Computational analysis of DNA repair pathways using gene expression data — ASN Events
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  2. Poster Session 2
  3. Computational analysis of DNA repair pathways using gene expression data

Computational analysis of DNA repair pathways using gene expression data (#255)

Chao Liu 1 , Kum Kum Khanna 2 , Sriganesh Srihari 1 , Peter T. Simpson 3 , MarK Ragan 1 , Kim-Anh Le Cao 4
  1. Institue for Molecular Bioscience University of Queensland, St Lucia, QLD, Australia
  2. QIMR-Berghofer Medical Research Institute, Brisbane, Queensland, Australia
  3. The University of Queensland Centre for Clinical Research, Brisbane, Queensland, Australia
  4. The University of Queensland Diamantina Institute, Brisbane, Queensland, Australia

Human DNA is constantly subject to threats posed by various endogenous and exogenous factors, such as ultraviolet radiation, cigarette smoke and oxidative by- products from cellular respiration. At least six DNA repair pathways have been developed to counteract these threats. The difference in activity of these repair pathways amongst subgroups of various cancers has been associated with radio- and chemoresistance, and more recently with response to poly [ADP-ribose] polymerase 1 (PARP1) inhibitor-related targeted therapy for breast and ovarian cancer (1-3). It is thus important to investigate systematically the status of all these six repair pathways in cancer, but to our knowledge no such studies have been done.

As DNA repair research is a fast-advancing area, we have first manually curated these pathways by combining literature search and domain expertise to provide up-to-date knowledge of these pathways (4). We then evaluated the sensitivity and specificity of four popular self-contained pathway analysis methods on the curated pathways, using three publicly available gene expression data sets for which the status of each repair pathway is already known. We chose to focus on self-contained methods because they can test pathway-phenotype association directly. Our preliminary results show all the four methods display good sensitivity, but poor specificity that is likely due to pathway crosstalk. Moreover, all these methods fail to identify defective repair pathways that are common in cancer and important for predicting therapy response.

In order to obtain a better activity estimate of these repair pathways, we propose further to develop a pathway analysis methodology that incorporates a recently proposed gene signature (5) for detecting deficiencies in the homologous recombination repair pathway,  and an algorithm that corrects for pathway crosstalk (6). This methodology will then be applied to breast cancer subgroups to investigate their DNA repair capabilities.

  1. 1. Bouwman, P. and Jonkers, J. (2012) The effects of deregulated DNA damage signalling on cancer chemotherapy response and resistance. Nat Rev Cancer 12, 587–598.
  2. 2. Lord, C.J. and Ashworth, A. (2012) The DNA damage response and cancer therapy. Nature 481, 287–294.
  3. 3. Curtin, N.J. (2012) DNA repair dysregulation from cancer driver to therapeutic target. Nat Rev Cancer 12, 801–817.
  4. 4. Liu, C., Srihari, S., Lê Cao, K.A., Chenevix-Trench, G., Simpson, P.T., Ragan, M.A. and Khanna, K.K. (2014) A fine-scale dissection of the DNA double-strand break repair machinery and its implications for breast cancer therapy. Nucleic Acids Research, Accepted.
  5. 5. Peng, G., Chun-Jen Lin, C., Mo, W., Dai, H., Park, Y.-Y., Kim, S.M., Peng, Y., Mo, Q., Siwko, S., Hu, R., et al. (2014) Genome-wide transcriptome profiling of homologous recombination DNA repair. Nature Communications 5, 3361.
  6. 6. Donato, M., Xu, Z., Tomoiaga, A., Granneman, J.G., Mackenzie, R.G., Bao, R., Than, N.G., Westfall, P.H., Romero, R. and Draghici, S. (2013) Analysis and correction of crosstalk effects in pathway analysis. Genome Research 23, 1885–1893.