To my mind (as a cancer biologist), the big caution here is in the mouse xenograft model. While it is good that they tested some different cell lines (in a dish), it is much easier to cure a xenograft tumor than it is to cure a sporadic tumor in a human. This is probably because culturing a cell line in the lab for a while tends to reduce the genetic heterogeneity among the cells, and so reduces the chance that there will be a resistant mutant among those cells. Clinical experience shows that there is very often a resistant mutant in the sporadic tumor of a human patient (particularly if the cancer has managed to spread, i.e., metastasized). In any case, they only tested one cell line in the mice. So it is time to try more.
Second off, the cancer stem cell hypothesis is highly controversial, and has been mostly demolished in breast cancer (the type of cancer in this experiment), where Kornelia Polyak has shown that what people are calling cancer stem cells, are probably just different clones / cell types in the tumor. That being said, if we can come up with drug combinations that can handle all the different cell types in a tumor, we'll be doing a lot better than present.
It certainly is promising that the combined therapy showed no relapse over ~60 days after the end of therapy, but I'd like to see the results taking out further to see if the mice would relapse later. I'd also like to see it demonstrated on other cell lines and more realistic models of cancer. There is a long history of drugs looking good in simple "pre-clinical" models, like mouse xenograft models, and then failing in clinical trials.
Also note that it usually takes decades between initial discovery and changing clinical practice, so don't hold your breath, but do cross your fingers.
Actually, the few times I know of that a good data set was put up on the web, it generated a lot of research and progress. I'm thinking of Pat Brown putting up some of the first data on gene expression arrays. Probably hundreds of people worked on that data - everything from statistical methods, to reverse engineering the gene network. It was great. This is probably most valuable when the data is from a new type of experiment that is likely to be widely used.
I hope to do something similar but there is a big problem for geneticists like me. If you post your volunteer's genetic data on the web, there is no way to anonymize it. It would be a simple thing for a medical insurance company to take a cheek swab, run the genetics and then match it against all public datasets to see if an applicant has a known disease. I know of patients that have lost their medical insurance because their insurer found out that they were participating in a research study, and inferred (incorrectly) that the patient had a disease.
I'm a cancer researcher and I agree. Though I'm in it more for the good of society and because it is an engaging problem. I would jump at the chance to cure cancer even if it put my institution out of business and I didn't get the recognition. The reality (of this fantasy) is that most institutions and researchers could easily move on to other diseases/problems. We do it all the time.
In addition, there is BIG money to be made from a drug that cures cancer. Even the ones that cure a small percent of cancer can make Gigabucks these days. This is why big pharma really does try to find new cancer drugs.
To further extend the point: While it is true that there are many types of cancer with different genetics, there is a further fundamental problem in cancer therapeutics. Tumors are composed of billions to trillions of cells with tens of thousands of mutations (by some measurements). Clinical experience shows that when you apply a drug to such a genetically diverse population, you often kill most of the cells but select for resistance mutations. Thus the tumor grows back from the remaining resistant cells and the patient relapses. This is one of the major reasons we have not been able to cure cancer. These results, while potentially important, will not solve that basic problem in cancer therapy. They will select for their own form of resistance mutations.
All of us who object to this policy should be writing our congressional representatives. Otherwise it is unlikely to get the attention it deserves. And yes, I am practicing what I preach.
Bush and his handlers are the masters of doublethink.
There is quite an insightful essay in the British magazine Prospect discussing how many of the things Orwell was worried about in 1984 resonate in today's U.S.
No one is claiming that DNA computing can solve a problem better than silicon computing. Yet. That is viewed as the holy grail of the field. DNA computing is simply one way that we may end up doing molecular level computing. It has plenty of drawbacks, and the jury is still out on whether or not it will ever be useful, but the excitement is over the fact that you can do such massively parallel (optimistically 10^20 operations) computations. My guess is that it will won't replace silicon as a general purpose medium for computation, but rather that we will find some specialized uses for it. Laura Landweber (big shot in the field and cool person) at Princeton has some good ideas along these lines. It is a hot new field, and a lot of people are now working on it. There is a lot of work to be done just in quantifying and then controlling errors in the processes. It might also be noted that we have only begun to mine biology for useful enzymes to be used in computation.
If you want to read more about DNA computing, the best source is the set of 5 DIMACS proceedings (DNA Computers, DNA Computers II, etc.). If you would like a more in depth review of the field, I published one in Evolutionary Computation 6:201-230. You can find a postscript version here. It was designed to be readable by computer types and to bring you up to speed such that you could start contributing to the field. I don't know how well it fared in this department. Unfortunately, it now a bit out of date. A more up to date version will come out soon in a collection on Molecular Computing edited by Tanya Sienko from MIT Press.
There is, at least one known form of contagious cancer. It started out in a dog and then developed (with metastasis) the ability to be sexually transmitted. It is called canine transmissible venereal sarcoma (CTVS). Yang even argues that it is a new form of parasitic life. Here is the reference:
T.J. Yang. 1995. Parasitic protist of metazoan origin. Evolutionary Theory 11:99-103.
Of course, I don't claim that cancer is likely to become a highly contagious disease. It is a hard enough problem as it is. I just bring this up because it shows how bizarre life can be.
The 1999 NIH budget for the National Cancer Institute was $2.9G, $235M of which was spent on AIDS (because AIDS patients are more susceptible to cancer due to the fact that the immune system often attacks tumors). The total amount spent on AIDS, across all the NIH institudes was $1.8G. So I think it is fair to say that the NIH spends more on cancer than AIDS.
These numbers come from http://www4.od.nih.gov/ofm/budget/00conference.stm - Carlo
Slashdot Mirror
To my mind (as a cancer biologist), the big caution here is in the mouse xenograft model. While it is good that they tested some different cell lines (in a dish), it is much easier to cure a xenograft tumor than it is to cure a sporadic tumor in a human. This is probably because culturing a cell line in the lab for a while tends to reduce the genetic heterogeneity among the cells, and so reduces the chance that there will be a resistant mutant among those cells. Clinical experience shows that there is very often a resistant mutant in the sporadic tumor of a human patient (particularly if the cancer has managed to spread, i.e., metastasized). In any case, they only tested one cell line in the mice. So it is time to try more.
Second off, the cancer stem cell hypothesis is highly controversial, and has been mostly demolished in breast cancer (the type of cancer in this experiment), where Kornelia Polyak has shown that what people are calling cancer stem cells, are probably just different clones / cell types in the tumor. That being said, if we can come up with drug combinations that can handle all the different cell types in a tumor, we'll be doing a lot better than present.
It certainly is promising that the combined therapy showed no relapse over ~60 days after the end of therapy, but I'd like to see the results taking out further to see if the mice would relapse later. I'd also like to see it demonstrated on other cell lines and more realistic models of cancer. There is a long history of drugs looking good in simple "pre-clinical" models, like mouse xenograft models, and then failing in clinical trials.
Also note that it usually takes decades between initial discovery and changing clinical practice, so don't hold your breath, but do cross your fingers.
Actually, the few times I know of that a good data set was put up on the web, it generated a lot of research and progress. I'm thinking of Pat Brown putting up some of the first data on gene expression arrays. Probably hundreds of people worked on that data - everything from statistical methods, to reverse engineering the gene network. It was great. This is probably most valuable when the data is from a new type of experiment that is likely to be widely used.
I hope to do something similar but there is a big problem for geneticists like me. If you post your volunteer's genetic data on the web, there is no way to anonymize it. It would be a simple thing for a medical insurance company to take a cheek swab, run the genetics and then match it against all public datasets to see if an applicant has a known disease. I know of patients that have lost their medical insurance because their insurer found out that they were participating in a research study, and inferred (incorrectly) that the patient had a disease.
I'm a cancer researcher and I agree. Though I'm in it more for the good of society and because it is an engaging problem. I would jump at the chance to cure cancer even if it put my institution out of business and I didn't get the recognition. The reality (of this fantasy) is that most institutions and researchers could easily move on to other diseases/problems. We do it all the time.
In addition, there is BIG money to be made from a drug that cures cancer. Even the ones that cure a small percent of cancer can make Gigabucks these days. This is why big pharma really does try to find new cancer drugs.
To further extend the point: While it is true that there are many types of cancer with different genetics, there is a further fundamental problem in cancer therapeutics. Tumors are composed of billions to trillions of cells with tens of thousands of mutations (by some measurements). Clinical experience shows that when you apply a drug to such a genetically diverse population, you often kill most of the cells but select for resistance mutations. Thus the tumor grows back from the remaining resistant cells and the patient relapses. This is one of the major reasons we have not been able to cure cancer. These results, while potentially important, will not solve that basic problem in cancer therapy. They will select for their own form of resistance mutations.
All of us who object to this policy should be writing our congressional representatives. Otherwise it is unlikely to get the attention it deserves. And yes, I am practicing what I preach.
There is quite an insightful essay in the British magazine Prospect discussing how many of the things Orwell was worried about in 1984 resonate in today's U.S.
- sigless in Seattle
If you want to read more about DNA computing, the best source is the set of 5 DIMACS proceedings (DNA Computers, DNA Computers II, etc.). If you would like a more in depth review of the field, I published one in Evolutionary Computation 6:201-230. You can find a postscript version here. It was designed to be readable by computer types and to bring you up to speed such that you could start contributing to the field. I don't know how well it fared in this department. Unfortunately, it now a bit out of date. A more up to date version will come out soon in a collection on Molecular Computing edited by Tanya Sienko from MIT Press.
Cheers, Carlo Maley
T.J. Yang. 1995. Parasitic protist of metazoan origin. Evolutionary Theory 11:99-103.
Of course, I don't claim that cancer is likely to become a highly contagious disease. It is a hard enough problem as it is. I just bring this up because it shows how bizarre life can be.
Enjoy, Carlo
These numbers come from http://www4.od.nih.gov/ofm/budget/00conference.stm - Carlo