Breast Cancer Index: Prospective Evaluation Insights from Kai Treuner PhD
By Kai Treuner, PhD
So the breast cancer index is a test that’s been developed for the extended endocrine decision making. So in particularly women that are hormone receptor positive that usually get 5 years of endocrine therapy at the 5 year point they have to face the question, is extension of that endocrine therapy beneficial for me or not? So that’s the answer that the BCI provides the breast cancer index we are providing a prognostic answer. So what is the risk of recurrence as well as the prediction of extended benefit. And so that is really helpful because many physicians can use the test to help the patients determine if 5 years of additional therapy to 10 years is actually benefiting the patient or it’s not.
What is the standard of care in this disease state and why you chose to pursue this trial?
Breast cancer index is a endocrine response genomic biomarker assay, and so we were always interested in additional endocrine therapies, and particularly for premenopause of women that is ovarian function suppression. So we had a longstanding interest to understand better premenopausal women decision making. And so we were looking at a trial that was available to us through the IBCSG, which is called the suppression of ovarian function trial. And it’s one of the landmark trials in the premenopausal space. And we were able to assess all of the specimens from that trial and perform the BCI testing. And this is the new data that was presented at the San Antonio Conference.
Can you please tell us about the trial design and why it’s set up this way?
The trial design of the soft trial looked at who benefits from the addition of ovarian suppression to standard endocrine therapy. So the standard of care is tamoxifen for 5 years for these women, and so the trial randomized women over 3000 women with early stage hormone receptor positive breast cancer to three arms. So one of them was. Standard of care Tamoxifen compared to Tamoxifen with ovarian suppression, and the last arm was Xin with ovarian suppression. And so what the trial has shown now, we have about 12 year of median follow up now consistently that the best outcome was seen in patients with XMS in OFS and slightly improved outcome with Tamoxifen OFS. So there’s clearly a benefit of ovarian suppression in these women and. That is the parent SOFT trial that we looked at to run the samples and perform the BCI testing because we were interested if BCI can predict who will benefit from OFS and who doesn’t, and clearly the results were positive and we were very pleased with that we were able to show that BCI is indeed able to predict OFS benefit and it’s very consistent with the parent SOFT trial where we see the best outcomes really in patients on the XE in OFS arm compared to Tamoxifen alone. So if you look at the entire population, there was about a 7% benefit for the XE in OFS compared to Tamoxifen alone. And when you now overlay the biomarker, we clearly see a separation in two groups, one of them that benefits and the other group does not benefit. And so that is very interesting to us because it shows the predictive power of this biomarker. And in addition, one of the most rigorous statistical tests is an interaction test. We saw that there was a treatment to biomarker interaction, which was statistically significant. And so we are very confident in the data. Importantly, we looked at all the subsets. So data that was presented at the conference also included the chemotherapy cohort versus no chemotherapy. We looked at young and older age, we used the 40 year old as a cutoff. And we also looked at nodal status, and in all of these subsets, we see the same answer. The benefit was always in one of the groups completely confined to that one group, and the other group did not benefit. Now, to our surprise, what we did not anticipate we would have the benefit in the BCI (H/I) of a low group. And so all of our existing evidence to that, was showing that the BCI (H/I) of a high group benefited from extended endocrine therapy. Now, it seems to be that there’s a different tumor biology in premenopause of women, which we find is very interesting, and so it deserves further study. But nevertheless, we consistently see that the benefit is always in the BCI (H/I) of a low group. The most important data is clearly the predictive ability, right? And as I already alluded to there was about 7% benefit for XM sn OFS compared to Tamoxifen alone. If you overlay the biomarker, it doubles, basically the benefit, and that is the BCI (H/I) low group. So they had about 12% benefit, and clearly in the other there was about 40% of patients they had no benefit of it all. So the important implications of that finding is if this is confirmed in another study, Is that you could use the BCI to inform decision making for OFS and really segregate the women that will benefit versus others that will not benefit. And the important aspect of that is really there’s a lot of toxicities potentially with ovarian suppression. So it’s a very significant treatment decision that they need to make. So it’s clearly the goal would be to avoid. Under treatment and overtreatment and sealy, that’s where genomic biomarker can really add the value. If we find this data gets confirmed in additional clinical trials.
What are the most common questions you get from your colleagues about the study?
So the most common question in is clearly why we think that the results are different than what we had hypothesized. And so I think, this is from a scientific point of view, very interesting, and it clearly deserves further study. We do believe that there’s a difference in the tumor biology between a premenopausal and a postmenopausal tumor. So therefore, we are planning to do additional investigations to really find out what those differences are and why it is actually seen in the other group than we expected. But regardless of that the data is so consistent in all the subsets that we feel quite confident that this is a predictive potential that could be really helpful for physicians in the future and patients to make this decision together. Will I benefit from ovarian suppression or do I not need ovarian suppression?
10 Key Takeaways from the parent SOFT Trial
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The SOFT trial was designed to investigate the effectiveness of ovarian function suppression (OFS) and tamoxifen, compared with tamoxifen alone, in reducing the risk of breast cancer recurrence in premenopausal women with hormone receptor-positive early-stage breast cancer.
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The SOFT trial enrolled 3,066 patients across 22 countries between 2003 and 2011.
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The primary endpoint of the SOFT trial was disease-free survival (DFS), which measures the length of time a patient lives without recurrence, new primary breast cancer, or death from any cause.
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The results of the SOFT trial showed that adding OFS to tamoxifen did not significantly improve DFS compared with tamoxifen alone.
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A subanalysis of the SOFT trial found that premenopausal women at high risk of recurrence may benefit from the addition of ovarian suppression.
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The SOFT trial also evaluated the effectiveness of aromatase inhibitors (AIs) compared with tamoxifen in reducing the risk of recurrence.
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The results of the SOFT trial showed that AIs did not significantly improve DFS compared with tamoxifen in premenopausal women.
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However, a subanalysis of the SOFT trial found that premenopausal women with a high risk of recurrence may benefit from switching to an AI after 2-3 years of tamoxifen therapy.
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The SOFT trial results suggest that individualized treatment plans may be necessary for premenopausal women with hormone receptor-positive early-stage breast cancer, based on their risk of recurrence.
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The SOFT trial provides valuable insights into the effectiveness of different hormonal therapies in premenopausal women with early-stage breast cancer, which can help inform clinical practice and improve patient outcomes.
What were the primary end points of this trial and where they met?
So the primary endpoint was a predictive analysis. So that is the breast cancer free interval, which looks at events of local, regional, distant, and contra later. Recurrence. So that was a endpoint that was also used in the parent SOFT trial, and those endpoints were met because we were showing, as I already alluded to, that the BCI (H/I) is predictive.
What are the key takeaways from this research and data?
So we are very excited about the new data for the Breast Cancer index. It clearly shows the power of the biomarker, and we are clearly looking forward to potentially getting confirmatory results that, ultimately, hopefully it ends up in a additional indicated use for this diagnostic test and really help inform the decision for variant suppression. So the data is ongoing, the analysis. So we hope to have this data out in the not to distant future.
Kai Treuner, PhD – About The Author, Credentials, and Affiliations
Kai Treuner, PhD, is a highly accomplished scientist and researcher who currently serves as the Senior Vice President and General Manager of the Breast and Skeletal Health Solutions division at Hologic, a leading medical technology company focused on women’s health.
Dr. Treuner earned his PhD in Physics from the Technical University of Munich in Germany, where he focused his research on X-ray and particle physics. Then he started working at Siemens Healthcare, where he helped make new imaging technologies for medical uses.
In 2006, Dr. Treuner joined Hologic as the Vice President of Research and Development for the company’s Breast Health division. In this job, he was in charge of making new technologies for detecting, diagnosing, and treating breast cancer, such as the groundbreaking 3D mammography system called Tomosynthesis.
Dr. Treuner’s contributions to the field of medical imaging have earned him many awards and patents over the course of his career. He has also published numerous scientific papers and presented at numerous international conferences.
As the head of Hologic’s Breast and Skeletal Health Solutions division, Dr. Treuner is still in charge of coming up with new ways to find and treat breast cancer and other health problems that affect women. His work has had a big effect on the field of medical imaging, and he is seen as one of the most knowledgeable people in the field.