Understanding Intraclonal Heterogeneity in Multiple Myeloma for Better Treatment

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• What is Intraclonal Heterogeneity?
• Impact on Treatment Resistance and Disease Aggression
• Detecting Heterogeneity with Transcriptomics
• DNA Sequencing for Cancer Evolution
• Prognostic and Therapeutic Applications
• Full Transcript

What is Intraclonal Heterogeneity?

Intraclonal heterogeneity is a fundamental characteristic of multiple myeloma and most cancers. Dr. Nikhil Munshi, MD, describes it as the process where cancer cells change as they proliferate. A single original cell divides into two, then four, then more. Each new generation of cells can acquire new nutritional, genetic, and genomic changes. This results in a tumor population that is not uniform. Instead, it is a heterogeneous mix of related but distinct cells. This diversity is not merely academic. It is the engine that drives many of the challenges in treating advanced cancers like myeloma.

Impact on Treatment Resistance and Disease Aggression

Intraclonal heterogeneity has severe clinical consequences. Dr. Nikhil Munshi, MD, identifies two primary negative impacts. The first is the development of drug resistance. Through a process of cell selection, tumor cells evolve traits that allow them to survive therapy. The second impact is accelerated growth. These evolved cells often become faster proliferating and more aggressive. In solid tumors, this heterogeneity can also predispose cells to metastasize. While myeloma is primarily a disease of the bone marrow, this evolutionary pressure can rarely lead to extramedullary spread. Dr. Nikhil Munshi, MD, emphasizes that this heterogeneity is a central problem in oncology.

Detecting Heterogeneity with Transcriptomics

Advanced technologies are crucial for detecting and analyzing intraclonal heterogeneity. One powerful method is transcriptomic analysis, or gene expression profiling. Dr. Nikhil Munshi, MD, explains its utility. Because tumor cells are different, each one has slight variations in which genes it expresses. Gene expression profiling measures these differences across a population of cells. Analyzing 100 myeloma cells from a bone marrow sample reveals both commonalities and critical minor differences. This transcriptomic data provides a snapshot of the tumor's functional state. It is a key tool for understanding the biology driving a patient's specific disease.

DNA Sequencing for Cancer Evolution

Looking at the DNA level offers an even deeper view of cancer evolution. Dr. Nikhil Munshi, MD, highlights the importance of genomic sequencing. Each cell within a heterogeneous population may have a slightly different mutational spectrum. Analyzing these mutations allows oncologists to reconstruct the cancer's "family tree." This phylogenetic analysis identifies the original founder cell. It also maps out the daughter and granddaughter cells that have evolved from it. This detailed genomic picture is critical. It helps identify the main cell populations that need to be targeted for a therapy to be effective and durable.

Prognostic and Therapeutic Applications

The analysis of heterogeneity has direct clinical applications for multiple myeloma patients. Dr. Nikhil Munshi, MD, details how this information is used. First, it aids in prognostication. Identifying specific gene mutations or expression patterns can predict if a patient's myeloma will be more aggressive. This knowledge allows doctors to tailor treatment intensity accordingly. Second, it guides therapy selection. Understanding which genes are overexpressed enables the selection of drugs that specifically target those pathways. Dr. Anton Titov, MD, discusses these concepts with experts to highlight how modern profiling moves treatment from a one-size-fits-all approach to a precision medicine model designed to overcome resistance.

Full Transcript

Dr. Anton Titov, MD: So multiple myeloma has a particular feature. It's called interclonal heterogeneity, and it relates to the gene expression profile in multiple myeloma. What is intraclonal heterogeneity of multiple myeloma? And how does gene expression profiling in multiple myeloma help to select the best therapy and also determine the prognostic factors?

Dr. Nikhil Munshi, MD: Yeah, so let me define first the interclonal heterogeneity. We believe that is at the crux of almost all cancer. And what it simply means is that as any cancer cells—in multiple myeloma cells—as it grows, it changes. It acquires new mutational changes, new genetic and genomic changes.

So from one cell, when it becomes 2, 4, 8, and more, those newer cells have gained something that is more than what the original cell has. Some of it happens automatically without having significant impact. But a lot of it, because of the cell selection, happens to make the tumor cells become more aggressive.

They become drug resistant. They become faster proliferating. And so when we look at the tumor cells—when we take the bone marrow and look at myeloma cells, 100 myeloma cells—there is a lot of things common between all the cells, but there are a lot of minor differences that have been acquired over time.

And so it is a heterogeneous population, not one single cell mimicking all the other cells, or clonal heterogeneity. As I said, it is important because this intraclonal heterogeneity provides the cancer cell ability to become resistant to treatment—that's number one. And number two, they grow faster.

In solid tumor, they also predispose the tumor cells to then metastasize. In myeloma, it is in all the bone marrow, so we don't have that big concern. They can become extramedullary rarely, but in solid tumor, it can metastasize. So this interclonal heterogeneity is a bad thing. That's one of our problems.

Now, how can we detect intraclonal heterogeneity? One simple way is what you asked about—transcriptomic analysis, look at the gene expression profile. Because when the tumor cells are different, each one of them has a slight difference in what genes they express or not. So that's one way, and we utilize it for various purposes. I'll tell you in a second.

The second way that we are now focusing even more is to look at the DNA level, because each cell may have a slightly different mutational spectrum. That can tell us which cell was the original cell, which are the daughter cells and the granddaughter cells, etc.

We can look at the whole heterogeneous population. We can develop a sort of family tree of the cancer in a particular person that tells us what is the main cell we need to target, and then whatever else is developing from that. So both transcriptome and genome sequencing are playing an important role.

Now, how does it play a role? Because doing such analysis helps us identify what the tumor cells have acquired that makes them bad cells, makes them more aggressive cells. So now looking at tumor cells and looking at what gene mutations we observe, I could be able to predict that this person's myeloma is going to be more aggressive, and we should treat it accordingly.

Similarly, when we look at the expression profile—gene expression profile—depending upon what genes are expressed or overexpressed, we can then decide: this is a bad sign, more progressive sign; this is more slow-growing tumor. So one thing it tells us is the aggressiveness of the disease, the prognostication.

And then, knowing which genes are up and down or have changed, we can then select drugs to more specifically target that particular gene to kill myeloma cells or control them better. And so this is how we utilize this technology for possible therapeutic purpose.