The whole genome of breast cancer

14 October 2019

Interview with 

Serena Nik-Zainal, University of Cambridge

The UK Biobank is a massive database of half a million people’s family histories, diets, and medical data… you name it. And starting now, they’re sequencing the whole genome - every single bit of DNA - for every single person. A big job, with a big price tag - and half of that investment is coming from big pharmaceutical companies. £25 million each. Why? The UK Biobank’s Mark Effingham explains...

Mark - This wealth of genetic data will enable those pharma companies to develop new treatments, and they will get a short period of exclusive access of nine months before those data become available to all UK Biobank researchers.

AstraZeneca’s Catherine Priestley agrees, saying that they’ll be looking at complex diseases such as IPF, idiopathic pulmonary fibrosis, and CKD, chronic kidney disease. “Defining the ‘right target’ is key to any drug discovery. To be able to do this well, you need to do this at scale to uncover rare variants.” As an example of how scale can help uncover the genes behind a complex disease, you can look at breast cancer. It’s the UK’s most common type of cancer - one in eight women gets it at some point. Serena Nik-Zainal from the University of Cambridge has been researching it for years, and in a new study, she tells Phil Sansom about how she looked at whole genomes and found something surprising...

Serena - We've studied a cancer type that’s quite aggressive called triple negative breast cancers. And we have used the genomics to see whether we can identify patients who are going to respond to treatments vs patients who don't respond to treatments. We take a DNA sample from the tumour itself and we also take a sample of DNA from the blood of the patient. And a tumour usually has a highly mutated genome, and so you can do the comparison and find all the mutations that have arisen that have contributed towards cancer development.

Phil - That's what makes it a tumour basically.

Serena - That's exactly what makes it a tumour. That's right.

Phil - Who were you looking at in the study?

Serena - In the south of Sweden they've been recruiting patients with breast cancer since 2010. Every woman with a breast cancer has been included in this study. The recruitment rate is in the order of 85%, over eleven and a half thousand patients recruited already.

Phil - Does it matter that they're all Swedish?

Serena - It certainly has got its own biases because it's a Swedish population. Having said that, there was no bias in who was included, so anybody in the whole of the south of Sweden was included.

Phil - What did you find?

Serena - Patterns of mutations. Specifically we had used an algorithm that we developed, a computational algorithm called HRDetect. HRDetect was trained using machine learning methods to identify tumours that had BRCA1 or BRCA2 genetic defects. These two genes are really important in fixing damage that happens to your DNA. So our DNA is always coming under attack from the environment, from within our cells. BRCA1 and BRCA2 are proteins that are involved in DNA repair. So when you have a mutation in BRCA1 and BRCA2 you can't fix damage and you get a lot of mutations in your genome. HRDetect is trained to identify those patterns and tell you what the likelihood is of any tumour having a BRCA1 or BRCA2 type of defect.

Phil - Here's the stupid question. Why can't you just look at BRCA1 or BRCA2? Because those are genes that you know where they are, right?

Serena - Yes. So if you could just be sure that by sequencing just a gene alone you would find the tumours, then yes that would be the cheapest way. But what we found is that actually about a third of the tumours, we cannot find the genetic cause or an alternative cause. We can't find it in about a third of the tumours. So we can see the patterns, and the patterns look identical to BRCA1 or BRCA2 tumours, but we can't find the genetic defect.

Phil - That's so weird that it's a BRCA1 tumor but it doesn't have the BRCA1 thing!

Serena - That's right. And we don't fully understand why that is. But we don't fully understand the whole genome either. And I suspect there are ways of turning off the gene that we don't fully understand yet.

Phil - Now what does that mean if you find these tumours that people didn't realise had these patterns, but actually they do - what does that mean for people?

Serena - For those women who have tumours that look like the BRCA1 and BRCA2 tumours, currently they're not getting the same treatments.

Phil - How many of them are there again?

Serena - More than 59% had a high score. And these tumours are believed to be sensitive to specific drugs, in particular drugs that were developed for BRCA1 and BRCA2 tumours called PARP inhibitors. Currently actually most women still don't get PARP inhibitors in this country. But even if they did it would have only been 1 to 5 percent, not 50-something percent. That increase is massive. So this drug was initially created for 1 to 5 percent of the population, not huge numbers, not 20-something percent, 50-something percent. So that’s a lot of women who potentially could be getting drugs that they're not getting.

Phil -  And because it's got that pattern it might work on them.

Serena - That's right. But we don't know whether that's definitely going to be the case. So we now need to do the clinical trials.

Phil - Why do you need whole genome sequencing for that?

Serena - The other ways of examining the genome are called exon sequencing or targeted sequencing. And exon sequencing captures about 1 to 2 percent of the genome, and targeted sequencing captures even less, 0.1 percent or less. If you don't look at 98 percent of the genome you're going to miss a lot of information. So I kind of think of it as going on a voyage and using very limited landmarks to try to get where you want to go. But today with whole genome sequencing you can have a complete world map.

Phil - Is it like going from “here there be dragons” to GPS?

Serena - Yeah, pretty much it is. Yeah.

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