This is the first project in a Center of Cancer Nanotechnology Excellence (CCNE) application""""""""Emory-GA Tech Nanotechnology Center for Personalized and Predictive Oncology."""""""" Its long-term goal is to develop targeted nanoparticle probes based on semiconductor quantum dots (QDs) and biodegradable nanoparticles for cellular and molecular imaging of cancer. The basic rationale is that nanometer-sized particles have novel optical, electronic, magnetic, and structural properties that are not available from either individual molecules or bulk solids. In this """"""""mesoscopic"""""""" size range of 10-100 nm, nanoparticles also have more surface areas and functional groups that can be linked to multiple diagnostic (e.g., optical, radioisotopic, or magnetic) and therapeutic (e.g., anticancer) agents. Recent research by this CCNE team has developed a new class of multifunctional nanoparticle probes for cancer targeting and imaging in living animals (Me and coworkers, Nature Biotechnology 2004, 22, 969-976). This development has raised exciting possibilities for in-vivo tumor imaging, but the current nanoparticle probes have drawbacks such as limited tissue penetration and potential toxicity concerns. Here we propose to address these problems by using both biomolecular engineering and biomedical nanotechnology. Specifically, we will develop tunable near-infrared-emitting quantum dots in the spectral range of 700 nm to 1700 nm to improve the tissue penetration depth. We will . also develop dual-modality optical and magnetic probes by linking gadolinium (Gd) chelates to near-infrared quantum dots to provide both signal intensity and depth information. Further, we will develop self-assembled polymeric nanoparticles that are biodegradable and nontoxic and could complement the properties of QD imaging probes. Focusing on primary and metastatic breast cancer cells, we will prepare and validate peptides (protein fragments) for selective targeting and imaging of over-expressed cancer biomarkers. In particular, we will use the amino terminal fragment (ATF) of plasminogen to target the urokinase plasminogen activator receptor (uPAR), and a single-chain antibody fragment (ScFv) to target the epidermal growth factor receptor (EGFR), both of which are signatures of human breast cancer and several other cancer types. With an interdisciplinary team of chemists, and engineers, and medical scientists, the proposed research represents a major effort in translating biomedical nanotechnology to tumor molecular imaging and treatment monitoring. Its potential practical outcomes include (a) new quantum dots with improved optical properties, (b) dual-modality nanoparticle probes for correlated optical and magnetic resonance imaging, (c) biodegradable nanoparticles containing both diagnostic and therapeutic agents, and (d) purified tumor-targeting ligands in large quantities, all of which will be shared with other CCNE centers and with the broader scientific community.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
5U54CA119338-05
Application #
7937744
Study Section
Special Emphasis Panel (ZCA1)
Project Start
Project End
2012-08-31
Budget Start
2009-09-01
Budget End
2012-08-31
Support Year
5
Fiscal Year
2009
Total Cost
$274,796
Indirect Cost
Name
Emory University
Department
Type
DUNS #
066469933
City
Atlanta
State
GA
Country
United States
Zip Code
30322
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Kaddi, Chanchala D; Wang, May D (2017) Models for Predicting Stage in Head and Neck Squamous Cell Carcinoma Using Proteomic and Transcriptomic Data. IEEE J Biomed Health Inform 21:246-253
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Tong, Li; Yang, Cheng; Wu, Po-Yen et al. (2016) Evaluating the impact of sequencing error correction for RNA-seq data with ERCC RNA spike-in controls. IEEE EMBS Int Conf Biomed Health Inform 2016:74-77
Phan, John H; Hoffman, Ryan; Kothari, Sonal et al. (2016) Integration of Multi-Modal Biomedical Data to Predict Cancer Grade and Patient Survival. IEEE EMBS Int Conf Biomed Health Inform 2016:577-580
Wu, Po-Yen; Wang, May D (2016) The Selection of Quantification Pipelines for Illumina RNA-seq Data Using a Subsampling Approach. IEEE EMBS Int Conf Biomed Health Inform 2016:78-81
Raharjo, I; Burns, T G; Venugopalan, J et al. (2016) Development of user-friendly and interactive data collection system for cerebral palsy. IEEE EMBS Int Conf Biomed Health Inform 2016:406-409
Kothari, Sonal; Wu, Hang; Tong, Li et al. (2016) Automated Risk Prediction for Esophageal Optical Endomicroscopic Images. IEEE EMBS Int Conf Biomed Health Inform 2016:160-163
Quan, Li; Wu, Jiangxiao; Lane, Lucas A et al. (2016) Enhanced Detection Specificity and Sensitivity of Alzheimer's Disease Using Amyloid-?-Targeted Quantum Dots. Bioconjug Chem 27:809-14
Mishra, Sameer; Kaddi, Chanchala D; Wang, May D (2016) Pan-cancer analysis for studying cancer stage using protein and gene expression data. Conf Proc IEEE Eng Med Biol Soc 2016:2440-2443

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