sounds silly, but that's basically what we do. And I'm not saying you shouldn't mop up your water if you have cancer. But I'm saying that it's not really the problem; that's the symptom of the problem.

What we really need to get at is the process that's going on, and that's happening at the level of the proteonomic actions, happening at the level of why is your body not healing itself in the way that it normally does? Because normally your body is dealing with this problem all the time. So your house is dealing with leaks all the time. But it's fixing them. It's draining them out and so on. So what we need is to have a causative model of what's actually going on. And proteomics actually gives us the ability to build a model like that.

David got me invited to give a talk at National Cancer Institute and Anna Barker was there. And so I gave this talk and said, "Why don't you guys do this?" And Anna said, "Because nobody within cancer would look at it this way. But what we're going to do, is we're going to create a program for people outside the field of cancer to get together with doctors who really know about cancer and work out different programs of research." So David and I applied to this program and created a consortium at USC where we've got some of the best oncologists in the world and some of the best biologists in the world, from Cold Spring Harbor, Stanford, Austin -- I won't even go through and name all the places -- to have a research project that will last for five years where we're really going to try to build a model of cancer like this. We're doing it in mice first. And we will kill a lot of mice in the process of doing this, but they will die for a good cause. And we will actually try to get to the point where we have a predictive model where we can understand, when cancer happens, what's actually happening in there and which treatment will treat that cancer.

So let me just end with giving you a little picture of what I think cancer treatment will be like in the future. So I think eventually, once we have one of these models for people, which we'll get eventually -- I mean, our group won't get all the way there -- but eventually we'll have a very good computer model -- sort of like a global climate model for weather. It has lots of different information about what's the process going on in this proteomic conversation on many different scales. And so we will simulate in that model for your particular cancer -- and this also will be for ALS, or any kind of system neurodegenerative diseases, things like that -- we will simulate specifically you, not just a generic person, but what's actually going on inside you.

And in that simulation, what we could do is design for you specifically a sequence of treatments, and it might be very gentle treatments, very small amounts of drugs. It might be things like, don't eat that day, or give them a little chemo therapy, maybe a little radiation. Of course, we'll do surgery sometimes and so on. But design a program of treatments specifically for you and help your body guide back to health -- guide your body back to health. Because your body will do most of the work of fixing it if we just sort of prop it up in the ways that are wrong. We put it in the equivalent of splints. And so your body basically has lots and lots of mechanisms for fixing cancer, and we just have to prop those up in the right way and get them to do the job.

And so I believe that this will be the way that cancer will be treated in the future. It's going to require a lot of work, a lot of research. There will be many teams like our team that work on this. But I think eventually, we will design for everybody a custom treatment for cancer.

So thank you very much.

(Applause)