My talk is about using cancer cell models or cancer cell lines to develop improved therapies for patients. The basic idea is that we can use these models to study cancer in a way that you couldn’t possibly do in patients for ethical and practical reasons. So I’m going to really focus on the strength of these models and how one might use them but also reflect a little bit on the limitations of the existing models. Then I’ll really conclude by talking about what are the potential to develop new models for the future and some really exciting developments in that field about how we can have more relevant models that better reflect the biology of the disease and then what are the prospects to really exploit those models for the future.
What are the main messages from your talk?
It’s going to be fairly broad-reaching but one of the key points would be that these models, to a reasonable extent, really reflect the biology of the disease. So this is an artificial system but it’s a system that you can experimentally manipulate that has a lot of advantages because obviously you can’t do these types of things in a real human being. There are also drawbacks to using experimental or model systems so I’m going to discuss some of the benefits of the system but also some of the limitations of that system so people really understand how to best use that system. Then I’ll conclude by really talking about some very exciting developments in how we can make these models for the future and new models that are coming on line and how we can begin to use those to address some of the concerns with those existing models that we have now.
What are the significant benefits with regards to clinical practice?
I think it has huge implications. So these models, in a sense, are universally used by cancer labs as really, what I want to call, a workhorse to study cancer as a disease and to develop new therapeutics. Therefore if we have better models that better reflect disease, that’s only going to give us results that translate into the clinic much more readily. So it really is a foundation thing about having good experimental models that mimic the disease as well as you possibly can.
What are the main obstacles?
One of the issues of cancer is it’s a very diverse disease, it’s not just one disease, it’s really many different diseases. Underpinning these is a set of genetic changes in the DNA and these vary by cancer type. So to have models for all these different cancer types, one really actually can’t just use one model or one cell line but you really need many, many different cell lines. This creates a huge logistical problem of acquiring these cell lines, accessing patient samples to get those, the work of making the cell lines, and then when you perform an experiment in a cell line you have to be very cautious about the interpretation about how widely applicable that is to other cancer types. So one of the key challenges is having a large enough collection of these models that we really encompass the diversity that is cancer which, as I said, is not one disease but really many different diseases with a different molecular underpinning.
What are the next steps?
My work is to really focus on generating some of these new models, understanding, again, where they add a lot of value, what their limitations are and then exploiting them as a resource or a tool to make new cancer therapies.
