The Cancer Molecular Imaging (CMI) Program Area is comprised of 29 members, including two """"""""split"""""""" members (shared between two program areas), representing two schools and eight departments at UCLA. The NCI and other peer-reviewed cancer-related support for this Program Area is $5.7M. CMI Program Area members have produced a total number of 411 publications, of which 27% are intra-programmatic, 4 1% are inter-programmatic and 44% were in collaboration with investigators at other institutions. The main goals of the CMI Program Area are to develop new molecular imaging technologies and methodologies to provide new insights into cancer biology, to improve the diagnosis and treatment of cancer, and to translate molecular imaging approaches to the clinic. Four themes support this goal: 1) Instrumentation and analytical tools. Next-generation instrumentation provides high-resolution, sensitive and quantitative noninvasive measurement of molecular biomarkers in vivo at low cost, and facilitates adoption of novel tracers in preclinical and clinical imaging centers. 2) Novel molecular Imaging approaches. CMI members develop novel, translatable PET tracers and reporter gene systems. These probes are employed preclinically to study cancer initiation, progression and metastasis, and to predict and monitor treatment response, laying the groundwork for ciinicai translation. 3) Imaging immune responses. Immune regulation plays a key role in the development and control of cancer, as evidenced by new developments in immunotherapeutics. The CMI Program Area is developing a range of probes for imaging immune responses and monitoring cancer immunotherapy in preclinical models and patients. 4) Translational molecular imaging. CMI investigators are advancing clinical molecular imaging of cancer through first-in-human studies of new radiotracers for deoxycytidine kinase activity (dCK), engineered immunoPET probes for imaging of cell surface markers, novel reporter systems for human use, and finally, new applications of current clinical molecular tracers and modalities (e.g., FDG, FLT, as well as MRI/MRSI to investigate metabolism in gliomas and prostate cancer) to improve patient outcomes.
Cancer molecular imaging allows us to visualize the specific alterations that have occurred in cancerous tissues, in preclinical models, and, importantly, in patients. As more and more targeted therapeutics are brought forward, diagnostic tools, including molecular imaging, are becoming critical to understanding cancer biology in specific individuals, and in selecting and monitoring the most appropriate targeted drugs. The CMI Program Area ensures that the next generation of imaging tools will be available to meet these needs.
|Van Dyk, Kathleen; Bower, Julienne E; Crespi, Catherine M et al. (2018) Cognitive function following breast cancer treatment and associations with concurrent symptoms. NPJ Breast Cancer 4:25|
|Robinett, Ryan A; Guan, Ning; Lux, Anja et al. (2018) Dissecting Fc?R Regulation through a Multivalent Binding Model. Cell Syst 7:41-48.e5|
|Chin, Chee Jia; Li, Suwen; Corselli, Mirko et al. (2018) Transcriptionally and Functionally Distinct Mesenchymal Subpopulations Are Generated from Human Pluripotent Stem Cells. Stem Cell Reports 10:436-446|
|Alban, Tyler J; Alvarado, Alvaro G; Sorensen, Mia D et al. (2018) Global immune fingerprinting in glioblastoma patient peripheral blood reveals immune-suppression signatures associated with prognosis. JCI Insight 3:|
|Yang, Qing; Fung, Wing K; Li, Gang (2018) Sample size determination for jointly testing a cause-specific hazard and the all-cause hazard in the presence of competing risks. Stat Med 37:1389-1401|
|Seo, Jai Woong; Tavaré, Richard; Mahakian, Lisa M et al. (2018) CD8+ T-Cell Density Imaging with 64Cu-Labeled Cys-Diabody Informs Immunotherapy Protocols. Clin Cancer Res 24:4976-4987|
|Ribas, Antoni; Wolchok, Jedd D (2018) Cancer immunotherapy using checkpoint blockade. Science 359:1350-1355|
|Wang, Hong; Chen, Xiaolin; Li, Gang (2018) Survival Forests with R-Squared Splitting Rules. J Comput Biol 25:388-395|
|Yu, Jingyi; Seldin, Marcus M; Fu, Kai et al. (2018) Topological Arrangement of Cardiac Fibroblasts Regulates Cellular Plasticity. Circ Res 123:73-85|
|Hong, Aayoung; Moriceau, Gatien; Sun, Lu et al. (2018) Exploiting Drug Addiction Mechanisms to Select against MAPKi-Resistant Melanoma. Cancer Discov 8:74-93|
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