Daniela Matei - US grants

DESCRIPTION (provided by applicant): This project will employ a non-traditional imaging technology called tissue-dynamics imaging (TDI) to measure the sensitivity of ovarian tumors to chemotherapy and biological treatments. Tissue-dynamics imaging is the first imaging technology to use intracellular motion as an internal vital-signs monitor of cancer tissue. TDI uses digital holography with low-coherence illumination to construct 3D holograms up to a millimeter inside tissue. Dynamic imaging detects diverse cellular motions as endogenous image contrast in a functional imaging approach. Three-dimensional solid tumor models, such as tumor exgrafts and multicellular tumor spheroids, are ideal in vitro models to study complex three-dimensional tumor microenvironments, tumor heterogeneity, and multicellular drug resistance. Tissue dynamics imaging provides the required depth capability, the sensitivity to cellular motions, and the signatures of different dynamical cellular functions to provide quantitative measurements of cellular functioning and proliferation. In the proposed work, tissue dynamics imaging will be used to generate the first drug-response phenotypic profiles in cancer biology applied to tumor exgrafts. The goal is to design a reproducible and sensitive predictive marker to therapy. Current approaches that measure chemo-resistance are cumbersome and not predictive of biological and clinical response. By bridging between the fields of cancer biology and coherent optical physics, we propose that by exploiting the intracellular dynamical properties of ovarian tumors or metastatic implants ex-vivo, this new technology can be adapted to overcome a problem of high clinical relevance for women with ovarian cancer. A commercial partner, Animated Dynamics LLC, will receive technology transfer and construct the first clinic-based TDI system.

Cancer is a deadly disease and costs us half a million American lives every year. Among many types of cancers, ovarian cancer is a particularly severe disease because it usually does not present many symptoms and when it does, it is usually too late. Ovarian cancer, like many other cancers, can theoretically be detected by sensing certain molecules in the blood sample of a patient. There are a number of such 'molecular markers' that could be measured. However, most current platforms are not sensitive enough to detect most of these markers in blood. Those that are sensitive are highly complicated, unreliable and expensive. This particular study aims to develop biomolecular detector that is very sensitive, low cost and capable to performing multiple tests at the same time very quickly. The prosed platform will have the capability of analyzing how a cancer patient responds to therapy using a minimally invasive blood test. Since these tests are relatively simple and cheap as compared to computed tomography scans and biopsies, the tests can be performed more frequently and help clinicians quickly see how the patient is responding to therapy and if necessary, increase the dosage, modify or stop the therapy. In addition, the platform paves the way to early diagnosis of ovarian cancer and can be adapted to many other cancers. The proposed study also includes educational activities that will teach high school students about diffraction and optical phenomenon that enables the operation of the proposed platform.

The proposed platform integrates three elements: a) immunomagnetic bead-based separation that concentrates small number of molecules onto a functionalized biochip surface by capturing rare molecules from large sample volumes, b) optical diffractometry to detect signals generated by self-assembled bead gratings, and c) high-speed spinning disk interferometry that interrogates multiple chips for fast and highly sensitive multiplexing. The study involves building the system, optimizing its performance via simulations and experimentation, and testing with three molecular markers of the ovarian cancer (vascular endothelial growth factor, platelet-derived growth factor, basic-fibroblast growth factor) that are generally difficult to detect using today's technology and hence have not been extensively studied in the context of ovarian cancer. The proposed system will detect these markers from the blood samples of ovarian cancer patients at sub-picomolar concentrations and most importantly, detect all of them at the same time. Unlike most current biosensing platforms that usually have one main strength but a lot of shortcomings, the proposed biomolecular detector system will have the capability of high throughput, high sensitivity, robustness, archivability of the detection chips for future analysis, without the need for fluorescence or microscopy.