An ovarian cancer blog which includes quality resource materials: education, research, social networking, genetics and (some) healthcare politics.
“Not everything that can be counted counts and not everything that counts can be counted.”
Albert Einstein
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They had the right idea of using the MTT assay (one of the cell-death assay endpoints) to assess platinum chemosensitivity in drug-sensitive and drug-resistant ovarian cell-lines. It's a shame they did this with cell-lines.
Cell-lines are useful for experimentation in labs as they are always available to researchers as a product and do not require harvesting (acquiring of tissue from a host) every time cells are needed in the lab.
Problem is, cell-lines don't recapitulate drug response patterns which exist in the body. For drug selection, it is better to directly remove tumor microclusters straight from the body and immediately test them, before they change.
I remember NCI had a huge lab working on microarrays to look for patterns of mRNA and protein expression which were predictive of chemotherapy response. They spent 2 years trying to find patterns which correlated using the NCI's various established ovarian cell-lines.
They thought they had something, but when they started to apply them to fresh tumor specimens, none of the results in the cell-lines was applicable to the fresh tumors. Everything they'd worked out in the cell-lines was not worth anything and they had to start over from square one.
I also remember Potti at Duke was also doing what these guys are doing: cell culture assays in cell-lines as gold standards of "resistant" vs "sensitive" tumors. Trying then to identify gene expression patterns which correlated with "sensitivity" vs "resistance." Lots of scientists have done this and lots of failure. Maybe these guys will be successful, maybe not.
The best study using this approach I ever read was a NEJM publication in 2004. However, there has been no follow up whatsoever in the past 8 years. I presume the authors concluded that the gene expression patterns weren't helpful, or they'd probably have published something by now (N Engl J Med. 2004 Aug 5;(6):533-42;601-3 Gene-expression patterns in drug-resistant acute lymphoblastic leukemia cells and response to treatment).
The gene expression markers (assays) actually can be calibrated to provide information both about the possibility of recurrence and also chemosensitivity. The problem is dissecting one from the other. Studies to date have just looked at whether people had a recurrence.
You can identify gene expression patterns (via assays) which correlate with this. But it can be hard and even impossible to tell what exactly you are measuring: is it intrinsic aggressiveness of the tumor? sensitivity to drug A? sensitivity to drug B? sensitivity to drug C? sensitivity to drug D? You find a gene expression panel which correlates with something, but picking apart the pieces is hard.
You can begin to do this if you combine gene expression studies with cell culture studies. Use the cell culture as the gold standard to define the difference between sensitivity and resistance. Then see which pattern correlates with which for individual tumors and individual drugs. It can theoretically be done (and certainly will be done, over time), but it's not easy.
They had the right idea of using the MTT assay (one of the cell-death assay endpoints) to assess platinum chemosensitivity in drug-sensitive and drug-resistant ovarian cell-lines. It's a shame they did this with cell-lines.
ReplyDeleteCell-lines are useful for experimentation in labs as they are always available to researchers as a product and do not require harvesting (acquiring of tissue from a host) every time cells are needed in the lab.
Problem is, cell-lines don't recapitulate drug response patterns which exist in the body. For drug selection, it is better to directly remove tumor microclusters straight from the body and immediately test them, before they change.
I remember NCI had a huge lab working on microarrays to look for patterns of mRNA and protein expression which were predictive of chemotherapy response. They spent 2 years trying to find patterns which correlated using the NCI's various established ovarian cell-lines.
They thought they had something, but when they started to apply them to fresh tumor specimens, none of the results in the cell-lines was applicable to the fresh tumors. Everything they'd worked out in the cell-lines was not worth anything and they had to start over from square one.
I also remember Potti at Duke was also doing what these guys are doing: cell culture assays in cell-lines as gold standards of "resistant" vs "sensitive" tumors. Trying then to identify gene expression patterns which correlated with "sensitivity" vs "resistance." Lots of scientists have done this and lots of failure. Maybe these guys will be successful, maybe not.
ReplyDeleteThe best study using this approach I ever read was a NEJM publication in 2004. However, there has been no follow up whatsoever in the past 8 years. I presume the authors concluded that the gene expression patterns weren't helpful, or they'd probably have published something by now (N Engl J Med. 2004 Aug 5;(6):533-42;601-3 Gene-expression patterns in drug-resistant acute lymphoblastic leukemia cells and response to treatment).
The gene expression markers (assays) actually can be calibrated to provide information both about the possibility of recurrence and also chemosensitivity. The problem is dissecting one from the other. Studies to date have just looked at whether people had a recurrence.
You can identify gene expression patterns (via assays) which correlate with this. But it can be hard and even impossible to tell what exactly you are measuring: is it intrinsic aggressiveness of the tumor? sensitivity to drug A? sensitivity to drug B? sensitivity to drug C? sensitivity to drug D? You find a gene expression panel which correlates with something, but picking apart the pieces is hard.
You can begin to do this if you combine gene expression studies with cell culture studies. Use the cell culture as the gold standard to define the difference between sensitivity and resistance. Then see which pattern correlates with which for individual tumors and individual drugs. It can theoretically be done (and certainly will be done, over time), but it's not easy.