Convergence of biomarkers, bioinformatics and nanotechnology for individualized cancer treatment

Abstract

Recent advances in biomarker discovery, biocomputing and nanotechnology have raised new opportunities in the emerging fields of personalized medicine (in which disease detection, diagnosis and therapy are tailored to each individual's molecular profile) and predictive medicine (in which genetic and molecular information is used to predict disease development, progression and clinical outcome). Here, we discuss advanced biocomputing tools for cancer biomarker discovery and multiplexed nanoparticle probes for cancer biomarker profiling, in addition to the prospects for and challenges involved in correlating biomolecular signatures with clinical outcome. This bio-nano-info convergence holds great promise for molecular diagnosis and individualized therapy of cancer and other human diseases.

Figure 1

Schematic diagram of the ‘bio-nano-info’ convergence, which uses bioconjugated nanoparticles such as multicolor quantum dots (QDs) to analyzemulitple biomakers for molecular imaging, diagnosis, and targeted therapy.

Figure 2

Flow diagram of biocomputing tools for discovery and validation of molecular biomarkers. As discussed in the text, genomic and proteomic data first need to be properly organized and annotated. Before further analyzing the data to identify differentially expressed biomarkers, the original data are evaluated and improved by removing technical artifacts and by combining multiple datasets to increase statistical significance. Candidate biomarkers can be identified using omniBiomarker (73), followed by their clinical validation using for example multiplexed immunostaining or RT-PCR.

Figure 3

Schematic illustration of the use of omniBioMarker for biomarker identification and clinical validation. (A): Shown is a knowledge-guided workflow for identifying differentially expressed genes. This protocol consists of three major steps. First, high-throughput –‘omics’ data are collected from microarray gene expression studies together with previous biological knowledge concerning the disease of interest (1). The biological knowledge is then used to guide the feature selection process and to identify those algorithms that will result in rankings with maximal biological relevance (2). Finally, candidate biomarkers are validated using RT-PCR or multiplexed nanotechnology (3). (B): Shown here are the results of renal cell carcinoma studies with a list of candidate biomarkers as the system output. Using omniBiomarker, several algorithms for feature ranking and selection can be tested simultaneously, yielding a short list of biomarkers that are most strongly correlated with biological function or clinical outcome. omniBiomarker is accessible via http://omnibiomarker.bme.gatech.edu/.

Figure 4

Graphic drawing showing how prior knowledge can be used to guess a shape from a limited number of data points. As information about the geometric shape is introduced (upper), the number of possible solutions shrinks. Eventually the true shape emerges as the only solution (middle). Likewise, the biomarker search space is large due to the variety of available feature selection algorithms, each of which produces a different list of candidate biomarkers (lower). As information about biomarkers is introduced, the number of valid feature selection algorithms decreases, leading to an optimal algorithm that can consistently identify the known biomarkers.