# Strategy for combining circulating tumor DNA (ctDNA) and magnetic resonance imaging (MRI) measures of tumor burden for prediction of response and outcome in neoadjuvant-treated early breast cancer > **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2021 · $670,223 ## Abstract ABSTRACT/PROJECT SUMMARY 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 we... ## Key facts - **NIH application ID:** 10100831 - **Project number:** 1R01CA255442-01 - **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO - **Principal Investigator:** Wen Li - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2021 - **Award amount:** $670,223 - **Award type:** 1 - **Project period:** 2020-12-03 → 2025-11-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10100831 ## Citation > US National Institutes of Health, RePORTER application 10100831, Strategy for combining circulating tumor DNA (ctDNA) and magnetic resonance imaging (MRI) measures of tumor burden for prediction of response and outcome in neoadjuvant-treated early breast cancer (1R01CA255442-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10100831. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*