Predicting Early and Late Recurrence to Improve Care

Investigator: Laura J. Esserman, MD
Sponsor: California Breast Cancer Research Program

Location(s): United States


Breast cancer is a heterogeneous disease and we are at a point where the many emerging genomic and clinical predictors can transform our approach to care and to selecting patients for clinical trials. The translational goal of our project is to validate the tools we need to build a prototype “personalized risk tool” to predict breast cancer’s early recurrence, late recurrence, and no recurrence, in both the absence and presence of adjuvant systemic therapy. Our current predictors, such as commercially available prognostic gene signatures (e.g., Mammaprint, Oncotype DX) fail to predict early metastases from hormone-independent (ER/PR negative) breast cancers, late metastases from hormone-dependent (ER or PR positive) breast cancers, or resistance to treatments.

In collaboration with our colleagues at the Karolinska Institute in Sweden, we will study a large group (>1300) of breast cancer cases with >20 years clinical follow-up. The goal is to generate a validated set of clinical and molecular predictors to determine the overall likelihood and timing of recurrence, and specify the order in which they should be used to predict the likely benefit from systemic therapy. In addition, we will analyze recurrence despite systemic intervention and resistance pathways; thus offering developmental opportunities for more effective systemic breast cancer therapies. We hope to validate our previously reported “recursive partitioning model” using the biomarkers assayed in this study. The objective is to generate novel prognostic classifications of breast cancer using genomic signatures and clinical variables.

The long-term benefit will be to test a prototype personalized risk predictor and decision tool based on our findings. For the first time we can evaluate the prognostic impact of tumor heterogeneity with respect to specific breast cancer subtypes, and we will employ our powerful “risk partitioning” approach to optimize the choice of gene predictors and their temporal impact on patient outcome. This will lead to a new paradigm for treating breast cancer by providing women with an understanding of their future risk of recurrence at the time of diagnosis, how their recurrence likelihood changes over time, and how much risk-reducing benefit they can expect from their choice of systemic treatment given detailed knowledge about their tumor biology. In conclusion, with this grant to study the Swedish breast cancer cohort we are poised to dramatically accelerate validation and adoption through the ATHENA Breast Health Network , a combined effort across the five University of California medial campuses, where tumor profiling is being conducted using similar approaches.