Related Experiment Video
Updated: Apr 17, 2026
Orthotopic Transplantation of Breast Tumors as Preclinical Models for Breast Cancer
Published on: May 18, 2020
The Neoadjuvant Model Is Still the Future for Drug Development in Breast Cancer
Angela DeMichele1, Douglas Yee2, Donald A Berry3
1University of Pennsylvania, Philadelphia, Pennsylvania.
Abstract:
The many improvements in breast cancer therapy in recent years have so lowered rates of recurrence that it is now difficult or impossible to conduct adequately powered adjuvant clinical trials. Given the many new drugs and potential synergistic combinations, the neoadjuvant approach has been used to test benefit of drug combinations in clinical trials of primary breast cancer. A recent FDA-led meta-analysis showed that pathologic complete response (pCR) predicts disease-free survival (DFS) within patients who have specific breast cancer subtypes. This meta-analysis motivated the FDA's draft guidance for using pCR as a surrogate endpoint in accelerated drug approval. Using pCR as a registration endpoint was challenged at ASCO 2014 Annual Meeting with the presentation of ALTTO, an adjuvant trial in HER2-positive breast cancer that showed a nonsignificant reduction in DFS hazard rate for adding lapatinib, a HER-family tyrosine kinase inhibitor, to trastuzumab and chemotherapy. This conclusion seemed to be inconsistent with the results of NeoALTTO, a neoadjuvant trial that found a statistical improvement in pCR rate for the identical lapatinib-containing regimen. We address differences in the two trials that may account for discordant conclusions. However, we use the FDA meta-analysis to show that there is no discordance at all between the observed pCR difference in NeoALTTO and the observed HR in ALTTO. This underscores the importance of appropriately modeling the two endpoints when designing clinical trials. The I-SPY 2/3 neoadjuvant trials exemplify this approach.
Insights
Neoadjuvant trials using pathologic complete response (pCR) can predict disease-free survival (DFS) in breast cancer. This approach reconciles seemingly discordant results between neoadjuvant and adjuvant trials, highlighting the importance of endpoint modeling.
Area of Science:
- Oncology
- Clinical Trial Design
- Biostatistics
Background:
- Adjuvant clinical trials for breast cancer are challenging due to declining recurrence rates.
- The neoadjuvant approach is increasingly used to evaluate drug combinations in primary breast cancer.
- Pathologic complete response (pCR) is a validated predictor of disease-free survival (DFS) for specific breast cancer subtypes, as shown by an FDA meta-analysis.
Purpose of the Study:
- To address the apparent discordance between neoadjuvant (NeoALTTO) and adjuvant (ALTTO) trials evaluating lapatinib in HER2-positive breast cancer.
- To demonstrate that pCR and DFS are not discordant endpoints when appropriately modeled.
- To underscore the importance of proper endpoint modeling in clinical trial design, using I-SPY 2/3 trials as examples.
Main Methods:
- Comparative analysis of the NeoALTTO and ALTTO trial designs and outcomes.
- Application of an FDA meta-analysis framework to reconcile pCR and hazard ratio (HR) findings.
- Illustrative examples from the I-SPY 2/3 neoadjuvant trials.
Main Results:
- The study reconciles the NeoALTTO and ALTTO trial results by demonstrating no discordance between pCR and DFS when endpoints are appropriately modeled.
- The FDA meta-analysis supports the predictive value of pCR for DFS in specific breast cancer subtypes.
- The findings emphasize the critical role of statistical modeling in interpreting clinical trial results.
Conclusions:
- The neoadjuvant approach with pCR as a surrogate endpoint is a viable strategy for accelerated drug approval in breast cancer.
- Appropriate statistical modeling is crucial for aligning findings from neoadjuvant and adjuvant breast cancer trials.
- The I-SPY 2/3 trials serve as a model for effective neoadjuvant trial design and endpoint analysis.
