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.