The goal of this research is to develop and validate prognostic imaging biomarkers for breast cancer. A major challenge in the management of breast cancer is distinguishing patients with indolent disease from those with aggressive lethal disease at diagnosis. Currently, there are no reliable biomarkers to distinguish these groups on an individual level. Consequently, all patients with breast cancer receive adjuvant therapies, but not all benefit equally. This one-size-fits-all approach causes overtreatment, leading to morbidity and mortality. The need for reliable biomarkers is highlighted by the randomized TAILORx trial, which identified a small group of low-risk breast cancer patients who had very low rates of recurrence without chemotherapy, based on the 21-gene Oncotype Dx assay. Unfortunately, a majority (67%) of patients fell in the intermediate-risk range according to the genomic assay, and uncertainty still remains regarding the need for chemotherapy among these patients. Clearly, better biomarkers are needed to improve prognostication and patient stratification in breast cancer. Built on extensive preliminary data, we hypothesize that imaging characteristics reflect underlying tumor pathophysiology, and that image-based phenotyping of both tumor and parenchyma will provide much improved accuracy for recurrence prediction. To test this hypothesis, we propose to: (1) develop and improve methods to explicitly quantify multiregional MRI phenotypes including those of intratumoral subregion and parenchyma, and systematically assess their reproducibility; (2) develop a prognostic imaging signature using a large retrospective cohort of >1000 patients curated by the Stanford Oncoshare Project, and validate it in the prospective multi-center I-SPY 1 cohort; (3) construct a radiogenomic signature to perform additional testing of its prognostic value in 13 public gene expression cohorts of >5000 breast cancer patients. To further improve prognostication, we will build a multifactorial model that integrates imaging with clinical and genomic markers. This research will advance the quantitative imaging field by moving beyond traditional gross-tumor features and incorporating additional parenchymal and intratumoral imaging characteristics. If successful, it will provide much needed, rigorously validated imaging biomarkers for breast cancer, which can be further tested for clinical utility in prospective trials. Ultimately, such biomarkers can be used to stratify patients and guide individualized therapy, by allowing clinicians to avoid overtreatment of indolent disease and intensify treatment in women with aggressive disease.

Public Health Relevance

Breast cancer causes a major disease burden in the United States. We propose to discover and validate prognostic imaging biomarkers for breast cancer. This research will enable more reliable prediction of recurrence risk of individual breast cancer based on diagnostic MRI scans. Moreover, we will combine imaging biomarkers with clinicopathological factors and genomic assay to further improve prognostication. If validated in prospective clinical trials, the new biomarkers could be used to stratify patients and guide management of breast cancer.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
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Special Emphasis Panel (ZRG1)
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Wang, Chiayeng
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Stanford University
Schools of Medicine
United States
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Wu, Jia; Cao, Guohong; Sun, Xiaoli et al. (2018) Intratumoral Spatial Heterogeneity at Perfusion MR Imaging Predicts Recurrence-free Survival in Locally Advanced Breast Cancer Treated with Neoadjuvant Chemotherapy. Radiology 288:26-35
Wu, Jia; Tha, Khin Khin; Xing, Lei et al. (2018) Radiomics and radiogenomics for precision radiotherapy. J Radiat Res 59:i25-i31
Wu, Jia; Li, Xuejie; Teng, Xiaodong et al. (2018) Magnetic resonance imaging and molecular features associated with tumor-infiltrating lymphocytes in breast cancer. Breast Cancer Res 20:101
Cui, Yi; Li, Bailiang; Pollom, Erqi L et al. (2018) Integrating Radiosensitivity and Immune Gene Signatures for Predicting Benefit of Radiotherapy in Breast Cancer. Clin Cancer Res 24:4754-4762