Optimizing PCR Assays for DNA Based Cancer Diagnostics

  • Ali Bashir
  • Qing Lu
  • Dennis Carson
  • Benjamin Raphael
  • Yu-Tsueng Liu
  • Vineet Bafna
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5541)

Abstract

Somatically acquired DNA rearrangements are characteristic of many cancers. The use of these mutations as diagnostic markers is challenging, because tumor cells are frequently admixed with normal cells, particularly in early stage tumor samples, and thus the samples contain a high background of normal DNA. Detection is further confounded by the fact that the rearrangement boundaries are not conserved across individuals, and might vary over hundreds of kilobases. Here, we present an algorithm for designing PCR primers and oligonucleotide probes to assay for these variant rearrangements. Specifically, the primers and probes tile the entire genomic region surrounding a rearrangement, so as to amplify the mutant DNA over a wide range of possible breakpoints and robustly assay for the amplified signal on an array. Our solution involves the design of a complex combinatorial optimization problem, and also includes a novel alternating multiplexing strategy that makes efficient detection possible. Simulations show that we can achieve near-optimal detection in many different cases, even when the regions are highly non-symmetric. Additionally, we prove that the suggested multiplexing strategy is optimal in breakpoint detection.

We applied our technique to create a custom design to assay for genomic lesions in several cancer cell-lines associated with a disruption in the CDKN2A locus. The CDKN2A deletion has highly variable boundaries across many cancers. We successfully detect the breakpoint in all cell-lines, even when the region has undergone multiple rearrangements. These results point to the development of a successful protocol for early diagnosis and monitoring of cancer.

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References

  1. 1.
    Campbell, P., Stephens, P., Pleasance, E., O’Meara, S., Li, H., Santarius, T., Stebbings, L., Leroy, C., Edkins, S., Hardy, C., et al.: Identification of somatically acquired rearrangements in cancer using genome-wide massively parallel paired-end sequencing. Nature Genetics 40(6), 722 (2008)CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Mitelman, F., Johansson, B., Mertens, F.: The impact of translocations and gene fusions on cancer causation. Nat. Rev. Cancer. 7, 233–245 (2007)CrossRefPubMedGoogle Scholar
  3. 3.
    Raphael, B., Volik, S., Yu, P., Wu, C., Huang, G., Waldman, F., Costello, J., Pienta, K., Mills, G., Bajsarowicz, K., Kobayashi, Y., Sridharan, S., Paris, P., Tao, Q., Aerni, S., Brown, R., Bashir, A., Gray, J., Cheng, J.F., Jong, P., Nefedov, M., Padilla-Nash, H., Collins, C.: A sequence based survey of the complex structural organization of tumor genomes. Genome Biology 9(3) (2008)Google Scholar
  4. 4.
    Ruan, Y., Ooi, H., Choo, S., Chiu, K., Zhao, X., Srinivasan, K., Yao, F., Choo, C., Liu, J., Ariyaratne, P., et al.: Fusion transcripts and transcribed retrotransposed loci discovered through comprehensive transcriptome analysis using Paired-End diTags (PETs). Genome Res. 17(6), 828–838 (2007)CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Bashir, A., Volik, S., Collins, C., Bafna, V., Raphael, B.: Evaluation of Paired-End Sequencing Strategies for Detection of Genome Rearrangements in Cancer. PLoS Computational Biology 4(4) (2008)Google Scholar
  6. 6.
    Beigel, R., Alon, N., Apaydin, S., Fortnow, L., Kasif, S.: An Optimal Multiplex PCR Protocol for Closing Gaps in Whole Genomes. In: Proceedings of the Fifth Annual International Conference on Computational Molecular Biology (RECOMB) (2001)Google Scholar
  7. 7.
    Lipson, D.: Optimization problems in design of oligonucleotides for hybridization based methods. Master’s thesis, Technion - Israel Institute of Technology (2002)Google Scholar
  8. 8.
    Liu, Y., Carson, D.: A novel approach for determining cancer genomic breakpoints in the presence of normal DNA. PLoS ONE 2(4), e380 (2007)CrossRefGoogle Scholar
  9. 9.
    Bashir, A., Liu, Y., Raphael, B., Carson, D., Bafna, V.: Optimization of primer design for the detection of variable genomic lesions in cancer. Bioinformatics 23(21), 2807 (2007)CrossRefPubMedGoogle Scholar
  10. 10.
    Dasgupta, B., Jun, J., Mandoiu, I.: Primer Selection Methods for Detection of Genomic Inversions and Deletions via PAMP. In: Proceedings of the 6th Asia-Pacific Bioinformatics Conference. Imperial College Press (2008)Google Scholar
  11. 11.
    Fan, J., Chee, M., Gunderson, K., et al.: Highly parallel genomic assays. Nature Reviews Genetics 7(8), 632 (2006)CrossRefPubMedGoogle Scholar
  12. 12.
    Brooks, R.: On colouring the nodes of a network. Proc. Cambridge Phil. Soc. 37, 194–197 (1941)CrossRefGoogle Scholar
  13. 13.
    Welsh, D., Powell, M.: An upper bound for the chromatic number of a graph and its application to timetabling problems. The Computer Journal 10(1), 85–86 (1967)CrossRefGoogle Scholar
  14. 14.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220(4598), 671–680 (1983)CrossRefPubMedGoogle Scholar
  15. 15.
    Rocco, J., Sidransky, D.: p16 (MTS-1/CDKN2/INK4a) in Cancer Progression. Experimental Cell Research 264(1), 42–55 (2001)CrossRefPubMedGoogle Scholar
  16. 16.
    Sasaki, S., Kitagawa, Y., Sekido, Y., Minna, J., Kuwano, H., Yokota, J., Kohno, T.: Molecular processes of chromosome 9p21 deletions in human cancers. Oncogene 22, 3792–3798 (2003)CrossRefPubMedGoogle Scholar
  17. 17.
    Tomlins, S., Rhodes, D., Perner, S., Dhanasekaran, S., Mehra, R., Sun, X., Varambally, S., Cao, X., Tchinda, J., Kuefer, R., et al.: Recurrent Fusion of TMPRSS2 and ETS Transcription Factor Genes in Prostate Cancer. Science 310(5748), 644–648 (2005)CrossRefPubMedGoogle Scholar
  18. 18.
    Wang, J., Cai, Y., Ren, C., Ittmann, M.: Expression of variant TMPRSS2/ERG fusion messenger RNAs is associated with aggressive prostate cancer. Cancer Res. 66, 8347–8351 (2006)CrossRefPubMedGoogle Scholar
  19. 19.
    Kitagawa, Y., Inoue, K., Sasaki, S., Hayashi, Y., Matsuo, Y., Lieber, M.R., Mizoguchi, H., Yokota, J., Kohno, T.: Prevalent Involvement of Illegitimate V (D) J Recombination in Chromosome 9p21 Deletions in Lymphoid Leukemia. Journal of Biological Chemistry 277(48), 46289–46297 (2002)CrossRefPubMedGoogle Scholar
  20. 20.
    Wang, D., Urisman, A., Liu, Y., Springer, M., Ksiazek, T., Erdman, D., Mardis, E., Hickenbotham, M., Magrini, V., Eldred, J., et al.: Viral discovery and sequence recovery using DNA microarrays. PLoS Biol. 1(2), E2 (2003)CrossRefGoogle Scholar
  21. 21.
    Lu, Q., Nunez, E., Lin, C., Christensen, K., Downs, T., Carson, D., Wang-Rodriguez, J., Liu, Y.: A sensitive array-based assay for identifying multiple TMPRSS2: ERG fusion gene variants. Nucleic Acids Research (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ali Bashir
    • 1
  • Qing Lu
    • 1
  • Dennis Carson
    • 1
  • Benjamin Raphael
    • 2
  • Yu-Tsueng Liu
    • 1
  • Vineet Bafna
    • 1
  1. 1.University of CaliforniaSan DiegoUSA
  2. 2.Brown UniversityProvidenceUSA

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