Akira Myomoto

MicroRNA profile predicts recurrence after resection in patients with hepatocellular carcinoma within the Milan Criteria.

Objective Hepatocellular carcinoma (HCC) is difficult to manage due to the high frequency of post-surgical recurrence. Early detection of the HCC recurrence after liver resection is important in making further therapeutic options, such as salvage liver transplantation. In this study, we utilized microRNA expression profiling to assess the risk of HCC recurrence after liver resection. Methods We examined microRNA expression profiling in paired tumor and non-tumor liver tissues from 73 HCC patients who satisfied the Milan Criteria. We constructed prediction models of recurrence-free survival using the Cox proportional hazard model and principal component analysis. The prediction efficiency was assessed by the leave-one-out cross-validation method, and the time-averaged area under the ROC curve (ta-AUROC). Results The univariate Cox analysis identified 13 and 56 recurrence-related microRNAs in the tumor and non-tumor tissues, such as miR-96. The number of recurrence-related microRNAs was significantly larger in the non-tumor-derived microRNAs (N-miRs) than in the tumor-derived microRNAs (T-miRs, P<0.0001). The best ta-AUROC using the whole dataset, T-miRs, N-miRs, and clinicopathological dataset were 0.8281, 0.7530, 0.7152, and 0.6835, respectively. The recurrence-free survival curve of the low-risk group stratified by the best model was significantly better than that of the high-risk group (Log-rank: P = 0.00029). The T-miRs tend to predict early recurrence better than late recurrence, whereas N-miRs tend to predict late recurrence better (P<0.0001). This finding supports the concept of early recurrence by the dissemination of primary tumor cells and multicentric late recurrence by the ‘field effect’. Conclusion microRNA profiling can predict HCC recurrence in Milan criteria cases.

Abstract 1937: Development of microRNA-based prediction model of Trastuzumab treatment response for HER2-positive breast cancer using FFPE specimens.

[Background and Aim] Although Trastuzumab has been used for HER2(+) breast cancer, the treatment response of Trastuzumab therapy depends on unknown mechanisms among individual cases. In order to avoid unnecessary adverse events and to lighten financial burden, pre-treatment prediction of trastuzumab treatment response would be beneficial for patients. Thus, in this study, we develop a prediction algorithm using microRNA expression profile using formalin-fixed paraffin-embedded (FFPE) specimens of HER2(+) breast cancer. [Materials and Methods] Eighty-three breast cancer patients who underwent trastuzumab-chemo combined therapy before operations were enrolled with written informed consent. FFPE specimens of pre-treatment core needle biopsy samples were collected, and regions containing cancer and adjacent stromal cells were laser-microdissected. Total RNA samples extracted from the microdissected specimens were subjected for microRNA microarray (3D-Gene®, Toray, Japan) analysis. Among these 83 patients, 39 cases had pCR (definition: complete response in IDC regions regardless presence of DCIS without lymph node metastasis), and the other 44 cases did not. According to the pCR/non-pCR information, we develop a prediction model using 35 signature microRNAs by a SVM technique. Prediction accuracy assessed by Leave-one-out validation was AUROC=0.889. The 35 signature microRNAs for trastuzumab treatment response included 7 out of 8 let-7 family members and miR-125a-5p/b-5p, which were downregulated in pCR specimens. [Conclusion] microRNA profile could predict treatment response of trastuzumab-chemo combined therapy for HER2(+) breast cancer, and the developed prediction algorithm might be a useful tool for clinical decision making. Citation Format: Fumiaki Sato, Zhipeng Wang, Takayuki Ueno, Akira Myomoto, Satoko Takizawa, Feng Ling Pu, Norikazu Masuda, Yoshiki Mikami, Yoshiki Mikami, Kazuharu Shimizu, Shigehira Saji, Masakazu Toi. Development of microRNA-based prediction model of Trastuzumab treatment response for HER2-positive breast cancer using FFPE specimens. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 1937. doi:10.1158/1538-7445.AM2013-1937

Abstract P5-10-09: Prediction of Trastuzumab treatment response for HER2-positive breast cancer by microRNA profiling

[Background and Aim] Although Trastuzumab has been used for HER2(+) breast cancer, the treatment response of Trastuzumab therapy depends on unknown mechanisms among individual cases. In order to avoid unnecessary adverse events and to lighten financial burden for patients, pre-treatment prediction of trastuzumab treatment response would be beneficial for patients. Thus, in this study, we develop a prediction algorithm using microRNA expression profile of breast cancer tissues. [Materials and Methods] Eighty-three breast cancer patients who underwent trastuzumab-chemo combined therapy before operations were enrolled with written informed consent. FFPE specimens of pre-treatment core needle biopsy samples were collected, and regions containing cancer and adjacent stromal cells were laser-microdissected. Total RNA samples extracted from the microdissected specimens were subjected for microRNA microarray (3D-Gene®, Toray, Japan) analysis. Among these 83 patients, 39 cases had pCR (complete response in IDC regions regardless presense of DCIS without lymphnode metastasis), and the other 44 cases did not. According to the pCR/non-pCR information, we develop a prediction model using 35 signature microRNAs by a SVM technique. Prediction accuracy assessed by Leave-one-out validation was AUROC=0.889. The 35 signature microRNAs for trastuzumab treatment response included 7 out of 8 let-7 family members and miR-125a-5p/b-5p. [Conclusion] microRNA profile could predict treatment response of trastuzumab-chemo combined therapy for HER2(+) breast cancer, and the developed prediction algorithm might be a useful tool for clinical decision making. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P5-10-09.

Abstract 4961: MicroRNA profile predicts recurrence after resection in patients with hepatocellular carcinoma within the Milan criteria

Objective: Hepatocellular carcinoma (HCC) is difficult to manage due to the high frequency of post-surgical recurrence. Early detection of the HCC recurrence after liver resection is important in making further therapeutic options, such as salvage liver transplantation. In this study, we utilized microRNA expression profiling to assess the risk of HCC recurrence after liver resection. Methods: We examined microRNA expression profiling in paired tumor and non-tumor liver tissues from 73 HCC patients who satisfied the Milan Criteria. We constructed prediction models of recurrence-free survival using the Cox proportional hazard model and principal component analysis. The prediction efficiency was assessed by the leave-one-out cross-validation method, and the time-averaged area under the ROC curve (ta-AUROC). Results: The univariate Cox analysis identified 13 and 56 recurrence-related microRNAs in the tumor and non-tumor tissues, such as miR-96. The number of recurrence-related microRNAs was significantly larger in the non-tumor-derived microRNAs (N-miRs) than in the tumor-derived microRNAs (T-miRs, P Conclusion: microRNA profiling can predict HCC recurrence in Milan criteria cases. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4961. doi:10.1158/1538-7445.AM2011-4961