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Carol Tang

Principal Investigator in-charge

Edwin Sandanaraj


Contact Information

National Neuroscience Institute
11 Jalan Tan Tock Seng, Singapore 308433
Tel: (65) 6357 7616 / (65) 6357 7634 (Lab)
Fax: (65) 6256 9178


Our newly formed division has its roots from our early industry collaboration in neuro-oncology. This collaboration focused on establishing patient stratification methods, using patient-derived xenografts and primary glioma cells. To overcome the low statistical power of primary cell lines, as with any such studies, we tapped into the large public clinical databases to form informative associations between gene activation pathways, somatic mutation profiles, clinical prognosis and imaging sequences. We established computational platforms to facilitate these associative studies (Choudhury et al, J Clin Invest, 2012; Koh et al, Antioxid Redox Signal, 2013; Ng et al, Clin Cancer Res, 2012Yeo et al, Cancer Res, 2012) .

In advancing genomic interrogative studies for the various neurological diseases at NNI, we envisage expanding our neuro-computing platform to assess the clinical phenotype-genotype relationship. NNI provides medical treatment services for the majority of local and regional patients with central nervous system disorders. As such, we have a substantial pool of patients and their associated clinical material for research endeavors. Our detailed longitudinal history of, for example, Parkinson’s disease patients, with recently acquired exome-profiling data, allows us to identify potential biomarkers and process networks associated with disease etiology. In addition, our efforts in “Precision Medicine” allow us to identify patient cohorts most likely to benefit from targeted treatment approaches.

Deployment of Informatics Strategies for Characterizing Clinical Neurological Disorders

Statistical models will be adapted for studies such as genomic, gene expression, methylation, Next-generation sequencing and microRNA analyses. These interrogation methods are common across all diseases in the various themes with focus on patient stratification and prognostic marker identification.

Synergy and Cohesion Between Disease Themes

  1. Systematic organization of clinical and genomics information in relational databases to explore scientific insights across neural disorders
    We have initiated, for different diseases, systematic archival of prospectively collected patient material, as well as publicly available patient clinical databases with deep profiling information. We also have access to collaborative databases with other clinical centres worldwide to facilitate this platform building
  2. Predictive analysis on the vast amount of in-house and public repositories to identify genomics markers for providing efficacious treatment
    These predictive analyses are being carried out in various studies. Information include but are not limited to qRT-PCR, methylation, microRNA, gene expression, genome copy number etc.
  3. Molecular characterization of clinical problems through high-throughput genomics and proteomics information in order to map key elements in biological pathways
    We have begun to undertake these approaches in Parkinson’s disease and brain tumors. We have generated several high impact works using these methods.The important points we wish to make are:
    1. Molecular heterogeneity exists that cannot be accounted for by current clinical indicator
    2. Genome-informed therapeutic strategies are viable. This forces a re-evaluation of current diagnostic approaches to identify patient cohorts based on molecular schemes for tailored therapies.
  4. Integrative analysis on genomics/proteomics data churned out from multiple platforms to subtype patient population leading to personalized treatment
    This is being put in place for brain tumors that will serve as a roadmap for other diseases. An example is the integration of gene expression, genome copy number and methylation data from The Cancer Genome Atlas to identify patient subtypes for genome-informed treatment regimens.
  5. Exploration on potential candidates targeting aberrant pathway in neural disorders using computational approaches
    Our platforms described above will enable us to build up in silico capability at identifying therapeutic targets, as well as small molecules for patient cohorts most likely to receive treatment benefit.

Selected Publications

  1. Choudhury Y, Tay FC, Lam DH, Sandanaraj E, Tang C, Ang BT, Wang S (2012) Attenuated adenosine-to-inosine editing of microRNA-376a* promotes invasiveness of glioblastoma cells. J Clin Invest 122: 4059-4076
  2. Koh LW, Koh GR, Ng FS, Toh TB,  Sandanaraj E, Chong YK, Phong M, Tucker-Kellogg G, Kon OL, Ng WH, Ng IH, Clement MV, Pervaiz S, Ang BT, Tang CS (2013) A distinct reactive oxygen species profile confers chemoresistance in glioma-propagating cells and associates with patient survival outcome. Antioxid Redox Signal 19: 2261-2279
  3. Ng FS, Toh TB, Ting EH, Koh GR, Sandanaraj E, Phong M, Wong SS, Leong SH, Kon OL, Tucker-Kellogg G, Ng WH, Ng I, Tang C, Ang BT (2012) Progenitor-like Traits Contribute to Patient Survival and Prognosis in Oligodendroglial Tumors. Clin Cancer Res 18: 4122-4135
  4. Yeo CW, Ng FS, Chai C, Tan JM, Koh GR, Chong YK, Koh LW, Foong CS, Sandanaraj E, Holbrook JD, Ang BT, Takahashi R, Tang C, Lim KL (2012) Parkin pathway activation mitigates glioma cell proliferation and predicts patient survival. Cancer Res 72: 2543-2553