Moffitt Postdoc Receives 2021 Laurence P. Clarke Young Scientist Award in Quantitative Imaging
Dr Wei Mu, a PhD graduate from the University of Chinese Academy of Science in Beijing, has been a postdoctoral fellow at the Moffitt Cancer Center for the past four years.
Her research interests involve the development of machine learning models to analyze multimodal medical images for the early diagnosis of cancer and to aid in the decision of individualized treatment planning. Her dissertation pioneered work in automatic segmentation methods and texture analysis of FDG PET images to improve prognostic models for both cervical and colorectal cancers. She continues her work in PET image analysis at Moffitt Cancer Center by developing additional PET/CT radiomics features through convolution of PET and CT images.
Dr. Mu has received several awards for her work over the last few years. She is the recipient of the Outstanding Student and Graduate Award from her undergraduate school and received several scholarships to the University of Chinese academy of Science. She was one of four finalists in the 2020 WMIS Young Investigator Award and recipient of the Women in Molecular Imaging Network Scholar Award from WMIS. She is an author on over a dozen peer-reviewed articles.
Drs. Bob Gillies and Shari Pilon-Thomas were recently awarded an NIH/NCI R01 for $1,878,475 over five years for a research project titled “Imaging Acidosis and Immune Therapy in PDAC." This is Dr. Pilon-Thomas's first R01. Congratulations!
CO-Is on the project are Drs. Arig Ibrahim Hashim, Barbara Centeno, and Jason Fleming.
Congratulations, Drs. Gillies and Pilon-Thomas!
Radiomics Workshop 2018
The annual Radiomics workshop sponsored by Moffitt will be held Oct. 15-16 at the Hyatt Regency Clearwater Beach, 301 S Gulfview Blvd, Clearwater, FL 33767. More information to be released soon.
Robert J. Gillies and his lab are focused on understanding cancers as complex, heterogeneous and dynamic systems. Along with his long-time collaborator, Robert A. Gatenby, they share a core belief that, due to genomic plasticity and microenvironmental heterogeneity, cancers can only be understood through the lens of Darwinian Evolution. Dr. Gillies is an experimentalist whose work spans molecular, cellular, animal models, and image analytics. He is the Martin Silberger Chair of Cancer Physiology, Director of the Center of Excellence in Cancer Imaging and Technology, and Scientific Director of the Small Animal Imaging Lab, SAIL.
May 2017, Cancer Research cover, from Arig Ibrahim-Hashim, Robert J. Gillies et al. "Defining Cancer Subpopulations by Adaptive Strategies Rather Than Molecular Properties Provides Novel Insights into Intratumoral Evolution"
Gillies Lab at the Moffitt PSOC Site Visit
External beam radiotherapy (XRT) is a widely used cancer treatment, yet responses vary dramatically among patients. These differences are not accounted for in clinical practice, partly due to a lack of sensitive early response biomarkers. In this study, investigators hypothesized that quantitative magnetic resonance imaging (MRI) measures reflecting tumor heterogeneity can provide a sensitive and robust biomarker of early XRT response.
The study's preclinical findings indicate that spatial heterogeneity analysis of T2 MRI can provide a translatable method for early radiotherapy response assessment. We propose that the method may in future be applied for personalization of radiotherapy through adaptive treatment paradigms.
EGFR, or epidermal growth factor receptor, is a common mutation found in non-small cell lung cancer patients, and can be a predictor for treatment. However, the current biopsy-based detection techniques are invasive, and may fail to yield actionable results due to insufficient quantity or quality of the tissue. In this work, we developed a deep learning model to predict EGFR mutation using 18F-FDG PET/CT images since it is widely used and the glucose radiotracer used could be affected by EGFR activation and inflammation. Further, we found that the EGFR deep learning score was positively associated with longer progression free survival in patients treated with tyrosine kinase inhibitors (TKI), and negatively associated with durable clinical benefit and longer progression free survival in patients being treated with immune checkpoint inhibitor (ICI). Therefore, together with our previously developed PD-L1 deep learning prediction model using PET/CT images, we developed a non-invasive treatment decision support to make treatment plan.
Immunotherapy has improved outcomes for patients with non-small cell lung cancer (NSCLC), yet durable clinical benefit (DCB) is experienced in only a fraction of patients. Therefore, we developed an effective and stable radiomics signature by the combination of PET, CT and the fusion Kullback–Leibler Divergence features, which may serve as a predictive biomarker for immunotherapy response. This signature could be used prior to initiation of immunotherapy to identify NSCLC patients most likely to benefit from immunotherapy, and could also be leveraged to improve the non-invasive treatment decision support in the treatment of advanced NSCLC patients.
Research Scientist, Mehdi Damaghi, in Gillies lab was co-lead author a paper recently published in Nature Communications that utilized a systems analysis of intracellular pH vulnerabilities for cancer therapy. The team, which included Dr. Bob Gillies, and John Cleveland, developed a computational methodology that explores how intracellular pH (pHi) can modulate metabolism. Experimental testing of the novel strategy revealed that it is particularly effective against aggressive phenotypes. The study suggests essential roles of pHi in cancer metabolism and provides a conceptual and computational framework for exploring pHi roles in other biomedical domains. Moffitt's Molecular Genomics and Analytical Microscopy cores were also utilized.
Their opinion posits that temporal changes in blood flow are commonly observed in malignant tumors, but the evolutionary causes and consequences are rarely considered. The authors propose that stochastic temporal variations in blood flow and microenvironmental conditions arise from the eco-evolutionary dynamics of tumor angiogenesis in which cancer cells, as individual units of selection, can influence and respond only to local environmental conditions.
Temporal variations in intratumoural blood flow, which occur through the promotion of cancer cell phenotypes that facilitate both metastatic spread and resistance to therapy, may have substantial clinical consequences.
Quantitative Imaging is an indispensable approach to study intratumoral heterogeneity. Dr. Gillies' group develops and applies Image Analytics to histological and radiological images to investigate the spatial relationships of cells and microenvironments in clinical and pro-clinical tumors.
Solid tumors are acidic due to elevated glycolysis combined with poor perfusion. Dr. Gillies has developed methods to image tumor pH, and study the causes and consequence of this acidity. Recent focus has been on developing methods to interfere with tumor acidosis for improving therapy in the clinic.