ASTER 70s is a landmark study for the older population with breast cancer, older meaning aged 70
and older, with the most frequent phenotype of breast cancer at this age which is ER positive,
oestrogen receptor positive, HER2 negative breast tumours. In whom we used a genomic grade index
gene signature to select those who were at higher risk to test the additional benefit of chemotherapy
in addition to endocrine treatment. So why is that? Because this population really has been left behind
in many investigations for a long time and the role of adjuvant chemotherapy remains controversial
because of poor presence of data in the literature or when present raising many questions of toxicity
or related to comorbidity which is something which increases with age and competes with cancer in
terms of prognosis.
So we set up this trial really to try to reverse this unfair and irrational situation. We aimed at enrolling
2,000 women after going for curative surgery for this primary tumour, screening for the genomic grade
index. Those with a tumour with a low genomic grade index, low GGI, were spared chemotherapy
whilst those with a high genomic grade index were randomised between endocrine treatment alone as
a standard arm or chemotherapy followed by endocrine treatment. We enrolled in four years between
April 2012 and April 2016 2,000 women and, of them, 1,100 had a high genomic grade and were
randomised according to these two arms – chemo or no chemo.
The primary endpoint was overall survival and the main result is that those selected with a high
genomic rate did not show a significant benefit from adding chemotherapy to endocrine treatment,
that’s the main message. That’s a message of caution, meaning that even if you use a powerful tool
to select those cases with a poor prognosis, or a worse prognosis, adding systematic chemotherapy
doesn’t improve easily the result, the outcome.
Do you have any plans for this data set in the future?
First, we have a large collection of data that we have collected longitudinally in the randomised
population with data on quality of life, on geriatric aspects, treatment acceptability, socioeconomic
data. So we are going to be able to analyse a bit further these results to try to identify if some of them,
some of these patients, could benefit, derive a benefit marginal or a bit more than marginal, from
chemotherapy. So there is plenty of correlative work that we’re going to be able to do, especially
because we have a large biobank also associated with the population.
Anything else to add?
One last aspect which is really important for this study is that we tried to revolutionise the concept and
the design of clinical trials in this older population. How did we do that? We deliberately chose to
make more flexible the inclusion criteria; we tried to simplify the process of participating to the trial
using a single informed consent for screening with the GGI and randomising when a high GGI was
observed. Finally, we also incorporated many information collected longitudinally to document all
aspects of quality of life which is so important at this age in this population.