Choosing the optimal chemotherapy regimen is still an unmet medical need for breast cancer patients. In this study, we reanalyzed data from seven independent data sets with totally 1079 breast cancer patients. The patients were treated with three different types of commonly used neoadjuvant chemotherapies: anthracycline alone, anthracycline plus paclitaxel, and anthracycline plus docetaxel. We developed random forest models with variable selection using both genetic and clinical variables to predict the response of a patient using pCR (pathological complete response) as the measure of response. The models were then used to reassign an optimal regimen to each patient to maximize the chance of pCR. An independent validation was performed where each independent study was left out during model building and later used for validation. The expected pCR rates of our method are significantly higher than the rates of the best treatments for all the seven independent studies. A validation study on 21 breast cancer cell lines showed that our prediction agrees with their drug-sensitivity profiles. In conclusion, the new strategy, called PRES (Personalized REgimen Selection), may significantly increase response rates for breast cancer patients, especially those with HER2 and ER negative tumors, who will receive one of the widely-accepted chemotherapy regimens.
Development of adipose tissue requires the differentiation of less specialized cells, such as human mesenchymal stem cells (hMSCs), into adipocytes. Since matrix metalloproteinases (MMPs) play critical roles in the cell differentiation process, we conducted investigations to determine if a novel mercaptosulfonamide-based MMP inhibitor (MMPI), YHJ-7-52, could affect hMSC adipogenic differentiation and lipid accumulation. Enzyme inhibition assays, adipogenic differentiation experiments, and quantitative PCR methods were employed to characterize this inhibitor and determine its effect upon adipogenesis. YHJ-7-52 reduced lipid accumulation in differentiated cells by comparable amounts as a potent hydroxamate MMPI, GM6001. However, YHJ-7-82, a non-inhibitory structural analog of YHJ-7-52, in which the zinc-binding thiol group is replaced by a hydroxyl group, had no effect on adipogenesis. The two MMPIs (YHJ-7-52 and GM6001) were also as effective in reducing lipid accumulation in differentiated cells as T0070907, an antagonist of peroxisome-proliferator activated receptor gamma (PPAR-gamma), at a similar concentration. PPAR-gamma is a typical adipogenic marker and a key regulatory protein for the transition of preadiopocyte to adipocyte. Moreover, MMP inhibition was able to suppress lipid accumulation in cells co-treated with Troglitazone, a PPAR-gamma agonist. Our results indicate that MMP inhibitors may be used as molecular tools for adipogenesis and obesity treatment research.
Matrix metalloproteinases (MMPs) are a family of metzincin enzymes that act as the principal regulators and remodelers of the extracellular matrix (ECM). While MMPs are involved in many normal biological processes, unregulated MMP activity has been linked to many detrimental diseases, including cancer, neurodegenerative diseases, stroke, and cardiovascular disease. Developed as tools to investigate MMP function and as potential new therapeutics, matrix metalloproteinase inhibitors (MMPIs) have been designed, synthesized, and tested to regulate MMP activity. This chapter focuses on the use of enzyme kinetics to characterize inhibitors of MMPs. MMP activity is measured via fluorescence spectroscopy using a fluorogenic substrate that contains a 7-methoxycoumarin-4-acetic acid N-succinimidyl ester (Mca) fluorophore and a 2,4-dinitrophenyl (Dpa) quencher separated by a scissile bond. MMP inhibitor (MMPI) potency can be determined from the reduction in fluorescent intensity when compared to the absence of the inhibitor. This chapter describes a technique to characterize a variety of MMPs through enzyme inhibition assays.
Despite the rapid progress in personalized cancer therapy (PCT) for breast cancer, no previous studies have used genomic predictors to choose among multiple chemotherapy regimens. It is unclear that given the current regimens how much PCT can improve the response rate for patients who will receive chemotherapy. In this study, we reanalyzed data from published studies of 1111 breast cancer patients who were treated with neoadjuvant chemotherapies. Those patients were divided into three regimen groups: an anthracycline alone, anthracycline plus paclitaxel, and anthracycline plus docetaxel. We developed a new strategy called PRES (Personalized REgimen Selection) to reassign the optimal regimen to each of the patients. First, a variable selection scheme was developed to identify significant genetic predictors for chemotherapy response. The selected genetic variables were then combined with clinical variables to build random forest models to predict the response of a patient to each regimen using pCR (pathological complete response) as the measure of response. The models were used to assign an optimal regimen to each patient to maximize the chance of pCR. We found that the expected rate of pCR was improved from 21.2% to 39.6% (95% CI: 34.6% - 43.0%). We also found that 31.1% of the patients may have been overtreated and 8.2% patients undertreated. A validation study on 21 cell lines showed that our prediction agrees with their paclitaxel-sensitivity profiles. We performed additional analysis on the Cancer Genome Atlas (TCGA) data and found that 18 of the 19 genes identified are significantly differentially expressed between normal and tumor tissues, and 2 of them, TAF6L and METRN (meteorin), are associated with overall survival. In conclusion, PRES could substantially increase response rates for breast cancer patients who will receive one of the widely-accepted chemotherapy regimens at present. Citation Format: Jinfeng Zhang, Kaixian Yu, Qingxiang Amy Sang, Winston Tan, Mayassa B. Dargham, Jun S. Liu, Ty Lively, Cedric Sheffield. Personalized chemotherapy regimen selection for breast cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2034.
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