Conquering Cancer: Paradigm Shifts - From Biology to Technology to Medical Applications
HYNES: Welcome back to the third session. I'm Richard Hynes from the Koch Institute. And I have the pleasure of chairing this session.
We have three truly excellent speakers for you this afternoon, two from MIT-- Susan Lindquist and Eric Lander-- and one from the West Coast, Lee Hood. I will introduce each of them as they come up to give their talk, rather than doing it all in one go at the beginning. And I'm not going to take any time to show slides and offer you my own opinions on things. We'll wait for the discussion. Maybe we'll do some of that then.
Just let me give you a quick overview of what this session is going to cover. We're going to first start off with Sue Lindquist, with an example of how one can take discoveries made in obscure organisms like fruit flies and yeast, take them all the way through from plants to implications for cancer and neurodegenerative disease. And then we're going to turn to the impact of genomics on medicine and cancer, with Eric Lander.
And finally we're going to end up with Lee Hood talking about personalized medicine and maybe a little bit of systems biology. I'm not sure exactly what he's going to say. But those are the two things for which he's best known, as well as inventing all the machines that Eric used to do the sequencing.
So as I said, I'm going to take more time. And I'd like for us to introduce Sue Lindquist, one of our own. Sue did her PhD at Harvard and then went to the University of Chicago, where she did a post-doc and stayed on for, I think, about 20 years on the faculty at the University of Chicago, where she, through all that period, did wonderful work. Some of which she's going to talk about the later extensions of in a minute.
10 years ago, she came to MIT, to the Whitehead, and was the director of the Whitehead for a period of time and has now gone back to her lab, which she loves and continues as a faculty member at MIT and at the Whitehead. And it's a great pleasure to introduce her to talk about heat-shock proteins and their implications. That wasn't the title she gave me, but it will do.
LINDQUIST: Thank you, Richard. It's a pleasure to be here today. Thanks for the invitation. I'm a fairly recent transplant to MIT. I came about 10 years ago, and I absolutely am crazy about it.
I'm going to tell you about some work that I started many years ago. In effect, this slide here is the only experiment that you'll see in this presentation that I did with my own hands. But basically, what this represents is a survival response, an illustration of the survival response, sometimes called the heat-shock response.
And what we have here is I grew a culture of yeast cells at 25 degrees. And I took two aliquots of the culture out. And one of them I put directly up at high temperatures, 50 degrees. And the other one I first gave about 30 minutes in an intermediate temperature and then put it up at high temperature. And so you can see that brief pre-treatment, just 30 minutes, provided incredible increase in the ability of the cells to survive that higher temperature insult.
And this is a very similar experiment, done by a graduate student in my lab, with Arabidopsis seedlings. Same basic idea, mild pre-treatment providing a tremendous amount of protection. You can do this with any organism on the planet. It's been done with fruit flies, it's been done with whole mice wrapped in electric blankets.
But basically, the same thing holds. A conditioning treatment allows a tremendous ability to survive a more severe treatment. And so what's going on in those cells while they're getting conditioned and learning to cope with the more severe conditions?
Before I go on to that, I should probably say that these mild conditioning pre-stress conditions will actually provide protection not just to heat but to all kinds of other stresses. They'll provide protection against oxidative stress, against heavy metal ions, against just osmotic stress, a huge array of different stresses.
So what magic is going on in those cells? Well, they're making a new set of proteins. So these are cells that were pulse labeled at 25 degrees, the normal growing temperature with a radioactive amino acid. And these cells over here were pulse labeled at that conditioning treatment.
And you can see that the pattern of protein synthesis is quite different. So they pull out all the stops. And they start to make new proteins that are going to help them to cope with the high temperatures and many other types of stress.
And it turns out that we were able to study this in a variety of different organisms. And we were working in different organisms, depending on what we wanted to do. But with yeast we could genetically manipulate these genes, way before we could do it in any higher organism.
And so we found something kind of interesting about hsp90 that turns out to have something interesting to do with cancer. This is my first little foray into the cancer field. Hsp90 we found is a very abundant protein normally. And it just gets up-regulated with heat. But normally the cells don't need anywhere near the amount that they're making. They can get away with much, much less.
But when they're under any condition of stress, they really need all they can make. And then we didn't know what was going on with this protein, what it was doing, until it was reported that-- by several different laboratories, kind of accidentally ran into it, they were working on cancer cells. And hsp90 was bound to the mutated oncogenic protein that was driving the cancer. And so we decided to take advantage of genetics in yeast and lower the hsp90 levels and see what happens when cells are able to use a lot of extra hsp90 or not.
So we expressed one of those cancer causing proteins, v-Src, in the yeast cell. And what we found was that in terms of just looking at Src accumulation, there was a little bit less Src accumulating. But pretty much the cells that had normal levels of hsp90 and cells that had much lower levels of hsp90 many were making pretty much the same amount of protein.
But when we looked at the activity of that oncogenic kinase, which is a tyrosine kinase, the activity was profoundly different. So the cells that had lower levels of hsp90, which could grow and multiply and do everything that a normal yeast cell can do, could not mature this oncogenic protein. And then we looked at the c-Src protein-- the normal, unmutated form of the protein-- and we found that well, we had to expose this 20 times longer, because of course it's a much less active kinase.
But when we did, there wasn't too much of a difference between the cells that had full bore, large amounts of hsp90 and the cells that didn't. So this protein, in other words, is not very needy of hsp90. This protein is very needy of hsp90.
And it turns out that this is not just true of yeast cells, not just an oddity of yeast cells. I had gotten the idea when I saw this that maybe hsp90 might be a really good therapeutic target in cancer, because you can in normal cells reduce the amount that you're using and they couldn't care less. But the ontogenic protein would not be able to be matured.
And I went to the intellectual property office at the University I was then working at, and they were quite rude. They laughed in my face. And so after a while, I kind of gave up on this story. But luckily other people did not.
And so here's an example of a experiment that was done by Luke Whitesell, a wonderful colleague of mine, looking at the ability of v-Src to transform and make these cells grow like crazy-- completely out of control-- or in the presence of an inhibitor of hsp90-- which causes them to revert to normal morphology-- and they're able to grow. And they do just fine, but they're normal cells.
And it's turned out that we understand very well now how this works. It's universal, these heat-shock proteins are conserved across all biological systems. And what happens is that the normal Src kinase folds back up on itself and keeps itself under control.
But the mutated form can't fold back on itself. And so it opens up, and it's splayed out. And it's actually very, very subject to degradation and aggregation and doesn't work very well. Except that hsp90 comes along.
It's a chaperone protein, that's a protein that helps other proteins to fold. It helps to get it folded, gets it inserted into the membrane. And then it goes away and leaves it free to be active. And we now know that this is actually true for many, many oncogenic kinases, in fact, the ones that were discussed earlier this morning as well.
And so it has become a fairly interesting new therapeutic strategy, just exactly what I had initially proposed a long time ago, is that you might be able to use inhibitors of this hsp90, protein because there's a large therapeutic window. Normal cells don't need very much. And these are just some of the pivotal licensing trials that are in progress right now. And this is an example of one of the earliest positive responses, seen in a patient with a breast cancer lung metastasis disappearing.
It's still early days. And we don't know how broadly this is going to be used. But the idea was that there might be a broad strategy here, to therapeutically target something basic about the biology of the cancer cells, rather than necessarily an individual target. I think they're both very, very useful.
So that's the way how one protein of this heat-shock response interfaces with cancer. What about the response as a whole? So I had, as I mentioned to you, I had kind of dropped this. And we were working on other aspects of the heat-shock protein functions, including hsp90s functions. But I had not really been enticed into working any further on cancer, until I came here.
And the Koch Cancer Institute is just simply so fantastic and so exciting and has so many wonderful people that I realized well, gee, I really would like to know how this ancient survival pathway interfaces with cancer cells. Are cancer cells using this survival pathway in more ways than just that one hsp90 protein is using it, to help mature and fold a cancer causing protein? And so because I was here, I was able to tackle this question.
And we took advantage of the fact that there's a master regulator of this whole response that's been conserved from yeast to man. And lots of different people have worked on it. And it turns out that it's a dispensable protein, except for stress responses. At normal conditions, you don't need it.
And so Ivor Benjamin had made a mouse that was deficient in this protein and could not make a stress response. And they could not survive a heart attack very well, or they could not survive a stroke very well. Otherwise, they were just perfectly fine. They lived a perfectly normal lifetime.
So we asked, what about their susceptibility to cancer? And the very first thing we did was to just use a rather simple chemical carcinogenesis protocol, where you paint the backs of the mice with a mutagen and a tumor promoter. And in the normal wild type mice, they're sprouting tumors all over the place here. And in the mice that are not capable of mounting that survival response, they're profoundly protected.
And this translates into a protection in terms of their lifetimes, as well. These are the mice have the knockout. And they're very, very-- they live a long time. These guys, the wild type, die.
And so that's a cancer that's driven by the Ras oncogene. How general is this? What about a cancer that's driven, as Jackie was talking about today, cancers driven by a very different type of mechanism, the loss of a tumor suppressor function?
So we went to Tyler Jacks. And we got his p53 mouse model. And here again, you can see that being deficient for the stress response provides profound protection. In other words, the cancer cells are using this survival pathway to help them survive in their really multi-faceted, deranged states.
So we looked at in a more detailed molecular mechanism how the cancer cells were using this stress response. And it turns out that the stress response was doing all sorts of things to help the cancer cells. It helped with cell proliferation.
It helped them to ignore apoptotic signals, the cell death signals. It changed all kinds of signal transduction pathways. It helped make the cells more growth factor independent.
And it changed their metabolism. As was mentioned earlier this morning, there's this Warburg effect. That really in part depends upon being able to mount the survival response. And when you think about it, such a profoundly multi-faceted survival response that affects so many aspects of the ability of all kinds of organisms to survive, it makes sense that it would be used in lots of different ways to provide a change in the biology of cancer cells.
So here's an example the Warburg effect. Cancers love sugars. And the difference between the heat-shock wild type cells, in terms of the uptake of sugars and the knockouts is really quite profound. That's just one example of data, I'm not going to dwell on this.
But of course, that's mice, right. So what about human cancers? And what about the maintenance of cancer, not just the initial induction of cancer?
And so one of the things we did was to go around to various labs at MIT and get lots of different cancer cell lines that had lots of different underlying genetic lesions from lots of different human tumors. And in fact, when we turned down the heat-shock response, they died. But what about renal tumors in a living human being?
This is a biopsy section of breast cancer. This is the normal tissue, and you can see it juxtaposed to the cancer tissue. And the brown staining is that master regulator of the stress response. So it's sitting in the cytoplasm and not doing anything much in normal cells.
When the cells in the cells that have been transformed in these tumors and are growing out of control, it gets expressed at a much higher level, and it goes into the nucleus, and it drives this program of survival. We at least can infer that this-- we know that the mice are using it. And the fact that the human tumors have so much HSF in the nucleus would suggest that they are being driven by this-- the cancer is helped and aided and abetted by the stress response.
That's not just specific to breast cancer. Here's prostate cancer. Here's lung cancer. And here's colon cancer.
Now it's not every cancer. It's only in about 70% of the cancers we've looked at. But it's in an awful lot of them.
And so we think this might be a strategy for therapeutically differentially targeting cancer cells in a very broad sense. And so we've set cells up with a response system that can report on whether or not they're making a heat-shock response, and whether or not they're inhibited for the production of other proteins as well. And I won't take you through all this. But I'll just say that we can very easily screen for both potentiators and inhibitors of cancer-- sorry, potentiators and inhibitors of HSF function.
And where did we do that? We did that at the Broad, where they have just as you will hear from Eric very soon, they have some really spectacular capabilities. And so we were able to screen, with the help of the Broad, 350,000 compounds for the ability to either inhibit the response or to potentiate the response. And the reason why we might also want to look for inducers of the response is that if you break the function-- it's an auto-regulated response. And so if you break the functionality of it, you'll wind up inducing a response as well.
Anyway, I'll just show you a couple of quick, very quick little vignettes of some things we're just starting to do now. And we found one compound-- I'm going to tell you just about two compounds that we followed. They're both natural products.
This one is from mahogany trees. And it actually had already been known to have anti-cancer properties. It was just not known why it might have anti-cancer properties.
And what's really cool about this compound is it's a very complex natural product. But total synthesis has been achieved, and that enables medicinal chemistry and SAR. And we've been doing that with John Porco's group at Boston University.
And this just shows you that cancer cells-- this is a transformed cell line. They're both transformed cell lines, but this one is a very highly malignant cell line, and this one is a very poorly malignant cell line. But basically, we seem to find-- we've tested a lot of lines-- the more deranged and metastatic and oncogenic the cell line is, the more it seems to depend upon the stress response.
And this is just the beginning. I showed you the glucose uptake before in the tissue culture cells. And this is-- a tumor is implanted in mice, treated with DMSO or tumors implanted in mice treated with this rocaglamide. And as you can see, it's interfering with that. We think it's interfering with a whole bunch of other stuff. We're just doing the survival trials on these mice now.
And finally, I just want to mention quickly another herbal compound that actually has been used as a wellness tonic in Ayurvedic medicine for 3,000 years. And you can buy it yourself in health food stores. But I don't recommend it, because the concentrations that are in different preparations of this vary by 100 fold in different health foods supplements.
Anyway, that compound, too, looks like it has some promise. This is an intracranial human glioma xenograft model, where we implanted glioma cells, which are a really highly, highly, malignant cell type into the brains of these mice. And what's cool about this compound is that people have been taking it because it helps them relax, and it makes them feel good. And that's because it gets into the brain.
And so in these mice that were not treated with Efferin, they die. But with Efferin, two different doses, it's slowing the advance of the cancer. And in some cases, it seems to be allowing the mice to live on without the cancer. This has been done a couple of times, but we need much larger numbers.
Anyway, I just want to say that I think that the environment here that's provided by the Koch and the Broad and the Whitehead and the biology department and this amazing town has just been a wonderful thing for me in terms of-- I'm not a cancer biologist. But it's enabled me to maybe try to make some kind of a contribution, in terms of understanding the generalized biology of cancer cells and how they might be using this stress response.
And I want to thank the people in my lab for all of the wonderful work they've done. These are the folks who did that initial work on v-Src that was initially rather ill treated when we first did it. And this is Mikko Taipale, who's working on the hsp90 now in the human cells and doing some really fabulous stuff, I didn't have time to show you his work.
Luke and Chengkai did all that work on the mice with the tumor models. Sandro Santagata did all of those sections and pathology and did the screen, set up the cells. Leslie, a natural products chemist and John Porco, a collaborator here at Boston University. And then all these amazing-- I can't begin to list all the people at the Koch Center who have helped and the Broad Institute who have helped. But I'm very grateful to you. Thank you
HYNES: Thank you Sue, for a wonderful talk and as you said, a great example of what collaboration can achieve and also a great example of taking basic science on flies and yeast and having it show its impact in cancer in not such a long time. That's really a delightful story.
So our next speaker, too, is an associate member of the Koch Institute, who lives across the street from the new Koch building Eric Lander, who is the director of the Broad Institute. Eric has two degrees in mathematics from Princeton and Oxford and no degree in biology that I know of. I was department head when we hired Eric. And he at that point was teaching a course in negotiation at the Harvard Business School. So he negotiated a good package. And--
--and ever since, he's negotiated from that point upwards. The Genome Center actually started in the Cancer Center, the old building. But the space wasn't big enough for Eric. So he moved off campus and set up the Whitehead MIT Genome Center, which played a big part in the sequencing of the human genome.
And once that was done, he looked around for something else to do and negotiated to set up the Broad, which has gone from strength to strength to exploit the genome. And he's going to tell us about some of that. Eric.
LANDER: Richard, thanks Richard. Well, 150 years of MIT. The Cancer Center represents one quarter of the history of MIT, about 36 years. It started in 1974. It's just fantastic to think that it's actually a whole quarter of MIT's history, or on the other hand, that it took a quarter of MIT's history to get a building worthy of the Cancer Center.
But it's happened. And I could not be more excited to look across from my window every day and see the Koch Institute, busily, busily functioning and bringing together science and engineering.
I haven't been around as long as that. But I have been around 25 years at MIT, so I've got one sixth of the history of MIT. And that's both exciting and mildly depressing, to think that I could represent some actual knowable fraction, meaningful fraction there, of that history. But it's been an amazing one sixth of MIT's history in the last 25 years.
I just want to talk a little bit about where things have been going over that 25-year period and think a little bit about where they're going to be going. So, yeah, we do have that. I want to talk about taking comprehensive views.
Mostly what I'm interested in is comprehensive views. Maybe it's because as a mathematician by origin, I like kind of stepping back and seeing the big picture. And it hasn't been possible to do that until relatively recently, really that last sixth of the history of MIT, has it been possible.
Now in cancer, much of the 20th century was devoted to arguing about what cancer was. The notion that cancer was a genetic disease was proposed as early as 1914 and then prominently forgotten. As late as 1970, you could find definitive reviews assuring us that the viral origin of the majority of malignant tumors is now documented beyond any reasonable doubt.
When people say things like that, you should reasonably doubt them. Because not too long thereafter, evidence began to accumulate that in fact, the majority of the causes of cancer was genetic, was rearrangements, mutations. That in fact, cancer was a disease of the genome, with Bishop and Varmus's discovery of viral oncogenes having cellular counterparts, with Bob Weinberg's incredibly important discovery of a first human proto oncogene.
And by 1986, one had a pretty good picture of some examples of point mutations, translocations, amplifications in the lesions, telling you that all the genetic mechanisms that could happen, do happen. And the question was, well, what's going on past there?
Renato Dulbecco wrote a very influential article in 1986 in Science Magazine, in which he said, look, we can either do this retail or wholesale. Either we're going the hand-to-hand combat, looking for a gene here or a gene there. Or we're going to step back and take the big picture. We're going to get the genome of, as he said, a selected species. He argued it should be the human.
And he and others around the mid 1980s, just around the time I had come down from Harvard Square to Kendall Square, were arguing for the idea that became the Human Genome Project. Well, that project got off the ground in 1990. It ran to about 2003. It produced first the draft sequence of the genome, around 2001 it was published. And a finished sequence of the genome completed around 2003 and gave us for the first time that big picture look.
Now I'll talk in a moment about what that's good for. But I have to say what has happened in the period since, roughly this last decade, is even more remarkable than the work that happened in the Human Genome Project in some ways.
The Human Genome Project, everybody agonized, should we do this project or not. It seemed so huge. How would we ever get the genome done? It was going to suck up all the money in biology.
And folks, look, somehow we're going to find ways to do it. Lee Hood, our next speaker, came up with ways to improve technologies to be able to do this. Many strategies were developed. And it turned out to be completely doable.
Boy it's good we went down that path. Because it's begun this virtuous cycle of improving and improving and improving technology. And my mind is blown by what's going on by this world of technology now. All sorts of new machines have come along, based on new approaches to sequence.
And to give you a sense of what the impact of these new ideas for sequencing has been, I'll just show you a graph I used to be very proud of. It showed our output of sequence from the year 1999 to 2006, when over at the Whitehead MIT Center for genome research and Broad Institute we went from 1 billion bases a year 70 billion bases a year. That felt really good.
And then as these new technologies came in, it looked like this. Two more years added to this graph looks like that. Let me add another year to that graph, looks like that. Let me complete it to the end of the year, like that.
About 125,000 billion bases of sequence last year. And we might pick up almost an order of magnitude this year. It is stunning. The cost of doing this has fallen by 100,000 fold already and will likely, before this technology levels out, fall by about a million fold. I don't know anything that's every decreased by a million fold.
It is stunning. It is really good we decided let's do the project. Let us figure out how to go down this path.
Now what has been the implication of this for cancer? Well, we're beginning to see that implication. We've seen some of it in the past decade. But for cancer, most of that impact, I think, is in the next five years or so, in terms of getting genomic information.
So what have we learned? Well, we've learned lots of things. I'm not going to talk about the many things we've learned about inherited diseases and human history and the evolution of the genome and what's in the genome. For these few minutes we have today, I just want to say what is it doing for us in cancer?
It's letting us understand the basis of disease in a completely unbiased fashion. By asking the cancer genome when it mutates, what's changed? And piling those data up for many, many patients and seeing.
What's the impact? Well, a few years after Renato Dulbecco wrote his article in 1986, the number of bona fide oncogenes in solid tumors-- I could get good numbers here for solid tumors-- was about a dozen. A decade later, around the time the Human Genome Project was completed, it was about 80.
Where are we now? Now, it's about 240. And lots of interesting surprises and things with therapeutic impact have come out.
Sequencing to look for mutations has pointed us to mutations in BRAFs in melanoma, in PIC3CA in colon cancer, EGFR mutations in lung cancer, IDH1 mutations in glioma. And each of these things has led either to therapeutic or diagnostic implications already, some of them already in the clinic, some soon to go into the clinic. It's led to discoveries of new types of oncogenes. Lineage specific transcription factors are turning up in multiple cancer types as important oncogenes.
There was an official dogma that translocations mattered in blood cancer but not in solid tumors. That's false. It turns out when you look, solid tumors have recurrent translocations. And then of course, combining genomic aberrations with functional studies, like RNAi, has let you intersect lists and find interesting and previously undescribed genes that play roles in cancer.
So this story, which I'm saying goes from about 2000 to 2010, began to get going. And around 2005, some of us were part of an NCI effort to create something that eventually became the Cancer Genome Atlas Project. The argument was kind of simple. It was, there was going to be a lot of information, and we should get it in a systematic fashion.
And just like the Human Genome Project, it provoked enormous controversy. The Human Genome Project folks said, oh, look, it's going to suck up all the money. Why don't we understand what we know. It's too expensive. Let's wait until it gets cheaper.
And of course, you know, the answer tends to be start down the road. Because unless you start, it doesn't get cheaper. Unless you start, you don't figure out how to do it.
And so let's start. And so a pilot project was organized. The pilot project ran for about three or four years. And now it's just beginning to get into its serious ramp-up phase.
Now, why were people concerned about this? Well, they were concerned because they said, you know, cancer is very complicated. It's hopelessly complicated. That was about half the objection. The other half of the objection was we already know all the genes in cancer.
And one wished you could get the two to argue it out with each other and have to only argue with one side at a time. But in any case, I come down squarely in the middle. Cancer is not hopelessly complicated. But we sure don't know everything, and we ought to be looking.
Of course, any one cancer is very complicated. All sorts of things go on in a cancer. There's lots of background noise.
But by looking at many cancers, many tumors of the same type, you can begin to pick out a common patterns. Genes are mutated 50% of the time, 20% of the time. With enough tumors, you can see that genes are mutated 3% of the time, and that's significantly above background, then it's telling you something.
So if you look at brain tumors, glioblastomas, you can find there are about 35 regions of the genome that show consistent amplification or deletion. And we can tell you a good story about 40% of them. The other 60%, we don't know what's going on.
That tells you it's not infinitely complicated, the level of amplifications and deletions. But we don't know everything. We can get pointers into what matters.
Now beyond looking at amplification and deletion, which you could do by some fairly easy tools, direct sequencing. Sequence the genome of a tumor. Sequence the genome of a normal. Compare them.
Any one individual differs from a reference sequence by about one letter in 1000. But an individual differs from a tumor by about one letter in a million. There are only about 30 or 40 or 50 coding changes in proteins. And 3,000 non-coding changes around the genome.
It's conceivable if you looked at enough of those, you could begin to sift them out. You could look at the copy number and the mutations and the rearrangements, et cetera.
I'll tell you a story about a paper that's coming out in the next couple of weeks from my colleagues Todd Golub and Gaddy Getz and Mike Chapman and others at the Broad and the Dana Farber about multiple myeloma. But I can tell you lots more stories like this. I'll just pick this is as a poster child example.
Multiple myeloma, it's about 20,000 new diagnoses a year. It's a malignancy of plasma cells, involving massive secretion of Ig. Well, we sequenced 50 genomes, or in some cases exomes-- the coding regions-- of multiple myeloma patients's tumors and normal pairs. Seven new pathways emerged from that.
Pathways, well, known pathways, KRAS, NRAS, p53. But 40 odd percent of the patients carry mutations affecting protein homeostasis. In retrospect, it makes sense. These things are protein factories, churning out protein. NF-kB pathway is clearly mutationally deranged in 25% of cases.
Two patients, only two, had mutations in the IRF4 transcription factor. But they had the identical mutation. And two more patients had mutations in a target of that transcription factor.
BRAF, one patient in our sample had a BRAF mutation. But there's a drug against BRAF. So we looked at a bunch more patients, and golly, about 4% of patients have BRAF mutations.
And I have no idea whether the BRAF drugs will work against them. But if you had somebody you loved with multiple myeloma and had that mutation, you'd sure want to know that.
The thrombin pathway is mutated in about 16% of patients. And that's an extracellular pathway. And what is that doing being oncogenic?
Maybe because thrombin comes back and acts on the par one receptor in a mytogenetic fashion. Maybe not, but you wouldn't have guessed that one. And et cetera, you get the point.
There's a lot of this going on, just at the Broad, right opposite the Koch, we've done about 1400 tumor normal pairs so far. Actually, this slide is a month old. We've done more than that, but I don't have the current number. This is roughly what's been going on there.
And this is just the start. As the costs of sequencing are falling down to $10,000, to $5,000, to I hope within another several years, $1,000, one will see tens of thousands, and frankly hundreds of thousands of cancer tumor normal pair sequence.
We already can see that there are important differences. They have very different mutation rates. Different types of cancers have different mutation rates, as you can see there. That's a log scale. They have different patterns of mutations, as you can see from the colorful pictures underneath that indicate the mutational patterns.
What do we have to do? What does the future look like? We need comprehensive views of cancer.
For patients, that's going to mean hundreds of thousands of tumor normal pairs. That's not a hard number to come by. In order to detect things that are running around at a couple of percent and are still significant, you need to look at a few thousand samples.
And there are 50 or 100 cancer types. Who knows, let's not argue about it. Let's begin to start and figure out sub-types and go deeper as we do that.
We need to do this for cell lines. There's a project that started here already to take 1,000 of the most important cancer cell lines and fully analyze them as well. And of course, animal models, all animal models, should be characterized mutationally in this same way.
But it isn't just sequencing. I've picked that to focus on right now. We need the same thing functionally.
We need to understand what are the essentials in cancer cells. Take RNAis against every gene in the genome. Knock out every gene in the genome in appropriate cellular models, in animal models, and be able to understand every essential gene. We need to be able to put back genes and ask which knockouts or additions can confer resistance on cells that have been insulted in various different ways, so we can build in advance. Look up tables of how a tumor will become resistant to drugs not 10 years later when the drug is in the clinic and patients now develop resistance in nine months, but right while you're developing the drug.
The real goal? Map this all back onto pathways. Map the mutations back onto the pathways, map the essentiality back onto the pathways. Map the potential sites of resistance back onto the pathways. Map the drugs available back onto the pathways.
And a physician of the future gets a report that says, aha, lesions here. Doesn't make sense to use the drug here, makes sense to use the drug there. And so on.
We need to think about not just the laboratory-- the structural information and the functional information-- but the clinical information. It's already time to start asking, when is this going to become the standard of care for cancer patients. It should be soon for anybody that I loved, that they could have this information. And when do we take all patients with cancer in the world and give them the opportunity to contribute their data freely to a common database, together with their clinical information and [INAUDIBLE] so that we can all be part of an experiment that we can learn from?
It has been a fantastic 150 years at MIT. It's been a fantastic 36 years of the Cancer Center. It's been a great 25 years that I've been here. But the next 25 and 36 and 150 years are going to be far more remarkable than that. And so I leave you with MIT plus 150, and I look forward to the discussion. Thanks.
HYNES: Thank you, Eric. I would like to remind you that we're going to have a panel discussion after the next speaker. So if you've got questions, and I'm sure those two talks must have stimulated many questions, write them on a card, and they will be collected. And we will try and address some of them in the panel discussion afterwards.
So a second wonderful talk, and now we're going to have a third one from Lee Hood, who's from the University of Washington and, in particular, from the Institute of Systems Biology, which he set up there. Lee is really actually our only M.D. on this group of speakers. He got his M.D. from Johns Hopkins and his other degrees from Caltech, where he spent many years and during that time, developed some of the earliest high throughput sequencing machine for both proteins and DNA, which initiated the revolution that Eric's just reviewed for you. And he moved from Caltech in the early '90s to Seattle, where he's been ever since.
His MIT connection is that in 2003, he won the Lemelson-MIT prize for innovation and invention. And I noticed in his long list of awards and things like that-- as the other two, I picked out one two only-- in 2008, Wired Magazine said he was one of the 15 people the president should listen to. So I hope you're getting his ear. But if you're not, you can tell Eric. Because Eric has the ear of the president. So Lee, good to have you.
HOOD: Thank you.
Well, I'm not sure he listened. But I'll tell you what I wanted to say to him anyway. What excites me about this symposia is the idea of paradigm changes. In fact, when I was a young assistant professor at Caltech in the '70s, I was really struck by reading a book called The Structure of Scientific Revolution, I think a really pioneering and transformational book that certainly changed my thinking about things.
I was fortunate enough to participate in a number of paradigm changes. And the first four that I participated in really set the framework for what I want to talk about, namely P4 medicine. And the essence of P4 medicine is how do you bring science to the patients, and how can you do it effectively?
So when I started at Caltech, as you heard in the 1970s, I was thinking about engineering and developing a series of instruments that essentially allowed us to translate biological information, DNA, later RNA, proteins, and the like. What that, interestingly enough, led to is what Eric talked about, the Human Genome Project. I got invited in '85 to the first official meeting on the Human Genome Project.
And I think the major conclusion we came to then was it was possible but difficult. And I must say, going out into the community and seeing 90% of the people firmly opposed for all the reasons that Eric enunciated-- and most of all, NIH was opposed to up until the very end-- was an interesting and fascinating experience.
Developing the automated DNA sequencer made me realize that, indeed, cross-disciplinary biology is going to be essential for the future. It is bringing in the engineers and physicists and the chemists and the computer scientists that really created the tools that allowed us to be able to move forward as we have. And I tried in the late '80s to set up such a department at Caltech, and the biologists weren't interested. Bill Gates let me do it at the University of Washington.
And that department, eventually, led to the Institute I set up now, the Institute for Systems Biology, whose mission was this systems science taking a systems approach to disease. And we'll talk just a little bit about that in a few moments.
But it was the sum total of all of these that really led to this thing I call P4 medicine-- predictive, personalized, preventive, and participatory. And what was interesting in thinking back over these paradigm changes, I think, were two important points. One is that each was really met with enormous skepticism. And the second was in every single case, we had to create new organizational structures, or I don't think they ever would have seen the light of day.
So what is P4 medicine all about? I would argue it's all about information. And my prediction is sometime in a 10 year future, individual patients will be surrounded by a virtual cloud of billions of data points. And we'll have the wherewithal to reduce that data dimensionality to simple hypotheses about health and disease. And a really important point about the data is it's going to be enormously heterogeneous. And indeed, I predict, that social data is going to be a very, very important aspect of this patient data mosaic.
So what then are the real foundations of P4 medicine, of systems biology? I would argue it is this view-- biology, medicine, is an informational science. The idea that there are two fundamental types of information, genome information and environmental signals.
The idea that those types of information are connected to the phenotype by biological networks and the execution of molecular machines. And of course the idea that there are many different levels of data that in a sense, if you're to really understand the system, have to be summed and integrated, so that you can explicate the various environmental modifications of each of these different levels of information, DNA to RNA to protein to networks and write up the hierarchy, ultimately to ecologies.
So by this token then, disease is a function of disease-perturbed networks, genetically or environmentally modified. It changes the envelope of information. And this altered dynamic envelope of changed information, both planes the pathophysiology of the disease, and gives us insights into diagnosis and therapy.
And indeed, I would like to give you just a very brief vignette of the disease that we've studied for more or less the last 10 years from a systems point of view. And that is prion neurodegeneration in mice. You can induce it at a particular time point. You can survey how the information in the brain changes at multiple time points across the full onset of the disease. You can subtract normal from diseased counterparts.
And if you do, you horrifyingly end up with a third of the mouse genes being changed, more or less. And you have to do signal to noise reduction procedures, especially that deal with the biological noise. That is, the phenotype is not one aspect that you'd be interested in, namely neurodegeneration, it's always many aspects of biology.
And you have to subtract out the unwanted aspects of biology. When we did this, we could get a 20-fold enrichment down to about 300 genes or so. And then we mapped them into four networks that were defined by histopathologic analysis, prion accumulation, glial activation, and then two aspects of neural degeneration. And for each, we were able to define networks that gave us fundamental insights.
And I would say two really important points came from looking at all of these networks. Number one is there are a lot of other networks that we never ever knew about. And the networks are disease-perturbed in a sequential manner, starting with prion replication and accumulation.
And number two, the dynamics of these networks explain virtually every aspect of the pathophysiology of the disease. And, of course, it led to interesting ideas to think about systems approach to disease.
I don't have enough to talk about the three different approaches that we've developed, one dealing with functional proteins that actually get secreted in the blood, as reflected by these disease-perturbed networks. A second organ-specific protein, I'll mention more about that. And a third we've done recently, and very fascinatingly in the brain, cell type specific proteins that get secreted in the blood that actually can let you interrogate how particular cell types are actually functioning.
So as you can see here, how do we go about identifying organ specific proteins that are secreted in the blood? And the procedure is not simple. You take deep transcriptome analyses of many different tissue types, organ types, in both human and mouse. And from those, you can deduce transcripts that are organ specific.
We can then actually test whether those organ specific transcripts make proteins that get secreted in the blood, using targeted proteomics mass spectrometry. And then finally, we have to be able to treat the blood. Because the range of proteins in the blood is of the order of 10 to the 11th or 10 to 12th, so we have to remove the top 20 or so proteins, so we can see down into the lesser dimensions of protein concentration.
And when we do that, we indeed can get significant numbers of organ specific proteins. Indeed, we have about 100 for the brain and 88 for the liver, the two organs we focused on. And these organ specific proteins, then, constitute fingerprints. And in a normal brain, the 100 proteins will display one set of concentration levels. In a disease-perturbed brain, you'll see those proteins whose cognate networks are disease-perturbed alter their concentration levels in a manner that's characteristic for each disease.
And indeed, we've been able to show with organ specific proteins not only can we do disease stratification, I'm not showing you that data. That is, we can take a disease and divide it into its distinct sub-types. But in the case of prion, we actually used 15 blood brain specific proteins that fell into each of the four networks to show from the blood, we could actually ascertain the order of disease perturbation in these networks.
And of course, it's these analyses that led to the idea that you have now biomarkers that can do early detection that can stratify disease, can assess progression. And interestingly enough, because they're organ specific and they have addresses for each organ, you can begin, for the first time, to assess multi-organ responses to particular diseases. And in some cases the results, as you might think, are really dramatic. We did start a company called Integrated Diagnostics that's now employing these strategies, and I think they've been employing them extremely successfully.
What about emerging technologies? What I'd like to talk about are three big new projects that we're working on at ISB. One is family genome sequencing, about which I'll say more in just a moment, that I think opens up the possibility for not just identifying genes encoding simple Mendelian traits but genes that encode complex genetic traits as well.
Number two, we and others have really started pushing for a second step genome like project, called the Human Proteome Project. I'll say a word about that in just a moment. And then finally, I'll end up showing you some of the clinical assays that we're developing that really allow new dimensions of patient data space to be explored.
The complete genome sequencing of families strategy I think is really strikingly interesting in many ways. Number one, we're having Complete Genomics do all of the sequencing for us. And my guess is genome sequencing is very soon going to become a commodity that will be done just like producing radioactivity. And it will be done, my guess is in five years will be well below $1,000 for a human genome.
The family that we sequenced was really revelatory. The parents were normal, the kids each had two different genetic diseases. But the ability to have the complete genome sequences of the families allowed us to use the principles of Mendelian genetics to do really striking things.
So number one, we could correct more than 70% of the sequencing errors. Number two, we automatically got rare variants. The only requirement was two or more members of the family have those rare variants. And separating them from sequencing errors had been the problem in the past.
We can delineate relatively precisely chromosomal recombination spots. We, for the first time, were able to do intergenerational mutation rates. In this family, roughly 30 mutations per kid.
But what was really interesting is it reduced enormously the search space in genomic haplotypes for disease genes. And indeed, from this family we were quickly able to identify four disease gene candidates for the two disease genes and then able to demonstrate by various methods just which genes went with which disease.
So our feeling is for simple Mendelian diseases, family genome sequencing will be powerful. We're now-- with Jim Gusella and people at several other institutes-- looking at a series of patients from families with Huntington's disease, to look for modifiers, early and late onset. And we have 65 complete sequences we've just begun analyzing in that regard.
And we hope to be able to go after complex genetic diseases, like Alzheimer's, after we've developed new techniques for stratification. And we have two in mind and I won't be able to talk about them.
The Human Proteome Project is something that was really pushed in part on the background of spectacular work Reudi Aebersold did while he was at ISB. And then later, when he moved to the ETH, he continued an extensive collaborations with us. And four projects that he initiated really have made the difference-- the Trans Proteomic Pipeline, to assess data quality; a protein atlas to store all the data, pioneered mass spectrometry that allows you to analyze hundreds of proteins quantitatively in an hour and complex mixtures. And then more recently, Rob Moritz has created preliminary assays for nearly all 20,000 human proteins. So this puts us in a really terrific position to be able to push forward the Human Proteome Project, to begin thinking about the unimaginable complexity of the proteome.
Making blood a window into health and disease isn't going to be done in the future by mass spectrometer. If you want to do 350 million samples two or three times a year, rather, we think it's going to be done by microfluidic protein chips. And together with Jim Keith, we're developing chips that we hope in the future could generate the ability to look at 2,500 proteins, 50 for each of 50 different organs, so you could have wellness, as opposed to disease assessment with this longitudinal information.
And Jim has designed a chip that now can measure 50 proteins, can do it from a fraction of a droplet of blood. It's a five minute assay. And actually, these chips are in the clinic at UCLA, looking at responses to cancer drugs and so forth.
So we have developed a series of technologies in genomics and in proteomics and as well in single cell analysis and in the use of iPS cells, to begin taking these clinical assays to patients. And I think iPS cells, as many other people do, offer enormous opportunities, not only for understanding fundamental disease mechanisms, but for disease stratification in very, very interesting ways.
And this, obviously, takes us to this thing we've called P4 medicine-- predictive, preventive, personalized, and participatory. And I think what is really interesting about P4 medicine is the challenges. And I would say there are two.
One are the technical challenges, about which this lecture is focused. The second are the societal challenges. And that includes the enormous cultural change that will have to come in the medical community, from P4 medicine, which really is in fact a paradigm change and so forth.
But what emerges from P4 medicine, I would say, are these two fundamental ideas. We will be able to deal in powerful, new ways with disease-- better diagnosis, better therapy, ultimately more effective forms of prevention. But I think the real future is going to focus on wellness.
We're doing a pilot project with Ohio State Medical school on developing metrics for assessing wellness, that is for assessing the dimensionality of the wellness space that surrounds each individual and assessing whether the vector of that individual is toward or away from wellness. And my own view is over the next 10 to 15 years the wellness aspects of medicine will overtake the disease aspects of medicine. And indeed, I predict there's going to be an enormous wellness industry in the future.
P4 medicine really has, I think, four important implications. One, it's going to cause a rethinking of the business plan of every industry in various sectors of the health care industry. And I think this represents enormous opportunities, if all companies can figure out how to adapt to new ideas.
Number two, I think there will be a digitalization of medicine. And this is in many dimensions. It's in the dimension of the billions of data bits that I talked about earlier. It's in the dimension of the fact that we will be able, actually, to use single molecules or single cells, each of the quantized units of information, to get actionable information for individual patients in the future. And it's all about the idea that we'll have iHealth pods in the future that we'll be able to assess parameters that will let us analyze real time health and wellness, with wireless connections to assessments and so forth.
So what I'd like to say, then, is how are we going to push this forward? How are we going to bring it to patients? I think there are enormous challenges, technically, but especially sociologically.
Many people have been responsible for what I chatted about. And I look forward to the panel discussion. Thanks.
HYNES: As expected, that was a tour de force. I should point out to the audience that Lee flew in on the red eye, which was delayed. And he wasn't even here by the end of the morning session. Actually, I wasn't worried. Because I remember being at a Gordon Conference when he flew in by helicopter to give his talk.
So we're now moving to the panel discussion. And we'll be joined and led by Duaa Mohammad, who's a postdoc in Michael Yaffe's lab. And she's going to field and ask the questions.
So if you have questions, hand them to the various people walking around in the audience. And they'll bring them up to Duaa. Are you going to do that from here or over there?
MOHAMMAD: From over there.
So I wanted to thank all the speakers for giving these fantastic talks. And we're going to start with some questions that were submitted by the audience online. What approach will be most important for personalized health care, those based on proteomic or genomic technologies? And do you think that these technologies will primarily be used to detect cancer, modulate treatment, or develop cures for cancer?
LINDQUIST: All of the above.
HOOD: You know, I think if you think about systems medicine, it is the integration of all of those types of data. And the total is always much greater than the sum of the parts. So I think there isn't really such a thing as genomic medicine or proteomic medicine. It's really holistic medicine, where we analyze all those different types of information.
LANDER: I'll say the same thing. If the point is to be able to take a comprehensive view, it's at all these different levels. And the point of a comprehensive view is to figure out where might you best intervene, how might you best detect. I think when you get up to 50,000 feet, you can see the landscape, strategies will emerge in all of these different dimensions.
MOHAMMAD: Fantastic. What do you see as--
LINDQUIST: How could I not agree? It's absolutely the case. And it was said earlier in this morning's session that taking multiple attack points is what we need. And that's the only way we're going to conquer these diseases.
MOHAMMAD: So all of the above?
LINDQUIST: All of the above is what I said, yeah.
MOHAMMAD: What do you see as the major scientific obstacles for improving cancer treatment? In which directions do you think the field is going to overcome these obstacles?
LINDQUIST: Maybe I'll step in there. I think one of the hugest barriers is dealing with metastatic cancer. And the property of the cancer cells that is so difficult to deal with is their changeability.
So they have this ability to evolve. They change. They don't stay the same.
And so a wonderful strategy is to be predictive and to stop it in the first place or get it early, et cetera. But there's going to be a certain fraction of cancers that that's not going to happen for. There are a number of people that don't have the kind of access to health care that would allow that. And the biological property, innate biological property of the fact that these things evolve and have harnessed mechanisms for changing, that you try one strategy and you get them. But the few of them manage to get out of it and can change in such a way that they can evade your strategy.
So I think paradoxically, the only way we're going to be able to stop this evolvability is going to be to evolve new mechanisms to do it and new kinds of paradigms. And that is to take advantage of the kinds of things these guys have been talking about, more than one strategy. Hitting a tumor with more than one type of a therapeutic approach.
LANDER: Simultaneously. I mean, what we know as geneticists is there will be a mutation rate. It will give rise to resistance.
One cell, I'll make up a number, in 10 to the sixth, nearly one in 10 to the sixth will find a way around. But you have more than 10 to the sixth cells. You're guaranteed somebody will. With two, you might get down to one in 10 to the 12th. With three, you're guaranteed to win.
That's what we do with HIV. We have three drugs for HIV, each of which is ineffective at controlling HIV on its own, because resistance is guaranteed to develop. All three together, highly effective.
What we don't know right now is when you apply a BRAF inhibitor, what mutations will occur to get around it? Actually, we do know. There was a nice paper on that recently.
And up regulation of the COT1 kinase. We could have known that eight years ago and been working on drugs for that. We need the picture, so as Sue says, we hit it simultaneously.
I'm going to be optimistic for a second and then pessimistic. Optimistically, we win in the long run on cancer. Because every cancer starts naive. It doesn't know what we're going to hit it with. And when we figure out its list of tricks, we will win.
Infectious disease is much harder. Because as we apply agents against infectious disease, it learns. And those mutations then get transmitted. So we'll win in the long run.
Now why am I pessimistic slightly? It's very hard to do clinical trials on combinations. It's hard with the FDA. It's hard to actually run those clinical trials.
Unless we get much more predictive in the laboratory, we are not going to be able to mount the n to the fifth clinical trials necessary to work out combinations. I think we'll do that. But I think that is going to be a hard part.
HYNES: Do you want to say something?
HOOD: I would just agree completely. I think triple drugs or more are going to be important. But I think the other thing that drug companies do not do well and frankly we don't do very well is the choice of targets. And I think we're just in the earliest stages of learning clever choices of targets. And I think the dynamics of networks are going to really inform us about very powerful strategies in this regard in the future.
MOHAMMAD: That actually transitions nicely into a question that we got from an audience member. And they ask, what about metastatic disease? Are there current studies on the genomic and proteomic level specifically designed to understand metastatic disease? Or are we still focusing on the primary tumor? And do you think that this is a reasonable course of action, because most cancer related deaths are due to metastatic disease?
LINDQUIST: So I'm going to let these guys talk about the genomic and proteomic approaches. But I think we, again, need to open this up and think about things in different ways. And so what I was talking about was the fact that there's these survival responses. That's a very broad general property of biology, and these cancer cells are using it. And so being able to look at the biology of those metastatic cells. The more deranged and crazy looking the cells are, they are the ones that are the most dependent upon that biological survival response.
So that's-- I can guarantee you that just targeting that, though, is not going to work. They're going to figure out another way to survive. They will figure out something else.
I think what's really quite remarkable about biology these days is the opening up of so many different approaches and so many different disciplines coming into it, and thinking about things in new and novel ways, out of the box. So I do think that there are going to be ways to attack metastatic cancer, but it's not going to be easy.
LANDER: The sort of studies that I was talking about with sequencing tumors, we're all patting ourselves on the back being able to get 50 or 100 or 200 tumors sequenced. And here, the goal was just look at anything and see what's going-- but we're very soon going to be at the point of saying, let's collect ten metastases and the primary from the same patient and do that across 100 patients. And let's begin to build up a whole picture of what metastasis looks like, distinguish it from the primary.
Right now, the last couple of years, people have been working out the technology to get the sequence-- analyze the data, figure out what's significant, et cetera. Once all that's in place, once the costs drop a little further, I think all of those comparison questions-- why do things metastasize to the lung versus the brain? Well, go collect a couple thousand that metastasized to the lung versus the brain. Begin to ask what's in common here, what's in common there.
I think that kind of information will become broadly available. And this next generation of fantastic young cancer scientists are going to start from those mechanisms that are unique to that problem that they're working on. It's going to be very exciting.
HYNES: There already are a couple of studies where people have studied within one patient by sequencing the genome of a metastasis and of a primary tumor, and there are differences. Whether they're pre-existing ones or whether they evolved, is yet unclear. How general they are, one needs more tumors. And metastases aren't so easy to come by in large amounts. But that's going to happen.
HOOD: So let me make one big warning about information that we're really missing in the way we've approached cancer studies. We recently did single cell analysis of a human glioblastoma cell line and found that there were three major quantized populations that could be distinguished by multi combinatorial analysis of their transcriptomes. If you sequence the whole tumor, you average out all of those discrete populations. And you lose what I think in the future is really going to be important noise.
Now my prediction is within a year we'll have really good methods for complete genome sequence analysis from one cell. And it is pretty obvious that very interesting experiments could then begin from a genomic point of view, predicting what the nature of these populations are and what their roles are and things like that.
LANDER: And indeed, just as Richard said with metastases, even in the past year, there have been a couple examples of single cell genomics already. They're still at the proof of concept level. You can get 85% of the genome. It's not falling off a log, but it will become falling off a log.
HYNES: And we're going to hear a little bit in the next session about isolating circulating tumor cells, among which may be the cells that are prone to metastasis, or maybe they're the ones which have already metastasized and are sitting in the bone marrow, doing nothing. Those will become accessible by these sorts of techniques. I think, just to pick up on something that Sue said, it's already clear from mouse models that there are differences in model systems among those tumors that metastasize to the lung or the breast or other places in the body.
And that seems to me, to put an optimistic note in here, that it's clear from those data that those cells set up a different niche for themselves, where they use different tricks to survive. Because they're not at home base, they don't have their normal signals. They have to generate some new ones.
There are very nice data on bone metastases, where they suborn the cycles of interaction among the bone cells and take advantage of them. That offers another place where you could specifically go after the metastasis. Because that's something the metastasis had to set up to grow in a distant site. So there are new targets coming out of that. And that leads one to hope that there will be some product from that fairly soon.
MOHAMMAD: Fantastic. Lee, referring to some of the stuff you spoke about at the end of your talk, what do you think are good economic and social models that could provide sustainable support to the objective of moving more quickly from bench to bedside?
HOOD: Well, if we ask it in a little broader context, how do we bring P4 medicine to patients, I think there's one thing that's incredibly important. And in fact, it's been incredibly important for all of those different paradigm changes I talked about. And that is demonstration projects. If you show you can do something that the old fashioned medicine can't do and it has value for patients, you're going to get a lot of response.
So the strategy we're thinking about now is creating a small network of large and small clinical centers, where we would with each of the clinical centers, as we did with Ohio State, have one to two pilot projects. With Ohio State, we have a project on wellness. We have a project on heart failure. And we're exploring with three or four other institutions, branching out in that similar vein.
And some of these pilot projects are going to succeed, and some of them are going to fail. But we have a really unique opportunity over the next few years, if a number of them succeed, in that we've set up a strategic partnership recently with the state of Luxembourg. And we've gotten to know the key three ministers that really run this country of 500,000 people. And they've actually agreed if things go well the next few years, they would consider having Luxembourg be a demonstration model nation for doing the kind of P4 things that we talked about here.
So my feeling about how you get things into acceptance-- and that's the real problem, getting them to patients-- is easy enough. I mean, terrific places like Boston has, you can take pretty fundamental research and apply it to patients. You want to ask how do you get it routinely applied to patients, so it really makes the difference. And I think these demonstration projects really will be the key. And if you really succeed there, then you don't have to spend all your time trying to negotiate with regulators and with politicians about whether this is a good thing.
LANDER: Are you saying that the solution is Luxembourg rather than the US Congress?
I'm trying to understand where you are going here.
HOOD: I would say Luxembourg first. I think the US Congress we don't have a ghost of a chance with at this point in time.
LANDER: Well, we better not give up the ghost on it though. Because it's still going to be pretty important. I like Luxembourg, but if we don't bring the US numbers--
HOOD: I understand you have connections, Eric. Maybe we can talk about this.
LANDER: It's unfortunately the executive branch. The legislative branch is-- no, no, no. It's incredibly important that we think about-- I know.
HOOD: I couldn't agree more.
LANDER: I'm using this simply as an opportunity to say that we have got to make sure that the time that is most exciting ever in science, we manage to connect with the public, so that they understand that the investments in science right now are going to pay huge dividends. Even as we're concerned-- not inappropriately-- with closing budget deficits, we better be concerned with these kind of investments. Because I couldn't explain-- I don't want to be explaining to my children why we closed some budget hole, and yet we failed to do these things.
HOOD: In that regard, let me tell you something that really terrifies me. So the US has invented essentially all the sequencing technologies. No question about that. What I'm terrified is there is an institution in China that may be the one that commercialized those on a scale that none of us can even think about competing with. And that is something we really have to be worried about. When you talk with them, their business plan is about destroying the competition. And they can do it in a straightforward way, because they have access to resources that none of the groups here do. So I see that--
LANDER: And Luxembourg is not enough to--
HOOD: And Luxembourg couldn't even touch this kind of thing.
LANDER: Sorry, we're just getting some tension-- we're agreeing too much. So anyway, back to you.
MOHAMMAD: Sue, did you--
MOHAMMAD: So in terms of structuring research in the future, how will the future of cancer research reflect the greater need for collaboration across research areas and researchers? And in particular it's interesting because in this session we had a talk from Sue's lab, that really I think was a very individual effort. And then we have Eric and Lee talking about large collaborative efforts. And so what will happen to the small independent researcher versus the large labs that can collaborate? Should the public money go to fewer labs with greater return than to a multitude of researchers?
LINDQUIST: We need both. I think that one of the most exciting developments is that the folks that have been doing these massive, big picture institutes and have been setting up technologies, they're collaborating with people like me. And that screen that I showed you, academics could not have touched that not too long ago. It was just a few years ago an academic lab could never have possibly screened 350,000 compounds for activity against a certain cell line. It just wouldn't have happened.
And now it's happening actually all over the place. There's the Broad, and there's several other centers around the country. We now have screens-- my own little research lab has I think five, yes five different RO3 grants to screen 350,000 compounds at different sequencing centers, the Broad being one of them. And that just opens up tools for individual investigation that wouldn't have been possible otherwise. And so I think the interchange and the dialogue between this kind of science is where it's all going to be.
LANDER: It's not scale versus individual creativity. Scale enables individual creativity. There's a postdoc behind every one of those screens.
LINDQUIST: You bet.
LANDER: And there's a team of people with capabilities behind the screens.
LINDQUIST: And I even have a graduate student behind one of those screens.
LANDER: And it's that collaboration. Scale enables individual creativity.
HOOD: So look. I think we all agree both are necessary. The really critical question is what is the balance of your mixed portfolio? And that's what NIH is struggling with. There's a committee set up to review glue grants that's going to make recommendations about this balance and so forth. And that is a really tough question.
MOHAMMAD: Fantastic. What is most likely to be the most important focus for future drug development? And this was just a general question, do you think that in the future drugs will be targeted towards specific proteins and pathways that you've identified are defective in that patient? Or do you think that for at least the acute short term, do you think that we should focus on drugs that have a more general action, such as HSF1? Or do you think that we should focus on drugs that may prevent cancer?
MOHAMMAD: And all of the above?
LANDER: No, not all of the above. The first category, you should focus on drugs that have the highest therapeutic index. You're going to get the highest therapeutic index by going against the pathway that's active in that patient. Sue points to where HSP pathways work, for example, and where they don't, for example. It just works or doesn't work.
You should be thinking about-- now, this isn't personalized to the level of every cancer patient is getting his around drug. This is there's an armamentarium of drugs. And we should be thinking about only applying drugs that are active against mechanisms relevant to the patient there.
I think those are the right kind of drugs. Why, particularly? Because those drugs have big therapeutic windows. When you target a specific protein, you have fewer off-target effects often. Not always, but often, as compared to more cytotoxic, more general drugs.
If we're going to do combination therapy, we need big therapeutic windows. Because you can't pile on multiple hemotoxic drugs. You're going to have to pile on things, each of which is somewhat forgiving and have therapeutic windows.
So I would actually not say all the above. I'd take your first as the most important thing for us be focusing on.
HOOD: The other thing I'd say, and I think I'm saying what Eric said in a very different way, it seems to me the real key is how can we engineer proximal disease-perturb networks and make them behave in a normal fashion, abrogating the downstream consequences? And that means learning how to re-engineer networks. And that means the most obvious targets may be the worse targets. That is, we really have to learn how to manipulate these pathways.
And let's remember that we should learn to manipulate them with multiple drugs. Again, to give you an example, Herceptin is the poster child of personalized medicine. You can do a simple genomic test and you can say this third of the women will respond very well.
What they don't tell you is a year or a year and a half later, those women are resistant to Herceptin. It's a single drug. So we have to from the very beginning be thinking about two or three drugs and the re-engineering of these networks more effectively.
LINDQUIST: I actually would like to introduce something else. And it was actually one of the choices. I think it might have been the last one, that I actually think is a very important one, and that is wellness. So I think these are diseases of aging. And aging is a process that's under genetic control. That's a process that's under genetic control and under metabolic control.
And I think both for cancer and for neurodegenerative diseases, both of these are staring us in the face. And they're equally horrifying in their prospect. I do think it's going to be possible to understand and learn strategies-- ways of eating, ways of exercising, or potentially some pills we could take-- that will actually extend the healthy lifespan of an individual. That's going to have an absolutely huge impact.
LANDER: The challenge is how do you run a clinical trial for it? Who supports it? Because you're not going to be able to patent the wellness strategy, necessarily. So the incentives of money coming from pharma won't be there in the same way.
LINDQUIST: I think this is where the US Congress, which is an aging group of individuals--
This is something we might be able to sell to Congress. And it is actually quite likely that they will do something.
HOOD: But you know, the other possibility is the wellness industry may be based on completely different companies than the health industry.
LANDER: We need the business models that would let them--
HOOD: Procter and Gamble and Coca Cola are really looking into wellness possibilities. So it's going to happen. The other real entree, I think, into wellness are these executive health plans. These are people that are willing to pay anything if you can give them some assurance maybe it will do some good. And bringing in individuals in that category and being able to work with them and have them pay for it is a terrific idea.
LANDER: For them.
HOOD: Well, and for us.
LANDER: But what for everybody else?
LINDQUIST: But there's one more thing.
HOOD: Well, if it works, then we can go to skeptics, right? It's the pilot project.
LANDER: We make guinea pigs of the billionaires.
HOOD: And we make them pay for it.
LINDQUIST: I think-- the reason I brought up those two particular examples that I talked about, in terms of the herbal remedies, is that I think for 3,000 years, one of those compounds has been used as a wellness tonic. And I actually think that Ayurvedic medicine has a long track line of things that do make a difference to people. And so there are clinical trials and there are other kinds of trials that are going on.
LANDER: So but which of them will you take?
LINDQUIST: I would only take something that I understood the science behind, believe me. But I do think, I think that that is probably our biggest health care challenge is really-- and I think it's something that's doable-- how you do the funding of it over the budget, or how you parse it out in terms of where you get the scientific data that you can go forward. I think that that could have an absolutely huge impact both on our health and well being, as well as on our economy.
MOHAMMAD: I think we're nearly out of time. So I wanted to thank everyone for speaking and for the fantastic discussion.
LANDER: Thank you to the moderator!
HYNES: We have a 15 minute break now. Please be back at 3:15.