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.
我承認我有一點點緊張, 因為我要說一些極端的東西, 談要如何以不同的角度看癌症, 但在座有很多人 卻比我還瞭解癌症。 但我也要說我其實沒有我應當的那麼緊張 因為我很確定我是對的。 (笑聲) 而且這個,事實上, 會是我們未來治療癌症的方法。 要談癌症, 我們必須先談這個-- 讓我用這張大投影片。 首先,我要讓你們從另一個角度看基因體。 我想要以一個更廣的角度 來看整件事情, 然後談一下你們沒那麼常聽到的東西,也就是蛋白質體學。 解釋了這些後, 我便可以談我認為和現今不同的 治療癌症的方式。
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.
所以讓我從基因體講起。 它是個熱門的話題。 它是我們學到最多的地方。 它是先驅。 但它有它的極限。 尤其是:你們可能聽過 基因就像是身體的藍圖。 如果這是正確的,那就太好了, 但這不是正確的。 基因像是你身體組成表。 它沒有解釋這中間如何串聯, 什麼引起什麼之類的事。 所以如果我要來比喻, 想像你要分辨一個好餐廳、 一個健康的餐廳 和一個壞餐廳, 而且你所有的只是一張他們庫存的 食材表。 所以有可能是,你去一家法式餐廳 你發現他們只有人造黃油 沒有奶油, 你會說:「喔!我知道哪裡出錯了。 我可以讓他們變健康。」 而且有可能有一些像那樣的特殊例子。 你當然可以從他們食品室有的東西中 輕易分辨 中式餐廳和法式餐廳。 所以食材表是能夠告訴你一些東西的, 而且有時候會告訴你哪裡出問題了。 如果他們有很多鹽, 你可能會猜他們放太多鹽之類的。 但知道的是有限的, 因為要真正知道一家餐聽好壞, 你需要吃吃看他們做的食物,你需要知道廚房中發生了什麼事, 你需要食材的最終產物。
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.
所以你如果看一個人, 看他的基因,這是相同的道理。 我們看得懂的基因部份 就像是食材表。 所以, 是有些時候我們可以看到 不好的食材。 囊胞性纖維症就是一個 你可以看組成就知道會有疾病, 且我們可以直接建立 這些組成和疾病的關連。 但大部份時候,你需要知道在廚房發生了什麼事, 因為,大部份生病的人之前是健康的, 但他們有一樣的基因組。 所以基因組只是告訴你 預先的定位。 所以你可以知道的是 你可以從組成成分表中 看出亞州人和歐洲人的差別。 但大部份的時候你沒有辦法看出 一個健康的人和不健康的人的差別-- 除了在很特殊的例子上。
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 black hole, 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.
既然如此,為什麼基因學 這麼重要? 首先, 我們可以讀它,這很棒。 在某些情況下非常有用。 它也是生物理論上 非常重要的榮耀。 這是一個生物學家們 一直想搞對的理論。 基因同時也是是達爾文 和孟德爾和其他人理論的基礎。 所以它是唯一一個他們預測且建構的理論。 孟德爾有點抽象地 說明基因這個概念。 然後達爾文在這個基礎上 建立了一整個理論。 然後華生和克力克 真的去找且找到了基因。 這在物理學上常常發生。 你預期會有黑洞, 你去找然後透過望遠鏡去看,真的找到了。 但在生物上這很少發生。 所以這個大榮耀--重要到 幾乎成爲生物學上的 信仰教條。 且達爾文演化論 就是核心理論。
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.
另外一個讓基因這麼受喜愛的原因是 我們可以測量它。它是數位的。 事實上, 感謝Kary Mullis, 你基本上只需要比你廚房再多一點用具 就可以測量你的基因組。 舉例來說,透過測量基因組, 我們從其間的相似性 學到很多關於我們和其他動物的關係、 或是我們跟其他人的關係--像家族表, 或是生命表。 僅僅只是比對基因間的相似情況, 我們可以得到很多資訊。 當然在醫學上 這是非常有用的, 因為醫生可以得到 與瞭解家庭病史相同的資訊。 事實上, 你的基因比你還瞭解你的家庭病史。 所以利用解讀基因組, 我們可以瞭解更多你不知道的關於你的家庭的資訊。 我們可以發現一些 你如果看夠多你的親戚 將可能會找到的資訊, 但這些可能是驚人的。 我做了「二十三和我」(基因檢測)的測驗, 且驚人地發現我是又胖又禿頭的。 (笑聲) 但有時候你會學到一些更有用的東西。
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.
但大部份時候 當你生病時你需要知道的 不是你的體質, 而是現在在你身上發生了什麼事。 要瞭解這個,你需要做的是 你需要看這些基因 製造的東西 和基因背後發生的事情。 而那正是蛋白質體學。 就像基因體學是研究所有的基因 蛋白質體學就是研究所有的蛋白質。 蛋白質是所有在你身上 在不同細胞間傳遞訊息的小東西。 而細胞則是體內工作的機器。 那是事情的發生地。 基本上, 人體是一場正在進行的對話, 是正在細胞內和細胞間進行的對話, 細胞互相告訴對方該生長還是死亡。 當你生病時, 這樣的對話出現問題。 所以訣竅是-- 不幸的,我們沒有像測量基因一樣 有個簡單的方式可以測量蛋白質。
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.
所以測量是個大問題-- 如果你試著測量所有的蛋白質,這是非常複雜的過程。 需要好幾百個步驟, 而且需要花很久很久的時間。 而且蛋白質含量也有關係的 十分之一的含量差異可以有嚴重的影響, 所以不是像DNA那樣有數位性的。 而且基本上我們的問題是 如果有人在一個很長的過程的中間, 他們停下來一下下, 且他們把東西留在生物酶中一秒鐘, 突然所有從那時後開始的測量值 就都不對了。 當他們這麼做時, 人們會得到非常不一致的結果。 有人很努力做這個。 我試了幾次, 看了這個問題然後放棄。
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."
我一直接到一個癌症學家的電話 他叫大維艾格斯。 在Applied Minds的人常常接到很多電話, 來自於需要幫忙解決他們的問題的人, 而我不覺得這個是我會想要回電的人, 所以我一直把他放到晚點再回的名單。 直到有一天, 我接到約翰杜爾、比爾貝客門、 高爾打來的電話, 都叫我要回大衛艾格斯的電話。 (笑聲) 所以我想「好吧,至少這個人人脈豐富。」 (笑聲) 所以我們開始談, 然後他說:「我們很需要一個更好的測量蛋白質的方式。」 我說:「我都看過了、做過了。 一點都不容易。」 他說:「不不,我真的很需要。 我的意思是,我每天看到病人死亡 因為我們不知道病人體內發生什麼事。 我們必須想個辦法。」 然後他給我看一些例子 和為什麼他很需要這個技術。 然後我瞭解到,哇,如果我們做得到的話 這會是很大的改變。 所以我說:「好,讓我們來看看。」
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.
Applied Minds有一些閒錢 可以讓我們不需要跟任何人要錢獲取得許可 就可以做些事情。 所以我們開始試試這件事情。 當我們在做的時候,我們瞭解到這是很基本的問題-- 就像喝一口咖啡-- 就是有很多人在做這個複雜的事 但事實上我們需要的 是自動化的流程 然後建造機器人 來測量蛋白質體。 所以我們這麼做了。 與大衛合作, 我們創立了一個叫做「Applied Proteomics」的公司, 專門建造這樣的機器流程線, 這樣可以很穩定的測量蛋白質。 且我會給你們看蛋白質測量是怎麼做的。
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.
基本上,我們做的是 我們從病人身上 取一滴血, 然後我們將這滴血中的蛋白質 依照它們的 重量、 它們的光滑程度來分類, 然後將它們放在一張圖上。 然後我們可以同時 看到這滴血當中 千百種的特色。 隔天我們可以取另一滴血, 然後你會發現你隔天的蛋白質是不一樣的-- 蛋白質在你吃過食物或睡過覺後都會不一樣。 他們真的告訴我們發生了什麼事。 所以這張圖, 對你們來說看起來像是一團大污點, 但卻是讓我對這件事感到興奮的原因 而且讓我們覺得我們是往對的方向前進。 所以如果我們把這張圖放大, 我可以給你們看這是什麼意思。 我們整理了這些蛋白質--從左至右 是這些片段的重量。 由上至下則是他們的光滑程度。 所以我們把他放大讓你們可以看清楚一些。 這邊每一條線 就是我們在看一個蛋白質時得到的訊號。 你們可以看到這些線是一群一群的 有蹦蹦蹦蹦蹦好幾條線。 這是因為我們很精準的測量重量-- 碳有不同的同位素, 所以如果有多一個中子, 我們就會測出是不一樣的物質。 也就是說我們把每一個同位素當作不一樣的來測量。
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.
所以這給你們一個 這個測量有多精密的概念。 所以看這張圖 有點像伽利略 在看星星一樣, 就像他第一次透過望遠鏡看, 然後你突然說「哇!這比我們想像的複雜許多。」 但我們可以看到這些東西 而且知道他們的特色。 這就有點像是從各種簽名中找出規律。 我們拿這些資訊做的是, 舉例來說,我們看兩個病人, 一個對某種藥有反應,另一個則沒反應。 然後問:「這兩者身體之間 有什麼不同?」 所以我們可以讓這樣的測驗夠精準到 讓我們可以把兩個病人的資料疊在一起然後看出不同。
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.
這裡綠色是愛莉絲 紅色是包伯。 我們把他們倆疊在一起。這是實際的數據。 你們可以看到,大部份相交呈黃色, 但有些是只有愛莉絲有, 而有些只有包伯有。 如果我們可以有類似的東西 來看對藥物有反應的人, 我們可以看到血液中, 他們有一些特別的狀況 讓他們可以對藥物有反應。 我們可能根本不知道這個蛋白質是什麼, 但我們可以看到 是否會對疾病有影響。 所以到這裡,我認為已經 是在醫學上很有用的了。 但我覺得 這只是我們以後 將如何治療癌症的開端。 所以讓我談談癌症。
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.
關於癌症-- 當我剛開始接觸的時候, 我事實上什麼都不知道。 但跟大衛艾格斯合作, 我開始觀察癌症治療, 還去看癌細胞移除手術。 按照我的觀察, 對我來說我們治療癌症的方式 是不合邏輯的。 為了瞭解它, 我必須學這個是從何而來。 我們治療癌症的方式有點像是治療感染性疾病。 我們治療的方式像是一個外來物侵入, 且我們需要殺死這個外來物。 所以這是標準範例。 另一個 理論在生物學是對的的例子-- 就是細菌和疾病的關係。 所以大部分醫生的訓練 就是去診斷 也就是說把你放在某一個類別中 然後給你一個科學上證明為有用的 治療方式。 這對治療感染性疾病是很有用的。 如果我們把你放在得到梅毒的類別, 那麽我們就給你盤尼西林。 我們知道這是有用的。 如果你有瘧疾,我們給你奎寧, 或它的一些衍生物。 這基本上是醫生被訓練來做的事。 這在感染性疾病上 有的效果-- 是幾近神奇的。 而且如果醫生們不這麼做, 在座很多人可能活不到現在。
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.
但現在讓我們以同樣的方式 對待這個叫做癌症的疾病。 問題出在於,在癌症中, 並沒有外來物 侵入人體。 是你自己,你有問題。 那個你身體中的對話 出了問題。 所以我們要如何診斷這個對話? 目前我們根據身體部位把它劃分-- 你知道的,就是在哪裡發生-- 所以我們根據身體部位 有不同的類別。 然後我們有臨床試驗, 有針對肺癌的藥的試驗, 有另一個針對前列腺癌,然後另一個給乳癌, 我們把他們看待成不同的疾病。 且這樣的分法 是基於發病表面的症狀。 但當然的,實情跟發病表面的症狀 是沒有太大關係的。 因為癌症是系統出問題。 事實上,我認為我們把癌症當作“一個東西” 這個看法本身就有問題。 我認為那是個很大的錯誤。 我認為癌症不該是名詞。 我們應該把癌症當作動詞, 像是我們正在做,不是我們有。 且那些腫瘤, 是癌症的症狀。 所以你的身體可能隨時都在癌症中。 但你的身體有很多系統, 會讓它在控制當中。
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 Drano 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's not really the problem; that's the symptom of the problem.
所以要給你們一個 我剛剛比喻的概念 想像癌症是個動詞, 想像我們對配管系統完全不了解, 還有我們談論的方式, 我們會回到家看到廚房漏水, 我們會說:「喔,我的房子有水。」 我們會把它分類--水電工會說:「水在哪裡?」 「在廚房裡。」「喔,你有廚房水。」 有點像是這樣理解的。 廚房水? 好,首先,我們要去那裡把水擦乾。 然後我們知道如果在廚房撒Draino清潔劑 會有幫助。 如果是客廳水, 可能要到屋頂上塗焦油有用。 這聽起來很可笑, 但這基本上這就是我們現在的做法。 我不是說當你有癌症的時候你不該把水清乾淨。 我是說它不是真正的問題; 那只是問題的症狀。
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.
我們需要修理的 是整個過程, 而那是在蛋白質的層面上 決定的。 要去瞭解為什麼你的身體不能像以往一樣 修復自己? 因為平常你的身體隨時都在處理各種問題。 所以你的房子也一直都在處理漏水。 是去解決這個問題,是將水排出系統外。 所以我們需要的 是一個整個過程 因果關係的模型。 蛋白質體學可以給我們 做這樣的模型的能力。
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.
大衛邀請我 到國家癌症學院演講 安娜芭克也在那。 我在那演了講 問說:「為什麼你們不這麼做?」 安娜說:「 因為在癌症中的人 不會這樣看這件事。 但我們要做的是,我們要創造一個計畫 讓癌症領域外的人 可以和真正 瞭解癌症的醫生合作, 然後做出不同的研究方向。」 所以大維和我申請了這個計畫 且在南加大 創造了一個聯合會, 在那我們有世界上最好的癌症學家, 一些世界上頂尖的生物學家, 從冷泉港, 從史丹佛,從奧斯丁-- 我不列舉全部的地方-- 這些人一起做這個研究計畫 花五年的時間, 我們要試著去建造一個癌症模型。 我們從老鼠開始。 在這個過程中 我們會犧牲很多老鼠, 但他們的犧牲會是有用的。 且我們要試著達到一個 可以預測的模型, 我們可以瞭解 什麼時候癌症會發生, 事實上發生了什麼事, 和什麼樣的治療方法可以治療癌症。
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.
讓我給你一個我認為 未來癌症治療的方向。 我認為總有一天, 當我們有針對人類的模型後, 我們有一天會做到的-- 我的意思是,我們這個團隊不會一直做到那-- 但總有一天我們會有一個很棒的電腦模型-- 有點像是世界天氣模型那樣。 這系統有很多不同的訊息, 可以提供在不同層面蛋白質的對話中 發生的事情。 所以我們可以 針對特定的模型 來跑這樣的模擬-- 且這也可以幫助ALS(脊椎硬化症) 或其他任何一種神經退化性疾病, 像是這一類的事-- 我們可以特別為你 來作模擬, 不只是一般不特定的人, 而是在你體內發生的事。
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 chemotherapy, 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.
在這樣的模擬中, 我們可以特別為你設計 一系列的治療, 這有可能是很簡單的治療,很少量的藥物。 有可能是,那天不要吃東西, 或是給他們一點點化療, 或一點點放射線治療。 當然,有時候我們也會開刀或做一些其他的事。 但完全針對你來設計的治療, 來幫助你的身體 回到健康的狀態-- 引導你回到健康。 因為你的身體會做大部份的修理動作, 我們只是需要把錯誤的地方稍微修正一下。 我們把它放在矯正器中。 所以你的身體基本上有很多可以 治療癌症的方法, 我們只是要把它引導到正確的方向 讓它們做這個工作。
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.
所以我相信這會是 未來治療癌症的方法。 這會需要很多努力, 很多研究。 會有很多像我們這樣的團隊 來研究這個。 但我認為最後, 我們可以針對每一個人設計 專屬個人的癌症療法。
So thank you very much.
謝謝大家。
(Applause)
(掌聲)