Interviewer - One of the things that seems to have revolutionised cancer genomics, and genomics generally, in recent years is next-generation sequencing. Why has next-generation sequencing changed everything and allowed us to make such progress recently? Interviewee - I think when we started with next-generation sequencing, back in 2007, it really wasn’t clear that it was going to have the impact that it’s had today and I think essentially, the technology moved from Sanger sequencing, the human genome project sequencing technology. You know - one genome, 15 years, three hundred million dollars, and that was never going to be useful in a truly medical context.
What we really want to do, particularly in dance,r is understand the the cancer genome, and the genome of the patient - the normal genome, and compare the two. And then we really can see what are the things that have gone wrong with cancer. But it wasn’t clear, back in 2007, that the technology would ever really get to that level but it was very interesting for targeted work, for understanding transcription factor biology, chip seek, understanding RNA, and now RNA seq almost completely replaced microarray technology we’d use for for a decade, or more. and the technology has just improved year-on-year, faster than Moore’s law, which is the most commonly quoted kind of the computing law. It’s like Moore’s Law on steroids.
And so today, we can, from the Human Genome Project, 15 years, three hundred million dollars, one genome, today we can do one genome for $1,000 in three days and that really is astounding progress. Interviewer - That is phenomenal because now you’re getting really close to the cost of a routine diagnostic test in a hospital laboratory, so you know, a bit of historpathology might cost a couple of hundred pounds, you now ,getting very close to the cost of a routine histology test for cancer, and you can analyse a whole genome. So how do we use that data for cancer patients? Interviewee - So, we’re, primarily the Cambridge Institute is a Research Institute - a translational research institute.
We’re trying to learn about cancer and move that data into the clinic, and there’s huge efforts to do that. Many of the really fantastic group leaders of the Institute are clinician scientists. They are MD PhDs. And so they’re there trying to understand cancer better and ultimately deliver technology that can be moved into a testing environment. But we’re not testing patients, we’re not making tests in the Institute.
Really, the real excitement is, by understanding the cancer genome, having the cancer genome and the normal genome from the patient, we can subtract all the the natural variation that that patient has against the background population, the reference genome, remove all of their personal structural variation, their own personal snips - single nucleotide variants, and now we’re left with just the things that are different between their genome and the cancer.
And that’s a mixture of many different types of mutation, and what we’ve learnt in projects like the Cancer Genome Project, is that there are many of those are background mutations, what we call passengers, and a few of those mutations, or those structural changes, are what are called driving mutations and those are the things we really want to understand, and so we’re really trying to build that picture of what are the true drivers of different types of cancer, so we can better categorise patients. And ultimately that understanding will move into being a clinical test. And it’s happening right now.
The costs are very, very similar to many tests that are there today and the costs are low compared to an overnight stay for a cancer patient in a hospital, in a high dependency ward. But that technical ability to generate the data is relatively straightforward today, but making sense of that data and giving something back that will inform a clinician on how to better treat a patient is still a lot of work needs to be done and that’s that’s taking longer than the technology. The technology is racing ahead.