Norita Sukri
Nanyang Technological University
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Publication
Featured researches published by Norita Sukri.
Journal of Clinical Oncology | 2009
Chiung-Ing Wong; T. S. Koh; Ross A. Soo; Septian Hartono; Choon-Hua Thng; Evelyn McKeegan; Wei Peng Yong; Chien-Shing Chen; Soo-Chin Lee; John Wong; Robert Lim; Norita Sukri; Siew-Eng Lim; Ai-Bee Ong; Joyce Steinberg; Neeraj Gupta; Rajendra S. Pradhan; Rod Humerickhouse; Boon-Cher Goh
PURPOSE To determine the safety and tolerability of ABT-869 at escalating doses and its effects on biomarkers relevant for antiangiogenic activity in patients with solid malignancies. PATIENTS AND METHODS Patients with solid malignancies refractory to or for which no standard effective therapy exists were enrolled onto escalating-dose cohorts and treated with oral ABT-869 once daily continuously. RESULTS Thirty-three patients were studied at doses of 10 mg/d, 0.1 mg/kg/d, 0.25 mg/kg/d, and 0.3 mg/kg/d. Dose-limiting toxicities in the first cycle (21 days) included grade 3 fatigue in a patient at 10 mg/d, grade 3 proteinuria and grade 3 hypertension in two separate patients at 0.25 mg/kg/d, and grade 3 hypertension and grade 3 proteinuria in two separate patients at 0.3 mg/kg/d, which was the maximum-tolerated dose. Other significant treatment-related adverse events included asthenia, hand and foot blisters, and myalgia. Oral clearance of ABT-869 was linear, with a mean of 2.7 +/- 1.2 L/h and half-life of 18.4 +/- 5.7 hours, with no evidence of drug accumulation at day 15. Two patients with lung cancer and one patient with colon cancer achieved partial response. Stable disease for more than four cycles was observed in 16 patients (48%). Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) showed dose-dependent reduced tumor vascular permeability that correlated with drug exposure. By day 15 of treatment, circulating endothelial cells were significantly reduced (P = .007), whereas plasma vascular endothelial growth factor was increased (P = .004). CONCLUSION ABT-869 by continuous once-daily dosing was tolerable at doses </= 0.25 mg/kg/d. Biomarker evidence of antiangiogenic activity and DCE-MRI evidence of tumor antiangiogenesis were observed together with promising clinical activity.
Pharmacogenetics and Genomics | 2008
Lu Fan; Boon-Cher Goh; Chiung-Ing Wong; Norita Sukri; Siew-Eng Lim; Sing-Huang Tan; Jia-Yi Guo; Robert Lim; Hui-Ling Yap; Yok-Moi Khoo; Philip Iau; How Sung Lee; Soo-Chin Lee
Objectives Doxorubicin is a cytotoxic drug with potential for severe myelosuppression that is highly variable and poorly predictable. Methods We correlated CBR1 and CBR3 genotypes with the pharmacokinetics and pharmacodynamics of doxorubicin in 101 Southeast Asian breast cancer patients receiving first-line doxorubicin. Results A common CBR3 11G>A variant was associated with lower doxorubicinol area under the concentration-time curve (AUC)/doxorubicin AUC metabolite ratio (P=0.009, GG vs. AA; trend test, P=0.004), lower CBR3 expression in breast tumor tissue (P=0.001, GG vs. AA), greater tumor reduction (P=0.015, GG vs. AA), and greater percentage reduction of leukocyte and platelet counts at nadir (trend test, P≤0.03). Chinese and Malays had higher frequency of the CBR3 11G>A variant than Indians (P≤0.002). Another variant CBR3 730G>A was associated with higher doxorubicinol AUC (P=0.009, GG vs. AA) and CBR3 expression in breast tumor tissue (P=0.001, GG vs AA). Conclusion Polymorphisms in CBR3 may explain interindividual and interethnic variability of doxorubicin pharmacokinetics and pharmacodynamics.
Pharmacogenetics and Genomics | 2009
Soo-Chin Lee; Xin Xu; Yi-Wan Lim; Philip Iau; Norita Sukri; Siew-Eng Lim; Hui Ling Yap; Wee-Lee Yeo; Patrick Tan; Sing-Huang Tan; Howard L. McLeod; Boon-Cher Goh
Objective Studying chemotherapy-induced gene expression changes in vivo, which could provide insights into mechanisms of chemotherapy resistance. Methods We analyzed and compared tumor gene expression changes of about 38 500 genes before and 3 weeks after doxorubicin or docetaxel treatment in 47 breast cancer patients. Results By using the median expression level of each probe set as the parameter, less than 5% of genes were upregulated or downregulated by more than 50% after treatment with either drug. Doxorubicin and docetaxel concordantly induced 251 genes predominantly involved in protein and macromolecule metabolism (upregulated), and cell cycle and DNA/RNA metabolism (downregulated). Doxorubicin treatment resulted in coregulation of a cluster of 345 probe sets involved in focal adhesion, Jak-Stat signaling pathway, cell adhesion molecules, and natural killer cell mediated cytotoxicity, whereas docetaxel treatment resulted in coregulation of a cluster of 448 probe sets involved in focal adhesion, neurodegenerative disorders, sphingolipid metabolism, and cell cycle. Tumors that were intrinsically sensitive or resistant to doxorubicin or docetaxel evoked distinct gene expression changes in response to the drug; doxorubicin-resistant tumors upregulated genes that were enriched for ErbB signaling, ubiquitin-mediated proteolysis, TGF-&bgr; signaling, and MAP-kinase signaling pathways, whereas docetaxel-resistant tumors upregulated genes that were enriched for focal adhesion and regulation of actin cytoskeleton. The drug-specific tumor gene expression changes were validated in independent in-vitro and in-vivo datasets. Conclusion Gene expression alterations of breast cancer were specific to doxorubicin and docetaxel treatment, and yielded mechanistic insights into resistance to either drug. Gene expression analysis provides more global perspectives on resistance pathways that could be exploited for therapeutic selection.
NMR in Biomedicine | 2011
Tong San Koh; Choon Hua Thng; Septian Hartono; Bee Choo Tai; Helmut Rumpel; Ai Bee Ong; Norita Sukri; Ross A. Soo; Chuing Ing Wong; Albert S. C. Low; Rod Humerickhouse; Boon Cher Goh
The aim of the present study was to compare three tracer kinetics methods for the analysis of dynamic contrast‐enhanced (DCE) MRI data, namely the generalized kinetics model, the distributed‐parameter model and the initial area under the tumor tracer curve (IAUC) method, in a Phase I study of an anti‐angiogenic drug ABT ‐869; and to explore their utility as biomarkers. Twenty‐eight patients with a range of tumors formed the study population. DCE MRI performed at baseline and 2 weeks post‐treatment was analyzed using all three methods, yielding percentage changes for various tracer kinetics parameters. Correlation analyzes were performed between these parameters and in relation to drug exposure. The association of these parameters with time‐to‐progression was examined using receiver‐operating characteristic and Kaplan–Meier curves. Significant correlation with drug exposure was found for the following parameters: normalized IAUC (IAUCnorm), fractional interstitial volume ve, fractional intravascular volume v1 and permeability PS. However, only ve and PS were effective in predicting late progression. A decrease in ve of more than 1.7% and a decrease in PS of more than 25.1% observed at 2 weeks post‐treatment could be associated with late progression. All three tracer kinetics methods have biomarker potential for assessing the effects of anti‐angiogenic therapy. Copyright
Pharmacogenetics and Genomics | 2009
Soo-Chin Lee; Xin Xu; Wee Joo Chng; Mark A. Watson; Yi-Wan Lim; Chiung-Ing Wong; Philip Iau; Norita Sukri; Siew-Eng Lim; Hui-Ling Yap; Shaik Ahmad Buhari; Patrick Tan; Jia-Yi Guo; Benjamin Chuah; Howard L. McLeod; Boon Cher Goh
Objective Tumor gene expression signatures have been used to classify, prognosticate, and predict chemotherapy sensitivity in breast cancer, although almost all efforts have been focused on the unchallenged baseline tumor. Most cancer patients receive systemic therapy, and exposure to drug may modify the tumors short-term and long-term outcomes. Drug-induced tumor gene signatures may thus be more predictive of treatment outcomes than the unperturbed tumor gene signatures. Methods Using a set of 47 breast cancer patients, we obtained paired prechemotherapy and postchemotherapy tumor biopsies and developed gene panels of baseline tumor (T1), postchemotherapy tumor (T2), and chemotherapy-induced relative change signatures (TΔ) to predict pathological response and progression-free survival (PFS). The signatures were validated in two independent test sets with paired prechemotherapy and postchemotherapy tumor samples, comprising of 18–20 patients each. Results T2 and TΔ were superior to T1 signatures in predicting for PFS (area under the curve of receiver operating characteristic 0.770 and 0.660 vs. 0.530) and pathological response (area under the curve of receiver operating characteristic 0.631 and 0.462 vs. 0.446) in the validation sets. In multivariate analysis for PFS with other clinical predictors, T2, but not T1, signatures remained as significant independent predictors. Conclusion Postchemotherapy tumor gene signatures outperformed baseline signatures and clinical predictors in predicting for pathological response and PFS, independent of clinical and pathological response to chemotherapy. Drug-induced tumor gene signatures may be more informative than unchallenged signatures in predicting treatment outcomes. These findings challenge the current practice of relying only on the baseline tumor to predict outcome, which overlooks the contributions of therapeutic interventions.
Cancer Chemotherapy and Pharmacology | 2008
Wei Peng Yong; Lingzhi Wang; Lai-San Tham; Chiung-Ing Wong; Soo-Chin Lee; Ross A. Soo; Norita Sukri; How Sung Lee; Boon Cher Goh
Therapeutic Drug Monitoring | 2006
Lai-San Tham; How Sung Lee; Lingzhi Wang; Wei Peng Yong; Lu Fan; Ai-Bee Ong; Norita Sukri; Ross A. Soo; Soo-Chin Lee; Boon Cher Goh
Journal of Clinical Oncology | 2016
Soo-Chin Lee; X. Xu; Patrick Tan; J. Lee; Philip Iau; Norita Sukri; Sing-Huang Tan; Siew-Eng Lim; Benjamin Chuah; Boon Cher Goh
Journal of Thoracic Oncology | 2007
Ross A. Soo; Choon-Hua Thng; Norita Sukri; Chiung-Ing Wong; Soo-Chin Lee; Chien-Shing Chen; Rod Humerickhouse; Boon Cher Goh
Journal of Clinical Oncology | 2007
Soo-Chin Lee; Mark A. Watson; X. Xu; Chiung-Ing Wong; Philip Iau; Norita Sukri; Siew-Eng Lim; Benjamin Chuah; Howard L. McLeod; Boon Cher Goh