Neil Hunt – TRANSCRIPT
Well, I hope none of you came here to hear about Netflix because I’m not going to say anything about Netflix at all. I have spent the last decade, though, figuring out how to use crowdsource data to make personalized recommendations and what I’ve become attracted to is the idea of using crowdsource data to solve perhaps a more socially important problem: “How do we find cures for cancer?” And you might think that’s an outrageous thing for somebody who has got no medical background at all, just technology and an entertainment background to propose.
So, let me start by posing a couple of questions: why is cancer different? Well, cancer isn’t one disease, cancer is thousands of diseases as Stéphane pointed out in the first session this morning for those of you who were here. And in fact, because it’s thousands of different diseases, there’s no single cure that can solve cancer. We need specific cures for each disease. So what tools can we apply to finding those cures? Because classical, clinical trials is not going to solve the problem for us. Let me give a little bit of background here.
This is a long tail distribution, and on the left, you’ve got a few things that happen frequently, and on the right, you’ve got a lot of things that happen infrequently. And so how is this relevant? Well, the stuff on the right actually constitutes most of the area on this curve. So if you can’t solve the problems that happen, the many problems that happen infrequently, you can’t solve the problem. If you can’t solve for the 10,000 cancers, you can’t solve cancer.
Now, 20th century medicine has done a miraculous job of solving problems for maladies and ahead of that curve. And so we have antibiotics, and we have vaccines, and we have Xanax and we have Tylenol, and hundreds of different drugs that tackle diseases that have a single cause where a single molecular mechanism can solve those problems, and that has done a remarkable job and elevated us, but perhaps many of you have the sense, as I do, that we’ve hit a bit of a stumbling block in the 21st century, in terms of making progress with medicine. And the diseases left over don’t respond to a one-size-fits-all solution. They need specific solutions for the particular problem that we’re dealing with.
Now, cancer is a scary disease. Cancer is just a software error. Cancer happens when cells replicate the DNA incorrectly. And incorrectly replicated DNA usually just kills the cell, and that’s not a problem because we’ve got plenty more. But sometimes that software error causes the cell to divide and multiply rapidly, and it threatens the life of the whole organism, and that’s when the cancer patient dies, and that’s not a good thing. And so, like any software problem, there are many ways that you can introduce defects into that DNA chain that cause the symptoms of cancer. And each of those defects is a separate disease that needs to be solved separately.
The treatments developed for cancer in the 20th century focused on the one thing that all cancers had in common: rapidly dividing cells. But chemotherapy focused at rapidly dividing cells affects all cells, so it’s kind of like carpet bombing a city in the aim of hitting the terrorist cell that’s hiding within the city. And that’s sometimes effective, but it always causes massive collateral damage, and that’s a problem, that’s not a good way to seek to solve the problem.
Cancer then, is a long tail disease. On the left, you’ve got some common cancers that occur frequently, and on the right, you’ve got many other cancers with obscure names that occur much less frequently. And the approaches we have today for solving cancer aim to solve – You pick a frequently occurring cancer, a lung cancer, and you study the molecular pathways that that cancer uses. Then you design a drug that intercepts those proteins involved in that molecular pathway. Then you enroll a clinical trial of 10,000 users, and then a decade later, and millions of dollars later in expense, perhaps you have a solution for one cancer and perhaps you have a failure.
So we have a hard time tackling the diseases in the long tail because there are so many of them, it’s impossible to master 10,000 patients for a meaningful clinical trial. But, actually, the problem is much worse than that. Even lung cancer is not one disease. Lung cancer is hundreds of different diseases caused by different genetic mutations down the chain here. And so each of those requires a different solution, and the drugs we design, tackle one of two of these things at most. The drugs we’re actually making pretty good progress with. This is the molecular model showing how the genetic mutations affect the various different proteins that drive the mechanisms that cause the cell proliferation, and different pathways are active in different ways for different genetic modifications that cause this particular cancer.
And the drug designers are able to build drugs that intercept particular pathways on this diagram, much more specific, they target the proteins fundamental to that cancer itself, much more specific than chemotherapy, much more effective, much less side effect. But we don’t know how to target these things effectively to deal with the thousand or a ten thousand different cancers that we know about today.
So I’d like to start with two stories that sort of illustrate the problem. Marty Tenenbaum was the Head of the AI lab where I worked as an intern in the 1980s, and he became a friend of mine. And in 1988, he was diagnosed with metastatic melanoma. Not the kind caused by exposure to the sunlight. This is the kind that kills you. And he was dying. And he went to see a number of different doctors, and there were a handful of clinical trials that showed promise for the kind of cancer he had. But each doctor had a different recommendation, and none had data to back up why their particular drug was the right solution for his cancer.
And so he collected what data he could and bet his life on the trial of Canvax. But it was a trial that ultimately failed, and the company went broke, and the drug is no longer on the market, but he had a very specific genetic mutation that that drug was able to solve. So that’s an example of learning from a failed trial, from a drug trial that did not lead to a solution to a problem in the classic sense, but did solve this one particular cancer.
Here’s a different guy, Lukas Wartman. Lucas was an oncologist, is an oncologist, I’m happy to say, at the University of Washington. And he spent his life studying Acute Lymphoblastic Leukemia. He was diagnosed in 2011 with the very disease that he’d spent his life studying. And all the treatments he’d helped develop did not solve his problem. So his colleagues took a novel approach. They sequenced the DNA of his cancer, and they sequenced his DNA, and compared the two, to find the specific mutations that were driving his cancer.
And they found an unusual discovery. They found that a gene called FLT3 was over expressed in his cancer. That’s very unusual in ALL, it’s less than 1% of all cases. But it turns out to be quite common in kidney cancer, and happily, there’s a targeted drug aimed at some kinds of kidney cancer called Sutent. And Sutent saved Lucas’s life, and he’s back to being an oncologist at the University of Washington.
And so that’s an example of a drug trial which had success in a limited sense for kidney cancer, but did not reveal the opportunities for other cancers. Now there are opportunities there. Clinical trials, the classic way that we seek to discover whether a drug is effective or not, are just not effective at finding solutions for the long tail of cancers. That’s subject to the tyranny of the average. What happens here is that if the drug is generally ineffective for most patients, it’s considered a failure, even if there is a couple of survivors who have a specific genetic mutation in that cancer that’s helped by this drug. This tailor is not captured in the clinical trials, is lost, and the drug has failed, and is not available for use, even though it has promise for other areas.