POT1: A Gene Behind Skin Cancer

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00:13.0 Hello. Thanks for tuning in.
00:14.1 My name is Daniela Robles
00:16.2 and I am really excited to be telling you today
00:19.1 about our journey
00:21.0 to investigate and finally solve
00:23.1 a devastating family mystery
00:25.1 that had to do with skin cancer.
00:27.1 Now, I've always been interested in cancer.
00:30.1 It is the complexity of this disease,
00:32.1 it is what we think cells are acting
00:35.2 in unpredictable ways in a tumor,
00:37.1 you know, doing crazy things
00:38.2 like going and invading other tissues,
00:41.1 but there are some commonalities
00:43.0 and there are some similarities
00:44.1 within cancer types and among cancer types,
00:46.0 and it is finding these similarities and commonalities
00:49.0 -- what makes a cancer a cancer --
00:51.0 is what I'm really, really interested in.
00:53.1 And this is why I decided to join
00:56.0 the experimental cancer genetics team
00:57.2 at Sanger for my PhD,
00:59.2 under the supervision of Dave Adams.
01:02.1 Now, I was working,
01:03.2 for the beginning of my PhD,
01:05.3 on mouse models of cancer
01:08.1 that recapitulate human disease,
01:10.1 and I did this for about eight months.
01:12.1 But all of that was about to change one day
01:15.0 when we got an intriguing call from this doctor,
01:18.1 Julia Newton-Bishop,
01:19.2 who works at the University of Leeds,
01:22.1 and she called us because she had an intriguing mystery.
01:26.1 She had been seeing, for many years,
01:28.1 families that had a rare type of skin cancer
01:32.0 called melanoma,
01:34.1 generation after generation,
01:36.0 and these families were just getting this disease
01:38.1 and she really had, you know,
01:40.1 no explanation as to why this was happening.
01:42.3 So she called my supervisor, Dave,
01:45.1 and said, "Okay, why don't we sit down
01:48.1 and we'll try to solve this question?
01:50.1 You know, find the reason why this is happening."
01:53.1 So, I tagged along to this meeting
01:55.1 because I thought I could help with the data analysis
01:57.2 that this project was likely to have,
01:59.1 and it was then, in that day,
02:02.1 at the British Library,
02:03.3 when my life changed.
02:05.2 I found what I wanted to do for the rest of my PhD.
02:09.1 I wanted to try and find out
02:11.1 why these families had cancer,
02:13.0 and it is because she told us
02:15.0 about their histories and their family trees,
02:18.2 and they told us how they just kept getting this disease
02:22.0 and just could get no answers as to why,
02:24.1 and she wanted to help them.
02:25.3 So, you know, what is this disease they have?
02:29.2 This disease is a rare type of skin cancer called melanona
02:34.1 and, well, melanoma is very, very aggressive,
02:37.1 and it is a rare type of skin cancer
02:39.1 because it's only 2% of all skin cancers,
02:43.3 but it causes, you know,
02:45.3 the majority of the deaths from these types of cancer,
02:48.1 actually about three-quarters of it.
02:50.2 And it manifests as black,
02:53.2 dark lumps in the skin.
02:55.1 People that find melanoma
02:57.2 after it has spread
02:59.1 have a chance of survival of less than 1 in 5.
03:02.1 So, you know, you can imagine why these people,
03:05.2 these families,
03:06.3 were really, really scared,
03:08.2 because when we talk about melanoma
03:11.0 we imagine that it has to do with the sun
03:12.2 and, you know, sunlight
03:14.2 and UV radiation causes skin cancer,
03:17.2 but this was...
03:19.0 I mean, that is true,
03:20.1 the majority of skin cancers are caused by that,
03:22.1 but it was not the case in these families.
03:23.2 These families were getting this cancer
03:25.1 without even going out to the sun,
03:26.3 so you can imagine they're worried.
03:28.2 And I want to show you know
03:31.0 a real family tree for one of the families
03:34.0 that this doctor was seeing.
03:35.1 So, here you see a couple and this couple had a daughter
03:39.2 who then had three children of her own,
03:42.0 and then one of these children
03:44.1 married and then had four children themselves.
03:46.1 So, you can, you know,
03:48.2 you may know a family like this,
03:49.3 it may look like your own family,
03:51.3 but what was striking about this particular one
03:54.3 is that more than half of the members
03:57.2 developed this deadly disease and then died soon after it.
04:00.2 So, you can imagine being part of this family
04:04.0 and asking, you know,
04:05.1 going to your doctor and saying,
04:06.3 Am I next? Are the other people in my family next?
04:09.2 And the doctor has no answers.
04:11.1 And this is why, you know, we teamed up
04:13.2 and tried to tackle this mystery.
04:17.2 Right.
04:19.1 So, what is, really, familial melanoma
04:20.2 in the context of all melanoma?
04:22.0 So, we know that the majority,
04:23.2 as I was telling you before,
04:25.1 are caused by the sun...
04:26.2 environmental factors,
04:28.1 the sun is the most important of them.
04:30.1 But about 10% of all melanomas are actually familial,
04:34.0 and what this means is that there are some
04:36.2 genes that are mutated in these families
04:38.1 that change how these genes behave
04:41.1 and they change how the cells behaves in their environement
04:44.1 and it can, you know, make the cell go crazy.
04:46.2 So it is these genes that we're interested in
04:49.1 in familial melanoma.
04:50.1 So, within those cases of familial melanoma,
04:53.1 we know that we can explain
04:55.0 about half of them,
04:56.1 so we know what genes
04:58.2 cause familial melanoma
05:00.0 in about half of the cases.
05:01.1 But we still have this orange spot here
05:03.3 where we really have no idea why it causes them.
05:06.2 We have no clues
05:08.3 what mutations are underlying these cases,
05:12.1 so they are completely unknown.
05:15.1 Now, you may be thinking,
05:16.3 well, you know, it's such a small proportion of all the cases,
05:21.0 why is that important?
05:23.0 The thing is that,
05:25.1 by studying the genetics of melanoma,
05:26.2 we can find out more about the disease,
05:28.3 and we can find what mechanisms
05:31.1 are important in the cell,
05:33.0 you know,
05:35.0 for going crazy and for causing cancer,
05:37.2 so what we discover in familial melanoma
05:39.1 may be, you know,
05:40.2 applicable to the rest of the disease,
05:42.1 so this is why it's important.
05:44.1 And, well, also to understand
05:46.3 what genes and what mutations
05:48.2 are important for melanoma,
05:50.0 we need to also know where melanoma comes from.
05:52.3 So, melanoma comes from melanocytes
05:56.0 and melanocytes are specialized cells in our skin.
05:59.1 So, you can see here,
06:01.1 they live in these...
06:03.1 some out here...
06:04.2 they live in the top layers of the skin,
06:07.1 between the epidermis and the dermis,
06:09.2 which are the two outermost layers,
06:11.2 and in a zoom-up, here,
06:13.2 you can see that they have this weird shape
06:16.1 with, like, many arms,
06:17.2 and these are called dendrites.
06:19.3 And, you know,
06:21.1 these dendrites are in touch with
06:23.3 maybe 30 different cells in the skin,
06:26.2 so, why do they look like that?
06:27.3 You know, what's the function?
06:29.3 So, it turns out that
06:32.0 these are the cells that defend your body
06:33.3 against DNA damage caused by the sun.
06:36.1 So, what happens is when you go out,
06:38.1 you know, in the sun and just,
06:40.1 you know, to get a tan or whatever,
06:43.1 sunlight reaches your skin and it's...
06:46.1 you know, it causes mutations in your DNA.
06:49.1 So cells need to defend themselves against this,
06:52.0 because you can imagine just gaining mutations
06:53.2 in the cells
06:55.2 would be really, really devastating for them.
06:57.1 So, this is the job of the melanocytes.
06:58.1 So, the melanocytes, when the see the sunlight,
07:00.0 then they emit,
07:02.1 they produce this substance called melanin.
07:04.2 And, you know, melanin is dark,
07:06.2 this is why, you know,
07:08.2 they look really discolored.
07:11.3 And what melanin does is it can absorb
07:14.1 all the UV light,
07:16.0 so keeping it away from the DNA
07:17.2 where it cannot do any more damage.
07:20.0 And melanocytes do not keep this melanin to themselves,
07:24.1 but with their arms they pass it to
07:26.3 all of the surrounding cells that they can touch
07:28.2 in little packets of this substance.
07:30.3 So, this is really the basis of why you get a tan.
07:34.0 It is really your skin fighting DNA damage,
07:38.0 so it's really a bad thing.
07:39.3 If you get a tan,
07:41.2 it's your body responding to DNA damage.
07:43.1 And also, of course, we know why these tumors look black,
07:47.0 it's because these cells are black.
07:48.3 And it also gives us an answer
07:50.2 as to why these cells
07:52.3 are so resistant to chemotherapy,
07:54.3 because their job is to protect everyone
07:58.0 against DNA damage,
07:59.1 and some forms of chemotherapy
08:01.2 work by inducing DNA damage in cells,
08:04.1 so these cells are intrinsically resistant to it.
08:07.1 Right.
08:08.1 So, this is where melanoma comes from
08:11.0 and we know, you know, a bit more about the biology of it.
08:12.3 Now... so, now we know that
08:16.2 studying the genetics of these families can inform us
08:19.1 a bit about the processes, generally, of melanoma.
08:22.1 So, this is the same family that I was showing you before,
08:25.1 but this is not the only family
08:28.1 that this doctor was seeing.
08:30.2 In fact, she had about 66 of them
08:33.0 that she could not explain by genetics,
08:36.0 and she said,
08:37.3 "Well, can you help me in finding the answer
00:08:40.29 in this whole group?"
08:41.3 And when we tackled this project
08:43.2 we called some other collaborators
08:45.3 that we knew were seeing similar cases,
08:49.0 and this is why we teamed up with some people
08:51.0 in the Netherlands
08:52.2 and some people, here, in Australia,
08:54.3 that also had these families,
08:56.2 and we decided to pull everything that we had together
09:00.0 and then look for the answer all together
09:02.1 by studying 105 families.
09:05.0 Right.
09:06.1 So we have 105 families,
09:07.2 several members of them developing melanoma,
09:10.0 what do we do with them.
09:11.1 Well, we know we have to read their DNA
09:13.1 because we know there's some mutation causing it,
09:15.2 so how do we do that?
09:17.2 How do we read DNA?
09:21.2 Well, the first thing we need to do is take a blood sample
09:24.1 or any sample that has DNA,
09:25.2 but in this case it was blood,
09:27.2 and we need to extract the DNA from that.
09:30.2 So, once we have the DNA
09:32.2 then we need to actually read it,
09:34.1 and we do this by a process called DNA sequencing.
09:37.2 And what DNA sequencing really is is, we know...
09:41.2 I'm going to explain it to you very simply in three steps...
09:43.2 you first cut the DNA in very small pieces
09:46.2 so you can process it,
09:48.1 and then you select, of those pieces,
09:50.1 the ones that code for genes,
09:52.2 because you just want to study the genes
09:54.2 in these people.
09:55.3 So, you do that by, you know, the double helix...
09:58.1 taking advantage of basepair complementarity
10:01.1 between these bases,
10:03.3 and you do that by, you know,
10:06.1 just pulling down the ones that code for genes with that.
10:08.2 And by the same process, then,
10:10.1 you can put these pieces that you selected
10:13.0 into the machine that I showed you before
10:15.0 and the machine is able to read it.
10:17.0 And it throws at you the sequence.
10:19.1 So, now you have the sequence of these fragments;
10:21.2 the machine has read it.
10:23.0 And, you know, it gives you
10:24.3 a file that looks a bit like this,
10:26.1 where it gives a name to the fragment
10:28.0 then it gives you the sequence,
10:29.2 and then it gives you some other metrics
10:31.2 so you know how the experiment really worked.
10:33.1 Now you have a bunch of fragments --
10:35.3 what do you do with them?
10:37.1 I mean, where are they, what do they mean?
10:39.2 You first need to find out where they lie in the human genome.
10:43.0 Now, we now have a reference human genome
10:46.0 and this is like a map of our genome.
10:48.0 We know what the sequence of chromosome 9 is
10:51.1 or chromosome 17 is,
10:52.3 and because of the sequence that we have of these fragments,
10:56.2 we can know where they belong.
10:58.1 And because we can now pair them and say,
11:00.3 okay, this fragment goes here,
11:02.2 then we can, you know,
11:04.1 find the differences between this reference
11:06.2 and the fragments that we put in the machine.
11:08.2 So, we are ready to identify DNA variants
11:12.1 and find mutations.
11:14.2 So, you know, we're ready to go.
11:16.1 So, which variant is it?
11:18.2 Unfortunately, this is like finding a needle in a haystack,
11:23.1 or in a lot of haystacks,
11:24.2 because you have so many of these mutations,
11:28.1 so many of them,
11:30.0 that you really don't know where to start.
11:32.1 It's a bit confusing,
11:33.3 so there are some things that you need to do first
11:35.2 with these ones.
11:37.1 So, how do we find the one that causes melanoma?
11:39.2 So, right...
11:41.0 there are some clues that you can say,
11:42.3 you know, I'm gonna refine my set a bit more.
11:45.3 I'm not gonna consider all the mutations,
11:47.2 just the ones that I know are likely to be causing it.
11:51.1 So, the first thing that you do is, like,
11:52.2 well, I know that these families have melanoma
11:54.2 and probably, you know,
11:56.1 it has to be the same variant,
11:59.3 the same mutation that is causing it.
12:02.2 So I'm just going to keep the ones that are found
12:04.2 in all the affected members.
12:05.2 So if you have one in this person here
12:07.2 but not here,
12:09.2 then you probably will discard that one
12:11.1 because it's probably not the causal one.
12:13.0 So, that's the first thing that we can do,
12:15.0 we can just discard the ones that they do not share,
12:17.2 and we have a smaller set.
12:19.0 Now, as with technology in every field,
12:23.1 there are a lot of errors associated
12:25.0 with the process of DNA sequencing.
12:27.2 The machine can probably tell you,
12:30.1 you know, the sequence of a fragment
12:33.3 and it may not be that right,
12:36.0 or when you map it to the genome
12:37.2 it may not map that well,
12:40.2 so you have to make sure that the mutations that you're considering
12:44.0 are high confidence.
12:45.3 So the first thing that you need to do is
12:47.1 remove all of the ones that you're not very confident about,
12:49.2 so that we can reduce our set even more.
12:51.3 So we do that.
12:54.1 And then, once we have that,
12:56.1 we do know what mutations
12:59.1 cause things like blue eyes and blond hair and skin color,
13:03.1 and we know that these don't do anything...
13:05.2 we know that these are not
13:08.1 the causal variants for melanoma,
13:09.1 so we can also remove those
13:11.1 and make our sets even smaller.
13:13.1 And finally, with the ones that we have,
13:16.1 we can try to predict what they do.
13:18.1 So, if this is a gene, here, in blue,
13:20.2 you can imagine that the lollipops are the mutations
13:23.3 and you can see, well, you know,
13:25.2 maybe the orange color is not that bad,
13:29.0 I mean, it changes the sequence maybe,
13:30.2 but, you know, it probably has no effect,
13:32.3 and the red one is like,
13:35.1 it completely changes the function
13:37.1 or it destroys the protein
13:38.3 #NAME?
13:40.2 So we can do that as well.
13:42.3 So, when we do all of these things...
13:44.3 what about now?
13:46.0 Can we find the variant?
13:48.0 Well, unfortunately, it is a smaller haystack,
13:50.0 but it is still a haystack.
13:51.3 You still have thousands of variants,
13:53.2 so what do you do?
13:57.1 You need to start looking at ways of,
14:00.2 let's say, ranking these variants,
14:02.2 so if, you know, the probability
14:04.2 that they have of causing melanoma.
14:06.1 So, you for example can say,
14:08.2 okay, I'm gonna to look at the genes that have mutations
14:11.2 in the most families.
14:13.0 So, for example, you may have a gene
14:15.1 that has a mutation in each of three different families
14:18.2 and that may tell you that it's more important
14:21.2 to melanoma than a gene that is only found in one family.
14:24.3 So you can do that,
14:26.1 or a completely different way...
14:28.3 you could also look at genes
14:31.3 that have the worst variants,
14:33.1 so the ones that really, really destroy the protein.
14:35.2 And, you know, here in this case,
14:37.2 you may have two genes that have,
14:40.0 you know, a good number of variants,
14:41.2 but this one, even if it's one,
14:44.1 it really destroys the protein,
14:45.1 so you may say, okay,
14:46.2 maybe this one is the causal one.
14:49.1 And that's a different way of doing it.
14:51.1 And these are valid ways of doing it,
14:52.2 this is where you have to think and try to just,
14:55.0 you know, come up with a way of saying,
14:57.1 okay, I'm going to test these variants.
14:58.2 So, these are fine to do,
15:01.1 but the one process that I want to tell you about
15:04.2 is the one that got us to the answer,
15:06.1 and with this one we decided to
15:09.0 even make the set smaller
15:11.2 by looking at just genes
15:13.3 that had damaging variants, rare ones
15:16.1 -- so, rare means not found in a population,
15:19.2 in any population we've looked at,
15:20.3 and not in people walking on the street,
15:22.1 no --
15:24.3 damaging, meaning it's predicted to really destroy the protein,
15:28.3 and also they are affected in the families
15:31.2 with the most information.
15:33.1 And what this means is that
15:35.1 we have some families for which
15:36.3 we have DNA for maybe one or two members,
15:39.1 but we have some other families
15:40.3 that we have information for more members,
15:42.2 so we know that the mutation
15:45.0 is shared amongst these individuals,
15:47.3 so we take those,
15:49.2 the ones that we have more information about.
15:50.3 And from those ones, then, we take,
15:53.0 you know, the mutations that are more damaging,
15:56.2 as I ways saying there.
15:58.0 So these are the genes that we decided to look at.
16:00.2 And, you know, it was only 5 genes.
16:03.1 This was amazing.
16:04.2 Normally you're dealing with haystacks of mutations in genes,
16:07.2 but when we did this we only had 5 genes,
16:10.1 and you're looking at them here.
16:14.1 So, in the first column you can see the gene name,
16:17.0 this is just how we refer to them.
16:18.2 In the second column,
16:20.2 you the see what the identity of these families
16:22.3 that had mutations in these genes were,
16:24.2 so you can see the first one
16:26.3 was two families, one of them was Dutch
16:29.1 and the other one was Australian,
16:30.1 and in brackets you can see
16:32.1 the number of members that we had
16:34.2 for each of these families.
16:35.2 So, three people for the first one and four people for the second one.
16:39.1 And in the last column you can see the function of the protein.
16:43.0 So, all of these functions
16:46.0 are important for cancers, you could say.
16:47.3 Protein phosphorylation, for example,
16:50.0 is important for a cell
16:52.0 to sense its outside environment,
16:55.1 but what really, really caught our attention
16:58.1 in this list of 5 genes is,
17:00.2 well, there's 2 that do the same thing...
17:03.2 it seems...
17:05.1 telomere maintenance.
17:07.0 So, we decided to look at these ones in more detail.
17:09.2 So, what are telomeres?
17:11.2 You can imagine, you know,
17:13.2 the chromosomes in your cells
17:16.1 to be like shoestrings, shoelaces.
17:19.1 And the telomeres would be the plasticy bits at the end,
17:23.0 so, these things,
17:24.1 that keep it protected,
17:25.2 keeps the shoelace protected
17:28.1 from, you know, fraying.
17:29.3 And these telomeres are really important
17:32.2 for the chromosomes because,
17:35.1 you know, they act as molecular clocks,
17:37.0 so they get shorter and shorter with each cell division,
17:40.1 and when they reach a certain length,
17:42.1 then they know they have to stop dividing cells.
17:44.1 So, you can imagine, if they are quite long
17:46.0 then the cells may divide more than they should;
17:48.0 if they are really short then they may die earlier,
17:51.1 which is bad for the cells.
17:53.1 And these two proteins that we found, here,
17:55.3 actually sit down at the telomeres,
17:58.2 and helps the cell protect these telomeres.
18:02.0 And they do that by
18:05.1 preventing a protein called telomerase
18:07.2 from coming and adding sequence to the end of telomeres,
18:10.0 and cells do this because they need to,
18:12.2 let's say, make up for all the
18:15.1 telomere that they lose every cell division,
18:17.1 so they have some way of keeping, you know,
18:19.2 keeping that length a bit more consistent.
18:22.2 And also they help the cell,
18:26.0 let's say, distinguish
18:28.1 between the ends of chromosomes
18:29.2 and a break in the middle,
18:30.3 because if a chromosome breaks in the middle,
18:32.1 the cell needs to know,
18:34.3 I need to repair that,
18:36.1 but not the ends of chromosomes.
18:37.2 Those are fine, those are the ends.
18:39.2 So this is what these two proteins actually do
18:42.2 and, well, yes, you can imagine
18:45.2 if they're not working as they should,
18:47.1 then, well, you know,
18:49.2 chromosomes can fray,
18:51.1 and if they're frayed then the cell will try to repair it,
18:53.1 and then may try to stick one chromosome to the next,
18:57.2 then, you know, that means that they cannot divide properly,
18:59.3 and then that would lead to cancer.
19:01.2 So, this was looking like a good hypothesis
19:03.2 as to why these families were getting cancer.
19:06.2 So we decided to look a bit more deeply into this,
19:10.2 and I'm going to talk about the POT1 gene
19:13.1 in the rest of the talk.
19:15.3 Right, so this the British family I was showing you.
19:19.0 Before... the green arrows indicate the family members
19:23.0 for which we had DNA
19:24.3 -- we didn't have it for all of them,
19:26.2 just the ones in the green arrows --
19:27.3 so all of them had a mutation in this gene called POT1.
19:30.1 And there was an Australian family, here, as well,
19:34.0 that also we had three members for it
19:36.1 and they all had a mutation in this gene as well.
19:39.1 So, what did these mutations do?
19:42.0 So, well, one... there were different mutations.
19:45.1 One of them, the one here,
19:48.2 it's called a missense mutation,
19:50.2 and what this really means is that it only changes
19:52.2 one of the amino acids,
19:55.1 or building blocks of a protein,
19:57.1 in the sequence,
19:59.0 so it's just one building block changed.
20:01.2 And the other one, the Australian family,
20:04.1 had another different type of mutation
20:06.2 called a splice acceptor,
20:08.2 and what this means is that it changes a lot of the protein sequence
20:13.0 or just destroys the structure of it.
20:14.3 So, this is what these mutations did.
20:17.0 But remember I told you we only looked at the families
20:19.2 with the most information --
20:21.1 we still had other families that we could look at.
20:23.2 So when we extended our search to these other families,
20:26.1 then we found another two British families
20:28.2 that had mutations in this gene,
20:30.1 and they were in the same category as the first one,
20:32.3 they were missesnse,
20:34.1 they changed one amino acid in the protein sequence
20:36.3 at position 94 and 273,
20:39.2 this is what this means,
20:41.2 and I'm going to show it to you now.
20:44.2 Right.
20:46.1 This is like a map of the protein,
20:47.3 this is a map of the protein called POT1.
20:50.1 And what this map is telling you
20:54.0 is that is has 634 of these amino acids
20:55.2 and these pink regions here
20:59.2 are what we call protein domains,
21:01.1 and this just means these are bits of the protein
21:03.3 that have a specific function.
21:05.2 In this case, it helps this protein bind to telomeres,
21:10.0 and we can see that the mutations
21:12.1 that we identified in the families
21:14.2 fall in those regions,
21:17.1 so it indicates that they may be important
21:19.2 for this function of binding to the telomeres.
21:21.3 And what we do when we want to,
21:24.3 you know, have a sense
21:26.2 if a mutation or an amino acid in a protein
21:29.2 may be important
21:31.1 is that we compare its sequence
21:33.1 to a lot of other species,
21:35.1 and we do that because
21:37.1 if an amino acid has been kept
21:40.0 where it is along a very, very long period of time,
21:43.2 then it may indicate that it hasn't mutated for a reason --
21:46.3 it's needed for a function.
21:48.1 And when we do that then we see that,
21:50.1 yes, you know,
21:52.1 those three positions there are highly conserved across evolution,
21:55.0 all the way from zebrafish, in some cases,
21:57.3 like this 273,
21:59.2 or from chicken up in the other cases,
22:02.2 so, yes, they are pretty well conserved.
22:04.2 They're telling us, you know,
22:06.1 this may be important for its function.
22:09.2 Okay, but we don't only have
22:12.0 two dimensional maps of proteins like this one,
22:14.0 we also have three dimensional maps,
22:16.1 and here's what I'm showing you here.
22:18.2 So, in green you can see these two OB domains,
22:23.0 so the two portions of the protein that helps it bind to DNA.
22:26.3 And you see the DNA, or the telomere, in orange,
22:30.2 you can see it here.
22:31.3 And here in these panels
22:33.2 is just a zoom-in of what's happening in the first panel,
22:35.2 with the positions that we identified just highlighted,
22:39.0 and what I want you to see here is that
22:42.1 these amino acids at these positions
22:44.1 are predicted to interact directly with the telomere,
22:48.2 which means that if you change them,
22:50.1 you know, the protein may not be able to interact with DNA anymore,
22:56.1 which means the function may be
22:58.1 lost or destroyed.
23:00.1 So we had to do an experiment to test this,
23:02.2 and we introduced the mutations
23:05.2 that we found in the families in this protein
23:07.1 and then assessed the ability of the protein
23:10.3 to bind to these telomeres, or DNA.
23:13.1 And this is what you're seeing here in this gel.
23:16.1 So, if the protein can bind to DNA,
23:20.1 then you'll see, you know,
23:22.1 the complexes appearing here.
23:23.2 If it can't, then you won't see them.
23:25.2 The first two lanes
23:27.3 just indicate controls to show if the experiment has worked
23:31.1 as we expect.
23:32.1 The third band is called a wild type,
23:34.2 that's what WT stands for,
23:36.1 that means the protein without any mutations,
23:38.2 so you can see,
23:40.1 yes, it binds where it should.
23:42.0 But when we add these three mutations to the protein,
23:45.0 then we see that it has completely lost the ability
23:48.0 to bind to DNA.
23:49.1 So now we know that these families
23:51.1 have a mutation in this protein
23:53.2 that is not allowing it to do its function
23:56.2 at the telomeres,
23:58.1 so this may mean something
24:00.2 for the telomeres themselves,
24:02.2 so we decided to look at telomere length,
24:05.2 because this protein helps maintaining of correct telomere length.
24:09.3 So, when we do that...
24:13.3 I'm going to show you here...
24:15.2 this is a distribution of telomere lengths
24:18.1 in melanoma cases
24:20.2 that do not have mutations in this gene,
24:22.1 so these are people that have melanoma,
24:24.1 but no mutations in this gene,
24:26.1 and we have measured how long the telomeres are
24:28.2 and we have plotted them in this distribution,
24:31.0 so you can see it there,
24:32.1 and the farther away you are at this side,
24:33.2 then the longer the telomere is.
24:35.2 And we put, on top of this graph,
24:38.1 the measurements for the people
24:40.1 that do have mutations in this gene,
24:42.2 and we see that, yes,
24:44.2 the first three rows here
24:47.2 are from people from these families
24:49.1 that have mutations in this gene,
24:51.1 and you can see that they are longer,
24:54.2 all the telomeres in these families are longer.
24:56.2 And the fourth row just shows you
24:58.2 telomere measurements for people in the same families,
25:01.3 but that do not have mutations in them,
25:03.3 and you can see that they fall pretty much in this distribution.
25:06.2 So, from this experiment,
25:09.0 we can conclude that these people that have these mutations in this gene
25:13.2 not only have a protein that doesn't work,
25:16.1 but this translates into a difference in their telomeres --
25:20.0 they are longer.
25:21.1 And they are not only longer,
25:23.2 but another groups investigated also,
25:25.1 you know, if they have frayed ends, and they do.
25:28.0 So, not only are they longer, but they are also frayed
25:31.0 and damaged,
25:32.3 so, well, you know, this is one of the reasons
25:34.3 why, you know, probably the most important reason
25:37.1 why they have cancer,
25:38.2 because they have unprotected telomeres.
25:42.1 So, all of this goes to tell you that we have found, now,
25:45.0 a new piece of the puzzle of melanoma genetics.
25:48.1 So, if we had this pie chart before,
25:50.2 now we know, you know,
25:52.2 we can explain another bit of this unknown portion
25:55.1 by mutation in this gene called POT1.
25:58.2 And what it really means for these families
26:01.0 is that they now know
26:03.2 why they have melanoma
26:05.2 and now people that belong to this family
26:07.0 can go to the doctor and the doctor can say,
26:08.2 "Okay, I can test you
00:26:10.10 and I can tell you if you are at high risk for developing it."
26:14.0 But there's another interesting thing going on in this pedigree,
26:17.1 and this is just a speculation,
26:18.2 we have no data yet to back this up,
26:23.1 but it is interesting if you see that
26:25.2 the ages keep getting younger and younger,
26:28.0 generation after generation,
26:29.2 and this may be because they have a defect in their telomeres,
26:33.0 and what it means is that...
26:35.1 telomeres also get longer in germ cells,
26:37.1 so when these people have a baby
26:39.1 then the baby is already starting with longer telomeres,
26:42.1 which means they may need
26:44.1 a bit less time to develop cancer,
26:46.2 and this may mean, you know,
26:48.2 what we're seeing here.
26:50.2 And at the moment,
26:52.0 this is a really exciting hypothesis for us.
26:53.3 We are at the moment trying to investigate it
26:57.1 with mouse models.
26:58.1 We have now created a mouse model in the lab,
27:00.1 a postdoc called Chi Wong,
27:02.1 introducing this mutation in the mice
27:04.1 and seeing, you know, monitoring them
27:06.0 when they have babies
27:07.2 and getting telomeres measurements.
27:09.1 And we are also trying to investigate
27:10.3 what other genetic events happen in these cells
27:13.2 that have these mutations
27:15.1 in order for them to develop cancer,
27:17.0 and we're doing that by looking at DNA from tumors, not only from blood.
27:21.1 So, what we have learned from this
27:23.2 is a new mechanism
27:25.2 that can predispose to familial melanoma,
27:27.0 which we didn't know before,
27:28.3 and how we think that this may be working is that...
27:31.1 you have the telomere in a normal individual
27:34.1 that does not have mutations here,
27:35.3 you have a normal POT1 protein that just,
27:39.1 you know, keeps binding to the telomere
27:41.2 and stops this telomerase enzyme
27:44.0 that adds the repeats at the end
27:46.1 from doing that when it shouldn't do it,
27:48.0 and also the DNA repair proteins,
27:49.2 which then will try to go and say,
27:52.1 This is the end, we should repair it!...
27:54.3 which it shouldn't do because it's the end of telomeres.
27:56.3 But if it's not working then telomerase can access it,
28:00.1 which means longer telomeres,
28:02.0 which is what we're seeing in these families,
28:04.1 and also that the DNA repair proteins
28:07.1 can go and recognize this as DNA damage,
28:09.1 try and repair it,
28:11.2 you know, bind two chromosomes together,
28:13.1 and this is what causes telomere damage
28:15.0 and chromosome instability.
28:17.2 So this is a new
28:21.0 mechanism that we have found,
28:23.3 in families,
28:25.3 that may be important for the overall melanoma biology.
28:28.3 So, it may be important
28:32.0 for melanocytes to regulate their own telomere length,
28:33.1 and when that goes wrong not only in families,
28:36.1 but maybe also in the environmental cases,
28:38.3 then it can go wrong,
28:40.2 and this is something that we're investigating at the moment.
28:42.1 And of course, overall,
28:44.1 all of this goes to say,
28:46.1 well, we're hoping that we're providing some help
28:49.1 or some answers
28:51.1 to the families that suffer from this cancer, and that...
28:53.3 before they really didn't know why,
28:56.0 and they didn't know if they were high risk,
28:58.3 and hopefully now we are able to tell them
29:01.0 why they have it,
29:02.2 what preventable measures they may take,
29:05.1 and hopefully, in the very long future,
29:07.1 you know, it could be used for therapy
29:09.2 or for treating their cancer
29:12.3 more specifically.
29:14.1 Okay, so I would like to finish now my presentation
29:17.0 by thanking a lot of people...
29:18.2 this was a very, very large project,
29:20.2 that you could have imagined
29:22.1 by having all of these people all over the world that we studied...
29:24.2 so I'd like to really thank Dave, my supervisor,
29:28.1 he's been fantastic
29:30.0 and provided a lot of support during my PhD;
29:32.0 of course Dr. Julia and her team at Leeds;
29:35.2 the team at Australia -- Lauren, Nick;
29:39.2 and finally a team in Oviedo, Andrew and Carlos,
29:41.3 who helped us with the experiments in telomere lengths.
29:44.2 These are my email addresses. I
29:47.2 f you would like to contact me I would be very happy to talk to you
29:51.0 about these things or other things.
29:53.1 And finally thanking my funding bodies,
29:56.1 Conacyt of Mexico
29:58.2 and the Wellcome Trust Sanger Institute.

This Talk

Speaker: Daniela Robles-Espinoza

Audience:

General Public
Educators of H. School / Intro Undergrad
Student
Educators of Adv. Undergrad / Grad
Researcher
Educators

Recorded: May 2015

Talk Overview

Daniela Robles-Espinoza explains that melanoma is a deadly skin cancer that is very rare in the human population. So it was perplexing when she learned about a number of families with alarming incidences of melanoma. How did these individuals get this rare type of skin cancer so readily? Robles-Espinoza and colleagues sequenced DNA from these families to pinpoint the genetic origins of the familial cases of melanoma. In several families, she discovered mutations in the POT1 gene, which regulates the length of chromosome ends, or telomeres. They performed in vitro experiments that showed the POT1 mutations completely disrupted POT1 function. Furthermore, they observed longer telomeres in the individuals afflicted with melanoma, suggesting the mutations had an effect on the physiology of the individuals and were causing, at least in part, the high rates of skin cancer in these families.

Speaker Bio

Daniela Robles-Espinoza

Daniela Robles-Espinoza recently received her PhD from the University of Cambridge, in the UK, and is now a postdoctoral fellow at the Experimental Cancer Genetics group in the Wellcome Trust Sanger Institute. She uses bioinformatics and computational methods to study the genetic factors that influence cancer development, especially melanoma, in high-risk families. She’s also interested… Continue Reading