A human chondrosarcoma cell line, CS-1, was treated successively with increasing concentrations of the marine chemotherapeutic Ecteinascidin-743 (ET-743), yielding a variant cell line displaying a significant degree of resistance to the cytotoxic action of this drug. Various experiments were performed to discern molecular aberrations between the parent and resistant cell line, and also identify potential molecular markers indicative of drug resistance. Although no significant differences in the levels of membrane transporters such as P-glycoprotein or multidrug resistance protein 1 (MRP1) were detected, the cell migratory ability of the ET-743-resistant cell variant was reduced, as was its attachment capability to gelatin-coated cell culture dishes. Staining of the actin-containing cytoskeleton with fluorescent-labeled phalloidin revealed marked differences in the cytoskeleton architecture between the parent and ET-743-resistant CS-1 cell lines. Comparison of serum-free conditioned medium from both cell lines showed conspicuous differences in the levels of several proteins, including a quartet of high molecular weight proteins (> or =140 kDa). The protein sequences of two of these high molecular weight proteins, present at significantly higher concentrations in conditioned medium obtained from the parent cell line, corresponded to subunits of types I and IV collagen. Analysis of type I collagen alpha1 chain mRNA revealed a significantly lower level in the ET-743-resistant CS-1 cell line. Thus, prolonged exposure to ET-743 may cause changes in cell function through cytoskeleton rearrangement and/or modulation of collagen levels.
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http://dx.doi.org/10.1016/j.bcp.2003.08.033 | DOI Listing |
Arch Pathol Lab Med
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the Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles (Petersen, Stuart, He, Ju, Ghezavati, Siddiqi, Wang).
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Objective.
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Institute for Molecular Bioscience, The University of Queensland, 306 Carmody Road, St Lucia, Brisbane, QLD 4072, Australia.
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State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, 2 Sipailou, Xuanwu District, Nanjing 210096, China.
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School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju 61005, Republic of Korea.
Combination therapies have emerged as a promising approach for treating complex diseases, particularly cancer. However, predicting the efficacy and safety profiles of these therapies remains a significant challenge, primarily because of the complex interactions among drugs and their wide-ranging effects. To address this issue, we introduce DD-PRiSM (Decomposition of Drug-Pair Response into Synergy and Monotherapy effect), a deep-learning pipeline that predicts the effects of combination therapy.
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