In earlier lessons of the Health Tree University course on cytogenetics testing in myeloma, we uncovered the power of tools like karyotyping, FISH, next generation sequencing, and single cell RNA sequencing. Now we're diving into another exciting and powerful technology, gene expression profiling. In this lesson, you'll discover how gene expression profiling works, what it can reveal about the behavior of your myeloma, and why this insight could be key to more personalized, effective treatment.
What is gene expression profiling?
So there's many different ways of looking at the genetic in a myeloma cell. And traditionally we've used FISH and cytogenetics. And then we did move or some places did move to using a test called gene expression profiling. Okay. And it was very much a big test that was done in Arkansas. And it was incredibly good at finding patients who had high risk disease.
So rather than looking at one chromosome or one gene, which is what the FISH test does, the gene expression profiling is able to look at many, many genes and form a picture of the how the myeloma cell is programed. And what they found is that by using that test, they could identify patients who would potentially not respond very well to treatment. In some cases, they were the same patients that we could pick out with the FISH test. But other cases, the patients for FISH test would be normal, but they would still have high risk disease by this gene expression profiling.
Now, what happened was it was quite a complicated test. And, it was more of a research test and didn't gain FDA approval. However, there is a test that's been made by a European company, which now does have approvals. And so it is the test is available and out there. The problem if that's the expression, is that many insurance companies in the US don't pay for it.
What are the names of gene expression profiling tests?
The two gene expression profiling tests have been available and or are now available is the Arkansas test was called the GEP 70, gene expression profiling 70. And then the European test was initially called EMC 92 and now has changed to Sky 92.
Gene expression Profiling is a laboratory test that analyzes the activity levels of many genes at once to determine which are turned on or off in a particular cell. In multiple myeloma, GEP helps identify specific gene patterns that can provide insight into the aggressiveness of the disease, predict treatment response, and classify myeloma into different risk groups.
just generally now there is a move trying to move from using some of these old tests like the genetics and the FISH, which are incredibly expensive. Moving to some of these newer tests, a little bit about like the gene expression, but also some more sophisticated tests, which we call a panel, or next generation sequencing, which are able to find all of the information that those other tests do combined.
Now, the problem there is that they are still in the research phase. They're beginning to move into getting FDA approvals. And so, people are working on that, but it's very much, around big data and around having, computational methods to read this. But I think from a myeloma perspective, there's a big push for it. It's used all the time in leukemia. Okay. For some reason, leukemia doctors are wired to do these tests. Myeloma doctors, for some reason, I still using the old tests. And so there's a big push to persuade people to move to these newer tests, that we can get a better idea on risk.
How do you interpret the heat map generated from a gene expression profiling test?
Different genes are expressed in different cells. So a muscle cell will express genes a liver cell, a plasma cell. They'll express their sets of genes. But what we really wanted to understand was how does a normal plasma cell express genes compared to a myeloma plasma cell? They're both plasma cells. You would expect them have certain genetic characteristics in common, but the genes that they express may be different in the tumor cell than in the normal plasma cell.
And the heat map that I'm showing here demonstrates every gene that I'm looking at is a row. So a different gene each row is a different gene. Every column is a different sample. And you'll notice on the left side there's a label there that says NPC those are normal plasma cells. And you'll notice there's a set of genes that are red. That means they're expressed at high level. There's a set of genes that are green. They're expressed at a very low level.
But what we really want to do is compare that then to the myeloma gene expression pattern for those same genes. And look what happens in myeloma. All those genes that are red in normal plasma cells aren't expressed very well in myeloma. All those genes that are not expressed much at all in normal plasma cells are now highly expressed in myeloma.
So we're understanding a pattern here. There's a pattern of genes that are changing in their expression that distinguish a myeloma plasma cell from a normal plasma cell. And that tells us something about the biology of this disease.
Now, look at those myeloma and the columns. And you'll notice that even though there's a block of red and a block of green, those two columns are not identical in every myeloma brings home my point. Every myeloma is different. So even though there are some common genes that are often upregulated or genes that are downregulated in their expression, every myeloma is a little different. And really we need to understand that.
In fact, you could take that pattern there and you could say, I've got a pattern. Where else do we see patterns that distinguish outcomes? One of those is in a grocery store barcode.
So I'm giving you an example here of a pattern. These are two grocery store barcodes. They have some things in common, some things that distinguish them. It's a barcode. Look at the bottom of the barcode. You'll notice that the bars all look very similar. That means those two products in the grocery store probably share something in common.
But look, as you move up the pattern, the pattern starts shifting, which says, wait a minute. Those two barcodes probably are defining two different products in the grocery store, but something that they have in common because the pattern is similar.
Turns out one of those code, the scans, says, well, this is wheat bread and one of them is white bread. They share a common feature. They're both bread, but they are distinct. One is white, one is wheat.
Let's think about that application of a barcode that might distinguish a patient who will respond to a therapy versus a patient that doesn't respond to a therapy. And can we in fact use a scanner to scan a pattern of gene expression that will distinguish a response and a not response for a particular therapy? And that's work that we've done in other labs have done as well.
So here's an example of a tumor one myeloma. This myeloma was being treated with bortezomib. And this is just a laboratory treatment of the myeloma.
And you can see in this graph what you're seeing is 100% of the cells are alive. When we started. But we started giving the this tumor in the lab, bortezomib or Velcade, which a lot of you are familiar with. And you'll notice that at 100%, as we increase the concentrate of the drug, we had fewer and fewer cells surviving, so that by the time we got to a relatively low concentration of drug, we killed all the myeloma cells.
That would tell me that that myeloma right there had a pretty good response to bortezomib, because we killed all the cells.
Now, the question is, if I test other myeloma, what happens?
Well, oh. What you can see here is I've got a lot of myeloma that look like that first one where they die very quickly in the drug. But look at all the myeloma that aren't responding very well. That's a demonstration of the heterogeneity of myeloma from different patients.
Some of those patients myeloma has responded killing all those tumor cells to bortezomib very quickly. But there are a lot of myeloma that didn't respond well to that at all.
How can I tell the difference?
What if I could generate a barcode of a heatmap of a gene expression that might distinguish a responder from a non responder.
So what we did is we collected on the left side all the guys that responded. And on the right side of this graph all the myeloma that didn't respond. And then we put them in these groups and said let's look at the gene expression pattern of responders. And non-responders and ask the computer if it can identify a set of genes that will distinguish those two.
And here is a result I now have another heatmap. But my heatmap is not distinguishing normal myeloma from a normal plasma cells. From myeloma plasma cells, the pattern on the left or all the myeloma is that responded to the drug. The pattern on the right are all the myeloma that did not respond to the drug.
That could be useful information. So, for example, if a new patient walks in the door and I say, you know what? Before we even treat you, let's do an expression analysis of your tumor.
And if we generate a heatmap of one tumor and it looks like that, you say, gee, that pattern matches the ones on the left that are responders, this will be a good drug for you to try.
If your pattern look like the one on the right, which were all non-responders, you might say, you know, based on your gene expression pattern, you might not respond to the drug. Maybe we should find other drugs that you will respond to.
We invite you to create a free HealthTree Cure Hub account. Go to Cure Hub website and enter your email here to create an account. After securely connecting your medical records, you'll be able to view your myeloma genetic profile.