David Agus :抗癌新策略

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http://dotsub.com/view/af3d4bac-c9ac-49b0-9540-abd79c9ab20f
David Agus :抗癌新策略
我是个癌症医生，三、四年前的一天我走出办公室 经过医院的药房， 一本封面为“为何我们被癌症战败的”的《财富》杂志 摆放在药房的橱窗里
作为一个癌症医生，你看到这个， 会有些沮丧。 里面有一篇克里夫的文章， 他本人也是癌症幸存者， 临床试验性治疗挽救了他的生命。 当时他的父母开车从纽约市到纽约州北部 接受试验性治疗， 他患有霍金斯病（淋巴瘤），试验性治疗救了他的命。 他在这篇文章里提出了一个很重要的问题。 这篇文章的核心是 用简化论者的眼光 来看待生物学、看待癌症。 这50年里，我们一直关注于 个体基因治疗 和对癌症的认识，而不是控制癌症。
这是一张让人吃惊的图表, 它使我们每天在我们这个领域保持清醒。 很明显，我们在心血管病方面 取得了显著的进步。 但看看癌症呢？50年里癌症死亡率 没有改变。 我们在某些疾病的治疗上取得了一点点成绩，象慢性粒细胞性白血病， 我们有一种药片可以使100%的病人缓解。 但是，总的来说，我们并没有在抗击癌症的战斗中取得进展。
所以今天我要讲的是 为什么我会这样想， 然后走出我自己舒适区， 告诉你我认为它会走向哪里， 新的方法在哪里—— 我们希望推进癌症的治疗。 因为这样是错的。
所以，首先癌症是什么？ 如果一个人血液检测不正常，就会去看医生。 医生会给病人扎一针， 我们今天诊断癌症的方法是通过型态辨识。 它看起来正常吗？还是看起来异常？
病理学家就象这样盯着这个塑料瓶看。 这是正常细胞，这是癌细胞。 这就是今天最先进的癌症诊断。 没有分子检测， 没有以前提到的基因测序， 也别对染色体有任何幻想。 这就是我们用的最先进的技术。
我很清楚作为一名癌症医生，我无法治疗晚期癌症。 顺便提一句，我坚信要早期诊断癌症， 这是你可以有效抗击癌症的唯一途径，通过早期发现它。 我们能够预防很多癌症。 前面我们提及预防心脏疾病， 对于癌症我们也同样可以这样做。 我和别人共同创立了一个公司叫基因导航公司， 如果你把痰吐到一个试管里， 我们可以看到35或40种疾病遗传标记物， 在许多癌症中都可以检测得到。 如果早期检测到这些标记分子， 那么就可以做些工作来预防癌症。 因为当前对于晚期癌症 我们还做不了很多，并不象某些统计数字所说的那样。
癌症是一种老年人的疾病。 为什么是老年人的疾病？ 因为在我们有了孩子之后，进化不再对我们感兴趣。 我们在生育年龄之内时进化保护我们， 但在我们35或40或45岁后， 进化和我们不再有什么关系了，因为我们已经有后代了。 所以如果你注意观察癌症的话，可以看到 小孩患癌症非常非常罕见，大约一年几千例。 但当年龄大了以后，就非常非常普遍了。
为什么癌症很难治疗？ 是因为它的多样化， 这种多样化对于癌症进化来说，是一个很理想的环境。 它挑选出那些坏的有攻击性的细胞， 我们叫做克隆选择。 但是如果我们开始认识到 癌症并不只是一个分子的缺陷，其实更复杂， 那么我们就会寻找新的治疗方法，就象我将向你们展示的那样。
癌症的根本问题之一是 我们用一些形容词、它的一些症状来进行描述。 我感觉疲倦，我有浮肿，我有疼痛等等。 还有一些解剖学描述， 你做了CT扫描，肝脏有一个3厘米的异物。 然后是身体部位的描述， 它在肝脏、在乳房、在前列腺。 就是这样。 我们用来描述癌症的词语非常非常少， 基本上是症状， 是疾病的临床表现。
让人兴奋的是在过去2到3年中， 政府投入4亿美元， 他们还投入了另10亿美元 给我们叫做癌症基因组图谱的项目。 目的是对癌症的所有基因进行测序， 它给了我们一个新的词汇，用一个新的词汇对癌症进行描述。 18世纪50年代中期法国 开始用身体部位描述癌症， 150年来一直这样。 我们把癌症叫做前列腺癌、乳腺癌， 这显然太老套了。 仔细想想，它没有任何意义。
所以，很明显，我们现在拥有的技术， 几年以后又会改变。 你不用再去乳腺癌诊所， 你会去HER2扩增诊所，或EGFR激活诊所， 他们会检测一些病理学损害 就是引发癌症的独特病因。 所以我们希望我们能从艺术医学 走向科学医学， 能象对传染性疾病那样， 检查微生物，细菌， 然后说这个抗生素有意义， 因为细菌对它有反应。 如果一个人接触了H1N1,服用达菲， 他的症状就会明显减轻， 并且会预防许多其它临床症状。 因为我们知道你有什么病，我们知道如何进行治疗。 虽然我们现在不能生产疫苗，但那是另一回事。
癌症基因图谱就要问世了。 所做的第一个癌症是脑癌。 下个月，12月底，就会看到卵巢癌， 几个月后是肺癌。 另外还有蛋白质组学方面，我要讲几分钟， 我认为从对疾病的认识和分类来讲 它将提升一个水平。 但记住，我不是要推动基因组学、 蛋白组学，做一个简化论者。 我这样做，我们才能够确定我们面临什么问题。 我们要达到什么目标现在还有很大的分歧。
今天的医疗保健，我们在疾病治疗上 花了很多钱—— 大部分钱花在一个人一生中最后两年。 而在明确我们所面临的问题上我们只花了很少的钱，或者没花。 如果我们能够开始向这个方向走，确定我们面临什么问题， 我们就会做得好得多。 如果我们做得能够再进一步并预防疾病， 我们就可以完全朝着另一个方向去做。 很明显，那就是我们需要的方向，向前走。
这是国家癌症研究院的网站。 在这里我要告诉你们它是错的。 国家癌症研究院网站 说癌症是遗传性疾病。 这个网站说，癌症就是有个体突变， 或有第2个，第3个， 那就是癌症。 但是，作为一个癌症医生，就我所了解的 它不是一种遗传性疾病。 你看那，那是个肝脏，有结肠癌， 你从显微镜看，有一个淋巴结， 癌症就是从那侵入的。 你看CT扫描能知道肿瘤在肝脏的哪个位置。 癌症是细胞与环境相互作用的结果， 使细胞的生长不再受控制。 它不是抽象的，它与环境相互作用。 这就是我们所说的系统。
作为一名癌症医生，我的目标不是去认识癌症。 我认为这50年来的根本问题 是我们一直致力于去认识癌症， 我们的目标是去控制癌症。 这是非常不同的优化方案， 对于我们所有人来说是非常不同的策略。
我参加了美国癌症研究协会的 一个最大的癌症研究会议，20，000人参加。 当时我说，我们犯了个错误， 我们都犯了个错误，包括我自己， 我们的重点错了，我们成为简化论者。 我们需要倒退一步。 无论你相信与否，观众中有嘘声。 人们感到不安了，但这是我们向前走的唯一一条路。
几年前我非常幸运遇到了Danny Hillis。 我们被推到了一起，但最初我们谁也没打算见面。 我说：“我真的想见一个从迪斯尼来的家伙吗？一个设计电脑的家伙？” 而他说，他真想会见另一个医生。 但人们说服了我们，我们凑到了一起， 我做了非常具有革新性的，绝对革新的项目。 我们一起设计、一起建立模型—— 许多主意都是来自Danny，来自他的团队—— 体内癌症模型是非常复杂的系统。 我会给你们显示一些数据， 我真的认为它可以用一种不同的新方法达到目标。
关键是当你看成这些变量、这些数据时， 你必须了解数据的输入。 如果我给你量体温超过30天， 然后我问平均体温是多少， 当它回落到98.7，我会说太好了。 但是如果其中一天 有6个小时你的体温峰值达到102， 然后你服用泰诺感觉好多了... 而我却丢失了这个数据。 所以医学上一个根本的问题 是你和我，以及我们所有的人， 我们一年看一次医生。 我们的数据元素互不关联，我们对此没有时间函数。
不久前，我们使用了这个叫做第一手生命的设备。 我已用了2个半月。 它真是个令人难以置信的装置，不是因为它告诉我 每天我有多少卡路里， 而是因为它24小时监测我一天中做了什么。 我没有意识到我在桌子前已经坐了3小时， 没有一点活动。 这个类似输入系统中有许多功能 与我们所了解的完全不同， 因为我们不是动态地进行测定。
你可以把癌症想象为一个系统， 它有输入、输出和中间状态。 状态相当于病史、 癌症病人；输入就是环境、 饮食、治疗、遗传变异； 输出就是症状： 有疼痛吗？肿瘤在发展吗？有浮肿吗等等。 许多情况是隐藏的。 所以我们能做的是我们要改变输入， 我们给与积极的化疗。 然后我们说输出好些吗？疼痛有所改善吗？等等。
所以，问题不仅仅是一个系统， 它是多维度上的多个系统， 是多系统中的一个系统。 在你观察新出现的系统时， 你在显微镜下看到神经细胞。 镜下的神经细胞非常漂亮， 有些小的突起， 当你把它们放到一起，放到一个复杂的系统中时， 你看到它变成了大脑， 大脑可以产生智慧。 我们谈论的是机体内的事， 癌症就是这样模仿它的，象个复杂的系统。 坏消息是这些旺盛—— 旺盛是一个关键词—— 系统要详细了解它们是很困难的。 好消息是你可以操纵它们， 也可以努力控制它们 即使你并不是完全了解其每个元素。
二月份的新英格兰医学杂志 刊登了一篇关于癌症的最基本的临床试验， 对象是停经前患乳腺癌的妇女。 这里有最糟糕的乳腺癌病例。 他们都接受化疗， 然后把他们随机分成2组， 1组用安慰剂， 另1组用唑来磷酸，一种影响骨代谢的药物， 它过去一直用于治疗骨质疏松， 一年用2次。 他们观察到 每年给这1800名妇女用2次药， 癌症的复发率减少了35%。 降低癌症复发率所用的药物 根本就没有接触到癌症。 它的概念是土地改变了，种子也就不生长了。 你改变了癌症系统， 对癌症有明显成效。
从没有人展示过——这是很令人震惊的—— 从没有人展示过大多数化疗 实际上触及了癌细胞。 从未展示过。 在组织培养皿中做了所有这些工作， 如果给肿瘤药物，那么对细胞也可以这样做， 但是培养皿所用剂量 与机体所用剂量是不同的。
如果我给乳腺癌妇女使用紫杉醇这种药物， 每三周使用一次，这是标准剂量， 大约40%转移癌患者 对这个药都有很大的反应。 一种反应是50%人的肿瘤缩小了。 记住，这不是一个数量级， 它是另一回事。 有人复发了，我每周给他们相同的药物， 又有30%的人有反应。 又复发了，我还是给他们同样的药物 96小时以上连续输注， 又20或30%的人有反应。 这样，你不能说对这三批病人我采用了同样的治疗机制。 它不是。我们对此机制也没有什么概念。 可能是化疗破坏了 那个复杂的系统， 就象骨代谢药破坏了那个系统而减少了复发一样， 化疗可能也是完全同样的作用。 关于这项试验还有一件离奇的事情， 它减少了新的原发癌，新的癌症，也是30%。
所以问题是，包括你的和我的问题，我们所有的系统都在变化， 它们是动态的。 这是一张可怕的幻灯片，没把它拿掉， 它展示的是世界上的肥胖人口。 我很遗憾，如果你读不到这些数字，有些小。 但如果你仔细看，红色和黑色的， 那些国家 75%以上的人口肥胖。 看看10年前，20年前，非常不同。 所以今天我们的系统与10年、20年前相比 有很大的不同。 我们今天的疾病 所反应的是过去几十年里的系统模式， 而在以后10年里或在这个基础上 将会发生巨大的变化。
这张照片，看起来挺漂亮，是整个蛋白质组400亿字节的 一张照片。 它只用一滴血经过超导磁， 我们就能够得出结论： 我们从哪可以开始看到机体所有蛋白质。 我们可以看整个系统了。 每个红点就是蛋白质被鉴定的地方。 这些磁力，我们在这里所能做的 是用这个技术我们能看到个体的中子。 这就是我们与Danny Hillis 和一个叫做应用蛋白组学的团队所做的事情， 我们可以看到个体中子的差异， 过去我们从来没有见过。 我们用后退一步取代了从简化论者的角度看待这个问题。
这个妇女，46岁， 肺癌复发。 她的脑部、肺脏、肝脏都有癌细胞。 她接受了紫杉醇卡铂、卡铂泰索帝、 Gemcitabene和诺维本。 我们有的每一种药她都用了，但是癌症继续发展。 她的三个孩子都在12岁以下， 这是她的CAT扫描。 这是什么？是我们为她做的横截面图。 中间是她的心脏， 心脏左边有一个很大的肿瘤， 如果不治疗，几周内肿瘤就会侵犯她并杀死她。 她每天服用一片药，药物目标是影响代谢过程的途径， 我也不确定在这个系统中，在这个癌症里，这个途径是否存在 但药物起效了，一个月后，肿瘤消失了。 六个月后，仍然没有复发。 3年后，癌症又复发了，她死于肺癌， 但是她通过服药又活了3年， 主要症状是痤疮。 就是这样。
临床试验已经做了， 我们参与了其中一部分， 在基本的临床试验中， 关键的一个试验我们叫它第三阶段， 我们拒绝使用安慰剂。 如果你的母亲、兄弟、姐妹是晚期肺癌， 生命只有几个星期的时间了，你愿意让他们使用安慰剂吗？ 很明显，答案是不。 所以这一组病人是这样做的。 试验中10%的病人有明显的反应，正如这里显示的， 然后我们把药物送到FDA， FDA说没有安慰剂， 我怎么知道病人是真正从这个药物获益的？ 所以这天早上FDA开会， 这是华尔街杂志的编辑部。 （笑声） 你知道，那个药物被批准了。
令人惊讶的一件事是另一个公司也恰好做了这项科学试验， 他们用一半安慰剂，一半药物。 我们从那也听说了一些重要的事情。 有意思的事情是他们在南美和加拿大做的， 在那些地方“给予安慰剂更道德一些”。 这个药物在美国也要得到批准， 我想在纽约州北部有3个美国病人 参与了试验。 试验发现 70%的无反应病人 比使用安慰剂的病人生存时间更长、生活质量更高。 它对我们所了解的癌症提出了挑战， 那就是你不需要有什么反应， 你不需要在疾病面前退缩。 如果我们能够延缓疾病的发展， 比我们在疾病面前退缩， 对于病人的存活、病人的后果及病人的感受会有更多好处。
问题是，如果我就是这个医生，今天我拿到你的CAT扫描， 你的肝脏有个2厘米的东西， 3个月后你回来找我，那个东西3厘米了， 那么那个药物对你是否有帮助？ 我怎么知道呢？ 它可能原本会长到10厘米，或我给你的药 没有任何作用而且非常昂贵？ 所以这是根本问题。 也就是这些新技术产生的原因。
所以很明显你进医生办公室的目标是—— 预防疾病的发生，对。 最终目标是防止疾病发生。 这是我们今天能做的 最有效、最经济的做法。 但如果你不幸患病了， 你就会去看医生，医生就会为你抽点血， 然后就知道如何治疗你的疾病。 我们的方法还是蛋白组学方面的， 就是这个系统， 一张大图。
这种技术的问题是 如果观察机体的蛋白质， 在高丰度蛋白和低丰度蛋白之间 有11个数量级的差异。 世界上没有一种技术能够跨越11个数量级。 所以我们与Danny Hillis和其他人所做的很多事情 是想引进工程原理，引进软件。 我们就可以看到频谱间的不同组分。
前面谈论过跨学科，谈论了合作。 我认为一个令人激动的事情是 其它领域的人们已开始介入。 昨天，国家癌症研究所公布了一个新的项目， 叫做物理科学和肿瘤学， 物理学家、数学家都介入研究癌症， 而这些人以前从未接触过。 Danny和我拿到了1600万美元，他们昨天公布了， 尝试解决这个问题。 一个全新的方法，不是给予高剂量的化疗药物， 而是通过不同的机制 能够有一种技术可以得到一张照片告诉我们机体内究竟发生了什么。
所以，用2秒钟，这些技术是如何工作的—— 因为我认为了解它是重要的。 它是怎么回事呢？你身体里的每个蛋白都是带电的， 磁性物质围绕蛋白质旋转， 最后有一个检测器， 它何时能碰到那个检测器要根据它的质量和电荷。 所以很精确地，如果它磁性很强， 你的分辨率也很高， 你就可以检测机体内所有的蛋白质， 就可以了解这个个体系统。
作为一名癌症医生， 你、我都不需要这么厚的纸质文件， 可以用办公室的数据流代替，就象这样， 一滴血产生千兆字节的数据。 电子数据可以描述疾病的每一个方面。 当然目标是我们可以从每一个问题中了解问题， 就能够前进一步，而不仅仅是反复遇到问题 而没有根本的了解。
结论是我们需要远离简化论的思想。 我们需要完全不同的想法。 所以我请求在座的每一位，用不同的方法去思考，提出新的思路。 去告诉我们这个领域里的每一个人， 因为在过去59年里，什么也没改变。 我们需要一个完全不同的方法。
当Andy Grove辞去英特尔董事会主席时—— 他是我的顾问之一，很强硬的一个人 当他辞职时，他说 “没有任何技术能够赢，技术本身才会赢”。 我坚信在医学领域，特别是癌症领域， 有一个广阔的技术平台 可以帮助我们前进， 也有希望在近期内帮助病人。
非常感谢。


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David Agus: A new strategy in the war against cancer
I'm a cancer doctor, and I walked out of my office and walked by the pharmacy in the hospital three or four years ago, and this was the cover of Fortune magazine sitting in the window of the pharmacy.

And so, as a cancer doctor, you look at this, and you get a little bit down-hearted. But when you start to read the article by Cliff, who himself is a cancer survivor, who was saved by a clinical trial, where his parents drove him from New York City to upstate New York to get an experimental therapy for -- at the time -- Hodgkin's disease, which saved his life. He makes remarkable points here. And the point of the article was that we have gotten reductionist in our view of biology, in our view of cancer. For the last 50 years, we have focused on treating the individual gene, in understanding cancer, not in controlling cancer.

So, this is an astounding table. And this is something that sobers us in our field everyday in that, obviously, we've made remarkable impacts on cardiovascular disease. But look at cancer. The death rate in cancer in over 50 years hasn't changed. We've made small wins in diseases like chronic myelogenous leukemia, where we have a pill that can put 100 percent of people in remission. But, in general, we haven't made an impact at all in the war on cancer.

So, what I'm going to tell you today, is a little bit of why I think that's the case, and then go out of my comfort zone and tell you where I think it's going, where a new approach -- that we hope to push forward in terms of treating cancer. Because this is wrong.

So, what is cancer, first of all? Well, if one has a mass or an abnormal blood value, you go to a doctor. They stick a needle in. They way we make the diagnosis today is by pattern recognition. Does it look normal? Does it look abnormal?

So, that pathologist is just like looking at this plastic bottle. This is a normal cell. This is a cancer cell. That is the state-of-the-art today in diagnosing cancer. There's no molecular test. There's no sequencing of genes that was referred to yesterday. There is no fancy looking at the chromosomes. This is the state-of-the-art in how we do it.

You know, I know very well, as a cancer doctor, I can't treat advanced cancer. So, as an aside, I firmly believe in the field of trying to identify cancer early. It is the only way you can start to fight cancer, is by catching it early. We can prevent most cancers. You know, the previous talk alluded to preventing heart disease. We could do the same in cancer. I co-founded a company called Navigenics, where, if you spit into a tube, and we can look look at 35 or 40 genetic markers for disease, all of which are delayable in many of the cancers. You start to identify what you could get, and then we can start to work to prevent them. Because the problem is, when you have advanced cancer, we can't do that much, today, about it, as the statistics allude to.

So, the thing about cancer is that it's a disease of the aged. Why is it a disease of the aged? Because evolution doesn't care about us after we've had our children. See, evolution protected us during our child-bearing years, and then, after age 35 or 40 or 45, it said it doesn't matter anymore, because they've had their progeny. So if you look at cancers, it is very rare, extremely rare to have cancer in a child, on the order of thousands of cases a year. As one gets older, very, very common.

Why is it hard to treat? Because it's heterogeneous, and that's the perfect substrate for evolution within the cancer. It starts to select out for those bad, aggressive cells, what we call clonal selection. But, if we start to understand that cancer isn't just a molecular defect, it's something more, then we'll get to new ways of treating it, as I'll show you.

So, one of the fundamental problems we have in cancer, is that, right now, we describe it by a number of adjectives, symptoms. I'm tired, I'm bloated, I have pain, etc. You then have some anatomic descriptions. You get that CAT scan. There's a three centimeter mass in the liver. You then have some body part descriptions. It's in the liver, in the breast, in the prostate. And that's about it. So, our dictionary for describing cancer is very, very poor. It's basically symptoms. It's manifestations of a disease.

What's exciting is that over the last two or three years, the government has spent 400 million dollars, and they've allocated another billion dollars, to what we call the Cancer Genome Atlas Project. So, it is the idea of sequencing all of the genes in the cancer, and giving us a new lexicon, a new dictionary to describe it. You know, in the mid-1850's in France, they started to describe cancer by body part. That hasn't changed in over 150 years. It is absolutely archaic that we call cancer by prostate, by breast, by muscle. It makes no sense, if you think about it.

So, obviously, the technology is here today, and, over the next several years, that will change. You will no longer go to a breast cancer clinic. You will go to a HER2 amplified clinic, or an EGFR activated clinic, and they will go to some of the pathogenic lesions that were involved in causing this individual cancer. So, hopefully, we will go from being the art of medicine more to the science of medicine, and be able to do what they do in infectious disease, which is look at that organism, that bacteria, and then say, this antibiotic makes sense, because you have a particular bacteria that will respond to it. When one is exposed to H1N1, you take Tamiflu, and you can remarkably decrease the severity of symptoms and prevent many of the manifestations of the disease. Why? Because we know what you have, and we know how to treat it, although we can't make vaccine in this country, but that's a different story.

The Cancer Genome Atlas is coming out now. The first cancer was done, which was brain cancer. In the next month, the end of December, you'll see ovarian cancer, and then lung cancer will come several months after. There's also a field of proteomics, that I'll talk about in a few minutes, which I think is going to be the next level in terms of understanding and classifying disease. But remember, I'm not pushing genomics, proteomics, to be a reductionist. I'm doing it so we can identify what we're up against. And there's a very important distinction there that we'll get to.

In health care today, we spend most of the dollars, in terms of treating disease -- most of the dollars in the last two years of a person's life. We spend very little, if any, dollars in terms of identifying what we're up against. If you could start to move that, to identify what you're up against, you're going to do things a hell of a lot better. If we could even take it one step further and prevent disease, we can take it enormously the other direction. And, obviously, that's where we need to go, going forward.

So, this is the website of the National Cancer Institute. And I'm here to tell you, it's wrong. So, the website of the National Cancer Institute says that cancer is a genetic disease. The website says, if you look, there's an individual mutation, and maybe a second, and maybe a third, and that is cancer. But, as a cancer doc, this is what I see. This isn't a genetic disease. So, there you see, it's a liver with colon cancer in it, and you see into the microscope, a lymph node where cancer has invaded. You see a CAT scan where cancer is in the liver. Cancer is an interaction of a cell that no longer is under growth control with the environment. It's not in the abstract; it's the interaction with the environment. It's what we call a system.

The goal of me as a cancer doctor is not to understand cancer. And I think that's been the fundamental problem over the last five decades, is that we have strived to understand cancer. The goal is to control cancer. And that is a very different optimization scheme, a very different strategy for all of us.

I got up at the American Association of Cancer Research, one of the big cancer research meetings, with 20,000 people there, and I said, we've made a mistake. We've all made a mistake, myself included, by focusing down, by being a reductionist. We need to take a step back. And, believe it or not, there were hisses in the audience. People got upset, but this is the only way we're going to go forward.

You know, I was very fortunate to meet Danny Hillis a few years ago. We were pushed together, and neither one of us really wanted to meet the other. I said, "Do I really want to meet a guy from Disney, who designed computers?" And he was saying, does he really want to meet another doctor. But people prevailed on us, and we got together, and it's been transformative in what I do, absolutely transformative. We have designed, and we have worked on the modeling -- and much of these ideas came from Danny, and from his team -- the modeling of cancer in the body as complex system. And I'll show you some data there where I really think it can make a difference and a new way to approach it.

The key is, when you look at these variables, and you look at this data, you have to understand the data inputs. You know, if I measured your temperature over 30 days, and I asked, what was the average temperature, and it came back at 98.7, I would say great. But if during one of those days your temperature spiked to 102 for six hours, and you took Tylenol and got better, etc., I would totally miss it. So, one of the fundamental problems in medicine is that you and I, and all of us, we go to our doctor once a year. We have discrete data elements; we don't have a time function on them.

Earlier it was referred to this direct life device. You know, I've been using it for two and a half months. It's a staggering device, not because it tells me how many kilocalories I do every day, but because it looks, over 24 hours, what I've done in a day. And I didn't realize that for three hours I'm sitting at my desk, and I'm not moving at all. And a lot of the functions in the data that we have as input systems here are really different than we understand them, because we're not measuring them dynamically.

And so, if you think of cancer as a system, there's an input and an output and a state in the middle. So, the states, are equivalent classes of history, and the cancer patient, the input is the environment, the diet, the treatment, the genetic mutations. The output are our symptoms. Do we have pain? Is the cancer growing? Do we feel bloated, etc.? Most of that state is hidden. So what we do in our field is we change and input, we give aggressive chemotherapy. And we say, did that output get better? Did that pain improve, etc.?

And so, the problem is that it's not just one system, it's multiple systems on multiple scales. It's a system of systems. And so, when you start to look at emergent systems, you can look at a neuron under a microscope. A neuron under the microscope is very elegant with little things sticking out and little things over here, but when you start to put them together in a complex system, and you start to see that it becomes a brain, and that brain can create intelligence, what we're talking about in the body, and cancer is starting to model it like a complex system. Well, the bad news is that these robust -- and robust is a key word -- emergent systems are very hard to understand in detail. The good news is you can manipulate them. You can try to control them without that fundamental understanding of every component.

One of the most fundamental clinical trials in cancer came out in February in the the New England Journal of Medicine, where they took women who were pre-menopausal with breast cancer. So, about the worst kind of breast cancer you can get. They had gotten their chemotherapy, and then they randomized them, where half got placebo, and half got a drug called Zoledronic acid that builds bone. It's used to treat osteoporosis, and they got that twice a year. They looked and, in these 1,800 women, given twice a year a drug that builds bone, you reduce the recurrence of cancer by 35 percent. Reduced occurrence of cancer by a drug that doesn't even touch the cancer. So the notion, you change the soil, the seed doesn't grow as well. You change that system, and you could have a marked effect on the cancer.

Nobody has ever shown -- and this will be shocking -- nobody has ever shown that most chemotherapy actually touches a cancer cell. It's never been shown. There's all these elegant work in the tissue culture dishes, that, if you give this cancer drug, you can do this effect to the cell, but the doses in those dishes are nowhere near the doses that happen in the body.

If I give a woman with breast cancer a drug called Taxol every three weeks, which is the standard, about 40 percent of women with metastatic cancer have a great response to that drug. And a response is 50 percent shrinkage. Well, remember that's not even an order of magnitude, but that's a different story. They then recur, I give them that same drug every week. Another 30 percent will respond. They then recur, I give them that same drug over 96 hrs by continuous infusion, another 20 or 30 percent will respond. So, you can't tell me it's working by the same mechanism in all three size. It's not. We have no idea the mechanism. So the idea that chemotherapy may just be disrupting that complex system, just like building bone disrupted that system and reduced recurrence, chemotherapy may work by that same exact way. The wild thing about that trial also, was it reduced new primaries, so new cancers, by 30 percent also.

So, the problem is, yours and mine, all of our systems are changing. They're dynamic. I mean, this is a scary slide, not to take an aside, but it looks at obesity in the world. And I'm sorry if you can't read the numbers, their kind of small. But, if you start to look at it, that red, that dark color there, more than 75 percent of the population of those countries are obese. Look a decade ago, look two decades ago, markedly different. So, our systems today are dramatically different than a decade or two ago. So the diseases we have today, which reflect patterns in the system over the last several decades, are going to change dramatically over the next decade or so based on things like this.

So, this picture, although it is beautiful, is a 40 gigabyte picture of the whole proteome. So this is a drop of blood that has gone through a superconducting magnet, and we're able to get resolution where we can start to see all of the proteins in the body. We can start to see that system. Each of the red dots are where a protein has actually been identified. The power of these magnets, the power of what we can do here is that we can see an individual neutron with this technology. So, again, this is stuff we're doing with Danny Hillis and a group called Applied Proteomics, where we can start see individual neutron differences, and we can start to look at that system like we never have before. So, instead of a reductionist view, we're taking a step back.

So this is a woman, 46 years old, who had recurrent lung cancer. It was in her brain, in her lungs, in her liver. She had gotten Carboplatin Taxol, Carboplatin Taxotere, Gemcitabene, Navelbine. Every drug we have she had gotten, and that disease continued to grow. She had three kids under the age of 12, and this is her CAT scan. And so what this is, is we're taking a cross-section of her body here. And you can see in the middle there is her heart, and to the side of her heart on the left there is this large tumor that will invade and will kill her, untreated, in a matter of weeks. She goes on a pill a day that targets a pathway, and again, I'm not sure if this pathway was in the system, in the cancer, but it targeted a pathway, and a month later, pow, that cancer's gone. Six months later it's still gone. That cancer recurred, and she passed away three years later from lung cancer, but she got three years from a drug whose symptoms predominately were acne. That's about it.

So, the problem is that the clinical trial was done, and we were a part of it, and in the fundamental clinical trial, the pivotal clinical trial, we call the phase three, we refused to use a placebo. Would you want your mother, your brother, your sister to get a placebo if they had advanced lung cancer and had weeks to live? And the answer, obviously, is not. So, it was done on this group of patients. 10 percent of people in the trial had this dramatic response that was shown here, and the drug went to the FDA, and the FDA said, without a placebo, how do I know patients actually benefited from the drug? So the morning the FDA was going to meet, this was the editorial in the Wall Street Journal. (Laughter) And so, what do you know, that drug was approved.

The amazing thing is another company did the right scientific trial, where they gave half placebo and half the drug. And we learned something important there. What's interesting is they did it in South America and Canada, where it's, "more ethical to give placebos." they had to give it also in the U.S. to get approval, so I think there were three U.S. patients in upstate New York who were part of the trial. But they did that, and what they found, is that 70 percent of the non-responders lived much longer and did better than people who got placebo. So it challenged everything we knew in cancer, is that you don't need to get a response. You don't need to shrink the disease. If we slow the disease, we may have more of a benefit on patient survival, patient outcome, how they feel, than if we shrink the disease.

The problem is that, if I'm this doc, and I get your CAT scan today, and you've got a two centimeter mass in your liver, and you come back to me in three months, and it's three centimeters, did that drug help you or not? How do I know? Would it have been 10 centimeters, or am I giving you a drug with no benefit and significant cost? So, it's a fundamental problem. And, again, that's where these new technologies can come in.

And so, the goal obviously is that you go into your doctor's office -- well, the ultimate goal is that you prevent disease, right. The ultimate goal is that you prevent any of these things from happening. That is the most effective, cost effective best way we can do things today. But if one is unfortunate enough to get a disease, you'll go into your doctor's office, he or she will take a drop of blood, and we will start to know how to treat your disease. The way we've approached it is the field of proteomics, again, this looking at the system. It's taking a big picture.

The problem with technologies like this is that if one looks at proteins in the body, there are 11 orders of magnitude difference between the high abundant and the low abundant proteins. So, there's no technology in the world that can span 11 orders of magnitude. And so, a lot of what has been done with Danny Hillis and others is to try to bring in engineering principles, try to bring the software. We can start to look at different components along this spectrum.

And so, earlier was talked about cross-discipline, about collaboration. And I think one of the exiting things that is starting to happen now is that people from those fields are coming in. Yesterday, the National Cancer Institute announced a new program called the physical sciences and oncology, where physicists, mathematicians, are brought in to think about cancer, people who never approached it before. Danny and I got 16 million dollars, they announced yesterday, to try to attach this problem. A whole new approach, instead of giving high doses of chemotherapy by different mechanisms to try to bring technology to get a picture of what's actually happening in the body.

So, just for two seconds, how these technologies work -- because I think it's important to understand it. What happens is every protein in your body is charged, so the proteins are sprayed in, the magnet spins them around, and then there's a detector at the end. When it hit that detector is dependent on the mass and the charge. And so accurately, if the magnet is big enough, and your resolution is high enough, you can actually detect all of the proteins in the body and start to get an understanding of the individual system.

And so, as a cancer doctor, instead of having paper in my chart, in your chart, and it being this thick, this is what data flow is starting to look like in our offices, where that drop of blood is creating gigabytes of data. Electronic data elements are describing every aspect of the disease. And certainly the goal is we can start to learn from every encounter and actually move forward, instead of just having encounter and encounter, without fundamental learning.

So, to conclude, we need to get away from reductionist thinking. We need to start to think differently and radically. And so, I implore everyone here, think differently. Come up with new ideas. Tell them to me or anyone else in our field, because, over the last 59 years, nothing has changed. We need a radically different approach.

You know, when Andy Grove stepped down as chairman of the board at Intel -- and Andy was one of my mentors, tough individual -- when Andy stepped down, he said, "No technology will win. Technology itself will win." And I'm a firm believer, in the field of medicine and especially cancer, that it's going to be a broad platform of technologies that will help us move forward and hopefully help patients in the near-term.

Thank you very much.