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Background: Recent evidences have suggested that there are a rare population of cells called cancer stem cells (CSCs) in breast cancer, which have the ability of extensive self-renewal and contribute to metastasis and treatment resistance. This study evaluated the effects of an mTOR inhibitor, RAD001 (Everolimus) on breast CSCs of primary breast cancer cells and 2 breast cancer cell lines (MCF-7, MDA-MB-231) in vitro. Methods: Primarily, we isolated primary breast cancer cells from the 1cm3 axenic tissue from breast cancer patients, which was been digested in collagenase typelV in 1-3 hours. After filtration, the cells from the organoid were plated in DMEM/F12(1:1) containing 10% FBS ,100U/ml penicillin,100µg/ml streptomycin and some growth factors overnight. The next day, we sorted CD44 +, CD24-/low, ESA + cells as breast CSCs by flow cytometry from primary breast cancer cells, breast cancer cell lines MCF-7 and MDA-MB-231. And then the three CSCs were treated with different concentrations of docetaxel alone(0,20µM,40µM,80µM, 160µM), mTOR inhibitor RAD001 alone (0,10nM,100nM,1µM,10µM), or in combination with docetaxel(20µM).the Inhibition of the drugs on different Breast CSCs was quantified by methyl thiazolyl tetrazolium (MTT) assay. All employed CSCs were divided into four groups, given respectively 24h treatment of RAD001(100nM), docetaxel(20µM), combination or contrals. Apoptosis and distribution of cell cycles were examined with flow cytometry. Experiments were performed in triplicate. Result: All three kinds of breast CSCs were resistant to the standard treatment doses of docetaxel in vitro(IC50=80 µM±5µM),compared with this drug inhibited normal breast cancer cell lines(IC50=16µM±2µM). Treatment with RAD001 resulted in growth inhibition of all employed CSCs in a dose-dependent manner, which is more effective than the treatment with docetaxel alone(P<0.001), and CSCs of MDA-MB-231 proved to be most sensitive to this drug(IC20=8nM).Combination studies showed that there was an additive growth inhibitory effect of a combination treatment on three CSCs in vitro compared with treatment with RAD001 alone (P<0.001)or docetaxel alone(P<0.001). In addition, an increase in G2-M cell cycle arrest were seen in the combination treatment group when compared with controls(P<0.05), suggesting that cell cycle arrest may contribute to the increased growth inhibitory effects of combination treatment seen in this study. In primary CSCs, combined treatment induced a mount-up population of early apoptosis, the phenomenon was not taken place in the treatment of RAD001 alone, however, it's no statistical significance(P>0.05). Discussion: We conclude that RAD001 has more effective inhibitory effect than docetaxel, and enhances the cytotoxic effects of docetaxel in models of breast CSCs in vitro by inducing cell cycle arrest, indicating that combination treatment with RAD001 and docetaxel may be an efficient therapy for refractory and metastatic breast cancer. But the drug interaction of RAD001 and docetaxel in the clinical situation has to be evaluated in further studies. Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr PD02-09.

In the treatment planning for urethra-sparing radiation therapy in localized prostate cancer, it is important to visualize the prostatic urinary tract to reduce the risk of urinary symptoms linked to the urethral dose. We developed a methodology for visualizing the prostatic urinary tract by post-urination magnetic resonance imaging (PU-MRI) without using a urethral catheter for urethra-sparing radiotherapy. Several super-resolution (SR) deep learning models were proposed for improving image quality. This study investigated whether these SR deep learning models improve the visibility of the prostatic urinary tract in PU-MRI. Materials/Methods: We enrolled 30 patients who had previously undergone real-time-image-gated spot scanning proton therapy (RGPT) by inserting fiducial markers at our institution from October 2019 to October 2020. PU-MRI was performed using a non-contrast high-resolution two-dimensional T2-weighted turbo spin-echo (TSE) imaging sequence. Four different SR deep learning models were constructed: the EDSR (enhanced deep SR network), WDSR (widely activated SR network), SRGAN (SR generative adversarial network), and RDN (residual dense network). All models were trained using a diverse 2K resolution high-quality image dataset. We imported PU-MRIs as lowresolution images and exported four SR images that were enlarged using one of the models each. To assess the performance of the proposed SR image compared to PU-MRI, we used the complex wavelet structural similarity index measure (CW-SSIM) as the quantitative metric. A 1-to-5 scale was used to subjectively evaluate the visibility of the prostatic urinary tract by two radiation oncologists. Results: The mean ( § standard deviation) of the CW-SSIM in the EDSR, WDSR, SRGAN, and RDN were 0.999 § 0.001, 0.999 § 0.002, 0.993 § 0.002, and 0.997 § 0.002, respectively. The mean prostatic urinary tract visibility scores for oncologist 1 and 2 were 3.70 § 0.64 and 3.53 § 0.99 for PU-MRI, 3.67 § 0.60 and 2.70 § 0.82 for EDSR, 3.70 § 0.53 and 2.73 § 0.81 for WDSR, 3.67 § 0.65 and 2.73 § 0.81 for SRGAN, and 4.37 § 0.61 and 3.73 § 0.93 for RDN, respectively. Conclusion: This study investigated whether the combination of PU-MRI and SR deep learning models improves visibility of the prostatic urinary tract. The results suggest that SR images using RDN are highly similar to the original image and subjectively improve the visibility of the prostatic urinary tract.

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