Hello.
My name is Mehmet Samur.
I'm from Dana-Farber Cancer Institute.
Here at ASH this year, we are showing our data on smoldering multiple myeloma.
More specifically, by looking at the whole genome sequencing and the genetic data, we are trying to differentiate the low-risk myeloma patients from smoldering myeloma patients and the high-risk smoldering myeloma patients.
In other words, we are trying to understand who is likely to progress to myeloma and who is not likely to progress to myeloma.
So we studied different genomic data, basically 180 precursor conditions with whole genome.
We have patients who are unfortunately progressed within five years of their smoldering myeloma.
We look at their myeloma genome to compare their smoldering myeloma genome.
And we also sequenced samples from patients diagnosed with smoldering myeloma.
And they did not progress to multiple myeloma with at least 6 or 7 years follow-up.
So they are on the more low-risk smoldering category.
So by comparing the smoldering genomes to myeloma genomes, we found that there are a lot of patients at smoldering stage.
Their genomic characteristic is exactly the same as their myeloma genome characteristics.
So, they've been diagnosed with smoldering myeloma based on the criteria we use today.
But when we look at their myeloma diagnostic genome, they exactly had the same clones present at both time points, indicating that they have the myeloma-making clone already at the SMM.
And maybe they would have been approached or put in trials or treated differently at the SMM.
So genomic information really provides a benefit there.
Then when we compare the non-progressors to progressors smoldering patients, we also found significant differences between them indicating genome information provides very useful background, very useful information on who carries high risk, who carries low risk.
And then what we think is that maybe, in the long term with larger studies, this should be like the genomic information maybe should be part of this diagnostic criteria.
And then we can, by looking at the genomic information, use everything we use today in combination, maybe we can define our future clinical trials, our future treatment decisions in a much more informed way than just by looking at the criteria we used today.
We created a risk model.
And our risk model is very good at deciding who is not likely to progress.
When we look at our sensitivity and specificity, an independent data set, we found it is achieving like 90% sensitivity, 70% specificity.
So it's good at deciding who is not going to progress.
But who is going to progress is a complex area.
And it's a very crowded area.
I think we still need a combination of multiple risk models to capture who is really likely to progress.
But our data also shows that if we can capture this group, they already have the myeloma cells existing technically with the SMM diagnosis today.
So they could have been benefited from early treatment, in that regard.
But again, overall, I think we still need the integrated risk models.
I don't think one will be enough.
We are still learning.
We are still finding the best solution to that.
And I believe in the future like we do in myeloma today, there will be a combination of genetic and other biomarkers we use in the clinic that assess the risk, in better terms.
HealthTree • December 13, 2024