The background of this analysis is that women with oestrogen receptor positive breast cancer continued to have distant recurrences five years after they finish endocrine treatment. There’s a really important clinical question to accurately predict who is going to develop a distant recurrence. Therefore we took the approach in using clinical pathological parameters to develop a simple prognostic tool to predict late distance recurrences.
We know that clinical pathological parameters are important not just for the first five years for prognosis, but some of these clinical features have also shown to be highly prognostic for late distance recurrences, in particular nodal status and, to a lesser extent, tumour size and grade. Recent data from a large meta-analysis from the Oxford overview has also shown that the risks of late distance recurrences can lie anything between 10% and 40%, and this is depending on the original tumour size, nodal status and grade. Therefore we took the approach to using this clinical information and incorporating it in a simple prognostic tool.
Do you know of any of the numbers that you could rattle off? I’m sure you’ve talked about it plenty of times already over the course of the conference but success rates, accuracy, that kind of data?
In terms of this tool, specifically? We have developed this tool first, it looked in the initial clinical parameters on its own to see which of these clinical parameters are prognostic for late distance recurrences, and we have observed that apart from five years of endocrine treatment all the clinical features are highly prognostic. This is nodal status, tumour size, grade and age. We incorporated these highly prognostic, univariate features into a multivariate model and have seen then that in the training data set, when we applied this prognostic tool in a training data set, that around 43% of patients are categorised as low risk of late distance recurrence and they only had a five-to-ten year distant recurrence risk of less than 3%. We then validated this tool in our validation data set, the BIG 198 trial, and have seen that the risk stratification using these risk groups from the training cohort that we found very similar risk stratification and 5-10 year distant recurrence risk in the validation cohort. Again, roughly 40% of patients were categorised as low risk, 25% as intermediate risk the remaining as high risk. We then combined these two datasets to get the increased risk estimates for late distant recurrence and of the 43% who were low risk, they had a 5-10 year distant recurrence risk of 3%. This indicates that extended endocrine therapy is of limited value in this patient group. When we compared it to the high-risk patient categorised by the CTS-5, they had a 5-10 year distant recurrence risk of almost 20% - this suggests that these women are good candidates for extended endocrine therapy.
For clinicians looking to take this data forwards, is there any way that they can start incorporating that into the care and maybe increasing that number up from the 20%, because we don’t want to be mistreating an extra 80% of people who wouldn’t be best suited to the extended care?
Yes. We aim to make this CTS-5 prognostic tool, this algorithm, available with a risk curve and readout table, so essentially in the end we would like to provide the clinicians with a risk calculator that they can use and simply import the clinical information, which they already have, so there is no further need for testing or any additional information needed. They can enter all this data into the risk tool which will give them then the CTS-5 value, with an associated 5-10 year distant recurrence risk. Women are then categorised into low, intermediate or high, and therefore they can then decide with the patient whether extended endocrine therapy is a good option for the patient.
That sounds like you’ve covered some of the past, present and future for the paper, is there anything else you can think of that we’ve not mentioned?
We have obviously trained this prognostic tool in a large clinical trial and validated in a large clinical trial. It would be good to see also if we can do further validation, specifically in pre-menopausal women, because at the moment this prognostic tool is only applicable to post-menopausal women, so in a pre-menopausal cohort it would be very nice to see if we can achieve the same risk stratification and therefore see if women could go on extended endocrine therapy if they are pre-menopausal.
