人类有望在两三年内治愈癌症.docx

1、人类有望在两三年内治愈癌症人类能否在两三年内治愈癌症？How one company is using artificial intelligence to develop a cure for cancerCould we be just two or three years away from curing cancer? Niven Narain, the president of Berg, a small Boston-based biotech firm, says that may very well be the case.我们是否真的在两三年之后，就能实现治愈癌症的愿景？波士

2、顿小型生物科技公司Berg的总裁尼文纳雷因表示，可能真是这样。With funding from billionaire real-estate tycoon Carl Berg as well as from Mitch Gray, Narain, a medical doctor by training, and his small army of scientists, technicians, and programmers, have spent the last six years perfecting and testing an artificial intelligence

3、platform that he believes could soon crack the cancer code, in addition to discovering valuable information about a variety of other terrible diseases, including Parkinsons.凭借亿万富翁、房地产业大鳄卡尔伯格和米奇格雷提供的资金，纳雷因和他带领的科学家、技术人员和编程人员团队耗时6年，完善并测试了一个人工智能平台，纳雷因认为，这个平台可能很快就会解开癌症的密码，同时为治疗包括帕金森症在内的一系列严重疾病提供有价值的信息。Th

4、anks to partnerships formed with universities, hospitals, and even the U.S. Department of Defense, Berg and its supercomputers have been able to analyze thousands of patient records and tissue samples to find possible new drug targets and biomarkers.凭借着跟多所大学、医院甚至美国国防部建立的合作关系，伯格公司及其超级计算机系统已经分析了成千上万的病

5、历和组织样本，以找到有可能全新的药物靶标和生物标志。All this data crunching has led to the development of Bergs first drug, BPM 31510, which is in clinical trials. The drug acts by essentially reprogramming the metabolism of cancer cells, re-teaching them to undergo apoptosis, or cell death. In doing so, the cancer cells die

6、 off naturally, without the need for harmful and expensive chemotherapy.经过庞大的数据计算，伯格公司开发出第一款新药BPM 31510，目前该药已经进入临床测试阶段。它可以重组癌细胞的新陈代谢，重新教会癌细胞如何死亡。在这个过程中，癌细胞就会自然死亡，使患者不必经历对身体伤害极大又十分昂贵的化疗过程。So far, Berg has concentrated most of its resources on prostate cancer, given the large amount of data available

7、on the disease. But thanks to recently announced partnerships, the firm is now building a new modeltargeting pancreatic cancer, which is one of the deadliest forms of cancers with a survivorship rate of only 7%.到目前为止，伯格公司的主要资源都集中在前列腺癌上，因为目前有大量关于前列腺癌的数据可供研究。不过拜一项最新合作所赐，该公司现在已经开始构建针对胰腺癌的新模型了。胰腺癌也是最凶险的

8、癌症之一，目前的存活率只有7%。Ambitious as that may be, it is really just the tip of the iceberg. In addition to mapping out prostate and pancreatic cancer, Berg hopes to analyze data from a whole host of other diseases, including breast cancer. Additionally, Berg thinks his companys artificial intelligence platf

9、orm can also revolutionize drug testing by creating individualized patient-specific treatment options, which he believes will ultimately reduce the risk of adverse drug interactions in clinical trials and hospitals by a significant degree.这个目标本身可谓雄心勃勃，但它还只是冰山的一角。除了治疗前列腺癌和胰腺癌之外，伯格公司还希望分析多种其它疾病的数据，包括乳

10、腺癌。另外，伯格公司还认为，它的人工智能平台可以根据病人的特异性制定专门针对个别患者的治疗方案，从而将掀起一场药物测试的革命，并显著降低药物的负面作用在临床实验和医疗实践中的风险。I sat down with Berg and Narain to discuss how the company works and what they hope to accomplish in the next few years. The following interview has been edited for publication.我采访了卡尔伯格和纳雷因，探讨了该公司的工作机制，以及他们在未来几

11、年内的目标。以下是采访摘要。Fortune: Carl, why did you decide to move from real estate into healthcare and has it panned out like you thought it would?财富：卡尔，你为什么选择从房地产业转向医疗行业？它的进展是否符合你的预期？Carl Berg: I have been in the venture capital business for 40 years but I never once touched biotech because I was concerned a

12、bout the risk associated with government approval its bad enough when youre doing venture capital but adding one more equation, like getting approval from the FDA Food and Drug Administration makes it a lot harder. But about eight years ago I said, instead of getting into a whole bunch of small comp

13、anies, I am in a position now where I can do something really big in a hope that it changes the world. So thats what motivated me, and then I met with Niven, and thats what got it started.卡尔伯格：我已经在风投界干了40年了，但我从来没有触碰过生物科技领域，因为我担心与政府审批有关的风险。做风投本身就不容易，又要多花一番工夫去获得美国食品药品监督管理局的认证，那就会更难。但大概8年前我曾说过，现在我不必再做一

14、堆小公司了，而是有能力做一些影响力足够大甚至有希望改变世界的事。这个目标激励了我，然后我认识了尼文，我们就是这样开始这项事业的。Did Niven convince you to go into biotech or did you find Niven?是尼文说服了你进入医疗行业，还是你找到了尼文？CB: I was considering a skin care product investment and I was introduced to Niven at the University of Miami. Niven was the project manager and abou

15、t a couple months into work on this product, Niven called me and said “Carl, this skin care product appears to have an effect on cancer.” To which I said “Sure, whenever you cure somebody, let me know.”卡尔伯格：当时我正考虑投资一款护肤产品，然后我在迈阿密大学经人介绍认识了尼文。尼文当时是那个项目的经理，那个项目开始大约一两个月后，尼文给我打电话说：“卡尔，这款护肤产品似乎对治疗癌症有效。”我说

16、：“好吧，如果你治好了谁，记得让我知道。”You didnt sound very convinced.你听起来好像不太相信。CB: Everybody knows that every cancer is different, so how could this one thing work? That didnt make any sense to me. And Niven said, “Can I fly out to California and show you my results?” And he came out, and we talked, and I got convi

17、nced that the technology he was using and the approach he was taking, could revolutionize the pharmaceutical market.卡尔伯格：人人都知道，每种癌症都是不一样的，那么这个东西怎么会有效呢？在我看来根本就说不通。这时尼文说：“我能飞到加州向你展示一下我的成果吗？”然后他就来了，经过一番交流，我相信他使用的技术和方法真的有可能在医药市场掀起一场革命。Niven, what did you say to convince Carl Berg that your work on skin

18、cream could possibly lead to a cure for cancer?尼文，你是怎样让卡尔伯格相信，你那款护肤产品上有可能治愈癌症？Niven Narain: When I met with Carl we were aligned philosophically that there has to be a better way to create a more efficient healthcare system one that really matches the right patients to the right drugs in a very prec

19、ise manner. So Carl supported taking this concept to the next level. Instead of treating humans with chemicals, that are screened to become drugs, we actually started with human tissue samples and work to understand the biology and develop drugs based on that. Using AI artificial intelligence instea

20、d of hypotheses.尼文纳雷因：当我见到卡尔时，我们原则上同意，肯定有办法建立一个更高效的医疗系统，它能够以非常精确的方式，将病人与正确的药物进行匹配。卡尔支持我们将这个理念引向深入。我们不是利用筛选过的化学制品治疗病人，而是从人体的细胞样本入手去了解人体生物学，然后据此研发药物的。我们使用的是人工智能，而不是各种假设。How exactly does artificial intelligence come into play here?人工智能究竟在这个过程中起了什么样的作用？NN: When you start with a hypothesis, you are dismi

21、ssing a lot of other areas that might actually have an impact on whatever you are trying to figure out. How many times do we see drugs get to late stage trials and fail because the early science either wasnt robust enough or focused on the wrong target?尼文纳雷因：如果你从一个假设入手，你就排除了很多其他可能产生真正效果的领域。有多少次药物在晚期

22、测试的失败，是因为它的早期科研不够扎实，或是选择了错误的靶标？At Berg, we use AI to create over 14 trillion data points on only one tissue sample. It is actually humanly impossible to go through all this data and use the traditional hypothesis inference model to glean any value out of all of it. So early on when we built what we

23、call an interrogative biology platform using AI to go through all that data. AI is actually able to take all the information from the patients biology, clinical samples, and demographics and really categorize which ones are similar and which ones are different and then stratify those in a way that h

24、elps us understand the difference between the healthy and diseased.在伯格公司，我们只针对一个组织样本就建立了超过14万亿个数据点。无论是使用人力，还是使用传统的推理假设模型，要想从所有这些数据中摘取有价值的信息，都是不可能的。所以当我们构建我们所称的疑问型生物平台时，我们使用了人工智能来分析所有数据。人工智能可以从病人的生物数据、临床样本和人口统计资料中摘取所有的信息，并且可以根据类似性和差异性进行分类和分层，从而帮助我们了解健康细胞和病变细胞之间的差异。Fourteen trillion data points sounds

25、 like information overload.14万亿个数据点听起来有点超负荷的感觉。NN: So there are two components: the upfront biological and there is something called omics. We go much deeper than just analyzing the genome, we look at all the genes in that tissue sample, all the proteins, metabolites, lipids, patients records, demog

26、raphics, age, sex, gender, etc. We combine the 30,000 genes in the body with about 60,000 proteins and a few hundred lipids, metabolites. Then we take those components and subject them to high order mathematic algorithm that essentially learns, uses machine learning, to learn the various association

27、s and correlations.尼文纳雷因：所以它有两个组成部分：首先是生物信息，然后还有所谓的“组学”。我们不仅仅是分析基因组，而是研究一个组织样本的所有基因、蛋白质、代谢分子、脂质、病历记录、人口统计学资料、年龄、性别等等信息。我们把人体的3万个基因与6万种蛋白蛋和几千种脂质、代谢分子的信息综合起来，然后把这些成分用具有机器学习功能的高阶数学算法进行计算，以了解它们的各种关联性和相关性。Omics its a fairly new term. It means youre going beyond just the genome. It means all the omics pro

28、teomics, metabolomics, and proteins. So we may be born with 30,000 genes, and those genes were born with certain mutations, but thats not the end of the story. You live in New York City, you are exposed to different things in the environment, your diet is different than someone who lives in Alabama

29、and your sleeping habits are different from some who lives in Utah. We believe all of these things have to be put together to tell the whole story of your omics the full profile of you.组学是一个相对较新的术语，它意味着你不能仅仅盯着基因组，而是所有的“组”比如蛋白质组、代谢组等等。虽然可能我们出生就带着3万个基因，而且这些基因可能还有某些天生的突变，但这并不是故事的结尾。你住在纽约市，暴露在环境中的不同物质里，

30、你的饮食与阿拉巴马州的某个人不一样，你的睡眠习惯也与犹他州的某个人不一样。所以我们认为，这些东西应该综合起来，才能完整描绘你的“组学”，即你的整体资料。But how does all of this get us to a cure for anything? Seems like a bunch of number crunching.但是这些东西怎样让我们治病？看起来只是一堆数据分析而已。NN: I know you cover the airline industry pretty intently, so you are probably familiar with those ai

31、rline route maps that show all the connections between hubs cities and destinations. So with the interrogative biology platform, the result of all that number crunching looks similar to a 3D version of those maps. But instead of those connections going between cities, they are going between genes an

32、d proteins. We then focus in on the big hubs and see what, if anything, is wrong. For example, in a system, if Dallas is in Oklahoma, obviously we know something is wrong, so the AI helps to push Dallas back into North Texas, and analyze what events happened in the biology to make that a normal process again. This is what we focus in on. The elements within the biology, the genes and proteins that made that a healthy process again.尼文纳雷因：我知道你经常报道航空业，你可能很熟悉航空公司的路线图了，它们展示了各个枢纽城市和目的地之间的联系。在我们的疑问型生物平台上，所有这些数据分析的结果看起来就像3D版的航空路线图。但这些联系并不是城市与城市之间的，而是基因与蛋白质之间。然后我