Muscle density as a predictive marker for toxicities during ovarian cancer treatment

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Published: 23 Nov 2018
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Dr Lucy Dumas - The Royal Marsden NHS Foundation Trust, London, UK

Dr Lucy Dumas speaks to ecancer at the International Society of Geriatric Oncology 2018 conference in Amsterdam about toxicities for women with ovarian cancer.

She used muscle density a a predictive marker and found that women with lower muscle density at the start of treatment had shorter overall survival.

Dr Dumas emphasises the importance of the importance of understanding the whole patient rather than just the cancer, for example by incorporating a geriatric assessment.

This service has been kindly supported by an unrestricted grant from Janssen Oncology.

I’m undertaking an MD(Res) with Susie Bannerjee at the Royal Marsden and my focus is looking at older women with ovarian cancer. My project has taken three parts but this component of it is looking at predictive markers, non-invasive predictive markers, to try and work out which women are most likely to benefit from and which women are more likely to suffer from toxicities relating to chemotherapy. We looked at all stages of ovarian cancer but the majority of women, unfortunately, are diagnosed with advanced disease, so stage 3, stage 4 cancer. We retrospectively looked at CT scans for women undergoing standard of care treatment and we looked at how dense their muscle was at the beginning of treatment predicted for how long their survival would be and whether or not they would have more toxicities from chemotherapy.

What did you find?

There is no standard threshold for low muscle density in the literature. There are a number of retrospective studies and people have drawn a different threshold depending on their data. I used a threshold based on a paper published in 2013 of 41 Hounsfield units, Hounsfield units is the measure of muscle density on a CT scan. I showed that patients with normal muscle density had a median, so average, overall survival of 56 months compared to 31 months in those with low muscle density, so a big difference. That remained statistically significant even after adjusting for increasing age, increasing stage at diagnosis and whether patients had had all of their cancer removed at surgery or not.

How well can low muscle density be examined?

That’s a really good question, it depends on how you examine it. We were using CT scans and you obviously only do a CT scan when a diagnosis of cancer is suspected. There was some data shown by a colleague of mine yesterday from the States who showed that actually in a longitudinal study it might be something that happens a lot earlier than we realise but you can’t pick it up on a CT scan that way, you need to use another form of imaging analysis. There are other ways of picking it up, so measures of function like how well someone’s grip strength works, for example, or how fast they can walk six metres or how fast they can get up out of a chair and walk a certain distance and then come back again may be early predictors of reduced muscle strength and therefore function which could be predictors for how well someone will cope with chemo and how well they will do overall.

How has this process progressed recently?

What’s coming in now, certainly in the older population, is our gathering understanding that we need to look at the whole of a patient. We probably need to spend a little bit more time than we have been doing to date, spending time thinking about how they are functionally and actually performing some kind of measure that is quantitative rather than what’s been done so far in standard of care anyway where geriatric assessment isn’t included is a sort of best guess and best estimate based on an oncologist’s often very significant experience. Now it’s about drawing down and trying to quantify those markers a little bit better and give patients more information about how well they are likely to do so that they can weigh up decisions along with us about what they want to go for treatment-wise.

How does the timeline look for the future?

The plan is to do this in a prospective study. We’re looking for funding at the moment but that’s an ongoing process. What we’d like is to asses this prospectively, so going forwards and looking at it in real time. The key is incorporating it with a geriatric assessment with a measure of function. What we’d like to do is incorporate other biomarkers alongside, so blood biomarkers that may also be useful predictors to see which patients, what the panel looks like which gives us the best idea of which patients are likely to do well and which patients might suffer. Therefore we might need to think about changing their dose or perhaps not even offering chemotherapy in some situations although in ovarian cancer that’s very unlikely. First line chemotherapy in ovarian cancer has very high response rates so we tend to want to offer it where we can.

What would be your take home message for clinicians?

I suppose the key is at the moment we’re not using it in routine practice. Sadly it’s still limited to the research setting so it can be used on baseline CTs but the CTs need to be assessed using specialist imaging software. So unfortunately it’s not something that can be used routinely yet but hopefully in the future will be. The key message for physicians out there who are looking at either thinking about incorporating this into clinical studies or ultimately into practice would be to think about the function of the muscle of the patient sitting in front of them. So thinking about a geriatric assessment and, most importantly, thinking about how to incorporate that into the decision making for their patients.