Extended Endocrine Therapy Decisions: Moving Beyond Clinical Risk to Predict Likelihood of Therapy Benefit Based on Tumor Biology

This sponsored article is made available in partnership with Biotheranostics, Inc., A Hologic Company.

For patients with hormone receptor-positive (HR+), early-stage breast cancer, late distant recurrence remains a concern well beyond initial treatment.^1,2 Nearly half of recurrences occur after year five, and recurrence risk persists across patient subgroups, including those initially considered to have low-risk disease.^3,4 As a result, the five-year mark becomes a pivotal decision point.

Extended endocrine therapy (EET) reduces late recurrence in select patients, but the absolute benefit is modest for most patients.^5-10 Cumulative toxicities, including bone loss, joint stiffness, vasomotor symptoms, and cardiovascular effects, make quality of life and adherence central considerations.^{8,10, 11} Thus, the key question becomes whether extended therapy will meaningfully benefit the individual patient.

Historically, this decision has relied on readily available clinicopathologic factors, along with prognostic tools such as the Clinical Treatment Score post-5 years (CTS5). Yet recurrence risk and therapeutic benefit are not synonymous. In fact, studies have shown that clinical risk factors cannot reliably identify which patients are likely to derive benefit from longer treatment.^{6,8, 11-12} Increasingly, genomic testing can refine these decisions by providing patient-specific insight into recurrence risk and likelihood of benefit from extended endocrine therapy.

Clinical Risk Assessment: Informative but Not Predictive

When a patient approaches the five-year mark, we revisit familiar variables: nodal involvement, tumor size and grade, age, and menopausal status.

These variables remain foundational in breast cancer management and help estimate overall recurrence risk. Yet EET trials demonstrate that the magnitude of benefit differs across patients.^13-17 Higher clinical risk does not consistently translate into greater benefit from prolonged therapy, and lower clinical risk does not eliminate the possibility of late recurrence.^10-13

In my practice, the year-five conversation is often nuanced, and the distinction between estimating risk and predicting benefit becomes central. A patient with positive nodes may appear to be an obvious candidate for longer therapy, while another with node-negative disease may seem to have a lower risk. In this case, estimating recurrence risk represents only part of the decision, and does not determine whether EET will have an impact on reducing that risk. When the question shifts from “How likely is recurrence?” to “Will additional therapy change that likelihood?,” further insight is needed.

Unlike clinicopathological factors and prognostic tools like CTS5, the emergence of predictive biomarkers addresses this gap by evaluating tumor biology directly. The Breast Cancer Index® (BCI)™ Test evaluates gene expression associated with late recurrence risk and likelihood of benefit from EET.^13-17 This genomic test interrogates the estrogen signaling pathway, looking at the ratio of two genes, HOXB13/IL17BR (H/I), which serves as a predictive biomarker for endocrine responsiveness, determining whether a patient is likely to benefit from EET.

A patient may have measurable residual risk. If her tumor biology suggests limited endocrine responsiveness, the incremental benefit of prolonged therapy may be small. Conversely, a patient with less striking clinicopathologic features may harbor tumor biology associated with continued endocrine sensitivity and derive meaningful benefit from extended treatment. This variability underscores the importance of predictive biomarkers in treatment decisions.

Clinical Validation of Predictive Performance

The predictive performance of the BCI Test has been evaluated in five studies involving more than 4,700 patients with early-stage, HR+ breast cancer treated with tamoxifen or aromatase inhibitors. ^13-17 Across these studies, the BCI Test identified patients likely to benefit from EET and those unlikely to derive significant benefit.^13-17

Among patients with clinically low-risk disease (T1N0), the BCI Test identified 25% as having higher risk of late distant recurrence and likely to benefit from extended therapy.¹⁸

Conversely, among patients with one positive node (N1), the BCI Test identified 22% with a limited (2.1%) risk of late distant recurrence,¹⁹ and 69% of those patients were not likely to benefit from extended endocrine therapy.²⁰

Given the ability of predictive biomarkers to go beyond our traditional assumptions based on clinicopathologic factors, in a prospective registry study of >2,800 patients, providers changed EET recommendations for 40% of patients after receiving BCI Test results.²¹ These findings illustrate the shift towards delivering personalized oncology care driven by the power of genomic testing. Leveraging these genomic assays empowers us, as physicians, to make better informed decisions based on the biology of a patient’s tumor and ultimately help avoid potential under- or over-treatment that may have resulted from assumptions based on clinicopathologic features alone.

Guideline Context

National oncology guidelines emphasize individualized decision-making. The Breast Cancer Index Test is the only genomic assay recognized by the NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) and the ASCO® Clinical Practice Guideline as predictive of which patients with early-stage, HR+, HER2-negative breast cancer are likely to benefit from EET.^22,23 Both organizations recognize its utility in node-negative disease and in patients with up to three positive nodes.^22,23 Recent NCCN updates further acknowledge the value of the BCI Test in N1 patients, highlighting the optimized risk estimates for N1 patients when tumor size and grade are incorporated into the prognostic algorithm.^19,24

Adherence and the Impact of Predictive Insight

Adherence is a critical component of extended therapy effectiveness, and the impact of patient preference and treatment-related toxicity on long-term compliance should not be underestimated. When patients are uncertain about the magnitude of benefit, adherence may decline.

Predictive genomic information can help address these uncertainties. In a decision impact study, 82% of patients recommended EET after BCI Testing reported they were more likely to take their medication as prescribed based on their test result.²⁵

As practice patterns evolve, there is growing interest in integrating predictive insights to guide treatment planning. The results can support shared decision-making grounded in tumor biology and aligned with patient values and may help ensure greater adherence.

Conclusion

Clinicopathologic factors remain central to early breast cancer management. However, at year five, the question is not simply who is at risk, but who is likely to benefit from additional endocrine therapy.

Nodal status, tumor size, and grade can help inform prognosis. Yet they do not define endocrine responsiveness and may misclassify patient risk.

The Breast Cancer Index Test provides validated predictive insight that helps identify patients who are likely to benefit from extended endocrine therapy, and those who are not, supporting more precise, biology-informed survivorship care.

More broadly, the continued evolution of genomic tools across oncology reflects an important shift in how we approach cancer care, moving beyond population-based risk models toward more individualized, biology-driven decision-making. As a practicing oncologist, I am encouraged by how these tools help provide clearer, more personalized guidance to patients. Ongoing research will continue to refine our ability to match the right duration of therapy to the right patient at the right time.

*Sami Diab, MD is the Associate Medical Director for the Breast Cancer Index Test. Statements reflect the authors' own personal views and interpretations.

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