Take Advantage of Gene Expression to Select The Most Relevant Models for Your Specific Project
"Cancer chemotherapy is in evolution from non-specific cytotoxic drugs that damage both tumour and normal cells to more specific agents. Targeted agents are directed at unique molecular features of cancer cells, ... [with the] aim to produce greater effectiveness with less toxicity...[However,] knowledge of the molecular profile of the tumor is necessary to guide selection of therapy for the patient" [1].
Just as the molecular profile of tumor is necessary to guide selection of therapy, the molecular profile of tumor cell lines is necessary to the experimental design of your project. Let's imagine you have a promising anticancer candidate. You designed it as a selective inhibitor of a protein which is over-expressed in tumor versus normal cells, or in a particular type of cancer. Or, you serendipitously discovered that it has an antiproliferative effect on tumor cells, which is very common in cancer research [2]. That's your starting point. Now, you need more data to confirm its potential anticancer activity before moving to animal studies.
What to do?
Imagine to have a big panel of tumor cell lines originated from the majority of cancer types and to have gene expression data for all of them. You may test your candidate on cell lines selected on histotype to assess its tumor specificity and identify genes whose expression is associated to drug potency. Or, you may test your candidate on cells chosen based on the expression of the target to evaluate its molecular specificity. These are just two examples of the possibilities you have with a panel of well-characterized cancer cell line models.
What to do?
Imagine to have a big panel of tumor cell lines originated from the majority of cancer types and to have gene expression data for all of them. You may test your candidate on cell lines selected on histotype to assess its tumor specificity and identify genes whose expression is associated to drug potency. Or, you may test your candidate on cells chosen based on the expression of the target to evaluate its molecular specificity. These are just two examples of the possibilities you have with a panel of well-characterized cancer cell line models.
|
61 Tumor Cell Lines with Available Gene Expression Profile
Free preliminary analysis of your target of interest
Gene expression analysis of customers' targets of interest in our panel of cell lines is often a preliminary data required to properly design the experiments in our contract research projects. Therefore, we provide this analysis to our customers for free.
|
Identify Chemosensitivity-Related Genes for Your Drug Candidates
As example, proliferation assays were carried out in 9 cell lines treated with various concentrations of cisplatin and GI50, the concentration required to reduce growth by 50%, was calculated at 48 hours of treatment (Figure 1). Gene expression profile of the 3 most sensitive cell lines were compared to the 3 most resistant cell lines. 3 genes, corresponding to 3 probes, were identified as up-regulated and 7 genes, corresponding to 10 probes, were identified as down-regulated in cisplatin resistant cells compared to sensitive ones (Figure 2). RAB25, member of RAS oncogene family, one of the genes identified as up-regulated in cisplatin resistant cells (ProbeSet 218186_at), has been reported to contribute to cisplatin resistance [3].
Figure 1 - GI50 (µM) of Cisplatin in 9 tumor cell lines.
|
Figure 2 - Differentially expressed genes.
|
1. Schilsky RL (2010). Personalized medicine in oncology: the future is now. Nature Review Drug Discovery, 9(5):363-6.
2. Hargrave-Thomas E, Yu B, Reynisson J (2012). Serendipity in anticancer drug discovery. World Journal of Clinical Oncology, 3(1): 1-6.
3. Link to Pubmed: RAB25 AND cisplatin resistance
2. Hargrave-Thomas E, Yu B, Reynisson J (2012). Serendipity in anticancer drug discovery. World Journal of Clinical Oncology, 3(1): 1-6.
3. Link to Pubmed: RAB25 AND cisplatin resistance
