Microarrays To Direct The Use Of Cancer Therapeutics

Microarrays are being heavily utilized in cancer research and are proving to be useful in all aspects of study, including the classification of cancer, the study of biochemical pathways, and the identification of potential targets for novel therapeutics. Gene expression technologies are also being used to distinguish on-target versus off-target effects of cancer therapeutics, mechanisms of resistance to treatment, mechanisms of therapeutic function, and prediction of drug response.

Resistance to chemotherapy drugs is a major barrier to the successful long-term treatment of cancer. In order to better understand the mechanisms involved, gene families were identified that appear to contribute to the evolution of drug resistance and may be regulated through a multiple pathway gene expression program. Microarray analysis of tumor samples will make feasible the identification of critical genes that are most relevant to clinical drug resistance, and the data can be used to develop strategies for

1 The Food and Drug Administration (FDA) Good Laboratory Practice (GLP) regulation [21 Code of Federal Regulations (CFR) Part 58].

circumvention of resistance (Holleman et al., 2004; Richardson and Kaye, 2005).

When cancer patients are given adjuvant chemotherapy, there may not be any benefit because either locoregional treatment alone has cured their cancer or the patient may be resistant to the regimens employed. If prognostic factors could be improved, then selection of patients for adjuvant therapy would be facilitated. Likewise, if the appropriate predictive factors could be identified, they would contribute to the ease of selecting an optimal therapeutic strategy. There are several ongoing studies exploring genomic prognostic factors for the purpose of optimizing the indications for adjuvant chemotherapy. A large randomized trial utilizing microarray in «ode-negative disease may avoid chemotherapy (MINDACT) is being conducted to discern genomic signatures of good prognosis breast cancer from breast cancers with a worse prognosis. This will be done by comparing the information obtained with genomic profiling to the classical clinicopathologic index. Other trials are being conducted to assess if neoadjuvant chemotherapy with docetaxel is more effective than an anthracycline-containing regimen for treating p53-mutated tumors. Within this context an additional study will evaluate the ability of gene profiles to predict p53 status (Mauriac et al., 2005).

The anthracycline antibiotic doxorubicin is a cancer chemotherapy agent used in multiple cancers, but resistant cells often emerge from the treated population. Using cDNA microarray and RNA interference (RNAi) analysis, genes were screened that regulate doxorubicin susceptibility in highly tumorigenic breast cancer cells. Genes associated with both proliferation and cell cycle arrest after treatment with doxorubicin were identified. A model in which a distinct transcriptional response to doxorubicin is induced in highly tumorigenic breast cancer cells that differs from less malignant cells was supported with these results. It may be possible to target the induced genes, which regulate drug susceptibility positively and negatively, for therapeutic intervention (Mallory et al., 2005).

Drug resistance in colon cancer has also been studied utilizing microarray technology. After being treated with 5-fluorouracil (5-FU) or oxaliplatin, HCT115 colorectal cancer cells that exhibited resistance to these agents were selected and a DNA microarray was used to analyze their transcrip-tional profile. On bioinformatic analysis, it was found that the drug resistant cells contained sets of genes that were constitutively dysregulated and then transiently altered after they were exposed to the chemotherapy drugs. The molecular signatures of sensitivity to 5-FU and oxaliplatin may be represented by these genes (Boyer et al., 2006).

Microarray data can be used to predict drug response. A combination of chemotherapies called M-VAC is a neoadjuvant therapy used for invasive bladder cancer, and consists of administering a regimen of methotrexate, vin-blastine, doxorubicin, and cisplatin. Some patients experience tumor shrinkage and improved prognosis, while others suffer from severe adverse drug reactions to this treatment without any obvious benefit. There is no existing method that can assist with the prediction of how an individual patient will respond to chemotherapy. Using cDNA microarrays, gene expression profiles of biopsy tissue from 27 invasive bladder cancers were analyzed in order to attempt to predict a response to M-VAC therapy. Laser capture microdissection (LCM) was used to purify the populations of cancer cells for this analysis. There were 14 genes shown to be predictive, and after devising a numerical prediction scoring system to clearly delineate responder tumors from nonresponder tumors, this system could accurately predict drug responses in 8 out of 9 test cases that were taken from the original 27 cases. The RT-PCR data for the 14 genes were highly concordant with the cDNA microarray data. A feasible prediction system for bladder cancer sensitivity to M-VAC neoadjuvant chemotherapy might be developed based on RT-PCR which could potentially be used in the clinic to personalize therapy (Takata et al., 2005).

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