Expert Perspectives On Advanced Hormone Receptor-Positive Breast Cancer - Episode 10
Sara Hurvitz, MD: Andrew, every patient is different, but how do you approach a low-risk, early-stage hormone receptor-positive breast cancer versus high-risk, nonmetastatic ER-positive breast cancer? Where are areas of unmet need?
Andrew Seidman, MD: It all depends on how you define low risk, but if we take this at a very high level, patients with low-risk hormone receptor-positive HER2-negative cancer are those who will benefit from endocrine therapy, who don’t need chemotherapy. And those patients who are higher risk, either defined by anatomic features, as Sara pointed out, or molecular features, as Massimo pointed out, still need chemotherapy. I agree with Massimo’s point that chemotherapy is not the panacea and answer that we want it to be. And hopefully the next generation of trials for those patients with high-risk ER-positive disease will explore more rational therapeutics, those that are now being integrated in metastatic disease, for example, CDK4/6 inhibition, PI3 kinase inhibition perhaps.
Sara Hurvitz, MD: Absolutely. I think it’s very hard for us to make a sweeping statement about how we treat each. For some patients we’re going to be using chemotherapy. We haven’t addressed whether ovarian suppression in a younger woman is enough and can supplant chemotherapy use, so many outstanding questions and many ongoing studies. Sara, a number of studies were presented and discussed at the San Antonio Breast Cancer Symposium. I’ve reviewed some of the slide sets I would say 4 or 5 times, and I’m still trying to grapple with the data and keep in mind the conclusions from each of these studies. Can you take us through some of the data that were presented at San Antonio relating to the use of biomarkers and assays to guide our treatment decisions for early stage disease?
Sara M. Tolaney, MD, MPH: I absolutely agree, there were a lot of data that came out of San Antonio that were really interesting but also very confusing in light of different data sets, and really puts into question how we should be approaching our patients with ER-positive disease to make decisions about who actually needs chemotherapy versus not. One such study was ADAPT, which was a unique study because it was trying to look at if you assessed tumor response to endocrine therapy by Ki-67, could that really help you understand who needs chemotherapy versus not?
They did a unique thing, which was they integrated recurrence score and this response to preoperative endocrine therapy by Ki-67 to help address this question. They had 2 general groups of patients. They had patients who had a recurrence score that was low, so less than 11 and up to 3 positive nodes, who could get endocrine therapy.
But then they also took the patients with intermediate recurrence scores and gave them preoperative endocrine therapy, and looked at how they did with that to see what happened to their Ki-67. If they had a good response, meaning that they had a Ki-67 under 10% and went on to get endocrine therapy, how did they do compared to the people who had the low recurrence scores. Can we take an intermediate-risk patient and get away without chemotherapy if they respond to preoperative endocrine treatment? Is that a way that we can identify those patients who don’t need chemotherapy? In fact, they did see that invasive disease-free survival was noninferior between those patients who did achieve a low Ki-67 from preoperative endocrine treatment. So it was saying we could select patients who could get away without chemotherapy by looking at response to the short course endocrine exposure, which is an interesting way to think about things.
We also have another study that was trying to look at a couple questions, but one also that was trying to address looking at short-term exposure to endocrine therapy and seeing if we can just continue on endocrine treatment or escalate to chemotherapy. That was the ALTERNATE trial. This trial was asking a couple of questions. It was looking at if there are differences between different endocrine agents, specifically is there a difference between an AI [aromatase inhibitor] and fulvestrant, or fulvestrant with AI? It was trying to address, again, if there are differences between those agents in terms of tumor sensitivity to those endocrine treatments. But it was also looking to see, can we take those patients who get that short course endocrine treatment, and if they don’t achieve response to that endocrine treatment, should we escalate treatment to chemotherapy and then look at their outcomes?
We’ve seen now 2 presentations. We saw the first presentation at ASCO [the American Society of Clinical Oncology 2020 annual meeting], where we looked at the differences between the 3 endocrine agents, and there was really no difference between anastrozole and fulvestrant or the combination in terms of looking at PEPI [preoperative endocrine prognostic index] 0 scores. Many of us wondered if fulvestrant was better in the early-stage setting, and this suggests that there isn’t a difference between the 3 endocrine arms.
But then at San Antonio we saw what happened to those patients who didn’t get suppression of their Ki-67 from that window of exposure to endocrine therapy and went on to get neoadjuvant chemotherapy. What you see was that the pCR [pathologic complete response] rate for those patients was low. We know in general pCR to chemotherapy is unfortunately quite low, but it was only about 5% overall for that patient population. And most patients did have residual cancer burdens that were 2 to 3, so about 50% were RCB-2 [residual cancer burden-moderate] and about a quarter of patients were RCB-3 [residual cancer burden-extensive]. It was again suggesting that chemotherapy, as we know, doesn’t achieve high rates of pCR even in those patients who aren’t responding to preoperative endocrine treatment.
Then we saw lots of other data. One challenge we’ve had with using the genomic assays is that it provides you with some prognostic information, but it’s not individualized based on clinical pathologic features for the patient. If we send a recurrence score, it’s purely looking at gene expression in the tumor. It’s not factoring in how big that tumor was, to help you understand the absolute benefit that that patient could have from treatment.
So I think it was very interesting and helpful, the presentation that we saw regarding how to come up with a calculator, if you will, to help understand your individualized patient’s benefit. Joe Sparano, MD, presented this calculator that you could get online, which I did actually yesterday in clinic, to calculate for my patient. That was interesting because I think we’ve all wanted to merge this genomic information with our traditional clinical pathologic features, and it did seem like it was providing better information in terms of prognosis.
I think as you alluded to, we've got a lot of different pieces of information, which are all confusing when you put them together, about how we really should be making decisions for our patients. But I think it is teaching us that it’s more than just tumor size and nodal status to help us decide who needs chemotherapy versus endocrine treatment.
Sara Hurvitz, MD: That was a great summary of some very complex data there. To add my 2 cents regarding the ADAPT study, I thought it was very elegantly designed. But the realism of testing Ki-67 in the neoadjuvant setting with endocrine therapy I don’t think is as simple as was presented, and I don’t think the uptake would be as good. We’re needing longer-term follow-up from these studies. I think we’re all begging for EFS [event-free survival] data, longer-term data to help us understand, for example, an alternate, whether chemotherapy is actually benefitting the patients who went on to do it, so doing randomized studies to query that.
I too have been using RSClin [recurrence score and clinical-pathological features]. I was involved in the “View From the Trenches” at the end of San Antonio, and Meredith Regan, ScD, put together this very nice table, different clinical scenarios and what the recurrence score would tell us about the patient, and the recurrence score adding in a couple features, and then using RSClin. And where RSClin I think is going to be most helpful is for those patients who have a score of like 26, 27 where we really don’t have good information about whether we should be using chemotherapy, and estimators for benefit from chemotherapy. I too have logged in and started using it a little bit in my patients to show them this pseudo individualized treatment response estimator.
Transcript Edited for Clarity