人類能否在兩三年內治癒癌症大綱
How one company is using artificial intelligence to develop a cure for cancer
人類能否在兩三年內治癒癌症？
Could 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.
我們是否真的在兩三年之後，就能實現治癒癌症的願景？波士頓小型生物科技公司Berg的總裁尼文·納雷因表示，可能真是這樣。
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 platform that he believes could soon crack the cancer code, in addition to discovering valuable information about a variety of other terrible diseases, including Parkinson's.
憑藉億萬富翁、房地產業大鱷卡爾·伯格和米奇·格雷提供的資金，納雷因和他帶領的科學家、技術人員和編程人員團隊耗時6年，完善並測試了一個人工智能平臺，納雷因認爲，這個平臺可能很快就會解開癌症的密碼，同時爲治療包括帕金森症在內的一系列嚴重疾病提供有價值的信息。
Thanks 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.
憑藉着跟多所大學、醫院甚至美國國防部建立的合作關係，伯格公司及其超級計算機系統已經分析了成千上萬的病歷和組織樣本，以找到有可能全新的藥物靶標和生物標誌。
All this data crunching has led to the development of Berg's 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 off naturally, without the need for harmful and expensive chemotherapy.
經過龐大的數據計算，伯格公司開發出第一款新藥--BPM 31510，目前該藥已經進入臨牀測試階段。它可以重組癌細胞的新陳代謝，重新教會癌細胞如何死亡。在這個過程中，癌細胞就會自然死亡，使患者不必經歷對身體傷害極大又十分昂貴的化療過程。
So far, Berg has concentrated most of its resources on prostate cancer, given the large amount of data available 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%.
到目前爲止，伯格公司的主要資源都集中在前列腺癌上，因爲目前有大量關於前列腺癌的數據可供研究。不過拜一項最新合作所賜，該公司現在已經開始構建針對胰腺癌的新模型了。胰腺癌也是最兇險的癌症之一，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 company's artificial intelligence platform 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.
這個目標本身可謂雄心勃勃，但它還只是冰山的一角。除了治療前列腺癌和胰腺癌之外，伯格公司還希望分析多種其它疾病的數據，包括乳腺癌。另外，伯格公司還認爲，它的人工智能平臺可以根據病人的特異性制定專門針對個別患者的治療方案，從而將掀起一場藥物測試的革命，並顯著降低藥物的負面作用在臨牀實驗和醫療實踐中的風險。
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.
我採訪了卡爾·伯格和納雷因，探討了該公司的工作機制，以及他們在未來幾年內的目標。以下是採訪摘要。
Fortune: Carl, why did you decide to move from real estate into healthcare and has it panned out like you thought it would?
財富：卡爾，你爲什麼選擇從房地產業轉向醫療行業？它的進展是否符合你的預期？
Carl Berg: I have been in the venture capital business for 40 years but I never once touched biotech because I was concerned about the risk associated with government approval - it's bad enough when you're 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 companies, I am in a position now where I can do something really big in a hope that it changes the world. So that's what motivated me, and then I met with Niven, and that's what got it started.
卡爾·伯格：我已經在風投界幹了40年了，但我從來沒有觸碰過生物科技領域，因爲我擔心與政府審批有關的風險。做風投本身就不容易，又要多花一番工夫去獲得美國食品藥品監督管理局的認證，那就會更難。但大概8年前我曾說過，現在我不必再做一堆小公司了，而是有能力做一些影響力足夠大甚至有希望改變世界的事。這個目標激勵了我，然後我認識了尼文，我們就是這樣開始這項事業的。
Did Niven convince you to go into biotech or did you find Niven?
是尼文說服了你進入醫療行業，還是你找到了尼文？
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 about 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."
卡爾·伯格：當時我正考慮投資一款護膚產品，然後我在邁阿密大學經人介紹認識了尼文。尼文當時是那個項目的經理，那個項目開始大約一兩個月後，尼文給我打電話說："卡爾，這款護膚產品似乎對治療癌症有效。"我說："好吧，如果你治好了誰，記得讓我知道。"
You didn't sound very convinced.
你聽起來好像不太相信。
CB: Everybody knows that every cancer is different, so how could this one thing work? That didn't 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 convinced that the technology he was using and the approach he was taking, could revolutionize the pharmaceutical market.
卡爾·伯格：人人都知道，每種癌症都是不一樣的，那麼這個東西怎麼會有效呢？在我看來根本就說不通。這時尼文說："我能飛到加州向你展示一下我的成果嗎？"然後他就來了，經過一番交流，我相信他使用的技術和方法真的有可能在醫藥市場掀起一場革命。
Niven, what did you say to convince Carl Berg that your work on skin cream could possibly lead to a cure for cancer?
尼文，你是怎樣讓卡爾·伯格相信，你那款護膚產品上有可能治癒癌症？
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 precise 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] instead of hypotheses.
尼文·納雷因：當我見到卡爾時，我們原則上同意，肯定有辦法建立一個更高效的醫療系統，它能夠以非常精確的方式，將病人與正確的藥物進行匹配。卡爾支持我們將這個理念引向深入。我們不是利用篩選過的化學制品治療病人，而是從人體的細胞樣本入手去了解人體生物學，然後據此研發藥物的。我們使用的是人工智能，而不是各種假設。
How exactly does artificial intelligence come into play here?
人工智能究竟在這個過程中起了什麼樣的作用？
NN: When you start with a hypothesis, you are dismissing 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 wasn't robust enough or focused on the wrong target?
尼文·納雷因：如果你從一個假設入手，你就排除了很多其他可能產生真正效果的領域。有多少次藥物在晚期測試的失敗，是因爲它的早期科研不夠紮實，或是選擇了錯誤的靶標？
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 call an interrogative biology platform using AI to go through all that data. AI is actually able to take all the information from the patient's 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 helps us understand the difference between the healthy and diseased.
在伯格公司，我們只針對一個組織樣本就建立了超過14萬億個數據點。無論是使用人力，還是使用傳統的推理假設模型，要想從所有這些數據中摘取有價值的信息，都是不可能的。所以當我們構建我們所稱的疑問型生物平臺時，我們使用了人工智能來分析所有數據。人工智能可以從病人的生物數據、臨牀樣本和人口統計資料中摘取所有的信息，並且可以根據類似性和差異性進行分類和分層，從而幫助我們瞭解健康細胞和病變細胞之間的差異。
Fourteen trillion data points sounds like information overload.
14萬億個數據點聽起來有點超負荷的感覺。
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, demographics, 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 associations and correlations.
尼文·納雷因：所以它有兩個組成部分：首先是生物信息，然後還有所謂的"組學"。我們不僅僅是分析基因組，而是研究一個組織樣本的所有基因、蛋白質、代謝分子、脂質、病歷記錄、人口統計學資料、年齡、性別等等信息。我們把人體的3萬個基因與6萬種蛋白蛋和幾千種脂質、代謝分子的信息綜合起來，然後把這些成分用具有機器學習功能的高階數學算法進行計算，以瞭解它們的各種關聯性和相關性。
Omics - it's a fairly new term. It means you're going beyond just the genome. It means all the omics - proteomics, metabolomics, and proteins. So we may be born with 30,000 genes, and those genes were born with certain mutations, but that's 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 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.
組學是一個相對較新的術語，它意味着你不能僅僅盯着基因組，而是所有的"組"--比如蛋白質組、代謝組等等。雖然可能我們出生就帶着3萬個基因，而且這些基因可能還有某些天生的突變，但這並不是故事的結尾。你住在紐約市，暴露在環境中的不同物質裏，你的飲食與阿拉巴馬州的某個人不一樣，你的睡眠習慣也與猶他州的某個人不一樣。所以我們認爲，這些東西應該綜合起來，才能完整描繪你的"組學"，即你的整體資料。
But how does all of this get us to a cure for anything? Seems like a bunch of number crunching.
但是這些東西怎樣讓我們治病？看起來只是一堆數據分析而已。
NN: I know you cover the airline industry pretty intently, so you are probably familiar with those airline 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 and 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·納雷因：我知道你經常報道航空業，你可能很熟悉航空公司的路線圖了，它們展示了各個樞紐城市和目的地之間的聯繫。在我們的疑問型生物平臺上，所有這些數據分析的結果看起來就像3D版的航空路線圖。但這些聯繫並不是城市與城市之間的，而是基因與蛋白質之間。然後我們把重點放在那些大的樞紐上，看看是否出了什麼問題。比如如果達拉斯市是在俄克拉荷馬州境內，我們都知道肯定有問題，這時人工智能就會把達拉斯推回北德克薩斯州，然後分析生物學中的哪些事件可以讓人體重啓正常的流程。這就是我們的研究重點，即生物的基本元素，以及能讓健康流程重啓的基因和蛋白質。
Have you had any success using this platform in a real world situation?
在真實世界中，你利用該平臺取得過成功嗎？
NN: We are in clinical trials for a drug, BPM 31510, which we developed using the interrogative platform. The results we have seen so far have been very encouraging. The platform predicted that the more metabolic, the better the treatment will work. And that is exactly what we are seeing in patients for certain types of cancer. For example, we tested this on a patient who had bladder cancer. It was a very aggressive cancer, which failed to respond to all other therapies. We then put him on BPM 31510, which targeted the metabolism of the cancer cell, and by week 18, the tumor was completely gone.
尼文·納雷因：我們正在測試一款名叫BPM 31510的藥物，它就是我們利用疑問型平臺研發的。目前顯示的結果非常令人鼓舞。該平臺顯示，新陳代謝越多，治療就會越有效。根據我們對患有某些癌症的病人的觀察，的確是這樣。比如我們在一名患有膀胱癌的病人身上測試了這款藥物，膀胱癌是一種非常兇險的癌症，幾乎對所有療法都沒有反應。我們在他身上使用了BPM 31510，該藥以癌細胞的新陳代謝爲靶向，到了第18周，他的腫瘤已經完全消失了。
Is this a patented process?
這種療法取得專利了嗎？
NN: We spent the lion's share of the first six years building the platform, developing it into various areas of focus, getting our early drugs into clinical trials and diversifying the use of the technology. And we have filed over 500 patents around the world that govern this specific elevated biology. So we have patents on the biological process, on the mathematics, the informatics, on each individual candidate biomarker, and drug targets. It is a very robust IP portfolio.
尼文·納雷因：我們把前六年的大部分時間花在構建平臺、研究各個重點領域、對早期藥物進行臨牀實驗和實現技術使用的多樣化上。我們在全球已經註冊了500多個專利。所以我們在生物學、數學、信息學上都有專利，對每個個體生物指標和藥物靶標也都有專利。總之我們有着非常堅實的知識產權資產。
Who are your competitors and where are you versus them in taking this process to the next level?
你們的競爭對手是誰？與他們相比，你們在今後的發展中處於何種地位？
NN: We get asked that fairly often. There are folks and entities that do pieces of what Berg does. They're leading companies focused on proteins or analytics, but there isn't one company we can identify or know of that has taken the biology, the omics, the clinical capability and put it all into an interrogative platform to really allow for a robust understanding of the biology to discover drugs in a different way. Also, we are allowing the data to generate hypotheses instead of hypotheses generating data, so it's a really different approach. We are fairly unique in that respect - both from a technology, but also from a commercial standpoint.
尼文·納雷因：我們經常會被問到這個問題。也有一些人和機構在做我們正在做的事。他們是一些蛋白質和分析學上的頂尖公司，但我們目前還沒有發現哪家公司把有關的生物學、組學研究和臨牀能力整合到一個疑問型平臺上，來對人體產生堅實的理解，並以一種新的方式開發藥物。另外，我們是用數據產生假設，而不是用假設產生數據，所以它是一種不同的方法。我們在這方面還是挺獨特的--無論是在技術上還是商業上。
Carl, for the last few years, you and Mitch Gray have been the only investors in Berg. How come?
卡爾，過去幾年裏，你和米奇·格雷一直是伯格公司的唯一投資人，爲什麼會這樣？
CB: I've learned that if you get too many people in the early stages of these things, especially within something as risky as this was, basically you have failed because people get upset and they get worried when anything goes wrong. Through all the years that I have been doing this I can kind of roll with the punches. If something goes haywire it doesn't upset me that much. I know that's what you're going to expect.
卡爾·伯格：如果你在這些東西的早期階段就讓太多人進入，尤其是這個項目又有比較高的風險，那麼你基本上肯定會失敗，因爲只要有什麼事情出了差錯，人們就會感到沮喪和擔心。憑藉多年的風投經歷，我基本上已經處變不驚了。就算出了大亂子，我也不會那麼沮喪。我知道那就是你需要預料到的。
Are you ready to open things up now?
你們現在打算開放融資了嗎？
CB: We are definitely planning on doing some other things and bringing in other investors, but we thought we ought to get to a certain point before we did that. I think we are now very close to that point.
卡爾·伯格：我們當然希望做些其他事情，並且引入新的投資人。但我們希望在此之前先達到某一個點。我認爲我們離那個點已經非常近了。
