My research interests are translational bioinformatics and cancer systems biology, primarily by developing computational and statistical learning methods for the analysis and integration of high-throughput cancer "omics" data in understanding and overcoming treatment resistance mechanisms in cancer. My lab acts as a "connector" to provide seamless integration of computational and statistical methods in experimental and clinical research. My lab is currently working in the following three interconnected research themes (RT):

RT1. Developing and Validating Predictive Biomarkers for Personalized Medicine. I have developed a novel machine learning algorithm, k-TSP (k-Top Scoring Pairs), to derive simple and accurate decision rules from high-throughput "omics" data. This algorithm won the best-automated machine learning algorithm in microarray analysis in the 2008 ICMLA Challenge. The k-TSP paper has received >400 citations since published in 2005. It has been widely used by experimental and computational biologists in their research. More recently, I have developed a novel approach to integrate other molecular markers with the k-TSP classifier to derive Integrative Genomics Classifiers, which are more robust and accurate than any individual markers alone as predictive biomarkers. Two of the predictive biomarkers that we developed are currently being retrospectively evaluated in clinical trials (ClinicalTrials.gov: NCT00735917 and NCT01016860). I received a Department of Defense Collaborative Idea Award (CA100512P1, PI: Tan, Collaborating PI: Eckhardt) to employ and develop predictive biomarkers for novel targeted therapies in colorectal cancer using the k-TSP algorithm from RNA-seq data.

Selected References

  1. John J. Tentler, Sujatha Nallapareddy, Aik Choon Tan, Anna Spreafico, Todd M. Pitts, M. Pia Morelli, Heather M. Selby+, Maria I. Kachaeva, Sara A. Flanigan, Gillian N. Kulikowski, Stephen Leong, John J. Arcaroli, Wells A. Messersmith and S. Gail Eckhardt (2010). Identification of Predictive Markers of Response to the MEK1/2 Inhibitor Selumetinib (AZD6244) in KRAS-Mutated Colorectal Cancer. Molecular Cancer Therapeutics 9(12):3351-3362. [PMID: 20923857] [Highlights of this issue]

  2. John J. Arcaroli, Basel M. Touban, Aik Choon Tan, Marileila Varella-Garcia, Rebecca W. Powell, S. Gail Eckhardt, Paul Elvin, Dexiang Gao and Wells A. Messersmith. (2010). Gene Array and FISH Biomarkers of Activity of Saracatinib (AZD0530), a Src Inhibit or, in a Preclinical Model of Colorectal Cancer. Clinical Cancer Research. 16(16): 4165-4177. [PMID: 20682712]

  3. Todd M. Pitts*, Aik Choon Tan*, Gillian N. Kulikowski, John J. Tentler, Amy M. Brown, Sara A. Flanigan, Stephen Leong, Christopher D. Coldren, Fred R. Hirsch, Marileila Varella-Garcia, Christopher Korch, and S. Gail Eckhardt. (2010). Development of an i ntegrated genomic classifier for a novel agent in colorectal cancer: approach to individualized therapy in early development. Clinical Cancer Research. 6(12): 3193-3204. [PMID: 20530704] [PMCID: PMC2889230]. [Commentated by: Douglas Yee (2010). How to Train Your Biomarker. Clinical Cancer Research. 6(12): 3091-3093]

  4. N.V. Rajeshkumar*, Tan, A.C.*, De Oliveira, E., Womack, C., Warren, M., Wombwell, H., Jenkins, M., Morgan, S., Green, T.P., Jimeno, A., Messersmith, W.A., and Hidalgo, M. (2009). Antitumor effects and biomarkers of activity of AZD0530, a Src inhibitor, in pancreatic cancer. Clinical Cancer Research. 15(12):4138-4146. [PMID: 19509160]

  5. Xu, L., Tan, A.C., Naiman, D.Q., Geman, D., and Winslow, R.L. (2005). Robust prostate cancer marker genes emerge from direct integration of inter-study microarray data. Bioinformatics 21: 3905-3911. [PMID: 16131522]

  6. Tan, A.C., Naiman, D.Q., Xu, L., Winslow, R.L., and Geman, D. (2005). Simple decision rules for classifying human cancers from gene expression profiles. Bioinformatics 21: 3896-3904. [PMID: 16105897] [PMCID: PMC1987374]

RT2. Overcoming Treatment Resistance. A detailed understanding of intrinsic and acquired resistance mechanisms to targeted anticancer agents should speed the development of treatment strategies with lasting clinical efficacy. I have pursued this research topic using two complimentary approaches to dissect the intrinsic and acquired resistance mechanisms:

(A). Deciphering the Cancer Genome. Cancer is the phenotypic end point of multiple genetic aberrations and epigenetic modifications that have accumulated within its genome. In order to decipher the cancer genome, I have developed computational methods to characterize the genome at various "omics" levels, and integrating them to reveal intrinsic resistance mechanisms and pathways in cancer. Recently, I have started to develop computational tools to analyze whole genome sequencing of cancer models for identifying the intrinsic and acquired resistance mechanisms. Similarly, by comparing the baseline gene expression profiles of sensitive and resistant cell lines to targeted therapy using systems biology approaches, we have identified several "actionable" over-expression pathways in the resistant lines. These intrinsic resistance mechanisms, when inhibited by pharmacologic agents or silenced by "knock-down" experiments in combination with targeted therapy demonstrated synergistic effects in both in vitro and in vivo cancer models. Multiple clinical trials for these combination studies are in preparation.

Selected References

  1. M. Pia Morelli, John J. Tentler, Gillian N. Kulikowski, Aik-Choon Tan, Erica L. Bradshaw-Pierce, Todd M. Pitts, Amy M. Brown, Sujatha Nallapareddy, John J. Arcaroli, Natalie J. Serkova, Manuel Hidalgo, Fortunato Ciardiello and S. Gail Eckhardt. (2011). Preclinical Activity of the Rational Combination of Selumetinib (AZD6244) in Combination with Vorinostat in KRAS Mutant Colorectal Cancer Models. Clinical Cancer Research. [Epub ahead of print Dec 15 2011][PMID: 22173548]

  2. Oliver A. Kent, Michael Mullendore, Erik Wentzel, Pedro López-Romero, Aik Choon Tan, Hector Alvarez, Kristen West, Michael F. Ochs, Manuel Hidalgo, Dan E. Arking, Anirban Maitra, and Joshua T. Mendell. (2009). A resource for analysis of miRNA expression and function in pancreatic ductal adenocarcinoma cells. Cancer Biology and Therapy. 8(21): 2005-2016. [PMID: 20037478] [PMCID: PMC2824894]

  3. Tan, A.C., Jimeno, A., Lin, S.H., Wheelhouse, J., Chan, F. Solomon, A., N.V. Rajeshkumar Rubio-Viqueira, B. and Hidalgo, M. (2009). Characterizing methylation patterns in pancreatic cancer genome. Molecular Oncology. 3(5-6): 425-438. [PMID: 19497796]

  4. Tan, A.C., Fan, J.-B., Karikari, C., Bibikova, M., Garcia, E.W., Zhou, L., Barker, D., Serre, D., Feldmann, G., Hruban, R.H., Klein, A.P., Goggins, M., Couch, F.J., Hudson, T.J., Winslow, R.L., Maitra, A., Chakravarti, A. (2008). Allele-Specific Expression in the germline of patients with familial pancreatic cancer: An unbiased approach to cancer gene discovery. Cancer Biology and Therapy 7:137-146. [PMID: 18059179]

(B). Charting Synthetic Lethality Networks. Together with my colleagues at the University of Colorado Cancer Center, we have recently developed a genome-wide synthetic lethality screen strategy by deep sequencing to identify genes and pathways when inhibited potentiate cancer cell killing with targeted therapy. My lab has developed a novel bioinformatics workflow (BiNGS!) to analyze and interpret these data to narrow down candidate synthetic lethality (SL) hits. Many of these hits have been validated in in vitro and in vivo models, and one of them has moved into a clinical trial as rational combination to treat leukemia.

Selected References

  1. Christopher C. Porter, Jihye Kim+, Susan Fosmire, Christy M. Gearheart, Annemie van Linden, Dmitry Baturin, Vadym Zaberezhnyy, Purvi R. Patel, Dexiang Gao, Aik Choon Tan, James DeGregori. (2011). Integrated genomic analyses identify WEE1 as a critical m ediator of cell fate and novel therapeutic target in acute myeloid leukemia. Leukemia. Accepted (12/16/2011).

  2. Jihye Kim+ and Aik Choon Tan. (2012). BiNGS!SL-seq: A Bioinformatics Pipeline for the Analysis and Interpretation of Deep Sequencing Genome-wide Synthetic Lethality Screen. Methods Mol Biol. 802:389-398. [PMID: 22130895].

[Read this Research Featured in C3 Magazine (Fall 2010 issue)]
RT3. Elucidating Tumor-Host Microenvironment Signaling. I have also investigated the specific signaling pathways that promote the growth of cancer cells in host microenvironment. In one study, we discovered that dying cancer cells release caspase-3 to stimulate tumor repopulation during radiotherapy. We identified the signaling cascade of this mechanism and found that the level of caspase-3 activation in tumors correlates with risk of relapse, suggesting that this pathway may be a determinant of therapeutic effects. In another study, we identified the co-expression of collagen and COX2 and their interactions promote tumor growth and invasion in postpartum involution mice and correlate with poor prognosis in young women with breast cancer. The data suggest that ibuprofen treatment during involution is a safe and effective approach to diminish pregnancy-associated cancer. My lab is continuing to develop and employ novel systems biology approaches to elucidate the signaling between tumor-host microenvironment.

Selected References

  1. Garrido-Laguna I, Uson M, Rajeshkumar NV, Tan AC, De Oliveira E, Karikari C, Villaroel MC, Solomon A, Taylor G, Sharma R, Hruban RH, Maitra A, Laheru D, Rubio-Viqueira B, Jimeno A, Hidalgo M. (2011). Tumor engraftment in nude mice and enrichment in stro ma-related gene pathways predicts poor survival and resistance to gemcitabine in patients with pancreatic cancer. Clin Cancer Res. 17(17); 5793-5800. [Epub ahead of print, Jul 8, 2011] [PMID: 21742805]

  2. Qian Huang*, Fang Li*, Xinjian Liu, Wenrong Li, Wei Shi, Fei-Fei Liu, Brian O'Sullivan, Zhimin He, Yuanlin Peng, Aik Choon Tan, Ling Zhou, Jingping Shen, Gangwen Han, Xiao-Jing Wang, Jackie Thorburn, Andrew Thorburn, Antonio Jimeno, David Raben, Joel S. Bedford and Chuan-Yuan Li. (2011). Caspase 3-mediated stimulation of tumor cell repopulation during cancer radiotherapy. Nature Medicine 17(7):860-866. [PMID: 21725296][Commented by: Connell, PP and Weichselbaum, RR (2011). A downside to apoptosis in cance r therapy? Nature Medicine 17(7):780-782]

  3. Traci R Lyons*, Jenean O'Brien*, Virginia Borges, Patricia J Keely, Matthew W Conklin, Kevin W Eliceiri, Andriy Marusyk, Aik Choon Tan and Pepper Schedin (2011). Postpartum mammary gland involution drives progression of ductal carcinoma in situ through collagen and COX-2. Nature Medicine 17(9):1109-1115. [Epub ahead of print, Aug 7, 2011][PMID: 21822285]