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Oxford Myeloma Workshop 2025 | Making sequencing decisions in myeloma: factors to consider and future outlooks
Sarah Gooding, MD, PhD, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK, outlines key factors to consider when making sequencing decisions in patients with multiple myeloma (MM), including their fitness and the agents used in previous lines of treatment. Dr Gooding emphasizes the need for targeted and individualized approaches and highlights the potential future use of multi-layered data and artificial intelligence (AI) -informed clinical decision-making. This interview took place at the 5th Oxford Myeloma Workshop in Oxford, UK.
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Transcript (AI-generated)
That’s also not a question with a quick answer, I think. So when considering sequencing in myeloma, there is an awful lot to think about. First, you have to think about the patient in front of you. How fit are they? How frail are they? How many lines of treatment overall may they be able to tolerate? Because that really influences your decision up front as well...
That’s also not a question with a quick answer, I think. So when considering sequencing in myeloma, there is an awful lot to think about. First, you have to think about the patient in front of you. How fit are they? How frail are they? How many lines of treatment overall may they be able to tolerate? Because that really influences your decision up front as well.
Secondly, of course, you have to think about what they’re already resistant to. And because our landscape is changing all the time, what a patient may have been exposed to already and may already be refractory to may not be the same, depending on when they were diagnosed and what drugs were available at that time. So you have to be extremely targeted and individualistic in the way you make these decisions and look at what the patients had before. The key, of course, is to use a combination of drugs that the patient is not already refractory to, and that will be different for everybody. So what we need are tests that give us some indication of what the patient may respond to.
Now, in an ideal world, it would be great to do some sort of individualized test to see how their tumor behaves, but we don’t often have access to those sorts of things. So we have to go by the things we do have access to, which, for example, may be the patient’s genetic background, whether that’s by FISH or whether that’s by a broader genetic analysis, for example, a targeted sequencing panel. We may, and although we’re not doing this in clinical practice at present, it might be great to do it in the future, we may be able to assess their immune health, you know, by looking at their T-cell states and what they’re expressing and their sort of implied T-cell functional health.
And I suspect that in the future we will have multi-layered data, which we will be able to apply to help us do algorithmic treatment decision making. You know, and I don’t think we’re there yet, but there’s an awful lot of research around the world looking to see how we can combine our multi-level data into something that will help us predict best treatment. And I think this is going to be really interesting in myeloma going forward. We’re going to need to use AI. We’re going to need to use multi-omics. And we’re going to need to work out how to deploy that in clinical practice in a way that’s cost-effective. So that’s definitely, I think, where a lot of thinking about future trial and diagnostic design will be going.
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