HiPerDART Targets and Objectives In applications related to clinical diagnostics, DNA microarrays have enormous potential for multiplex analysis, yet issues related to technical challenges (low reproducibility, poor signal-to-noise, and lengthy workflows) have limited their practicality and slowed regulatory approval. We believe that the SuNS technology offers significant advantages in microarray manufacturing, such that these challenges may be overcome. By making a large up-front investment in the template and then replicating it many times, we can effectively dilute our production costs while maintaining high performance features in the array. From a manufacturing standpoint, we will address three fundamental features of microarrays, which have so far limited their utility. Probe Density: Signal-to-noise and assay sensitivity, key features of microarray utility, are both determined by probe density within a feature. The higher the target-capture capacity of the feature per unit area is, the better the performance of the microarray. There is a fundamental limit, however, to the oligo density which can be achieved on a surface, due to the radii of gyration of the DNA strands. The approach here proposed is to maximize probe density by concatamerizing oligos in the “z-direction” with respect to the array surface. The HiPerDART consortium envisions accomplishing this by utilizing rolling-circle amplification (RCA) technology. Extension from a common primer sequence will take place within the micro-wells, each with a different circularized DNA molecule. The process is illustrated below: FIGURE 5: EXTENSION FROM A COMMON PRIMER SEQUENCE Utilizing the common primer sequence, the resulting long-mer strands are themselves enzymatically replicated prior to being printed onto a replica surface. The HiPerDART consortium believes that by increasing probe density using RCA technology, we can dramatically increase signal-to-noise and assay sensitivity to an unprecedented degree. HydroSuNS: A compelling feature of the SuNS technology is the flexibility of substrate material onto which DNA molecules may be printed. A good substrate shall have properties such that it simultaneously (a) provides ideal conditions for SuNS printing, and (b) optimizes microarray assay performance. Scientists in our consortium recently have discovered that indeed such a surface exists, and that it is the surface of a hydrogel polymer. Hydrogel as ideal substrate for SuNS printing: There are essentially two technical challenges to consider with any SuNS approach. The first challenge is to achieve nanometric conformal contact between two surfaces over a macroscopic area, while the second is to minimize damage to the template DNA which may result from repeated cycles of surface-to-surface contact. Although literature exists for related systems4, 5, Molecular Stamping is the first to overcome these two challenges of SuNS by printing onto the surface of a hydrogel. FIGURE 6: TEMPLATE AND REPLICA ARRAY Array Hybridization Conventional microscope slides (75 x 25 mm²) are most commonly used as microarray substrates. The following methods for hybridizing microarrays are commonly used today: 1. The coverslip method is the standard experimental procedure. A thin glass or plastic coverslip is placed on top of the array slide and the sample solution is drawn in the gap between cover slip and slide by capillary action. Sample volumes are limited to a few microliters per slide. 2. Gasket-type hybridization chambers are formed by a cover and a gasket which is usually attached to the slide with an adhesive. Hybridization in these gasket-type chambers may take place with or without agitation. Typically those hybridization chambers hold 50-500 µL of sample. 3. If volumes of more than a few mL are used, the entire slide is placed into containers. The containers may be agitated or left static during the hybridization reaction. Within the HiPerDART project, a microarray cartridge / microfluidics hybridization device will be developed which will enable very low hybridization volumes, while simultaneously reducing the time required for hybridization and improving the reproducibility of assay results. Our preliminary cartridge concept is depicted below. FIGURE 7: MICROARRAY CARTRIDGE In close collaboration with the SME partner thinXXS, the HiPerDART consortium will utilize the latest microfluidic device prototyping technology to develop the microinjection moulding required for this project. One key element of the design will be to incorporate one-way channels so that each array is guaranteed to remain isolated from the others during the assay implementation. Molecular profiling in colorectal cancer Colorectal cancer is the third most common cancer worldwide after lung and breast. Cumulative risk in European countries is near 6%, both in men and women. Five year survival estimates range from 90% in Stage I to < 5% in stage IV, and is less accurate (45-80%) in stages II & III (Ries et al, 2005; Howlander et al, 2006). Adjuvant chemotherapy is standard for stage III (Coleman et al, 2001; van Cutsem et al, 2005) but not for stage II (Benson et al, 2004; Sobrero and Koehne, 2006; Andre et al, 2006), where the challenge is to identify the 25% of patients not cured by surgery alone. Clinical and pathological risk factors (T4, G3, number of assessed lymph nodes, perforation or vascular invasion) have been identified but lack standardisation (Ries et al, 2005; Coleman et al, 2001). We need better prognostic biomarkers that would allow selecting high risk stage II patients for adjuvant therapy and possibly to spare it in the patients with low risk stage III. Despite the large amount of literature on molecular biomarker candidates, none is routinely used in the clinic due to lack of proper standardisation and validation (Anwar et al, 2004). Cumulative genetic alterations can lead to abnormal transcription of a large number of genes, which can be measured by mRNA expression levels. The MAQC1 and MAQC2 initiatives (coordinated by FDA) and EMERALD-QC (EU project) aim at deriving consensus protocols to warrant predictive and stable genomic signatures from gene expression and other genomic levels. Inherent to this approach is the hypothesis that every tumor contains informative gene expression signatures that, at the time of diagnosis, can direct the biologic behaviour over time. Recently, several microarray gene expression profiles have shown promise in predicting the prognosis of stage II and III colon cancer (Eschrich et al, 2005; Barrier et al, 2006; Lin et al, 2007) but need to be validated before being routinely used, which is jeopardized by the current cost of assessing the large number of genes in these profiles. In this project we aim to develop a prognostic predictor that has clinical usefulness in the management of colorectal cancer patients. We will use already existing data on genomic expression analyzed with Affymetrix microarrays in 150 tumors from the MECC study (Israel) and 26 tumors from the Bellvitge study (Barcelona). The patients from these studies are well characterized clinically and have been followed up for more that 5 years. The information of genomic expression in tumors will be complemented with two additional sources of data: genomic expression and germline polymorphisms in histologically normal colonic mucosa resected from patients with colorectal cancer and copy number variation. These data will be measured in a limited sample, but large enough to ensure power to detect the most relevant signals. Genomic expression of normal mucosa and inherited genomic variation has been shown to have relevant prognostic value prior to the development of the tumor. In this study we aim to use a whole-genome approach, together with appropriate data mining techniques, to select the most informative prognostic markers. Validation of classifiers and signatures Reliable and robust predictive models are essential to realize the promises of personalized medicine. Valid biomarkers derived from high-throughput technologies need to undergo a quality control process that can address the control of variability along all steps of the processing pipeline. A first consensus on intra- and inter-lab reproducibility and comparability between alternative platforms was reached by the FDA-led initiative Microarray Quality Control (MAQC). Through a consortium of 60 organizations (including 32 data analysis teams), the FDA is now leading an international effort (MAQC-II) for defining best practices on the validation of classifiers and signatures based on gene expression data from microarrays (edkb.fda.gov/MAQC). Beside the need of controlling selection bias (Ambroise et 2002) through carefully designed Data Analysis Protocols (DAP), a specific concern regards the stability of derived classifiers and signatures. The lists of biomarkers can be drastically different for changes along the process (generation, signal processing, normalization, etc), with slight perturbation of the training dataset producing different markers (Davis et al 2006, Simon 2006). In the HiPerDART project, we will evaluate and control variability of the new technology by adopting BioDCV, a state-of-the-art microarray profiling platform that implements a complete validation DAP (Furlanello et al 2003, 2005) and a rich family of machine learning algorithms for feature ranking. The platform can develop predictive signatures on genome-wide chips (Riccadonna et al. 2007), and it has been recently extended with a set of algebraic indicators of stability for ranked gene lists (Jurman et al 2008) – currently tested in the MAQC-II initiative for potential inclusion as a guideline. We will compare signatures produced with the HiPerDART or alternative platforms, and within the same platform, for different technical implementation of the upstream data production and preprocessing phases. Quality control and best practices defined from the EMERALD consortium will also be considered and implemented.