Gene expression profile testing for breast cancer and the use of chemotherapy, serious adverse effects, and costs of care

Division of General Medicine and Primary Care, Brigham and Women's Hospital, 1620 Tremont Street, Boston, MA 02120, USA.
Breast Cancer Research and Treatment (Impact Factor: 3.94). 06/2011; 130(2):619-26. DOI: 10.1007/s10549-011-1628-6
Source: PubMed


As gene expression profile (GEP) testing for breast cancer may provide additional prognostic information to guide the use of adjuvant chemotherapy, we examined the association between GEP testing and use of chemotherapy, serious chemotherapy-related adverse effects, and total charges during the 12 months following diagnosis. Medical record review was conducted for women age 30-64 years, with incident, non-metastatic, invasive breast cancer diagnosed 2006-2008 in a large, national health plan. Of 534 patients, 25.8% received GEP testing, 68.2% received chemotherapy, and 10.5% experienced a serious chemotherapy-related adverse effect. GEP testing was most commonly used in women at moderate clinical risk of recurrence (52.0 vs. 25.0% of low-risk women and 5.5% of high-risk). Controlling for the propensity to receive GEP testing, women who had GEP were less likely to receive chemotherapy (propensity adjusted odds ratio, 95% confidence interval 0.62, 0.39-0.99). Use of GEP was associated with more chemotherapy use among women at low risk based on clinical characteristics (OR = 42.19; CI 2.50-711.82), but less use among women with a high risk based on clinical characteristics (OR = 0.12; CI 0.03-0.47). Use of GEP was not associated with chemotherapy for the moderate risk group. There was no significant relationship between GEP use and either serious chemotherapy-associated adverse effects or total charges. While GEP testing was associated with an overall decrease in adjuvant chemotherapy, we did not find differences in serious chemotherapy-associated adverse events or charges during the 12 months following diagnosis.

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    • "Gene expression profiling is emerging as a tool for classifying breast cancers, guiding therapy, and predicting treatment responses (Cheang et al, 2008; Haas et al, 2011). However, genome and transcriptome analyses alone provide only a partial picture, as alternative splicing of mRNA, combined with more than 100 unique post-translational protein modifications, mean that each gene may give rise to multiple protein species (Banks et al, 2000). "
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