Next we're going to explore what the genetic landscape of a tumor looks like.

Tumor cells harbor a large number of mutations and

these mutations affect the gene expression and ultimately, the proteins

that are expressed on the cell surface and also

within the tumor within, within the nucleus of the cell.

So what I wanted to talk about next is how we actually measure tumor gene expression.

And one of the things that we've been doing for a number of years is to measure

single markers things like HER2, estrogen receptor, and progesterone receptor.

These are three common markers that have been evaluated on

the surface of breast tumors for a number of years.

But now, with some of the newer genomic technologies, we're

able to look at genome wide measures of gene expression.

And, we do this using expression arrays or other methods, such as RNA-seq.

I just wanted to describe these very briefly, as

you may encounter them in, in your clinical practice.

So, expression arrays, basically, what an expression micro array is, is it's a

slide for example, a glass slide, to which DNA probes have been added.

These probes are short segments of DNA that represent a gene.

And we can put tens of thousands, thousands to tens of thousands

of these on a slide and evaluate them all at, at one go.

So we could put on markers for all,

you know, 20 some thousand genes in the genome.

And they're single-stranded DNA molecules.

What we do next is we take a tumor sample and we isolate the RNA.

What does RNA represent?

It represents all of the genes that are expressed in that tumor.

So we take the RNA from the tumor and we label it with fluorescent

markers and then we mix it with this fixed probe on

the slide, and we see if it sticks or not, if it anneals.

It will anneal or bind if it's complimentary

to this, to the DNA that's on the slide.

If it's fully complimentary, we'll get a fluorescence signal.

And that signal will tell you number one, is

that specific gene that this probe represents being expressed in

that tumor sample, and the intensity of that signal

will tell you how much that gene is being expressed.

So it's both a qualitative and quantitative assay.

If the probe does not anneal or bind

to this complimentary DNA, you won't get any signal.

So this is a nice way to evaluate across the genome which genes are expressed

and how much they are expressed, relative to each other in one experiment.

Another approach to do essentially the same thing, but

using next generation sequencing, is called RNA sequencing or RNA-seq.

In this case, what you're doing is you're taking

the tumor sample and you're again isolating the RNA,

and you've got your little pieces of RNA here,

which represent different genes that are expressed in that tumor.

And then we sequence each of these.

And in sequencing these you'll recall from a

couple lectures back, we end up aligning these sequences.

And we have what's called a read depth, which tells

you how many times you've observed these different fragments of sequence.

And if you basically count those, that will give you a

good indication of the quantitative measure of gene expression in this tumor.

So either using expression micro-arrays or RNA-seq, we're

able to quantify the amount of gene expression in

each of these tumors and identify which genes

are turned on and which genes are turned off.

Another thing that we might want to be able to do is actually

measure, at the level of DNA, the somatic mutations that occur in a tumor.

And so if you think about a tissue and the blue represents the tumor

and the yellow represents the surrounding normal tissue.

What we can do is we can sequence the DNA

from both the tumor and the normal and compare the two.

Why would we want to do this?

Well if we want to know which of these mutations are somatic, in other

words, which of these are the acquired mutations that may be driving this cancer.

We need to compare it with the normal tissue to ensure that

these weren't some inherited mutation that,

that was found in the tissue normally.

So, we take the DNA from the tumor, and we sequence it.

And we take the DNA from the normal surrounding tissue and we sequence that.

The normal tissue will just give you germ line variance.

The tumor DNA will give you germ line variance and somatic changes.

And by comparing these two, you can essentially subtract out and, and,

and end up with what these somatic changes are in that tumor.

A number of different groups are carrying out, carrying

out large-scale efforts to characterize tumors at the molecular level.

We have the Cancer Genome Atlas in

the US and the International Cancer Genome Consortium.

Both of these groups have under taken projects to

sequence hundreds, if not thousands, of tumors across a number

of different tumor types, and deposit that information into

the public domain, so that any researcher can evaluate it.

One of the databases that houses information about

somatic variation in tumors is the COSMIC database.

In this database, you can access information, again about

how many genes are mutated, what the specific mutations are.

It's a very rich resource for any researcher who

wants to learn more about the genetic landscape of tumors.

Okay, so how many genes are actually mutated in the average solid tumor?

Some work was done much of this based on the Cancer Genome Atlas

project to look at how many mutations were found across different tumor types.

And this was published in Volgastein et al, from 2013 and Science.

What they report is that on average 33 to 66 genes are mutated in a solid tumor.

And this number varies depending on the cancer type and most of

these variants are actually single nucleotide

variants which is what we would expect.

It mirrors what is going on in, in

the mutational landscape of a, of a normal genome.

So why do these mutations, why do these tumor

types vary in the number of mutated genes in cancer?

Well, if you look at the diagram here, it shows you

what the different cancers are and how many genes are mutated.

And, and it begins to make a little sense.

You look at something like the pediatric cancers down here.

They're characterized by, by a very low mutational burden.

Why is that?

Well, they're pediatric patients, and we

know that cancer accumulates over a lifetime.

The older a patient is, the more likely

that, that their tumor will have acquired additional mutations.

Then, you have examples where the cancer is

due to an environmental carcinogen, lung cancers and melanoma.

They have a relatively high burden, because we have

several mechanisms that are contributing to the mutational burden.

And then on the far left here, on

the extreme off the charts, we have colorectal cancer.

And this makes sense.

As I mentioned, Lynch syndrome, one of the familial colorectal

cancer syndromes, is actually due to mutations in DNA repair enzymes.

So it makes sense that a tumor cell, a colorectal cancer

tumor cell of this nature would have acquired a lot more mutations.

[BLANK_AUDIO].

In the cancer world, we talk about drivers and passengers.

Drivers are mutations that provide a selective growth advantage.

Passenger mutations are just random mutations that have

occurred but they're really not driving this neo-plastic growth.

They're just benign, just along for the ride.

And so how do we actually identify what the driver mutations

are and, and how do they differ from the passenger mutations?

So to do this again, researchers have looked at hundreds of tumors across any

given tumor type and tried to look at a couple of different features of them.

The first feature is the frequency, and if you look

at the, the diagram here for colorectal cancer, looking across hundreds

of color rectal cancer samples, you can see a plot of

the frequency, with which different genes are mutated in these tumors.

And, you can begin to see patterns.

There are some genes that are mutated at a relatively low frequency, these are hills,

there are some genes that are not mutated at all, those don't even show up here.

And there are some genes where, when

you look across different tumor samples from different

patients and the same kind of cancer, you see these genes are mutated quite often.

Things like APC, which is one of the colon cancer genes that we know about.

KRAS, TP53 and others all show high levels of mutation in colorectal cancer.

So the idea is that maybe these are actually drivers.

They're the ones propelling the disease forward.

So it's important to recognize them in that way.

Another way to identify driver genes, is by look at different patterns of mutation.

Not just the frequency but the patterns.

Something like what was found in the IDH1 gene in brain tumors.

For this tumor, what we find is not only was the IDH1

gene mutated quite often, but it was essentially the same mutation every time.

So this is telling us that not only is this gene probably a

driver of cancer, but this specific mutation is probably a driver as well.

One of the questions that we've been trying to answer

is, how many driver genes exist out there, and some of

the research based on sequencing of nearly 3,300 tumors reveal that there

are about 125 mutational mutation driver genes that were identified.

71 of them, tumor suppressor genes, 54 oncogenes, so.

Now we have a ballpark of how many different genes

that we're, we're dealing with in terms of driving cancer forward.

One of the things with cancer, the thing that usually kills you, is a metastasis.

That is when the cancer invades the blood stream

and is able to blood or lymph system, and is

able to circulate and arrive at different places in

the body and lodge itself and begin to grow there.

The most common sites for metastasis include

the brain, lungs, liver, and lymph nodes.

So one of the questions that researchers have been trying to answer is

whether there are specific genes that

become mutated which then lead to metastasis.

Unfortunately, research to date has not been able to

identify any such genes controlling metastasis in that way.

And the recent thinking is that Metastatic

events are really just a stochastic process.

Once a tumor cell acquires a certain number of

mutations, it just becomes metastatic and, and, and moves on.

Metastatic disease accounts for

about 90% of cancer deaths, so

this is an important area to focus on.

Module 3: Tumor Genetic Landscape

Genomic and Precision Medicine

Meet the Instructors