Colorectal carcinoma is a major health issue in developed western countries. Currently, clinical staging is the most commonly used predictor of overall prognosis but remains relatively poor in predicting recurrence. Specific genomic instability events such as allelic imbalances in chromosomes have potential utility as prognostic biomarkers. While there is an urgent need for better biomarkers that utilize genomic instability events, current technologies to measure this phenomenon have significant limitations. Better technologies are required with improved reproducibility and prognostic potential in clinical studies. We describe the molecular inversion probe (MIP) technology and its application in genome-wide detection of gene copy number changes and allelic imbalances (1). For genomics-based biomarker discovery, molecular inversion probes (MIPs) have many advantages over current high-throughput technologies, including high reproducibility, the ability to interrogate gene copy number at a large number of designated, unrestricted positions across the entire genome and simultaneous genotyping of SNP alleles quantitatively. We propose to design a large number (2,000) molecular inversion probes set that will cover all human chromosomes and 320 known major cancer-related genes important in colorectal tumorigenesis and other cancers. To validate the application of MIPs for quantifying gene copy number and allelic imbalance analysis, we will analyze a panel of colorectal cancer cell lines, normal cell lines and other tumor cell lines containing known genomic instability events. We have implemented the bioinformatics necessary to design the probes, will optimize the parameters of the MIPs assay and refine the bioinformatics for data analysis. The cancer MIP probes will be used to analyze clinical colorectal cancer sample and identify particular gene copy number or allelic imbalance events that can distinguish different types of genomic instability.
| Ji, Hanlee; Welch, Katrina (2009) Molecular inversion probe assay for allelic quantitation. Methods Mol Biol 556:67-87 |
| Lin, Guixian; He, Xuming; Ji, Hanlee et al. (2006) Reproducibility Probability Score--incorporating measurement variability across laboratories for gene selection. Nat Biotechnol 24:1476-7 |
