Project 1: Imaging biomarkers
We have been developing image-based biomarkers to improve prognostication and prediction of therapy response in cancer. Our approach incorporates quantitative radiomic profiling of the tumor and intratumoral subregions, as well as surrounding parenchyma. We are also exploring the biological basis of clinically relevant imaging phenotypes by correlating with molecular data. Importantly, we have shown that the newly identified imaging markers complement established biomarkers and their integration further improves prediction accuracy. We have been conducting imaging biomarker studies on breast, lung, brain, head and neck cancers, using a variety of modalities including PET, CT, and MRI (Cui et al. Radiology, 2016, Wu et al. Radiology, 2016, Wu et al. Radiology, 2017, Wu et al. Clin Cancer Res, 2017, Wu et al. Radiology, 2018, Wu et al. J Nucl Med, 2019).
Project 2: Molecular biomarkers
In addition to imaging, we are also developing molecular biomarkers to predict therapy response and prognosis in cancer. We developed an individualized immune-based gene expression signature that predicts survival of patients with non-small cell lung cancer and validated it in over 2,000 patients (Li et al. JAMA Oncology, 2017). We integrated radiosensitivity and immune gene expression signatures to predict which patients are most likely to benefit from radiotherapy in breast cancer (Cui et al. Clin Cancer Res, 2018). Recently we identified 6 immune subtypes of squamous cell carcinoma of esophagus, head and neck, lung, and cervix that are associated with distinct molecular characteristics and clinical outcomes (Li et al. Clin Cancer Res, 2019). In ongoing work, we are developing biomarkers for early cancer detection, prognostication, and prediction through analyses of the genetic and epigenetic profiles.
Project 3: MRI-based radiation treatment planning
MRI has important advantages over the current gold standard – CT for radiation treatment planning, including improved target delineation. However, the lack of electron density information in MRI has been a major technical hurdle for its clinical adoption. We developed a unifying Bayesian approach that combines both geometry and intensity information for more reliable electron density mapping using MRI (Gudur et al. PMB, 2014). In ongoing work we are further improving the accuracy and robustness of this new technique.
Our lab is currently being supported by multiple R01 grants from the National Cancer Institute (R01 CA193730, R01 CA222512, R01 CA233578) at the NIH. My research was also supported by an NIH Pathway to Independence Award (K99/R00 CA166186) from 2012 to 2017.