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Combining groundbreaking research and developments in cancer biomarkers, nanotechnology and molecular targeted medicine, a new realm of therapy is possible: personalized and predictive medicine. Developing a method to detect the overexpression of several tumor marker genes simultaneously, knowing that a single cell generally expresses more than one altered gene, should have a high predictive value for identifying cancer cells amidst the normal cellular background. Theoretically, a cancer's unique molecular profile can be used to predict its invasive and metastatic potential, its ability to evade immune surveillance, and its potential response to treatment. Fluorescent probes have been developed to detect the levels of expression of various biomarkers in tumor cells and tissues. Expression of biomarker messenger RNAs (mRNAs) or the presence of a specific mutation in an oncogene in cancer cells can be detected using molecular beacons (MBs) that only emit fluorescent signals after binding to its specific target mRNAs. Antibodies or ligands labeled with fluorophores or fluorescent quantum dots (QDs) have been successfully used to identify specific proteins expressed in cells. Furthermore, multiplex imaging using both MBs and antibodies labeled with a fluorescent probe on the same sample may provide important information correlating the level of mRNA expression and the subsequent level of protein production for a given biomarker. This technology will be useful in research investigating cancer biology, molecular imaging and molecular profiling. With the identification of biomarkers that are related to aggressive tumor types, we may be able to predict within certain patient populations who will develop invasive cancers, and what their prognosis will be given different treatment modalities, ultimately delivering medical care and treatment strategies that are specifically tailored to each individual patient, making personalized and predictive medicine a reality.

Multiplexed Fluorescence Imaging of Tumor Biomarkers in Gene Expression and Protein Levels for Personalized and Predictive Medicine