Neoadjuvant chemotherapy (NAC), which is treatment given before surgery, has become a standard-of-care for breast cancer patients diagnosed with locally advanced disease. NAC offers a unique opportunity for real- time monitoring of tumor response and evaluation of drug efficacy. Patients who achieve pathologic complete response (pCR) have an excellent outcome. Thus, the challenge of NAC is to bring each patient to pCR; and, among non-responders, to identify those with a high probability of recurring for additional therapy in the adjuvant setting. Biomarkers that accurately predict NAC response and metastatic recurrence are key to achieving these objectives. We hypothesize that a multimodal approach for monitoring of tumor burden during NAC?i.e., by magnetic resonance imaging (MRI)-based functional tumor volume (FTV) and liquid biopsy-based circulating tumor DNA (ctDNA) analyses?can yield robust and accurate predictors of response to NAC and metastatic recurrence; and in turn, aid in therapeutic decisions regarding escalation or de-escalation of treatment to improve patient outcomes. Here, we propose a correlative study to the neoadjuvant I-SPY 2 TRIAL, a multicenter, adaptive randomization phase II trial that evaluates the efficacy of novel therapies in combination with standard NAC. Integrated within I-SPY 2, is an ongoing study that evaluates MRI FTV as predictor of response and outcome, and an infrastructure for discovery and validation of companion diagnostic markers, including ctDNA. The proposed study aims to: (1) perform serial ctDNA profiling in patients receiving NAC; (2) combine serial ctDNA profiles with available FTV data to develop breast cancer subtype-specific predictors of pCR, and (3) build prognostic models that combine ctDNA and FTV information to improve on the predictive performance of residual cancer burden (RCB) assessed at surgery. The deliverables of this proposed study include: (1) serial ctDNA profiles in a large cohort of early breast cancer patients; (2) a prediction tool that will calculate the probability of pCR (or residual cancer burden, RCB 0) at an early time point during treatment, and (3) a prognostic tool that will provide accurate risk assessment for early metastatic recurrence in patients who have residual disease after NAC (non-pCR or RCB 1/2/3). Our ultimate goal is to use the pCR prediction tool in the clinical trial setting to identify good responders who may be eligible for early surgical treatment to reduce exposure to toxicities from unnecessary additional therapies; and poor responders who may benefit from a switch in therapy to increase the likelihood of achieving a pCR. Furthermore, we envision that the prognostic tool developed here will help guide treatment choices in the adjuvant trial setting by providing aggressive adjuvant therapies to patients who are at high-risk of early metastatic recurrence, while de-escalating or forgoing further treatment for those who were potentially cured by NAC and surgical treatment and, therefore, not likely to recur.

Public Health Relevance

Pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) is strongly associated with improved outcomes, thus the major challenges of NAC are (1) to bring each patient to pCR; and (2) to identify those who are at risk of metastatic recurrence in patients who do not achieve pCR. To this end, biomarkers for prediction of treatment response and accurate estimation of patients? risk of early metastatic recurrence are needed to guide escalation or de-escalation of treatment in the neoadjuvant and adjuvant settings. In this proposed study, our overarching goal is to develop strategies for combining liquid biopsy (i.e., circulating tumor DNA) and MRI-based (i.e., functional tumor volume) measures of tumor burden to build robust predictors of response and metastatic recurrence, with the ultimate goal of improving outcomes in high-risk early breast cancer patients receiving NAC.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
Project #
Application #
Study Section
Clinical Translational Imaging Science Study Section (CTIS)
Program Officer
Zhang, Huiming
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of California San Francisco
Schools of Medicine
San Francisco
United States
Zip Code